![\"Creative](\"http://i.creativecommons.org/l/by-nc-sa/4.0/80x15.png\")
\n"
},
{
"path": "Demos/Data Analysis Workflow/report/report.html",
"content": "\n\n\n\n\n\n\n\n\n\n\n\n/report/report.Rmd.exploratory analysis
statistical analysis
predictive modeling
functional analysis
#set working directory\nwd<-"C:/Users/Dmitry/Dropbox/Software/TeachingDemos/Demos/Data Analysis Workflow/"\nsetwd(wd)\n\n#load dependancies\npkg<-c("ggplot2","dplyr","R.utils","fdrtool","caret","randomForest","pROC")\nout<-lapply(pkg, function(x) {\n if(!require(x,character.only = TRUE)) install.packages(x,character.only = TRUE)\n }\n)\n\n\n#bioConductor\nsource("https://bioconductor.org/biocLite.R")\nif(!require("pcaMethods")) biocLite("pcaMethods")\nif(!require("pathview")) biocLite("pathview")\nif(!require("KEGGREST")) biocLite("KEGGREST")\n\n#load devium functions\n#sourceDirectory( "R",recursive=TRUE)\nsource("http://pastebin.com/raw.php?i=UyDBTA57")#load data\nsetwd(wd)\nload(file="data/data cube") # data.objtable(data.obj$raw$sample.meta$group)## \n## healthy sick \n## 27 27table(data.obj$raw$sample.meta$batch)## \n## batch_1 batch_2 \n## 28 26data.cube<-data.obj$raw\nargs<-list( pca.data = data.cube$data,\n pca.algorithm = "svd",\n pca.components = 8,\n pca.center = FALSE,\n pca.scaling = "none",\n pca.cv = "q2"\n )\n \n#calculate and view scree plot \nres<-devium.pca.calculate(args,return="list",plot=TRUE)#plot results\n#scores highlighting healthy and sick\np.args<-list( \n pca = res,\n results = "scores",\n color = data.cube$sample.meta[,"group",drop=FALSE],\n font.size = 3\n )\n\ndo.call("plot.PCA",p.args)#loadings\np.args<-list( \n pca = res,\n results = "loadings",\n color = data.cube$variable.meta[,"type",drop=FALSE],\n font.size = 3\n )\n\ndo.call("plot.PCA",p.args)args<-list( pca.data = data.cube$data,\n pca.algorithm = "svd",\n pca.components = 8,\n pca.center = TRUE,\n pca.scaling = "uv",\n pca.cv = "q2"\n )\n \n#calculate and view scree plot \nres2<-devium.pca.calculate(args,return="list",plot=TRUE)#loadings\np.args<-list( \n pca = res2,\n results = "loadings",\n color = data.cube$variable.meta[,"type",drop=FALSE],\n font.size = 3\n )\n\ndo.call("plot.PCA",p.args)#plot results\n#scores highlighting healthy and sick\np.args<-list( \n pca = res2,\n results = "scores",\n color = data.cube$sample.meta[,"group",drop=FALSE],\n font.size = 3\n )\n\ndo.call("plot.PCA",p.args)p.args<-list( \n pca = res2,\n results = "scores",\n color = data.cube$sample.meta[,"batch",drop=FALSE],\n font.size =3\n )\n\ndo.call("plot.PCA",p.args)![Metabolomics and Beyond: Challenges and Strategies for Next-generation Omic Analyses](\"https://imdevsoftware.files.wordpress.com/2015/09/clipboard01.png?w=300&h=225\"){kind=link}
#get summaries and t-test stats\ndata.cube<-data.obj$normalized\ndata<-data.cube$data\nmeta<-data.cube$sample.meta[,"group",drop=FALSE] \n\n#get summary\n.summary<-stats.summary(data,comp.obj=meta,formula=colnames(meta),sigfigs=3,log=FALSE,rel=1,do.stats=TRUE)## Generating data summary... \n## Conducting tests... \n## Conducting FDR corrections...stats.obj<-cbind(data.cube$variable.meta,.summary)\nstats.obj %>% arrange(group_p.values) %>% head(.)## ID description type\n## 1 C00077 ornithine metabolite\n## 2 C02477 tocopherol alpha metabolite\n## 3 C00097 cysteine metabolite\n## 4 C00031 glucose metabolite\n## 5 C00170 5'-deoxy-5'-methylthioadenosine metabolite\n## 6 C00385 xanthine metabolite\n## healthy.mean.....std.dev sick.mean.....std.dev mean.sick_mean.healthy\n## 1 7300 +/- 2900 3910 +/- 2100 0.54\n## 2 1160 +/- 1400 2600 +/- 1200 2.24\n## 3 5160 +/- 2500 8460 +/- 3400 1.64\n## 4 335000 +/- 2e+05 144000 +/- 140000 0.43\n## 5 216 +/- 83 355 +/- 160 1.64\n## 6 574 +/- 350 1670 +/- 1500 2.91\n## group_p.values group_adjusted.p.values group_q.values\n## 1 9.433317e-06 0.001886663 0.000900412\n## 2 1.505034e-04 0.007714558 0.003632462\n## 3 1.507843e-04 0.007714558 0.003633836\n## 4 1.542912e-04 0.007714558 0.003650646\n## 5 2.004055e-04 0.008016221 0.003825749\n## 6 3.909048e-04 0.013030159 0.006072846#write.csv(stats.obj,file="results/statistical_results.csv")top.met<-stats.obj %>% filter(type =="metabolite") %>% \n arrange(group_p.values) %>% dplyr::select(ID) %>% \n dplyr:: slice(.,1) %>% unlist(.) %>% as.character(.)\nid<-as.character(stats.obj$ID) %in% top.met\ntmp.data<-data.frame(data[,id,drop=FALSE],meta)\n#make plot\nggplot(tmp.data,aes_string(x="group",y=colnames(tmp.data)[1],fill="group")) + \n geom_boxplot() + ggtitle(as.character(stats.obj$description[id])) +ylab("")top.met<-stats.obj %>% filter(type =="protein") %>% \n arrange(group_p.values) %>% dplyr::select(ID) %>% \n dplyr:: slice(.,1) %>% unlist(.) %>% as.character(.)\nid<-as.character(stats.obj$ID) %in% top.met\ntmp.data<-data.frame(data[,id,drop=FALSE],meta)\n#make plot\nggplot(tmp.data,aes_string(x="group",y=colnames(tmp.data)[1],fill="group")) + \n geom_boxplot() + ggtitle(as.character(stats.obj$description[id])) +ylab("")mtry parameter is optimized to maximize the are under the reciever operator characteristic curve (AUCROC).#create a classification model using random forests\n#generate training/test set\nset.seed(998)\ndata<-data.cube$data\ninTraining <- createDataPartition(data.cube$sample.meta$group, p = 2/3, list = FALSE)\ntrain.data <- data[ inTraining,]\ntest.data <- data[-inTraining,]\ntrain.y <- data.cube$sample.meta$group[ inTraining] %>% droplevels()\ntest.y <- data.cube$sample.meta$group[ -inTraining] %>% droplevels()\n\n#set model parameters\nfitControl <- trainControl(## 10-fold CV\n method = "repeatedcv",\n number = 3,\n ## repeated ten times\n repeats = 3,\n classProbs = TRUE,\n summaryFunction = twoClassSummary\n )\n \n \n#fit model to the training data\nset.seed(825)\nfit<- train(train.y ~ ., data = train.data,\n method = "rf",\n trControl = fitControl,\n metric = "ROC",\n tuneLength = 3 \n )mtry or the number of variables randomly sampled as candidates at each split.fit## Random Forest \n## \n## 36 samples\n## 199 predictors\n## 2 classes: 'healthy', 'sick' \n## \n## No pre-processing\n## Resampling: Cross-Validated (3 fold, repeated 3 times) \n## \n## Summary of sample sizes: 24, 24, 24, 24, 24, 24, ... \n## \n## Resampling results across tuning parameters:\n## \n## mtry ROC Sens Spec ROC SD Sens SD Spec SD \n## 2 0.7870370 0.7222222 0.7777778 0.08098544 0.2041241 0.1666667\n## 101 0.8549383 0.7777778 0.7592593 0.10090044 0.1443376 0.2060055\n## 200 0.8750000 0.8333333 0.7222222 0.09107554 0.1178511 0.2204793\n## \n## ROC was used to select the optimal model using the largest value.\n## The final value used for the model was mtry = 200.#predict the test set\npred<-predict(fit,newdata=test.data)\nprob<-predict(fit,newdata=test.data,type="prob")\nobs<-test.y\ntable(pred,obs)## obs\n## pred healthy sick\n## healthy 7 2\n## sick 2 7#get performance stats\ntwoClassSummary(data=data.frame(obs,pred,prob),lev=levels(pred))## ROC Sens Spec \n## 0.9135802 0.7777778 0.7777778x<-roc(obs,prob[,levels(pred)[1]],silent = TRUE)\nplot(x)## \n## Call:\n## roc.default(response = obs, predictor = prob[, levels(pred)[1]], silent = TRUE)\n## \n## Data: prob[, levels(pred)[1]] in 9 controls (obs healthy) > 9 cases (obs sick).\n## Area under the curve: 0.9136#need to get variable names\nvip<-varImp(fit)$importance # need to keep rownames\nvip<-vip[order(vip[,1],decreasing=TRUE),,drop=FALSE][1:10,,drop=FALSE]\nid<-colnames(train.data) %in% gsub('`','',rownames(vip))\ntmp.data<-data.frame(importance=vip[,1],variable=factor(stats.obj$description[id],levels=stats.obj$description[id],ordered=FALSE))\n\n#plot\nggplot(tmp.data, aes(x=variable,y=importance)) + geom_bar(stat="identity") + coord_flip()id<-as.character(stats.obj$description) %in% as.character(tmp.data[1,2])\ntmp.data<-data.frame(data[,id,drop=FALSE],meta)\n#make plot\nggplot(tmp.data,aes_string(x="group",y=colnames(tmp.data)[1],fill="group")) + \n geom_boxplot() + ggtitle(as.character(stats.obj$description[id])) +ylab("")results/statistical_results_sig.csv). We can view the full analysis results in results/IMPaLA_results.csv. next lets take an enriched pathway and fisualize the fold changes between sick and healthy in the enriched species.#format data to show fold changes in pathway\n#get formatted data for pathview\nlibrary(KEGGREST)\nlibrary(pathview)\ndata<-stats.obj\n#metabolite\nmet<-data %>% dplyr::filter(type =="metabolite") %>%\n dplyr::select(ID,mean.sick_mean.healthy) %>% \n mutate(FC=log(mean.sick_mean.healthy)) %>% dplyr::select(-mean.sick_mean.healthy)\n#protein\nprot<-data %>% dplyr::filter(type =="protein") %>%\n dplyr::select(ID,mean.sick_mean.healthy) %>% \n mutate(FC=log(mean.sick_mean.healthy)) %>% dplyr::select(-mean.sick_mean.healthy)\n\n#set rownames\nrownames(met)<-met[,1];met<-met[,-1,drop=FALSE]\nrownames(prot)<-prot[,1];prot<-prot[,-1,drop=FALSE] \n\n#select pathway to view\npath<-"Glycolysis / Gluconeogenesis"head(met)## FC\n## C00379 0.41871033\n## C00385 1.06815308\n## C00105 0.07696104\n## C00299 -0.24846136\n## C00366 0.33647224\n## C00086 -0.05129329head(prot)## FC\n## SPTAN1 -0.3424903\n## CFH 0.1133287\n## VPS13C 0.3148107\n## XRCC6 1.0715836\n## APOA1 -0.1392621\n## SUPT16H 0.7129498data(korg)\norganism <- "homo sapiens"\nmatches <- unlist(sapply(1:ncol(korg), function(i) {\n agrep(organism, korg[, i])\n}))\n(kegg.code <- korg[matches, 1, drop = F])## kegg.code\n## [1,] "hsa"setwd(wd)\npathways <- keggList("pathway", kegg.code)\n#get code of our pathway of interest\nmap<-grepl(path,pathways) %>% pathways[.] %>% names(.) %>% gsub("path:","",.)\nmap## [1] "hsa00010"#create image\nsetwd("report")\npv.out <- pathview(gene.data = prot, cpd.data = met, gene.idtype = "SYMBOL", \n pathway.id = map, species = kegg.code, out.suffix = map, keys.align = "y", \n kegg.native = T, match.data = T, key.pos = "topright")© Dmitry Grapov (2015)
![](\"http://image.slidesharecdn.com/dataanalysisworkflowspart12015-151010014252-lva1-app6891/95/data-analysis-workflows-part-1-2015-1-638.jpg?cb=1444443320\")
\n\n\n##### The following is an example of a data analysis strategy for an integrated metabolomic and proteomic data set. This tutorial is meant to give examples of some of the major common steps in an omic integration analysis workflow. You can check out all of the code in `/report/report.Rmd`.\n\n\n1. exploratory analysis\n\n2. statistical analysis\n\n3. predictive modeling\n\n4. functional analysis\n\n\n\n```r\n#set working directory\nwd<-\"C:/Users/Dmitry/Dropbox/Software/TeachingDemos/Demos/Data Analysis Workflow/\"\nsetwd(wd)\n\n#load dependancies\npkg<-c(\"ggplot2\",\"dplyr\",\"R.utils\",\"fdrtool\",\"caret\",\"randomForest\",\"pROC\")\nout<-lapply(pkg, function(x) {\n if(!require(x,character.only = TRUE)) install.packages(x,character.only = TRUE)\n }\n)\n\n\n#bioConductor\nsource(\"https://bioconductor.org/biocLite.R\")\nif(!require(\"pcaMethods\")) biocLite(\"pcaMethods\")\nif(!require(\"pathview\")) biocLite(\"pathview\")\nif(!require(\"KEGGREST\")) biocLite(\"KEGGREST\")\n\n#load devium functions\n#sourceDirectory( \"R\",recursive=TRUE)\nsource(\"http://pastebin.com/raw.php?i=UyDBTA57\")\n```\n\n\n\n```r\n#load data\nsetwd(wd)\nload(file=\"data/data cube\") # data.obj\n```\n\n##### This data set contains 200 measurements for 54 samples. The samples are comprised of sick and healthy patients measured across two analytical batches.\n\n\n```r\ntable(data.obj$raw$sample.meta$group)\n```\n\n```\n## \n## healthy sick \n## 27 27\n```\n\n```r\ntable(data.obj$raw$sample.meta$batch)\n```\n\n```\n## \n## batch_1 batch_2 \n## 28 26\n```\n\n****\n\n### Exploratory Analysis\n\n****\n\n##### A critical aspect of any data analysis should be to carry out an exploratory data analysis to see if there are any strange trends. Below is an example of a Principal Components Analysis (PCA). Lets start by looking at the raw data and caclculate PCA with out anys scaling. \n\n##### PCA has three main components we can use to evaluate our data. \n##### 1. Variance explained by each component\n\n```r\ndata.cube<-data.obj$raw\nargs<-list( pca.data \t\t= data.cube$data,\n\t\t\tpca.algorithm \t= \"svd\",\n\t\t\tpca.components \t= 8,\n\t\t\tpca.center \t\t= FALSE,\n\t\t\tpca.scaling \t= \"none\",\n\t\t\tpca.cv \t\t\t= \"q2\"\n\t\t\t)\n\t\t\t\n#calculate and view scree plot\t\t\t\nres<-devium.pca.calculate(args,return=\"list\",plot=TRUE)\n```\n\n \n\n##### The scree plot above shows the total variance in the data explained (top) and the cumulative varince explained (bottom) by each principal component (PC). The green bars in the bottom plot show the cross-validated variance explained which can be used to give us an idea bout the stability of calculated components. How many PCs to keep can be determined based on a few criteria 1) each PC should explain some minnimum variance and 2) calculate enough PCS to explain some target variance. The hashed line in the top plot shows PCs which explain less than 1% variance and the hashed line in the bottom plot shows how many PCs arerequired to explain 80% of the varince in the data. Based on an evaluation of the scree plot we may select 2 or 3 PCs. The cross-validated varince explained (green bars) also suggest that the variance explained does not increase after the first 2 PCs.\n\n##### 2. The sample scores can be used to visualize multivariete similarities in samples given all the varibles for each PC. Lets plot the scores and highlight the sick and healthy groups.\n\n```r\n#plot results\n#scores highlighting healthy and sick\np.args<-list( \n \t\tpca = res,\n\t\t\tresults = \"scores\",\n\t\t\tcolor = data.cube$sample.meta[,\"group\",drop=FALSE],\n\t\t\tfont.size = 3\n\t\t\t)\n\ndo.call(\"plot.PCA\",p.args)\n```\n\n \n\n#### Based on the scores above the sick and healthy samples look fairly similiar. Lets next look at the variable loadings.\n#### 3. Variable loadings show the contribution of each varible to the calculated scores.\n\n\n```r\n#loadings\np.args<-list( \n \t\tpca = res,\n\t\t\tresults = \"loadings\",\n\t\t\tcolor = data.cube$variable.meta[,\"type\",drop=FALSE],\n\t\t\tfont.size = 3\n\t\t\t)\n\ndo.call(\"plot.PCA\",p.args)\n```\n\n \n\n#### Evaluation of the loadings suggest that variance variables X838 abd X454 explain ~90% of the varince in the data. Because we did not scale the data before conducting PCA, variables with the largest magnitude will contribute most to varince explained. \n\n#### Next lets recalculate the PCA and mean center and scale all the variables by their standard deviation (autoscale).\n#### Variance explained\n\n```r\nargs<-list( pca.data \t\t= data.cube$data,\n\t\t\tpca.algorithm \t= \"svd\",\n\t\t\tpca.components \t= 8,\n\t\t\tpca.center \t\t= TRUE,\n\t\t\tpca.scaling \t= \"uv\",\n\t\t\tpca.cv \t\t\t= \"q2\"\n\t\t\t)\n\t\t\t\n#calculate and view scree plot\t\t\t\nres2<-devium.pca.calculate(args,return=\"list\",plot=TRUE)\n```\n\n \n\n#### Variable loadings\n\n```r\n#loadings\np.args<-list( \n\t\t\tpca = res2,\n\t\t\tresults = \"loadings\",\n\t\t\tcolor = data.cube$variable.meta[,\"type\",drop=FALSE],\n\t\t\tfont.size = 3\n\t\t\t)\n\ndo.call(\"plot.PCA\",p.args)\n```\n\n \n\n#### Sample scores\n\n```r\n#plot results\n#scores highlighting healthy and sick\np.args<-list( \n\t\t\tpca = res2,\n\t\t\tresults = \"scores\",\n\t\t\tcolor = data.cube$sample.meta[,\"group\",drop=FALSE],\n\t\t\tfont.size = 3\n\t\t\t)\n\ndo.call(\"plot.PCA\",p.args)\n```\n\n \n\n#### There are some noticible differences in PCA after we scaled our data.\n1. Variable magnitude no longer drives the majority of the variance.\n2. We can see more resolution in variable loadings for the first 2 PCs.\n3. There is an unexplained group structure in the score.\n\n#### Next we can try mapping other meta data to score to see if we can explain the cluster pattern. Lets show the analytical batches in the samples scores.\n\n```r\np.args<-list( \n \t\tpca = res2,\n\t\t\tresults = \"scores\",\n\t\t\tcolor = data.cube$sample.meta[,\"batch\",drop=FALSE],\n\t\t\tfont.size =3\n\t\t\t)\n\ndo.call(\"plot.PCA\",p.args)\n```\n\n \n\n#### We can see in the scores above that the analytical batch nicely explains 35% of the varince in the data. This is a common problem in large data sets which is best handled using various data normalization methods. Here is some more information about implementing data normalizations.\n\n###### [Metabolomics and Beyond: Challenges and Strategies for Next-generation Omic Analyses](https://imdevsoftware.files.wordpress.com/2015/09/clipboard01.png?w=300&h=225)\n\n[](https://www.youtube.com/watch?v=4AhBN5Q1oMs)\n\n##### [Evaluation of data normalization methods](http://www.slideshare.net/dgrapov/case-study-metabolomic-data-normalization-example)\n\n****\n\n#### [Part 3](http://www.slideshare.net/dgrapov/data-analysis-workflows-part-2-2015?related=1)\n\n\n![](\"http://image.slidesharecdn.com/dataanalysisworkflowspart22015-151010012953-lva1-app6891/95/data-analysis-workflows-part-2-2015-1-638.jpg?cb=1444443361\")
\n \n\n### Statistical Analysis\n\n****\n\n##### Next lets carry out a statistical analysis and summarise the changes between the sick and ghealthy groups. Below we identify significantly altered analytes using a basic t-test with adjustment for multiple hypotheses tested. We probably want to use more sophisticated and non-parametric tests for real applications.\n\n\n```r\n#get summaries and t-test stats\ndata.cube<-data.obj$normalized\ndata<-data.cube$data\nmeta<-data.cube$sample.meta[,\"group\",drop=FALSE] \n\n#get summary\n.summary<-stats.summary(data,comp.obj=meta,formula=colnames(meta),sigfigs=3,log=FALSE,rel=1,do.stats=TRUE)\n```\n\n```\n## Generating data summary... \n## Conducting tests... \n## Conducting FDR corrections...\n```\n\n```r\nstats.obj<-cbind(data.cube$variable.meta,.summary)\nstats.obj %>% arrange(group_p.values) %>% head(.)\n```\n\n```\n## ID description type\n## 1 C00077 ornithine metabolite\n## 2 C02477 tocopherol alpha metabolite\n## 3 C00097 cysteine metabolite\n## 4 C00031 glucose metabolite\n## 5 C00170 5'-deoxy-5'-methylthioadenosine metabolite\n## 6 C00385 xanthine metabolite\n## healthy.mean.....std.dev sick.mean.....std.dev mean.sick_mean.healthy\n## 1 7300 +/- 2900 3910 +/- 2100 0.54\n## 2 1160 +/- 1400 2600 +/- 1200 2.24\n## 3 5160 +/- 2500 8460 +/- 3400 1.64\n## 4 335000 +/- 2e+05 144000 +/- 140000 0.43\n## 5 216 +/- 83 355 +/- 160 1.64\n## 6 574 +/- 350 1670 +/- 1500 2.91\n## group_p.values group_adjusted.p.values group_q.values\n## 1 9.433317e-06 0.001886663 0.000900412\n## 2 1.505034e-04 0.007714558 0.003632462\n## 3 1.507843e-04 0.007714558 0.003633836\n## 4 1.542912e-04 0.007714558 0.003650646\n## 5 2.004055e-04 0.008016221 0.003825749\n## 6 3.909048e-04 0.013030159 0.006072846\n```\n\n```r\n#write.csv(stats.obj,file=\"results/statistical_results.csv\")\n```\n\n#### We can visualize the differences in means for the top most altered metabolite and protein as a box plot.\n\n```r\ntop.met<-stats.obj %>% filter(type ==\"metabolite\") %>% \n arrange(group_p.values) %>% dplyr::select(ID) %>% \n dplyr:: slice(.,1) %>% unlist(.) %>% as.character(.)\nid<-as.character(stats.obj$ID) %in% top.met\ntmp.data<-data.frame(data[,id,drop=FALSE],meta)\n#make plot\nggplot(tmp.data,aes_string(x=\"group\",y=colnames(tmp.data)[1],fill=\"group\")) + \n geom_boxplot() + ggtitle(as.character(stats.obj$description[id])) +ylab(\"\")\n```\n\n \n\n```r\ntop.met<-stats.obj %>% filter(type ==\"protein\") %>% \n arrange(group_p.values) %>% dplyr::select(ID) %>% \n dplyr:: slice(.,1) %>% unlist(.) %>% as.character(.)\nid<-as.character(stats.obj$ID) %in% top.met\ntmp.data<-data.frame(data[,id,drop=FALSE],meta)\n#make plot\nggplot(tmp.data,aes_string(x=\"group\",y=colnames(tmp.data)[1],fill=\"group\")) + \n geom_boxplot() + ggtitle(as.character(stats.obj$description[id])) +ylab(\"\")\n```\n\n \n\n****\n\n### Predictive Modeling\n\n****\n\n#### Next we can try a generate a non-linear multivarite classification model to identify important variables in our data. Below we will train and validate a random forest classifier. The full data set is split into 2/3 trainning and 1/3 test set while keeping the propotion of sick and healthy samples equivalent. The model is trained using 3-fold cross-validation repeated 3 times and the ```mtry``` parameter is optimized to maximize the are under the reciever operator characteristic curve (AUCROC).\n\n\n```r\n#create a classification model using random forests\n#generate training/test set\nset.seed(998)\ndata<-data.cube$data\ninTraining <- createDataPartition(data.cube$sample.meta$group, p = 2/3, list = FALSE)\ntrain.data <- data[ inTraining,]\ntest.data <- data[-inTraining,]\ntrain.y <- data.cube$sample.meta$group[ inTraining] %>% droplevels()\ntest.y <- data.cube$sample.meta$group[ -inTraining] %>% droplevels()\n\n#set model parameters\nfitControl <- trainControl(## 10-fold CV\n method = \"repeatedcv\",\n number = 3,\n ## repeated ten times\n repeats = 3,\n classProbs = TRUE,\n summaryFunction = twoClassSummary\n\t\t\t\t)\n\t\t\t\t\t\t \n\t\t\t\t\t\t \n#fit model to the training data\nset.seed(825)\nfit<- train(train.y ~ ., data = train.data,\n method = \"rf\",\n trControl = fitControl,\n metric = \"ROC\",\n tuneLength = 3 \n )\n```\n\n#### Below the optimal model is chosen while varying the ```mtry``` or the number of variables randomly sampled as candidates at each split. \n\n```r\nfit\n```\n\n```\n## Random Forest \n## \n## 36 samples\n## 199 predictors\n## 2 classes: 'healthy', 'sick' \n## \n## No pre-processing\n## Resampling: Cross-Validated (3 fold, repeated 3 times) \n## \n## Summary of sample sizes: 24, 24, 24, 24, 24, 24, ... \n## \n## Resampling results across tuning parameters:\n## \n## mtry ROC Sens Spec ROC SD Sens SD Spec SD \n## 2 0.7870370 0.7222222 0.7777778 0.08098544 0.2041241 0.1666667\n## 101 0.8549383 0.7777778 0.7592593 0.10090044 0.1443376 0.2060055\n## 200 0.8750000 0.8333333 0.7222222 0.09107554 0.1178511 0.2204793\n## \n## ROC was used to select the optimal model using the largest value.\n## The final value used for the model was mtry = 200.\n```\n\n#### Next we can evaluate the model performance based on predictions for the test set. We can also look at the ROC curve. \n\n```r\n#predict the test set\npred<-predict(fit,newdata=test.data)\nprob<-predict(fit,newdata=test.data,type=\"prob\")\nobs<-test.y\ntable(pred,obs)\n```\n\n```\n## obs\n## pred healthy sick\n## healthy 7 2\n## sick 2 7\n```\n\n```r\n#get performance stats\ntwoClassSummary(data=data.frame(obs,pred,prob),lev=levels(pred))\n```\n\n```\n## ROC Sens Spec \n## 0.9135802 0.7777778 0.7777778\n```\n\n#### We can also look at the ROC curve. \n\n```r\nx<-roc(obs,prob[,levels(pred)[1]],silent = TRUE)\nplot(x)\n```\n\n \n\n```\n## \n## Call:\n## roc.default(response = obs, predictor = prob[, levels(pred)[1]], silent = TRUE)\n## \n## Data: prob[, levels(pred)[1]] in 9 controls (obs healthy) > 9 cases (obs sick).\n## Area under the curve: 0.9136\n```\n\n#### Having validated our model next we can look at the most important variables driving the classification. We can look at the differences in performance when each variable is randomly permuted or the VIP.\n\n```r\n#need to get variable names\nvip<-varImp(fit)$importance # need to keep rownames\nvip<-vip[order(vip[,1],decreasing=TRUE),,drop=FALSE][1:10,,drop=FALSE]\nid<-colnames(train.data) %in% gsub('`','',rownames(vip))\ntmp.data<-data.frame(importance=vip[,1],variable=factor(stats.obj$description[id],levels=stats.obj$description[id],ordered=FALSE))\n\n#plot\nggplot(tmp.data, aes(x=variable,y=importance)) + geom_bar(stat=\"identity\") + coord_flip()\n```\n\n \n\n```r\nid<-as.character(stats.obj$description) %in% as.character(tmp.data[1,2])\ntmp.data<-data.frame(data[,id,drop=FALSE],meta)\n#make plot\nggplot(tmp.data,aes_string(x=\"group\",y=colnames(tmp.data)[1],fill=\"group\")) + \n geom_boxplot() + ggtitle(as.character(stats.obj$description[id])) +ylab(\"\")\n```\n\n \n\n****\n\n### Functional Analysis\n\n****\n\n#### Finally we can identify enriched biological pathways based on the integrated changes in genes and proteins. [IMPaLA: Integrated Molecular Pathway Level Analysis](http://impala.molgen.mpg.de/) can be used to calculate enriched pathways in genes or proteins and metabolites.To do this we can querry the significantly alterd proteins and metabolites for enriched pathways (see `results/statistical_results_sig.csv`). We can view the full analysis results in `results/IMPaLA_results.csv`. next lets take an enriched pathway and fisualize the fold changes between sick and healthy in the enriched species. \n\n\n```r\n#format data to show fold changes in pathway\n#get formatted data for pathview\nlibrary(KEGGREST)\nlibrary(pathview)\ndata<-stats.obj\n#metabolite\nmet<-data %>% dplyr::filter(type ==\"metabolite\") %>%\n dplyr::select(ID,mean.sick_mean.healthy) %>% \n\tmutate(FC=log(mean.sick_mean.healthy)) %>% dplyr::select(-mean.sick_mean.healthy)\n#protein\nprot<-data %>% dplyr::filter(type ==\"protein\") %>%\n\tdplyr::select(ID,mean.sick_mean.healthy) %>% \n\tmutate(FC=log(mean.sick_mean.healthy)) %>% dplyr::select(-mean.sick_mean.healthy)\n\n#set rownames\nrownames(met)<-met[,1];met<-met[,-1,drop=FALSE]\nrownames(prot)<-prot[,1];prot<-prot[,-1,drop=FALSE]\t\n\n#select pathway to view\npath<-\"Glycolysis / Gluconeogenesis\"\n```\n\n#### Lets take a looka at the Glycolysis / Gluconeogenesis pathway. Our data needs to be formatted as below. You can also take a look at the following more detailed example of [mapping fold changes to biochemical pathways](https://github.com/dgrapov/TeachingDemos/blob/master/Demos/Pathway%20Analysis/KEGG%20Pathway%20Enrichment.md).\n\n#### Metabolite data showing KEGG ids and log fold change\n\n```r\nhead(met)\n```\n\n```\n## FC\n## C00379 0.41871033\n## C00385 1.06815308\n## C00105 0.07696104\n## C00299 -0.24846136\n## C00366 0.33647224\n## C00086 -0.05129329\n```\n\n#### Protein data showing the Entrez gene name and log fold changes\n\n```r\nhead(prot)\n```\n\n```\n## FC\n## SPTAN1 -0.3424903\n## CFH 0.1133287\n## VPS13C 0.3148107\n## XRCC6 1.0715836\n## APOA1 -0.1392621\n## SUPT16H 0.7129498\n```\n\n#### Next we need to get the pathway code for or pathway of interest.\n\n```r\ndata(korg)\norganism <- \"homo sapiens\"\nmatches <- unlist(sapply(1:ncol(korg), function(i) {\n agrep(organism, korg[, i])\n}))\n(kegg.code <- korg[matches, 1, drop = F])\n```\n\n```\n## kegg.code\n## [1,] \"hsa\"\n```\n\n#### Now we can visualize the changes between sick and healthy in the Glycolysis / Gluconeogenesis pathway.\n\n```r\nsetwd(wd)\npathways <- keggList(\"pathway\", kegg.code)\n#get code of our pathway of interest\nmap<-grepl(path,pathways) %>% pathways[.] %>% names(.) %>% gsub(\"path:\",\"\",.)\nmap\n```\n\n```\n## [1] \"hsa00010\"\n```\n\n```r\n#create image\nsetwd(\"report\")\npv.out <- pathview(gene.data = prot, cpd.data = met, gene.idtype = \"SYMBOL\", \n pathway.id = map, species = kegg.code, out.suffix = map, keys.align = \"y\", \n kegg.native = T, match.data = T, key.pos = \"topright\")\n```\n\n\n****\n\n#### This concludes this short tutorial. You may also find the following links useful.\n\n* [Software tools](https://github.com/dgrapov)\n* [More examples and demos](https://imdevsoftware.wordpress.com/)\n\n© Dmitry Grapov (2015) \n"
},
{
"path": "Demos/Data Analysis Workflow/results/IMPaLA_results.csv",
"content": "pathway_name,pathway_source,num_overlapping_genes,overlapping_genes,num_all_pathway_genes,P_genes,Q_genes,num_overlapping_metabolites,overlapping_metabolites,num_all_pathway_metabolites,P_metabolites,Q_metabolites,P_joint,Q_joint\nMetabolism,Reactome,3,GLS;IVD;ACSS2,1427 (1485),0.505,1,26,C00077;C00158;C00116;C00366;C00385;C00170;C01835;C00245;C00836;C00180;C00315;C00041;C00047;C00106;C00148;C00791;C00020;C00025;C00197;C00097;C00327;C00130;C00135;C03546;C00989;C00346,808 (1017),3.54E-13,8.03E-10,5.43E-12,5.61E-09\nTransmembrane transport of small molecules,Reactome,0,,594 (623),1,1,16,C00077;C00106;C00116;C00148;C00245;C00158;C00791;C00020;C00025;C00097;C01571;C00366;C00315;C00041;C00047;C00135,204 (216),4.50E-13,8.03E-10,1.32E-11,6.85E-09\nMetabolism of amino acids and derivatives,Reactome,2,IVD;GLS,145 (152),0.0292,1,14,C00077;C00148;C00245;C00791;C00020;C00197;C00025;C00170;C00097;C00315;C00327;C00041;C00047;C00135,181 (190),2.62E-11,1.87E-08,2.21E-11,7.61E-09\nMetabolism of amino acids and derivatives,Wikipathways,0,,0 (0),1,1,14,C00077;C00148;C00245;C00791;C00020;C00197;C00025;C00170;C00097;C00315;C00327;C00041;C00047;C00135,174 (187),1.52E-11,1.36E-08,1.52E-11,1.36E-08\nSLC-mediated transmembrane transport,Reactome,0,,261 (265),1,1,14,C00077;C00106;C00148;C00135;C00245;C00158;C00791;C00025;C00097;C01571;C00315;C00041;C00047;C00366,160 (165),4.77E-12,5.68E-09,1.29E-10,3.34E-08\nCentral carbon metabolism in cancer - Homo sapiens (human),KEGG,1,GLS,66 (67),0.116,1,8,C00158;C00148;C00031;C00197;C00025;C00097;C00041;C00135,37 (37),2.36E-10,9.37E-08,6.92E-10,1.43E-07\nABC transporters - Homo sapiens (human),KEGG,0,,44 (44),1,1,12,C00077;C00116;C00148;C00245;C00379;C00031;C00025;C00047;C00315;C00041;C01835;C00135,123 (123),6.60E-11,3.37E-08,1.61E-09,2.39E-07\nAmino acid transport across the plasma membrane,Reactome,0,,31 (31),1,1,8,C00077;C00148;C00245;C00025;C00097;C00041;C00047;C00135,32 (32),6.61E-11,3.37E-08,1.62E-09,2.39E-07\nTransport of glucose and other sugars_ bile salts and organic acids_ metal ions and amine compounds,Reactome,0,,98 (101),1,1,10,C00158;C00148;C00366;C00791;C00245;C00097;C00315;C00041;C00047;C00135,78 (83),2.28E-10,9.37E-08,5.30E-09,6.86E-07\nUrea cycle and metabolism of arginine_ proline_ glutamate_ aspartate and asparagine,EHMN,1,GLS,105 (107),0.178,1,11,C00077;C00148;C00020;C00025;C00170;C00097;C00315;C00327;C00041;C00047;C00989,125 (125),1.52E-09,4.53E-07,6.24E-09,7.18E-07\ntRNA charging,HumanCyc,0,,41 (44),1,1,7,C00148;C00020;C00025;C00097;C00041;C00047;C00135,24 (24),3.55E-10,1.27E-07,8.07E-09,7.78E-07\nClass I MHC mediated antigen processing & presentation,Reactome,2,SEC24C;SEC13,208 (224),0.0561,1,7,C00148;C00020;C00025;C00097;C00041;C00047;C00135,35 (35),6.48E-09,1.54E-06,8.27E-09,7.78E-07\nGlucose Homeostasis,Wikipathways,0,,0 (0),1,1,6,C00077;C00116;C00031;C00327;C00047;C00135,21 (21),8.50E-09,1.78E-06,8.50E-09,1.78E-06\nAmino acid and oligopeptide SLC transporters,Reactome,0,,49 (49),1,1,8,C00077;C00148;C00245;C00025;C00097;C00041;C00047;C00135,45 (45),1.26E-09,4.10E-07,2.71E-08,2.34E-06\nTransport of inorganic cations/anions and amino acids/oligopeptides,Reactome,0,,94 (95),1,1,8,C00077;C00148;C00245;C00025;C00097;C00041;C00047;C00135,48 (48),2.17E-09,5.97E-07,4.55E-08,3.63E-06\nAmino acid synthesis and interconversion (transamination),Reactome,1,GLS,17 (17),0.0311,1,6,C00077;C00148;C00020;C00197;C00025;C00041,32 (32),1.33E-07,1.83E-05,8.42E-08,6.22E-06\nAdaptive Immune System,Reactome,3,SEC24C;EVL;SEC13,558 (608),0.0819,1,7,C00148;C00020;C00025;C00097;C00041;C00047;C00135,48 (48),6.59E-08,1.21E-05,1.08E-07,7.46E-06\nEndosomal/Vacuolar pathway,Reactome,0,,12 (12),1,1,6,C00148;C00025;C00097;C00041;C00047;C00135,20 (20),6.10E-09,1.54E-06,1.22E-07,7.86E-06\nProton/oligonucleotide cotransporters,Reactome,0,,4 (4),1,1,6,C00148;C00025;C00097;C00041;C00047;C00135,21 (21),8.50E-09,1.78E-06,1.66E-07,1.01E-05\nTransport of inorganic cations-anions and amino acids-oligopeptides,Wikipathways,0,,0 (0),1,1,6,C00148;C00025;C00097;C00041;C00047;C00135,31 (32),1.09E-07,1.56E-05,1.09E-07,1.56E-05\nγ-glutamyl cycle,HumanCyc,0,,11 (12),1,1,7,C00077;C00148;C00025;C00097;C00041;C00047;C00135,43 (47),2.97E-08,5.89E-06,5.44E-07,3.13E-05\nAntigen processing-Cross presentation,Reactome,0,,32 (32),1,1,6,C00148;C00025;C00097;C00041;C00047;C00135,29 (29),7.11E-08,1.21E-05,1.24E-06,6.42E-05\nleukotriene biosynthesis,HumanCyc,0,,6 (7),1,1,6,C00148;C00025;C00097;C00041;C00047;C00135,29 (30),7.11E-08,1.21E-05,1.24E-06,6.42E-05\ntRNA Aminoacylation,Wikipathways,0,,0 (0),1,1,7,C00148;C00020;C00025;C00097;C00041;C00047;C00135,65 (65),5.67E-07,6.53E-05,5.67E-07,6.53E-05\nGlutathione synthesis and recycling,Reactome,0,,14 (17),1,1,6,C00148;C00025;C00097;C00041;C00047;C00135,30 (31),8.83E-08,1.37E-05,1.52E-06,7.16E-05\nUrea cycle and metabolism of amino groups,Wikipathways,0,,20 (20),1,1,6,C00077;C00148;C00791;C00025;C00315;C00327,30 (30),8.83E-08,1.37E-05,1.52E-06,7.16E-05\nNa+/Cl- dependent neurotransmitter transporters,Reactome,0,,19 (19),1,1,6,C00148;C00245;C00097;C00041;C00047;C00135,31 (31),1.09E-07,1.56E-05,1.85E-06,8.35E-05\nPhase II conjugation,Wikipathways,0,,0 (0),1,1,8,C00148;C00020;C00025;C00097;C00180;C00041;C00047;C00135,102 (115),9.38E-07,9.57E-05,9.38E-07,9.57E-05\nNucleotide Metabolism,Wikipathways,0,,19 (19),1,1,5,C00077;C00385;C00130;C00366;C00315,17 (18),1.44E-07,1.90E-05,2.41E-06,0.000104\nAmine compound SLC transporters,Reactome,0,,30 (31),1,1,6,C00148;C00245;C00097;C00041;C00047;C00135,35 (35),2.35E-07,2.89E-05,3.82E-06,0.000152\nS-methyl-5-thio-α-D-ribose 1-phosphate degradation,HumanCyc,0,,4 (4),1,1,6,C00148;C00025;C00097;C00041;C00047;C00135,35 (35),2.35E-07,2.89E-05,3.82E-06,0.000152\nGlutathione conjugation,Reactome,0,,35 (38),1,1,6,C00148;C00025;C00097;C00041;C00047;C00135,38 (41),3.92E-07,4.67E-05,6.18E-06,0.000237\nImmune System,Reactome,4,SEC24C;EVL;XRCC6;SEC13,994 (1069),0.106,1,7,C00148;C00020;C00025;C00097;C00041;C00047;C00135,87 (103),4.20E-06,0.000273,6.98E-06,0.000258\nGlycine_ serine_ alanine and threonine metabolism,EHMN,1,GLOD4,80 (80),0.139,1,7,C00077;C00020;C00197;C00025;C00097;C00041;C00047,88 (88),4.54E-06,0.000289,9.61E-06,0.000311\nCytosolic tRNA aminoacylation,Reactome,0,,26 (29),1,1,7,C00148;C00020;C00025;C00097;C00041;C00047;C00135,66 (66),6.30E-07,6.62E-05,9.63E-06,0.000311\nMitochondrial tRNA aminoacylation,Reactome,0,,23 (23),1,1,7,C00148;C00020;C00025;C00097;C00041;C00047;C00135,66 (66),6.30E-07,6.62E-05,9.63E-06,0.000311\ntRNA Aminoacylation,Reactome,0,,45 (48),1,1,7,C00148;C00020;C00025;C00097;C00041;C00047;C00135,66 (66),6.30E-07,6.62E-05,9.63E-06,0.000311\nMetabolism of nucleotides,Wikipathways,0,,0 (0),1,1,7,C00106;C00366;C00385;C00025;C00020;C00130;C00041,97 (111),8.73E-06,0.000528,8.73E-06,0.000528\nChloramphenicol Action Pathway,SMPDB,0,,0 (0),1,1,4,C00020;C00148;C00041;C00135,20 (20),1.55E-05,0.000609,1.55E-05,0.000609\nRoxithromycin Action Pathway,SMPDB,0,,0 (0),1,1,4,C00020;C00148;C00041;C00135,20 (20),1.55E-05,0.000609,1.55E-05,0.000609\nJosamycin Action Pathway,SMPDB,0,,0 (0),1,1,4,C00020;C00148;C00041;C00135,20 (20),1.55E-05,0.000609,1.55E-05,0.000609\nMethacycline Action Pathway,SMPDB,0,,0 (0),1,1,4,C00020;C00148;C00041;C00135,20 (20),1.55E-05,0.000609,1.55E-05,0.000609\nRolitetracycline Action Pathway,SMPDB,0,,0 (0),1,1,4,C00020;C00148;C00041;C00135,20 (20),1.55E-05,0.000609,1.55E-05,0.000609\nStreptomycin Action Pathway,SMPDB,0,,0 (0),1,1,4,C00020;C00148;C00041;C00135,20 (20),1.55E-05,0.000609,1.55E-05,0.000609\nSpectinomycin Action Pathway,SMPDB,0,,0 (0),1,1,4,C00020;C00148;C00041;C00135,20 (20),1.55E-05,0.000609,1.55E-05,0.000609\nKanamycin Action Pathway,SMPDB,0,,0 (0),1,1,4,C00020;C00148;C00041;C00135,20 (20),1.55E-05,0.000609,1.55E-05,0.000609\nGentamicin Action Pathway,SMPDB,0,,0 (0),1,1,4,C00020;C00148;C00041;C00135,20 (20),1.55E-05,0.000609,1.55E-05,0.000609\nNetilmicin Action Pathway,SMPDB,0,,0 (0),1,1,4,C00020;C00148;C00041;C00135,20 (20),1.55E-05,0.000609,1.55E-05,0.000609\nNeomycin Action Pathway,SMPDB,0,,0 (0),1,1,4,C00020;C00148;C00041;C00135,20 (20),1.55E-05,0.000609,1.55E-05,0.000609\nTobramycin Action Pathway,SMPDB,0,,0 (0),1,1,4,C00020;C00148;C00041;C00135,20 (20),1.55E-05,0.000609,1.55E-05,0.000609\nParomomycin Action Pathway,SMPDB,0,,0 (0),1,1,4,C00020;C00148;C00041;C00135,20 (20),1.55E-05,0.000609,1.55E-05,0.000609\nMinocycline Action Pathway,SMPDB,0,,0 (0),1,1,4,C00020;C00148;C00041;C00135,20 (20),1.55E-05,0.000609,1.55E-05,0.000609\nTetracycline Action Pathway,SMPDB,0,,0 (0),1,1,4,C00020;C00148;C00041;C00135,20 (20),1.55E-05,0.000609,1.55E-05,0.000609\nLincomycin Action Pathway,SMPDB,0,,0 (0),1,1,4,C00020;C00148;C00041;C00135,20 (20),1.55E-05,0.000609,1.55E-05,0.000609\nClindamycin Action Pathway,SMPDB,0,,0 (0),1,1,4,C00020;C00148;C00041;C00135,20 (20),1.55E-05,0.000609,1.55E-05,0.000609\nAzithromycin Action Pathway,SMPDB,0,,0 (0),1,1,4,C00020;C00148;C00041;C00135,20 (20),1.55E-05,0.000609,1.55E-05,0.000609\nArbekacin Action Pathway,SMPDB,0,,0 (0),1,1,4,C00020;C00148;C00041;C00135,20 (20),1.55E-05,0.000609,1.55E-05,0.000609\nTigecycline Action Pathway,SMPDB,0,,0 (0),1,1,4,C00020;C00148;C00041;C00135,20 (20),1.55E-05,0.000609,1.55E-05,0.000609\nErythromycin Action Pathway,SMPDB,0,,0 (0),1,1,4,C00020;C00148;C00041;C00135,20 (20),1.55E-05,0.000609,1.55E-05,0.000609\nAmikacin Action Pathway,SMPDB,0,,0 (0),1,1,4,C00020;C00148;C00041;C00135,20 (20),1.55E-05,0.000609,1.55E-05,0.000609\nClomocycline Action Pathway,SMPDB,0,,0 (0),1,1,4,C00020;C00148;C00041;C00135,20 (20),1.55E-05,0.000609,1.55E-05,0.000609\nDoxycycline Action Pathway,SMPDB,0,,0 (0),1,1,4,C00020;C00148;C00041;C00135,20 (20),1.55E-05,0.000609,1.55E-05,0.000609\nDemeclocycline Action Pathway,SMPDB,0,,0 (0),1,1,4,C00020;C00148;C00041;C00135,20 (20),1.55E-05,0.000609,1.55E-05,0.000609\nOxytetracycline Action Pathway,SMPDB,0,,0 (0),1,1,4,C00020;C00148;C00041;C00135,20 (20),1.55E-05,0.000609,1.55E-05,0.000609\nLymecycline Action Pathway,SMPDB,0,,0 (0),1,1,4,C00020;C00148;C00041;C00135,20 (20),1.55E-05,0.000609,1.55E-05,0.000609\nTelithromycin Action Pathway,SMPDB,0,,0 (0),1,1,4,C00020;C00148;C00041;C00135,20 (20),1.55E-05,0.000609,1.55E-05,0.000609\nClarithromycin Action Pathway,SMPDB,0,,0 (0),1,1,4,C00020;C00148;C00041;C00135,20 (20),1.55E-05,0.000609,1.55E-05,0.000609\nTroleandomycin Action Pathway,SMPDB,0,,0 (0),1,1,4,C00020;C00148;C00041;C00135,20 (20),1.55E-05,0.000609,1.55E-05,0.000609\nProtein digestion and absorption - Homo sapiens (human),KEGG,0,,87 (89),1,1,6,C00148;C00025;C00097;C00041;C00047;C00135,47 (47),1.45E-06,0.000144,2.09E-05,0.000657\nArgininemia,SMPDB,0,,13 (13),1,1,5,C00077;C00327;C00041;C00020;C00025,27 (27),1.77E-06,0.000151,2.52E-05,0.000669\nCitrullinemia Type I,SMPDB,0,,13 (13),1,1,5,C00077;C00327;C00041;C00020;C00025,27 (27),1.77E-06,0.000151,2.52E-05,0.000669\nCarbamoyl Phosphate Synthetase Deficiency,SMPDB,0,,13 (13),1,1,5,C00077;C00327;C00041;C00020;C00025,27 (27),1.77E-06,0.000151,2.52E-05,0.000669\nArgininosuccinic Aciduria,SMPDB,0,,13 (13),1,1,5,C00077;C00327;C00041;C00020;C00025,27 (27),1.77E-06,0.000151,2.52E-05,0.000669\nUrea Cycle,SMPDB,0,,13 (13),1,1,5,C00077;C00327;C00041;C00020;C00025,27 (27),1.77E-06,0.000151,2.52E-05,0.000669\nOrnithine Transcarbamylase Deficiency (OTC Deficiency),SMPDB,0,,13 (13),1,1,5,C00077;C00327;C00041;C00020;C00025,27 (27),1.77E-06,0.000151,2.52E-05,0.000669\nAminoacyl-tRNA biosynthesis - Homo sapiens (human),KEGG,0,,66 (66),1,1,6,C00148;C00025;C00097;C00041;C00047;C00135,52 (52),2.67E-06,0.000222,3.70E-05,0.000956\nGene Expression,Reactome,3,EEF1D;HNRNPF;RPL9,1182 (1251),0.379,1,7,C00148;C00020;C00025;C00097;C00041;C00047;C00135,97 (104),8.73E-06,0.000528,4.50E-05,0.000991\nPhase II conjugation,Reactome,0,,94 (102),1,1,8,C00148;C00020;C00025;C00097;C00180;C00041;C00047;C00135,122 (145),3.69E-06,0.000244,4.98E-05,0.000991\nDefective AHCY causes Hypermethioninemia with S-adenosylhomocysteine hydrolase deficiency (HMAHCHD),Reactome,0,,94 (102),1,1,8,C00148;C00020;C00025;C00097;C00180;C00041;C00047;C00135,122 (145),3.69E-06,0.000244,4.98E-05,0.000991\nDefective GCLC causes Hemolytic anemia due to gamma-glutamylcysteine synthetase deficiency (HAGGSD),Reactome,0,,94 (102),1,1,8,C00148;C00020;C00025;C00097;C00180;C00041;C00047;C00135,122 (145),3.69E-06,0.000244,4.98E-05,0.000991\nDefective UGT1A1 causes hyperbilirubinemia,Reactome,0,,94 (102),1,1,8,C00148;C00020;C00025;C00097;C00180;C00041;C00047;C00135,122 (145),3.69E-06,0.000244,4.98E-05,0.000991\nDefective GSS causes Glutathione synthetase deficiency (GSS deficiency),Reactome,0,,94 (102),1,1,8,C00148;C00020;C00025;C00097;C00180;C00041;C00047;C00135,122 (145),3.69E-06,0.000244,4.98E-05,0.000991\nDefective GGT1 causes Glutathionuria (GLUTH),Reactome,0,,94 (102),1,1,8,C00148;C00020;C00025;C00097;C00180;C00041;C00047;C00135,122 (145),3.69E-06,0.000244,4.98E-05,0.000991\nDefective UGT1A4 causes hyperbilirubinemia,Reactome,0,,94 (102),1,1,8,C00148;C00020;C00025;C00097;C00180;C00041;C00047;C00135,122 (145),3.69E-06,0.000244,4.98E-05,0.000991\nDefective MAT1A causes Methionine adenosyltransferase deficiency (MATD),Reactome,0,,94 (102),1,1,8,C00148;C00020;C00025;C00097;C00180;C00041;C00047;C00135,122 (145),3.69E-06,0.000244,4.98E-05,0.000991\nDefective SLC35D1 causes Schneckenbecken dysplasia (SCHBCKD),Reactome,0,,94 (102),1,1,8,C00148;C00020;C00025;C00097;C00180;C00041;C00047;C00135,122 (145),3.69E-06,0.000244,4.98E-05,0.000991\nDefective TPMT causes Thiopurine S-methyltransferase deficiency (TPMT deficiency),Reactome,0,,94 (102),1,1,8,C00148;C00020;C00025;C00097;C00180;C00041;C00047;C00135,122 (145),3.69E-06,0.000244,4.98E-05,0.000991\nDefective OPLAH causes 5-oxoprolinase deficiency (OPLAHD),Reactome,0,,94 (102),1,1,8,C00148;C00020;C00025;C00097;C00180;C00041;C00047;C00135,122 (145),3.69E-06,0.000244,4.98E-05,0.000991\nFoxO signaling pathway - Homo sapiens (human),KEGG,0,,127 (134),1,1,3,C00020;C00031;C00025,5 (5),4.73E-06,0.000296,6.27E-05,0.00122\nGlycolysis / Gluconeogenesis - Homo sapiens (human),KEGG,3,ADH5;PGK2;ACSS2,66 (67),0.000232,0.303,2,C00031;C00197,31 (31),0.0253,0.229,7.68E-05,0.00147\nMetabolism of proteins,Reactome,5,EEF1D;EIF5A2;SEC24C;SEC13;RPL9,662 (693),0.00618,1,6,C00020;C00025;C00170;C01571;C06423;C00315,148 (163),0.000998,0.0166,8.01E-05,0.00151\nArginine and proline metabolism - Homo sapiens (human),KEGG,1,GLS,60 (61),0.106,1,6,C00077;C00148;C00791;C00025;C00315;C00327,91 (91),7.00E-05,0.00227,9.49E-05,0.00175\nDisease,Reactome,4,SEC24C;XRCC6;SEC13;ACSS2,1747 (1852),0.412,1,14,C00077;C00158;C00148;C00020;C00197;C01835;C00025;C00097;C00180;C00315;C00327;C00041;C00047;C00135,533 (699),2.61E-05,0.00099,0.000133,0.00242\nBiological oxidations,Reactome,1,ACSS2,174 (189),0.278,1,9,C00148;C00020;C00025;C00097;C00180;C00315;C00041;C00047;C00135,220 (278),3.96E-05,0.00147,0.000137,0.00244\nOne carbon donor,Wikipathways,0,,1 (1),1,1,4,C00077;C00245;C00097;C00315,19 (23),1.25E-05,0.000609,0.000153,0.00265\npurine nucleotides degradation,HumanCyc,0,,12 (12),1,1,4,C00385;C00020;C00130;C00366,19 (19),1.25E-05,0.000609,0.000153,0.00265\nAmino acid conjugation of benzoic acid,Wikipathways,1,ACSS2,4 (4),0.00741,1,2,C00020;C00180,9 (9),0.00219,0.0312,0.000195,0.00331\nUrea cycle,Reactome,0,,9 (10),1,1,4,C00077;C00327;C00020;C00025,21 (21),1.91E-05,0.00074,0.000226,0.00378\nTaurine and hypotaurine metabolism - Homo sapiens (human),KEGG,0,,10 (10),1,1,4,C00041;C00245;C00097;C00025,22 (22),2.32E-05,0.000889,0.00027,0.00444\nMetabolism of carbohydrates,Wikipathways,0,,0 (0),1,1,5,C00158;C00020;C00197;C01835;C00025,66 (69),0.000157,0.00459,0.000157,0.00459\nMetabolism of nucleotides,Reactome,0,,81 (83),1,1,7,C00106;C00366;C00385;C00025;C00020;C00130;C00041,122 (135),3.95E-05,0.00147,0.00044,0.00712\nGamma carboxylation_ hypusine formation and arylsulfatase activation,Reactome,1,EIF5A2,35 (35),0.0631,1,3,C00020;C00170;C00315,23 (23),0.000758,0.0135,0.000524,0.00725\nHyperornithinemia with gyrate atrophy (HOGA),SMPDB,0,,20 (20),1,1,5,C00327;C00077;C00148;C00020;C00025,52 (52),4.94E-05,0.00163,0.000539,0.00725\nCreatine deficiency_ guanidinoacetate methyltransferase deficiency,SMPDB,0,,20 (20),1,1,5,C00327;C00077;C00148;C00020;C00025,52 (52),4.94E-05,0.00163,0.000539,0.00725\nL-arginine:glycine amidinotransferase deficiency,SMPDB,0,,20 (20),1,1,5,C00327;C00077;C00148;C00020;C00025,52 (52),4.94E-05,0.00163,0.000539,0.00725\nHyperornithinemia-hyperammonemia-homocitrullinuria [HHH-syndrome],SMPDB,0,,20 (20),1,1,5,C00327;C00077;C00148;C00020;C00025,52 (52),4.94E-05,0.00163,0.000539,0.00725\nGuanidinoacetate Methyltransferase Deficiency (GAMT Deficiency),SMPDB,0,,20 (20),1,1,5,C00327;C00077;C00148;C00020;C00025,52 (52),4.94E-05,0.00163,0.000539,0.00725\nProlinemia Type II,SMPDB,0,,20 (20),1,1,5,C00327;C00077;C00148;C00020;C00025,52 (52),4.94E-05,0.00163,0.000539,0.00725\nProlidase Deficiency (PD),SMPDB,0,,20 (20),1,1,5,C00327;C00077;C00148;C00020;C00025,52 (52),4.94E-05,0.00163,0.000539,0.00725\nHyperprolinemia Type I,SMPDB,0,,20 (20),1,1,5,C00327;C00077;C00148;C00020;C00025,52 (52),4.94E-05,0.00163,0.000539,0.00725\nHyperprolinemia Type II,SMPDB,0,,20 (20),1,1,5,C00327;C00077;C00148;C00020;C00025,52 (52),4.94E-05,0.00163,0.000539,0.00725\nOrnithine Aminotransferase Deficiency (OAT Deficiency),SMPDB,0,,20 (20),1,1,5,C00327;C00077;C00148;C00020;C00025,52 (52),4.94E-05,0.00163,0.000539,0.00725\nArginine: Glycine Amidinotransferase Deficiency (AGAT Deficiency),SMPDB,0,,20 (20),1,1,5,C00327;C00077;C00148;C00020;C00025,52 (52),4.94E-05,0.00163,0.000539,0.00725\nArginine and Proline Metabolism,SMPDB,0,,20 (20),1,1,5,C00327;C00077;C00148;C00020;C00025,52 (52),4.94E-05,0.00163,0.000539,0.00725\nmolybdenum cofactor biosynthesis,HumanCyc,0,,4 (4),1,1,3,C00020;C00041;C00097,10 (13),5.52E-05,0.00181,0.000596,0.00791\nMetabolic disorders of biological oxidation enzymes,Reactome,1,ACSS2,584 (641),0.672,1,10,C00077;C00148;C00020;C00025;C00097;C00180;C00315;C00041;C00047;C00135,305 (382),9.00E-05,0.00285,0.000649,0.0085\nMetabolism of water-soluble vitamins and cofactors,Wikipathways,0,,0 (0),1,1,5,C00020;C00041;C00047;C00097;C00025,83 (96),0.000462,0.00988,0.000462,0.00988\nurate biosynthesis/inosine 5_-phosphate degradation,HumanCyc,0,,6 (6),1,1,3,C00385;C00130;C00366,11 (11),7.55E-05,0.00241,0.000792,0.0101\nGlucose-Alanine Cycle,SMPDB,0,,8 (8),1,1,3,C00031;C00041;C00025,11 (11),7.55E-05,0.00241,0.000792,0.0101\nAlanine_ aspartate and glutamate metabolism - Homo sapiens (human),KEGG,1,GLS,35 (35),0.0631,1,3,C00158;C00041;C00025,28 (28),0.00136,0.0214,0.000892,0.0113\nSynthesis_ Secretion_ and Deacylation of Ghrelin,Wikipathways,0,,0 (0),1,1,2,C01571;C06423,5 (5),0.000621,0.0121,0.000621,0.0121\ntriacylglycerol degradation,HumanCyc,0,,15 (15),1,1,4,C01571;C00116;C06423;C01601,32 (56),0.000108,0.00334,0.00109,0.0134\nPurine catabolism,Reactome,0,,11 (11),1,1,4,C00385;C00020;C00130;C00366,32 (32),0.000108,0.00334,0.00109,0.0134\nCOPII (Coat Protein 2) Mediated Vesicle Transport,Reactome,2,SEC24C;SEC13,9 (10),0.000117,0.228,0,,3 (3),1,1,0.00118,0.0142\nER to Golgi Transport,Reactome,2,SEC24C;SEC13,9 (10),0.000117,0.228,0,,3 (3),1,1,0.00118,0.0142\nGluconeogenesis,Reactome,0,,32 (33),1,1,4,C00158;C00020;C00197;C00025,33 (33),0.000122,0.00375,0.00122,0.0145\nadenosine nucleotides degradation,HumanCyc,0,,9 (9),1,1,3,C00385;C00020;C00366,13 (13),0.000129,0.00395,0.00129,0.0151\nSREBP signalling,Wikipathways,2,SEC24C;SEC13,60 (60),0.00543,1,1,C00031,3 (5),0.024,0.219,0.0013,0.0151\nHypoacetylaspartia,SMPDB,0,,14 (14),1,1,4,C00020;C00327;C00130;C00025,34 (34),0.000137,0.00408,0.00136,0.0153\nCanavan Disease,SMPDB,0,,14 (14),1,1,4,C00020;C00327;C00130;C00025,34 (34),0.000137,0.00408,0.00136,0.0153\nAspartate Metabolism,SMPDB,0,,14 (14),1,1,4,C00020;C00327;C00130;C00025,34 (34),0.000137,0.00408,0.00136,0.0153\n[2Fe-2S] iron-sulfur cluster biosynthesis,HumanCyc,0,,0 (0),1,1,2,C00041;C00097,6 (6),0.000927,0.0155,0.000927,0.0155\nPurine metabolism,Reactome,0,,34 (36),1,1,5,C00385;C00020;C00130;C00366;C00025,66 (67),0.000157,0.00459,0.00153,0.017\nTrans-sulfuration pathway,Wikipathways,0,,10 (10),1,1,3,C00245;C00097;C00025,14 (14),0.000164,0.00476,0.00159,0.0175\nGPCR downstream signaling,Wikipathways,0,,0 (0),1,1,5,C00077;C00020;C00116;C00047;C00025,101 (121),0.00114,0.0183,0.00114,0.0183\nPost-translational protein modification,Reactome,3,EIF5A2;SEC24C;SEC13,281 (290),0.0144,1,4,C00020;C00025;C00170;C00315,113 (126),0.0122,0.129,0.00169,0.0184\nglutamine degradation/glutamate biosynthesis,HumanCyc,1,GLS,3 (3),0.00556,1,1,C00025,4 (4),0.0319,0.26,0.00171,0.0184\nSynthesis_ secretion_ and deacylation of Ghrelin,Reactome,0,,8 (9),1,1,2,C01571;C06423,3 (3),0.000188,0.00542,0.0018,0.0192\nguanosine ribonucleotides de novo biosynthesis,HumanCyc,0,,13 (13),1,1,3,C00020;C00130;C00025,15 (15),0.000204,0.00577,0.00193,0.0201\nMolybdenum cofactor biosynthesis,Reactome,0,,6 (6),1,1,3,C00020;C00041;C00097,15 (20),0.000204,0.00577,0.00193,0.0201\nGlutathione metabolism - Homo sapiens (human),KEGG,0,,51 (51),1,1,4,C00077;C00025;C00097;C00315,38 (38),0.000213,0.006,0.00202,0.0201\nXanthine Dehydrogenase Deficiency (Xanthinuria),SMPDB,0,,37 (37),1,1,5,C00385;C00020;C00130;C00025;C00366,72 (72),0.000237,0.00601,0.00222,0.0201\nAdenylosuccinate Lyase Deficiency,SMPDB,0,,37 (37),1,1,5,C00385;C00020;C00130;C00025;C00366,72 (72),0.000237,0.00601,0.00222,0.0201\nAICA-Ribosiduria,SMPDB,0,,37 (37),1,1,5,C00385;C00020;C00130;C00025;C00366,72 (72),0.000237,0.00601,0.00222,0.0201\nAdenine phosphoribosyltransferase deficiency (APRT),SMPDB,0,,37 (37),1,1,5,C00385;C00020;C00130;C00025;C00366,72 (72),0.000237,0.00601,0.00222,0.0201\nPurine Metabolism,SMPDB,0,,37 (37),1,1,5,C00385;C00020;C00130;C00025;C00366,72 (72),0.000237,0.00601,0.00222,0.0201\nMolybdenum Cofactor Deficiency,SMPDB,0,,37 (37),1,1,5,C00385;C00020;C00130;C00025;C00366,72 (72),0.000237,0.00601,0.00222,0.0201\nAdenosine Deaminase Deficiency,SMPDB,0,,37 (37),1,1,5,C00385;C00020;C00130;C00025;C00366,72 (72),0.000237,0.00601,0.00222,0.0201\nGout or Kelley-Seegmiller Syndrome,SMPDB,0,,37 (37),1,1,5,C00385;C00020;C00130;C00025;C00366,72 (72),0.000237,0.00601,0.00222,0.0201\nLesch-Nyhan Syndrome (LNS),SMPDB,0,,37 (37),1,1,5,C00385;C00020;C00130;C00025;C00366,72 (72),0.000237,0.00601,0.00222,0.0201\nXanthinuria type I,SMPDB,0,,37 (37),1,1,5,C00385;C00020;C00130;C00025;C00366,72 (72),0.000237,0.00601,0.00222,0.0201\nXanthinuria type II,SMPDB,0,,37 (37),1,1,5,C00385;C00020;C00130;C00025;C00366,72 (72),0.000237,0.00601,0.00222,0.0201\nPurine Nucleoside Phosphorylase Deficiency,SMPDB,0,,37 (37),1,1,5,C00385;C00020;C00130;C00025;C00366,72 (72),0.000237,0.00601,0.00222,0.0201\nMitochondrial DNA depletion syndrome,SMPDB,0,,37 (37),1,1,5,C00385;C00020;C00130;C00025;C00366,72 (72),0.000237,0.00601,0.00222,0.0201\nMyoadenylate deaminase deficiency,SMPDB,0,,37 (37),1,1,5,C00385;C00020;C00130;C00025;C00366,72 (72),0.000237,0.00601,0.00222,0.0201\nsphingosine and sphingosine-1-phosphate metabolism,HumanCyc,0,,9 (9),1,1,4,C01571;C06423;C01601;C00346,40 (59),0.000261,0.00657,0.00242,0.0217\nacetate conversion to acetyl-CoA,HumanCyc,1,ACSS2,3 (3),0.00556,1,1,C00020,6 (6),0.0475,0.336,0.00244,0.0217\nMethionine Adenosyltransferase Deficiency,SMPDB,0,,19 (20),1,1,4,C00020;C00170;C00097;C00315,41 (42),0.000288,0.00685,0.00263,0.0217\nGlycine N-methyltransferase Deficiency,SMPDB,0,,19 (20),1,1,4,C00020;C00170;C00097;C00315,41 (42),0.000288,0.00685,0.00263,0.0217\nHypermethioninemia,SMPDB,0,,19 (20),1,1,4,C00020;C00170;C00097;C00315,41 (42),0.000288,0.00685,0.00263,0.0217\nMethylenetetrahydrofolate Reductase Deficiency (MTHFRD),SMPDB,0,,19 (20),1,1,4,C00020;C00170;C00097;C00315,41 (42),0.000288,0.00685,0.00263,0.0217\nHomocystinuria-megaloblastic anemia due to defect in cobalamin metabolism_ cblG complementation type,SMPDB,0,,19 (20),1,1,4,C00020;C00170;C00097;C00315,41 (42),0.000288,0.00685,0.00263,0.0217\nCystathionine Beta-Synthase Deficiency,SMPDB,0,,19 (20),1,1,4,C00020;C00170;C00097;C00315,41 (42),0.000288,0.00685,0.00263,0.0217\nS-Adenosylhomocysteine (SAH) Hydrolase Deficiency,SMPDB,0,,19 (20),1,1,4,C00020;C00170;C00097;C00315,41 (42),0.000288,0.00685,0.00263,0.0217\nMethionine Metabolism,SMPDB,0,,19 (20),1,1,4,C00020;C00170;C00097;C00315,41 (42),0.000288,0.00685,0.00263,0.0217\nurea cycle,HumanCyc,0,,6 (7),1,1,3,C00327;C00077;C00020,17 (17),0.000301,0.00685,0.00274,0.0217\nPyruvate Carboxylase Deficiency,SMPDB,0,,5 (5),1,1,3,C00020;C00041;C00025,17 (17),0.000301,0.00685,0.00274,0.0217\nPrimary Hyperoxaluria Type I,SMPDB,0,,5 (5),1,1,3,C00020;C00041;C00025,17 (17),0.000301,0.00685,0.00274,0.0217\nAlanine Metabolism,SMPDB,0,,5 (5),1,1,3,C00020;C00041;C00025,17 (17),0.000301,0.00685,0.00274,0.0217\nSpermidine and Spermine Biosynthesis,SMPDB,0,,6 (6),1,1,3,C00077;C00170;C00315,17 (17),0.000301,0.00685,0.00274,0.0217\nLactic Acidemia,SMPDB,0,,5 (5),1,1,3,C00020;C00041;C00025,17 (17),0.000301,0.00685,0.00274,0.0217\nguanosine nucleotides de novo biosynthesis,HumanCyc,0,,16 (16),1,1,3,C00020;C00130;C00025,17 (17),0.000301,0.00685,0.00274,0.0217\nGastrin-CREB signalling pathway via PKC and MAPK,Reactome,0,,203 (222),1,1,4,C00077;C00116;C00047;C00025,42 (46),0.000316,0.0071,0.00287,0.0223\nG alpha (q) signalling events,Reactome,0,,175 (190),1,1,4,C00077;C00116;C00047;C00025,42 (46),0.000316,0.0071,0.00287,0.0223\nTransport of glucose and other sugars_ bile salts and organic acids_ metal ions and amine compounds,Wikipathways,0,,0 (0),1,1,3,C00158;C00148;C00366,29 (29),0.00151,0.0232,0.00151,0.0232\nGlucose metabolism,Reactome,0,,67 (70),1,1,4,C00158;C00020;C00197;C00025,43 (43),0.000347,0.00774,0.00311,0.024\nHypusine synthesis from eIF5A-lysine,Reactome,1,EIF5A2,4 (4),0.00741,1,1,C00315,6 (6),0.0475,0.336,0.00315,0.0241\nthio-molybdenum cofactor biosynthesis,HumanCyc,0,,1 (1),1,1,2,C00041;C00097,4 (6),0.000375,0.00821,0.00333,0.025\nalanine biosynthesis/degradation,HumanCyc,0,,2 (2),1,1,2,C00041;C00025,4 (4),0.000375,0.00821,0.00333,0.025\nProton-coupled neutral amino acid transporters,Reactome,0,,2 (2),1,1,2,C00041;C00148,4 (4),0.000375,0.00821,0.00333,0.025\nMethionine and cysteine metabolism,EHMN,0,,79 (80),1,1,5,C00020;C00041;C00245;C00097;C00025,80 (80),0.000389,0.00847,0.00344,0.0256\nVitamin B12 Metabolism,Wikipathways,0,,50 (51),1,1,4,C00031;C00097;C02477;C00791,46 (59),0.000451,0.00976,0.00392,0.029\nMercaptopurine Action Pathway,SMPDB,0,,47 (47),1,1,5,C00020;C00385;C00130;C00025;C00366,83 (83),0.000462,0.00988,0.00401,0.0294\nAzathioprine Action Pathway,SMPDB,0,,47 (47),1,1,5,C00020;C00385;C00130;C00025;C00366,84 (84),0.000488,0.0103,0.00421,0.0305\nThioguanine Action Pathway,SMPDB,0,,47 (47),1,1,5,C00020;C00385;C00130;C00025;C00366,84 (84),0.000488,0.0103,0.00421,0.0305\nMitochondrial Iron-Sulfur Cluster Biogenesis,Wikipathways,0,,0 (0),1,1,2,C00041;C00097,9 (12),0.00219,0.0312,0.00219,0.0312\nSelenium Micronutrient Network,Wikipathways,0,,76 (83),1,1,5,C00385;C00031;C02477;C00097;C00366,85 (104),0.000516,0.0108,0.00442,0.0313\n2-Hydroxyglutric Aciduria (D And L Form),SMPDB,0,,22 (23),1,1,4,C00020;C00041;C00025;C00097,48 (48),0.000531,0.0108,0.00454,0.0313\nHomocarnosinosis,SMPDB,0,,22 (23),1,1,4,C00020;C00041;C00025;C00097,48 (48),0.000531,0.0108,0.00454,0.0313\nSuccinic semialdehyde dehydrogenase deficiency,SMPDB,0,,22 (23),1,1,4,C00020;C00041;C00025;C00097,48 (48),0.000531,0.0108,0.00454,0.0313\n4-Hydroxybutyric Aciduria/Succinic Semialdehyde Dehydrogenase Deficiency,SMPDB,0,,22 (23),1,1,4,C00020;C00041;C00025;C00097,48 (48),0.000531,0.0108,0.00454,0.0313\nGlutamate Metabolism,SMPDB,0,,22 (23),1,1,4,C00020;C00041;C00025;C00097,48 (48),0.000531,0.0108,0.00454,0.0313\nHyperinsulinism-Hyperammonemia Syndrome,SMPDB,0,,22 (23),1,1,4,C00020;C00041;C00025;C00097,48 (48),0.000531,0.0108,0.00454,0.0313\nVitamin E metabolism,EHMN,0,,43 (43),1,1,3,C00020;C00047;C02477,21 (21),0.000576,0.0116,0.00487,0.0334\nNAD Biosynthesis II (from tryptophan),Wikipathways,0,,8 (8),1,1,3,C00020;C00041;C00025,22 (23),0.000663,0.0121,0.00552,0.0336\nDefective MMACHC causes methylmalonic aciduria and homocystinuria type cblC,Reactome,0,,83 (88),1,1,5,C00020;C00041;C00025;C00097;C00047,90 (101),0.000672,0.0121,0.00558,0.0336\nMetabolism of vitamins and cofactors,Reactome,0,,83 (88),1,1,5,C00020;C00041;C00025;C00097;C00047,90 (101),0.000672,0.0121,0.00558,0.0336\nDefective GIF causes intrinsic factor deficiency,Reactome,0,,83 (88),1,1,5,C00020;C00041;C00025;C00097;C00047,90 (101),0.000672,0.0121,0.00558,0.0336\nDefective AMN causes hereditary megaloblastic anemia 1,Reactome,0,,83 (88),1,1,5,C00020;C00041;C00025;C00097;C00047,90 (101),0.000672,0.0121,0.00558,0.0336\nDefective MMAB causes methylmalonic aciduria type cblB,Reactome,0,,83 (88),1,1,5,C00020;C00041;C00025;C00097;C00047,90 (101),0.000672,0.0121,0.00558,0.0336\nDefective MMAA causes methylmalonic aciduria type cblA,Reactome,0,,83 (88),1,1,5,C00020;C00041;C00025;C00097;C00047,90 (101),0.000672,0.0121,0.00558,0.0336\nDefects in vitamin and cofactor metabolism,Reactome,0,,83 (88),1,1,5,C00020;C00041;C00025;C00097;C00047,90 (101),0.000672,0.0121,0.00558,0.0336\nDefective MUT causes methylmalonic aciduria mut type,Reactome,0,,83 (88),1,1,5,C00020;C00041;C00025;C00097;C00047,90 (101),0.000672,0.0121,0.00558,0.0336\nMetabolism of water-soluble vitamins and cofactors,Reactome,0,,83 (88),1,1,5,C00020;C00041;C00025;C00097;C00047,90 (101),0.000672,0.0121,0.00558,0.0336\nDefective TCN2 causes hereditary megaloblastic anemia,Reactome,0,,83 (88),1,1,5,C00020;C00041;C00025;C00097;C00047,90 (101),0.000672,0.0121,0.00558,0.0336\nDefective CD320 causes methylmalonic aciduria,Reactome,0,,83 (88),1,1,5,C00020;C00041;C00025;C00097;C00047,90 (101),0.000672,0.0121,0.00558,0.0336\nDefective MTRR causes methylmalonic aciduria and homocystinuria type cblE,Reactome,0,,83 (88),1,1,5,C00020;C00041;C00025;C00097;C00047,90 (101),0.000672,0.0121,0.00558,0.0336\nDefects in cobalamin (B12) metabolism,Reactome,0,,83 (88),1,1,5,C00020;C00041;C00025;C00097;C00047,90 (101),0.000672,0.0121,0.00558,0.0336\nDefective HLCS causes multiple carboxylase deficiency,Reactome,0,,83 (88),1,1,5,C00020;C00041;C00025;C00097;C00047,90 (101),0.000672,0.0121,0.00558,0.0336\nDefective BTD causes biotidinase deficiency,Reactome,0,,83 (88),1,1,5,C00020;C00041;C00025;C00097;C00047,90 (101),0.000672,0.0121,0.00558,0.0336\nDefects in biotin (Btn) metabolism,Reactome,0,,83 (88),1,1,5,C00020;C00041;C00025;C00097;C00047,90 (101),0.000672,0.0121,0.00558,0.0336\nDefective LMBRD1 causes methylmalonic aciduria and homocystinuria type cblF,Reactome,0,,83 (88),1,1,5,C00020;C00041;C00025;C00097;C00047,90 (101),0.000672,0.0121,0.00558,0.0336\nDefective CUBN causes hereditary megaloblastic anemia 1,Reactome,0,,83 (88),1,1,5,C00020;C00041;C00025;C00097;C00047,90 (101),0.000672,0.0121,0.00558,0.0336\nDefective MTR causes methylmalonic aciduria and homocystinuria type cblG,Reactome,0,,83 (88),1,1,5,C00020;C00041;C00025;C00097;C00047,90 (101),0.000672,0.0121,0.00558,0.0336\nDefective MMADHC causes methylmalonic aciduria and homocystinuria type cblD,Reactome,0,,83 (88),1,1,5,C00020;C00041;C00025;C00097;C00047,90 (101),0.000672,0.0121,0.00558,0.0336\nD-Glutamine and D-glutamate metabolism - Homo sapiens (human),KEGG,1,GLS,4 (4),0.00741,1,1,C00025,12 (12),0.0927,0.469,0.00569,0.034\nSulfur amino acid metabolism,Reactome,0,,23 (25),1,1,4,C00097;C00025;C00170;C00245,52 (59),0.000723,0.0129,0.00595,0.0354\nAntigen Presentation: Folding_ assembly and peptide loading of class I MHC,Reactome,2,SEC24C;SEC13,23 (24),0.000809,0.79,0,,7 (7),1,1,0.00657,0.0389\nPurine metabolism,EHMN,0,,220 (228),1,1,5,C00385;C00020;C00130;C00025;C00366,94 (94),0.00082,0.0145,0.00665,0.0391\nGastrin-CREB signalling pathway via PKC and MAPK,Wikipathways,0,,0 (0),1,1,3,C00077;C00047;C00025,36 (40),0.00285,0.0393,0.00285,0.0393\nGlycolysis,Reactome,0,,28 (29),1,1,3,C00158;C00020;C00197,24 (24),0.000862,0.0151,0.00694,0.0399\nNAD de novo biosynthesis,HumanCyc,0,,12 (15),1,1,3,C00020;C00041;C00025,24 (24),0.000862,0.0151,0.00694,0.0399\n3-Phosphoglycerate dehydrogenase deficiency,SMPDB,0,,26 (27),1,1,4,C00020;C00041;C00025;C00097,55 (55),0.000895,0.0151,0.00718,0.0399\nNon Ketotic Hyperglycinemia,SMPDB,0,,26 (27),1,1,4,C00020;C00041;C00025;C00097,55 (55),0.000895,0.0151,0.00718,0.0399\nGlycine and Serine Metabolism,SMPDB,0,,26 (27),1,1,4,C00020;C00041;C00025;C00097,55 (55),0.000895,0.0151,0.00718,0.0399\nDimethylglycinuria,SMPDB,0,,26 (27),1,1,4,C00020;C00041;C00025;C00097,55 (55),0.000895,0.0151,0.00718,0.0399\nHyperglycinemia_ non-ketotic,SMPDB,0,,26 (27),1,1,4,C00020;C00041;C00025;C00097,55 (55),0.000895,0.0151,0.00718,0.0399\nDimethylglycine Dehydrogenase Deficiency,SMPDB,0,,26 (27),1,1,4,C00020;C00041;C00025;C00097,55 (55),0.000895,0.0151,0.00718,0.0399\nDihydropyrimidine Dehydrogenase Deficiency (DHPD),SMPDB,0,,26 (27),1,1,4,C00020;C00041;C00025;C00097,55 (55),0.000895,0.0151,0.00718,0.0399\nSarcosinemia,SMPDB,0,,26 (27),1,1,4,C00020;C00041;C00025;C00097,55 (55),0.000895,0.0151,0.00718,0.0399\nGlycolysis and Gluconeogenesis,EHMN,1,PGK2,68 (70),0.119,1,3,C00158;C00020;C00197,52 (52),0.00807,0.0921,0.00764,0.0423\nFolate-Alcohol and Cancer Pathway,Wikipathways,1,ADH5,8 (8),0.0148,1,1,C00097,9 (10),0.0703,0.401,0.00817,0.045\nMetabolism of carbohydrates,Reactome,0,,256 (261),1,1,5,C00158;C00020;C00197;C01835;C00025,100 (120),0.00109,0.0177,0.0085,0.0454\nMyoclonic epilepsy of Lafora,Reactome,0,,256 (261),1,1,5,C00158;C00020;C00197;C01835;C00025,100 (120),0.00109,0.0177,0.0085,0.0454\nGlycogen storage diseases,Reactome,0,,256 (261),1,1,5,C00158;C00020;C00197;C01835;C00025,100 (120),0.00109,0.0177,0.0085,0.0454\nsuperpathway of tryptophan utilization,HumanCyc,0,,43 (47),1,1,4,C00020;C00041;C00025;C00097,58 (70),0.00109,0.0177,0.00856,0.0454\nBeta-mercaptolactate-cysteine disulfiduria,SMPDB,0,,9 (9),1,1,3,C00020;C00025;C00097,26 (26),0.0011,0.0177,0.00856,0.0454\nCysteine Metabolism,SMPDB,0,,9 (9),1,1,3,C00020;C00025;C00097,26 (26),0.0011,0.0177,0.00856,0.0454\nCystinosis_ ocular nonnephropathic,SMPDB,0,,9 (9),1,1,3,C00020;C00025;C00097,26 (26),0.0011,0.0177,0.00856,0.0454\nethanol degradation IV,HumanCyc,1,ACSS2,6 (6),0.0111,1,1,C00020,13 (13),0.1,0.491,0.00866,0.0457\nfatty acid β-oxidation,HumanCyc,0,,19 (20),1,1,4,C00020;C01571;C06423;C01601,60 (85),0.00124,0.0199,0.00957,0.0503\nSynthesis of diphthamide-EEF2,Reactome,0,,8 (8),1,1,2,C00020;C00170,7 (7),0.00129,0.0204,0.00988,0.0511\nspermidine biosynthesis,HumanCyc,0,,2 (2),1,1,2,C00170;C00315,7 (7),0.00129,0.0204,0.00988,0.0511\nspermine biosynthesis,HumanCyc,0,,2 (2),1,1,2,C00170;C00315,7 (7),0.00129,0.0204,0.00988,0.0511\nfatty acid β-oxidation (peroxisome),HumanCyc,0,,19 (20),1,1,4,C00020;C01571;C06423;C01601,61 (85),0.00132,0.0208,0.0101,0.052\nethanol degradation II,HumanCyc,1,ACSS2,8 (8),0.0148,1,1,C00020,12 (12),0.0927,0.469,0.0104,0.0532\nGPCR downstream signaling,Reactome,0,,930 (986),1,1,5,C00077;C00020;C00116;C00025;C00047,107 (126),0.00147,0.023,0.0111,0.0565\noxidative ethanol degradation III,HumanCyc,1,ACSS2,7 (7),0.0129,1,1,C00020,15 (15),0.115,0.54,0.0111,0.0565\nacyl-CoA hydrolysis,HumanCyc,0,,3 (3),1,1,3,C01571;C06423;C01601,29 (39),0.00151,0.0232,0.0113,0.0567\nMineral absorption - Homo sapiens (human),KEGG,0,,50 (51),1,1,3,C00148;C00031;C00041,29 (29),0.00151,0.0232,0.0113,0.0567\nAmmonia Recycling,SMPDB,0,,12 (12),1,1,3,C00020;C00025;C00135,29 (29),0.00151,0.0232,0.0113,0.0567\nsuperpathway of purine nucleotide salvage,HumanCyc,0,,59 (61),1,1,3,C00020;C00130;C00025,30 (30),0.00167,0.0254,0.0124,0.0613\nMetabolism of polyamines,Reactome,0,,14 (14),1,1,3,C00077;C00170;C00315,30 (34),0.00167,0.0254,0.0124,0.0613\nConjugation of benzoate with glycine,Reactome,0,,5 (6),1,1,2,C00020;C00180,8 (8),0.00171,0.0256,0.0126,0.0614\nlipoate biosynthesis and incorporation,HumanCyc,0,,3 (3),1,1,2,C00020;C06423,8 (8),0.00171,0.0256,0.0126,0.0614\nL-cysteine degradation II,HumanCyc,0,,2 (2),1,1,2,C00025;C00097,8 (8),0.00171,0.0256,0.0126,0.0614\nBiotin transport and metabolism,Reactome,0,,11 (11),1,1,2,C00020;C00047,8 (8),0.00171,0.0256,0.0126,0.0614\nGlutamate Neurotransmitter Release Cycle,Reactome,1,GLS,20 (21),0.0365,1,1,C00025,6 (6),0.0475,0.336,0.0127,0.0617\nEthanol Degradation,SMPDB,1,ACSS2,7 (7),0.0129,1,1,C00020,18 (18),0.136,0.616,0.0129,0.0621\nMembrane Trafficking,Reactome,2,SEC24C;SEC13,153 (162),0.0322,1,1,C00020,7 (7),0.0551,0.361,0.013,0.0624\nbeta-Alanine metabolism - Homo sapiens (human),KEGG,0,,30 (30),1,1,3,C00106;C00135;C00315,31 (31),0.00184,0.0274,0.0134,0.0638\nEthanol oxidation,Reactome,1,ACSS2,10 (10),0.0184,1,1,C00020,13 (13),0.1,0.491,0.0134,0.0638\nTransport to the Golgi and subsequent modification,Reactome,2,SEC24C;SEC13,36 (37),0.00199,1,0,,23 (24),1,1,0.0143,0.0678\nsphingomyelin metabolism/ceramide salvage,HumanCyc,0,,8 (8),1,1,3,C01571;C06423;C01601,32 (55),0.00202,0.03,0.0146,0.0685\nglutathione biosynthesis,HumanCyc,0,,3 (4),1,1,2,C00025;C00097,9 (9),0.00219,0.0312,0.0156,0.0708\nL-glutamine tRNA biosynthesis,HumanCyc,0,,2 (2),1,1,2,C00020;C00025,9 (9),0.00219,0.0312,0.0156,0.0708\ndiphthamide biosynthesis,HumanCyc,0,,2 (2),1,1,2,C00020;C00170,9 (9),0.00219,0.0312,0.0156,0.0708\nproline degradation,HumanCyc,0,,2 (2),1,1,2,C00148;C00025,9 (10),0.00219,0.0312,0.0156,0.0708\nCarnosinuria_ carnosinemia,SMPDB,0,,9 (9),1,1,3,C00106;C00025;C00135,33 (34),0.00221,0.0312,0.0157,0.0708\nUreidopropionase deficiency,SMPDB,0,,9 (9),1,1,3,C00106;C00025;C00135,33 (34),0.00221,0.0312,0.0157,0.0708\nGABA-Transaminase Deficiency,SMPDB,0,,9 (9),1,1,3,C00106;C00025;C00135,33 (34),0.00221,0.0312,0.0157,0.0708\nOrganic cation/anion/zwitterion transport,Reactome,0,,13 (13),1,1,3,C00315;C00366;C00791,33 (36),0.00221,0.0312,0.0157,0.0708\nBeta-Alanine Metabolism,SMPDB,0,,9 (9),1,1,3,C00106;C00025;C00135,33 (34),0.00221,0.0312,0.0157,0.0708\nphospholipases,HumanCyc,0,,41 (42),1,1,3,C01571;C06423;C01601,33 (55),0.00221,0.0312,0.0157,0.0708\nsuperpathway of conversion of glucose to acetyl CoA and entry into the TCA cycle,HumanCyc,1,PGK2,48 (52),0.0855,1,2,C00158;C00197,34 (36),0.0301,0.26,0.0179,0.0804\nPentose phosphate pathway - Homo sapiens (human),KEGG,0,,28 (28),1,1,3,C00031;C00197;C00257,35 (35),0.00262,0.0369,0.0182,0.0813\nSulfur relay system - Homo sapiens (human),KEGG,0,,10 (10),1,1,2,C00041;C00097,10 (10),0.00273,0.0377,0.0188,0.0826\nProline catabolism,Reactome,0,,2 (2),1,1,2,C00148;C00025,10 (10),0.00273,0.0377,0.0188,0.0826\nL-cysteine degradation I,HumanCyc,0,,2 (2),1,1,2,C00025;C00097,10 (10),0.00273,0.0377,0.0188,0.0826\nactions of nitric oxide in the heart,BioCarta,0,,41 (43),1,1,2,C00327;C00020,10 (10),0.00273,0.0377,0.0188,0.0826\nCori Cycle,Wikipathways,1,PGK2,15 (15),0.0275,1,1,C00031,13 (15),0.1,0.491,0.019,0.0829\nTriglyceride Biosynthesis,Reactome,0,,43 (45),1,1,3,C00158;C00020;C00116,37 (40),0.00308,0.0422,0.0209,0.0905\nPurine ribonucleoside monophosphate biosynthesis,Reactome,0,,11 (12),1,1,3,C00020;C00130;C00025,37 (37),0.00308,0.0422,0.0209,0.0905\nAbacavir transport and metabolism,Wikipathways,0,,0 (0),1,1,2,C00020;C00130,17 (24),0.00795,0.0916,0.00795,0.0916\nleucine degradation,HumanCyc,1,IVD,12 (12),0.0221,1,1,C00025,20 (20),0.15,0.653,0.0222,0.092\nDNA Repair,Reactome,1,XRCC6,117 (120),0.196,1,2,C00020;C00106,25 (32),0.0169,0.169,0.0222,0.092\nserine biosynthesis (phosphorylated route),HumanCyc,0,,4 (4),1,1,2,C00197;C00025,11 (11),0.00332,0.0438,0.0222,0.092\nasparagine biosynthesis,HumanCyc,0,,3 (3),1,1,2,C00020;C00025,11 (11),0.00332,0.0438,0.0222,0.092\ntaurine biosynthesis,HumanCyc,0,,3 (3),1,1,2,C00245;C00097,11 (11),0.00332,0.0438,0.0222,0.092\nPhosphate bond hydrolysis by NUDT proteins,Reactome,0,,6 (6),1,1,2,C00020;C00130,11 (22),0.00332,0.0438,0.0222,0.092\nSphingolipid Metabolism,SMPDB,0,,22 (22),1,1,3,C00031;C00836;C00346,38 (39),0.00333,0.0438,0.0223,0.092\nGaucher Disease,SMPDB,0,,22 (22),1,1,3,C00031;C00836;C00346,38 (39),0.00333,0.0438,0.0223,0.092\nGloboid Cell Leukodystrophy,SMPDB,0,,22 (22),1,1,3,C00031;C00836;C00346,38 (39),0.00333,0.0438,0.0223,0.092\nMetachromatic Leukodystrophy (MLD),SMPDB,0,,22 (22),1,1,3,C00031;C00836;C00346,38 (39),0.00333,0.0438,0.0223,0.092\nFabry disease,SMPDB,0,,22 (22),1,1,3,C00031;C00836;C00346,38 (39),0.00333,0.0438,0.0223,0.092\nKrabbe disease,SMPDB,0,,22 (22),1,1,3,C00031;C00836;C00346,38 (39),0.00333,0.0438,0.0223,0.092\nTransport of Glycerol from Adipocytes to the Liver by Aquaporins,Wikipathways,0,,0 (0),1,1,1,C00116,1 (1),0.00806,0.0921,0.00806,0.0921\nHistidine Metabolism,SMPDB,0,,15 (15),1,1,3,C00020;C00025;C00135,39 (40),0.00358,0.0467,0.0238,0.0969\nHistidinemia,SMPDB,0,,15 (15),1,1,3,C00020;C00025;C00135,39 (40),0.00358,0.0467,0.0238,0.0969\nthe visual cycle I (vertebrates),HumanCyc,0,,17 (18),1,1,3,C01571;C06423;C01601,39 (58),0.00358,0.0467,0.0238,0.0969\nGlycerophospholipid Biosynthetic Pathway,Wikipathways,0,,0 (0),1,1,2,C00116;C00346,18 (25),0.0089,0.0984,0.0089,0.0984\nformaldehyde oxidation,HumanCyc,1,ADH5,2 (2),0.00371,1,0,,11 (11),1,1,0.0245,0.0993\npurine nucleotides de novo biosynthesis,HumanCyc,0,,59 (62),1,1,3,C00020;C00130;C00025,40 (40),0.00385,0.05,0.0253,0.102\nAlpha9 beta1 integrin signaling events,PID,0,,24 (26),1,1,2,C00327;C00315,12 (13),0.00396,0.0504,0.0259,0.102\nguanosine nucleotides degradation,HumanCyc,0,,4 (4),1,1,2,C00385;C00366,12 (12),0.00396,0.0504,0.0259,0.102\nTaurine and Hypotaurine Metabolism,SMPDB,0,,5 (5),1,1,2,C00245;C00097,12 (12),0.00396,0.0504,0.0259,0.102\nchrebp regulation by carbohydrates and camp,BioCarta,0,,40 (42),1,1,2,C00020;C00031,12 (12),0.00396,0.0504,0.0259,0.102\nadenosine ribonucleotides de novo biosynthesis,HumanCyc,0,,38 (40),1,1,2,C00020;C00130,12 (12),0.00396,0.0504,0.0259,0.102\nSignaling by GPCR,Reactome,0,,1046 (1108),1,1,5,C00077;C00020;C00116;C00025;C00047,134 (153),0.00396,0.0504,0.0259,0.102\nValproic Acid Metabolism Pathway,SMPDB,1,IVD,11 (11),0.0202,1,1,C00020,29 (29),0.21,0.847,0.0275,0.108\nSignal Transduction,Reactome,0,,1966 (2077),1,1,6,C00077;C00116;C00020;C00025;C00327;C00047,198 (231),0.0044,0.0558,0.0283,0.111\nClass C/3 (Metabotropic glutamate/pheromone receptors),Reactome,0,,36 (41),1,1,3,C00077;C00047;C00025,42 (54),0.00443,0.0559,0.0284,0.111\nStimuli-sensing channels,Reactome,0,,94 (102),1,1,2,C00020;C00366,13 (14),0.00465,0.0577,0.0296,0.114\nNon-homologous end-joining - Homo sapiens (human),KEGG,1,XRCC6,13 (13),0.0239,1,0,,0 (0),1,1,0.0239,1\ncoenzyme A biosynthesis,HumanCyc,0,,7 (7),1,1,2,C00020;C00097,13 (13),0.00465,0.0577,0.0296,0.114\nCoenzyme A biosynthesis,Reactome,0,,8 (9),1,1,2,C00020;C00097,13 (13),0.00465,0.0577,0.0296,0.114\nSerine biosynthesis,Reactome,0,,3 (3),1,1,2,C00197;C00025,13 (13),0.00465,0.0577,0.0296,0.114\nMHC class II antigen presentation,Reactome,2,SEC24C;SEC13,59 (65),0.00526,1,0,,3 (3),1,1,0.0329,0.124\nHistidine metabolism,EHMN,0,,35 (36),1,1,3,C00020;C00025;C00135,45 (45),0.00539,0.0652,0.0335,0.124\nTaste transduction - Homo sapiens (human),KEGG,0,,49 (53),1,1,2,C00130;C00025,13 (13),0.00465,0.0577,0.0296,0.114\nConjugation of carboxylic acids,Reactome,0,,8 (9),1,1,2,C00020;C00180,14 (15),0.0054,0.0652,0.0336,0.124\nAmino Acid conjugation,Reactome,0,,8 (9),1,1,2,C00020;C00180,14 (15),0.0054,0.0652,0.0336,0.124\ncitrulline-nitric oxide cycle,HumanCyc,0,,5 (5),1,1,2,C00327;C00020,14 (14),0.0054,0.0652,0.0336,0.124\nproline biosynthesis,HumanCyc,0,,4 (4),1,1,2,C00148;C00025,14 (14),0.0054,0.0652,0.0336,0.124\nHistidine catabolism,Reactome,0,,5 (5),1,1,2,C00025;C00135,14 (14),0.0054,0.0652,0.0336,0.124\nhistidine degradation,HumanCyc,0,,6 (6),1,1,2,C00025;C00135,14 (14),0.0054,0.0652,0.0336,0.124\nNAD biosynthesis from 2-amino-3-carboxymuconate semialdehyde,HumanCyc,0,,5 (6),1,1,2,C00020;C00025,14 (14),0.0054,0.0652,0.0336,0.124\nDisulfiram Action Pathway,SMPDB,1,ACSS2,25 (25),0.0454,1,2,C00020;C00025,75 (77),0.122,0.566,0.0343,0.126\nProximal tubule bicarbonate reclamation - Homo sapiens (human),KEGG,1,GLS,23 (23),0.0419,1,1,C00025,17 (17),0.129,0.59,0.0336,0.124\nThe citric acid (TCA) cycle and respiratory electron transport,Reactome,0,,147 (156),1,1,3,C00158;C00020;C00989,47 (49),0.00609,0.0729,0.0371,0.136\nBile salt and organic anion SLC transporters,Reactome,0,,13 (14),1,1,2,C00158;C00791,15 (17),0.0062,0.0731,0.0377,0.136\nCreatine metabolism,Reactome,0,,11 (11),1,1,2,C00077;C00791,15 (15),0.0062,0.0731,0.0377,0.136\nornithine de novo biosynthesis,HumanCyc,0,,5 (5),1,1,2,C00077;C00025,15 (15),0.0062,0.0731,0.0377,0.136\nVitamin B5 (pantothenate) metabolism,Reactome,0,,12 (13),1,1,2,C00020;C00097,15 (15),0.0062,0.0731,0.0377,0.136\nPeptide hormone metabolism,Reactome,0,,49 (50),1,1,2,C01571;C06423,15 (16),0.0062,0.0731,0.0377,0.136\nglycolysis,HumanCyc,1,PGK2,25 (26),0.0454,1,1,C00197,19 (20),0.143,0.636,0.0392,0.14\nPhosphatidylinositol phosphate metabolism,EHMN,0,,92 (95),1,1,3,C00116;C03546;C00346,49 (49),0.00684,0.0804,0.0409,0.146\nAbacavir metabolism,Reactome,0,,5 (5),1,1,2,C00020;C00130,16 (23),0.00705,0.0823,0.042,0.149\nlysine degradation I (saccharopine pathway),HumanCyc,0,,4 (4),1,1,2,C00025;C00047,16 (16),0.00705,0.0823,0.042,0.149\nValine_ leucine and isoleucine degradation,EHMN,1,IVD,49 (49),0.0872,1,2,C00020;C00025,62 (62),0.0884,0.466,0.0452,0.16\nBranched-chain amino acid catabolism,Reactome,1,IVD,17 (17),0.0311,1,1,C00025,36 (36),0.254,0.98,0.0461,0.162\nAbacavir transport and metabolism,Reactome,0,,10 (10),1,1,2,C00020;C00130,17 (24),0.00795,0.0916,0.0464,0.162\nBiogenic Amine Synthesis,Wikipathways,0,,15 (15),1,1,2,C00025;C00135,17 (17),0.00795,0.0916,0.0464,0.162\n4-hydroxybenzoate biosynthesis,HumanCyc,0,,1 (1),1,1,2,C00020;C00025,17 (17),0.00795,0.0916,0.0464,0.162\nTransport of glycerol from adipocytes to the liver by Aquaporins,Reactome,0,,2 (5),1,1,1,C00116,1 (1),0.00806,0.0921,0.0469,0.163\nLysine metabolism,EHMN,0,,61 (62),1,1,3,C00020;C00047;C00025,53 (53),0.00851,0.0968,0.0491,0.17\ngluconeogenesis,HumanCyc,1,PGK2,26 (27),0.0472,1,1,C00197,25 (26),0.184,0.767,0.0499,0.171\nGamma-glutamyl-transpeptidase deficiency,SMPDB,0,,11 (11),1,1,2,C00025;C00097,18 (18),0.0089,0.0984,0.0509,0.171\n5-oxoprolinase deficiency,SMPDB,0,,11 (11),1,1,2,C00025;C00097,18 (18),0.0089,0.0984,0.0509,0.171\nGamma-Glutamyltransferase Deficiency,SMPDB,0,,11 (11),1,1,2,C00025;C00097,18 (18),0.0089,0.0984,0.0509,0.171\nGlutathione Metabolism,SMPDB,0,,11 (11),1,1,2,C00025;C00097,18 (18),0.0089,0.0984,0.0509,0.171\nGlutathione Synthetase Deficiency,SMPDB,0,,11 (11),1,1,2,C00025;C00097,18 (18),0.0089,0.0984,0.0509,0.171\n5-Oxoprolinuria,SMPDB,0,,11 (11),1,1,2,C00025;C00097,18 (18),0.0089,0.0984,0.0509,0.171\nserine and glycine biosynthesis,HumanCyc,0,,8 (8),1,1,2,C00197;C00025,18 (18),0.0089,0.0984,0.0509,0.171\nGlutathione metabolism,Wikipathways,0,,20 (20),1,1,2,C00025;C00097,18 (18),0.0089,0.0984,0.0509,0.171\nGlycosphingolipid metabolism,EHMN,0,,69 (69),1,1,3,C00031;C00836;C00346,55 (55),0.00943,0.104,0.0534,0.175\nTyrosine metabolism,EHMN,0,,105 (110),1,1,4,C00020;C00041;C00025;C00097,105 (105),0.00945,0.104,0.0535,0.175\nPurine metabolism - Homo sapiens (human),KEGG,0,,170 (176),1,1,4,C00385;C00020;C00130;C00366,92 (92),0.00595,0.0715,0.0364,0.134\nFatty Acid Biosynthesis,SMPDB,0,,2 (2),1,1,2,C01571;C06423,19 (31),0.0099,0.108,0.0556,0.175\nPyrimidine metabolism,Reactome,0,,25 (25),1,1,3,C00106;C00041;C00025,56 (57),0.00991,0.108,0.0556,0.175\nWarburg Effect,SMPDB,0,,45 (45),1,1,3,C00158;C00031;C00025,56 (56),0.00991,0.108,0.0556,0.175\nGlycolysis and Gluconeogenesis,Wikipathways,1,PGK2,49 (49),0.0872,1,1,C00031,15 (16),0.115,0.54,0.056,0.175\nDefective ALG2 causes ALG2-CDG (CDG-1i),Reactome,2,SEC24C;SEC13,119 (122),0.0202,1,1,C00025,84 (94),0.497,1,0.0562,0.175\nDefective DPAGT1 causes DPAGT1-CDG (CDG-1j) and CMSTA2,Reactome,2,SEC24C;SEC13,119 (122),0.0202,1,1,C00025,84 (94),0.497,1,0.0562,0.175\nDefective ALG1 causes ALG1-CDG (CDG-1k),Reactome,2,SEC24C;SEC13,119 (122),0.0202,1,1,C00025,84 (94),0.497,1,0.0562,0.175\nDefective ALG11 causes ALG11-CDG (CDG-1p),Reactome,2,SEC24C;SEC13,119 (122),0.0202,1,1,C00025,84 (94),0.497,1,0.0562,0.175\nAsparagine N-linked glycosylation,Reactome,2,SEC24C;SEC13,119 (122),0.0202,1,1,C00025,84 (94),0.497,1,0.0562,0.175\nDefective RFT1 causes RFT1-CDG (CDG-1n),Reactome,2,SEC24C;SEC13,119 (122),0.0202,1,1,C00025,84 (94),0.497,1,0.0562,0.175\nDefective MPDU1 causes MPDU1-CDG (CDG-1f),Reactome,2,SEC24C;SEC13,119 (122),0.0202,1,1,C00025,84 (94),0.497,1,0.0562,0.175\nDefective ALG8 causes ALG8-CDG (CDG-1h),Reactome,2,SEC24C;SEC13,119 (122),0.0202,1,1,C00025,84 (94),0.497,1,0.0562,0.175\nDefective ALG6 causes ALG6-CDG (CDG-1c),Reactome,2,SEC24C;SEC13,119 (122),0.0202,1,1,C00025,84 (94),0.497,1,0.0562,0.175\nDefective ALG14 causes congenital myasthenic syndrome (ALG14-CMS),Reactome,2,SEC24C;SEC13,119 (122),0.0202,1,1,C00025,84 (94),0.497,1,0.0562,0.175\nDiseases associated with N-glycosylation of proteins,Reactome,2,SEC24C;SEC13,119 (122),0.0202,1,1,C00025,84 (94),0.497,1,0.0562,0.175\nDefective ALG9 causes ALG9-CDG (CDG-1l),Reactome,2,SEC24C;SEC13,119 (122),0.0202,1,1,C00025,84 (94),0.497,1,0.0562,0.175\nDefective ALG3 causes ALG3-CDG (CDG-1d),Reactome,2,SEC24C;SEC13,119 (122),0.0202,1,1,C00025,84 (94),0.497,1,0.0562,0.175\nDefective B4GALT1 causes B4GALT1-CDG (CDG-2d),Reactome,2,SEC24C;SEC13,119 (122),0.0202,1,1,C00025,84 (94),0.497,1,0.0562,0.175\nDefective ALG12 causes ALG12-CDG (CDG-1g),Reactome,2,SEC24C;SEC13,119 (122),0.0202,1,1,C00025,84 (94),0.497,1,0.0562,0.175\nDefective MAN1B1 causes MRT15,Reactome,2,SEC24C;SEC13,119 (122),0.0202,1,1,C00025,84 (94),0.497,1,0.0562,0.175\nDefective MGAT2 causes MGAT2-CDG (CDG-2a),Reactome,2,SEC24C;SEC13,119 (122),0.0202,1,1,C00025,84 (94),0.497,1,0.0562,0.175\nDefective MOGS causes MOGS-CDG (CDG-2b),Reactome,2,SEC24C;SEC13,119 (122),0.0202,1,1,C00025,84 (94),0.497,1,0.0562,0.175\nRetrograde endocannabinoid signaling - Homo sapiens (human),KEGG,0,,94 (103),1,1,2,C00116;C00025,19 (19),0.0099,0.108,0.0556,0.175\nGABAergic synapse - Homo sapiens (human),KEGG,1,GLS,84 (90),0.145,1,1,C00025,9 (9),0.0703,0.401,0.057,0.177\nCysteine and methionine metabolism - Homo sapiens (human),KEGG,0,,37 (38),1,1,3,C00041;C00097;C00170,57 (57),0.0104,0.113,0.0579,0.179\nNonhomologous End-joining (NHEJ),Reactome,1,XRCC6,6 (6),0.0111,1,0,,2 (2),1,1,0.061,0.187\nGlyoxylate and dicarboxylate metabolism - Homo sapiens (human),KEGG,0,,25 (25),1,1,3,C00158;C00197;C00025,58 (58),0.0109,0.118,0.0602,0.185\nlysine degradation II (pipecolate pathway),HumanCyc,0,,6 (6),1,1,2,C00025;C00047,21 (21),0.012,0.128,0.0652,0.197\nPantothenate and CoA Biosynthesis,SMPDB,0,,6 (6),1,1,2,C00020;C00097,21 (21),0.012,0.128,0.0652,0.197\nLysine catabolism,Reactome,0,,8 (8),1,1,2,C00047;C00025,21 (21),0.012,0.128,0.0652,0.197\nVitamin B5 - CoA biosynthesis from pantothenate,EHMN,0,,31 (32),1,1,2,C00020;C00097,21 (21),0.012,0.128,0.0652,0.197\nPyruvate Dehydrogenase Complex Deficiency,SMPDB,1,ACSS2,22 (22),0.0401,1,1,C00020,46 (46),0.312,1,0.0674,0.2\nPrimary hyperoxaluria II_ PH2,SMPDB,1,ACSS2,22 (22),0.0401,1,1,C00020,46 (46),0.312,1,0.0674,0.2\nPyruvate kinase deficiency,SMPDB,1,ACSS2,22 (22),0.0401,1,1,C00020,46 (46),0.312,1,0.0674,0.2\nLeigh Syndrome,SMPDB,1,ACSS2,22 (22),0.0401,1,1,C00020,46 (46),0.312,1,0.0674,0.2\nPyruvate Metabolism,SMPDB,1,ACSS2,22 (22),0.0401,1,1,C00020,46 (46),0.312,1,0.0674,0.2\nPyruvate Decarboxylase E1 Component Deficiency (PDHE1 Deficiency),SMPDB,1,ACSS2,22 (22),0.0401,1,1,C00020,46 (46),0.312,1,0.0674,0.2\nFatty Acid Beta Oxidation,Wikipathways,1,ACSS2,34 (34),0.0613,1,1,C00116,29 (32),0.21,0.847,0.0689,0.204\nHemostasis,Reactome,0,,471 (501),1,1,3,C00020;C00327;C00116,62 (63),0.0131,0.139,0.0698,0.206\nTransfer of Acetyl Groups into Mitochondria,SMPDB,0,,9 (9),1,1,2,C00158;C00031,22 (22),0.0132,0.139,0.0702,0.207\nIron uptake and transport,Wikipathways,0,,0 (0),1,1,2,C00020;C00366,29 (31),0.0224,0.215,0.0224,0.215\nPassive Transport by Aquaporins,Wikipathways,0,,0 (0),1,1,1,C00116,3 (3),0.024,0.219,0.024,0.219\nRilpivirine Action Pathway,SMPDB,0,,0 (0),1,1,1,C00020,3 (3),0.024,0.219,0.024,0.219\nISG15 antiviral mechanism,Wikipathways,0,,0 (0),1,1,1,C00020,3 (3),0.024,0.219,0.024,0.219\nSUMOylation,Wikipathways,0,,0 (0),1,1,1,C00020,3 (3),0.024,0.219,0.024,0.219\nGlutamatergic synapse - Homo sapiens (human),KEGG,1,GLS,106 (116),0.18,1,1,C00025,8 (8),0.0628,0.379,0.0618,0.189\nIon channel transport,Reactome,0,,162 (173),1,1,2,C00020;C00366,23 (24),0.0144,0.151,0.0753,0.221\nRecycling of bile acids and salts,Reactome,0,,15 (16),1,1,2,C00020;C00245,24 (24),0.0156,0.163,0.0804,0.234\nPhenylalanine and tyrosine catabolism,Reactome,0,,9 (10),1,1,2,C00041;C00025,24 (24),0.0156,0.163,0.0804,0.234\nAMPK signaling pathway - Homo sapiens (human),KEGG,0,,121 (124),1,1,2,C00020;C00031,23 (23),0.0144,0.151,0.0753,0.221\nDeadenylation of mRNA,Reactome,0,,31 (32),1,1,1,C00020,2 (3),0.0161,0.166,0.0824,0.238\nPresynaptic function of Kainate receptors,Reactome,0,,19 (21),1,1,1,C00025,2 (2),0.0161,0.166,0.0824,0.238\nbasic mechanisms of sumoylation,BioCarta,0,,6 (7),1,1,1,C00020,2 (2),0.0161,0.166,0.0824,0.238\nFatty acid_ triacylglycerol_ and ketone body metabolism,Wikipathways,0,,0 (0),1,1,3,C00158;C00020;C00116,81 (89),0.0266,0.239,0.0266,0.239\nNon-alcoholic fatty liver disease (NAFLD) - Homo sapiens (human),KEGG,0,,146 (151),1,1,1,C00031,2 (2),0.0161,0.166,0.0824,0.238\nLysine Degradation,SMPDB,0,,13 (13),1,1,2,C00047;C00025,25 (27),0.0169,0.169,0.0857,0.24\nHyperlysinemia I_ Familial,SMPDB,0,,13 (13),1,1,2,C00047;C00025,25 (27),0.0169,0.169,0.0857,0.24\n2-aminoadipic 2-oxoadipic aciduria,SMPDB,0,,13 (13),1,1,2,C00047;C00025,25 (27),0.0169,0.169,0.0857,0.24\nPyridoxine dependency with seizures,SMPDB,0,,13 (13),1,1,2,C00047;C00025,25 (27),0.0169,0.169,0.0857,0.24\nSaccharopinuria/Hyperlysinemia II,SMPDB,0,,13 (13),1,1,2,C00047;C00025,25 (27),0.0169,0.169,0.0857,0.24\nHyperlysinemia II or Saccharopinuria,SMPDB,0,,13 (13),1,1,2,C00047;C00025,25 (27),0.0169,0.169,0.0857,0.24\nmethionine salvage cycle III,HumanCyc,0,,10 (10),1,1,2,C00170;C00315,25 (25),0.0169,0.169,0.0857,0.24\nTryptophan catabolism,Reactome,0,,11 (13),1,1,2,C00041;C00025,25 (26),0.0169,0.169,0.0857,0.24\nGlutaric Aciduria Type III,SMPDB,0,,13 (13),1,1,2,C00047;C00025,25 (27),0.0169,0.169,0.0857,0.24\nNeurotransmitter Release Cycle,Reactome,1,GLS,43 (44),0.0769,1,1,C00025,31 (31),0.223,0.892,0.0868,0.243\nOrganic cation transport,Reactome,0,,8 (8),1,1,2,C00315;C00791,26 (29),0.0182,0.181,0.091,0.253\nDegradation of cysteine and homocysteine,Reactome,0,,8 (8),1,1,2,C00245;C00097,26 (29),0.0182,0.181,0.091,0.253\nGlycerophospholipid Biosynthetic Pathway,Wikipathways,0,,0 (0),1,1,2,C00116;C00346,35 (51),0.0318,0.26,0.0318,0.26\nMethylhistidine Metabolism,SMPDB,0,,0 (0),1,1,1,C00135,4 (4),0.0319,0.26,0.0319,0.26\nZidovudine Action Pathway,SMPDB,0,,0 (0),1,1,1,C00020,4 (4),0.0319,0.26,0.0319,0.26\nZalcitabine Action Pathway,SMPDB,0,,0 (0),1,1,1,C00020,4 (4),0.0319,0.26,0.0319,0.26\nStavudine Action Pathway,SMPDB,0,,0 (0),1,1,1,C00020,4 (4),0.0319,0.26,0.0319,0.26\nNevirapine Action Pathway,SMPDB,0,,0 (0),1,1,1,C00020,4 (4),0.0319,0.26,0.0319,0.26\nLamivudine Action Pathway,SMPDB,0,,0 (0),1,1,1,C00020,4 (4),0.0319,0.26,0.0319,0.26\nEmtricitabine Action Pathway,SMPDB,0,,0 (0),1,1,1,C00020,4 (4),0.0319,0.26,0.0319,0.26\nEfavirenz Action Pathway,SMPDB,0,,0 (0),1,1,1,C00020,4 (4),0.0319,0.26,0.0319,0.26\nAbacavir Action Pathway,SMPDB,0,,0 (0),1,1,1,C00020,4 (4),0.0319,0.26,0.0319,0.26\nDidanosine Action Pathway,SMPDB,0,,0 (0),1,1,1,C00020,4 (4),0.0319,0.26,0.0319,0.26\nDelavirdine Action Pathway,SMPDB,0,,0 (0),1,1,1,C00020,4 (4),0.0319,0.26,0.0319,0.26\nSphingolipid metabolism - Homo sapiens (human),KEGG,0,,46 (47),1,1,2,C00836;C00346,25 (25),0.0169,0.169,0.0857,0.24\nFatty Acyl-CoA Biosynthesis,Reactome,0,,21 (22),1,1,2,C00158;C00020,27 (30),0.0195,0.193,0.0964,0.264\nNicotinate metabolism,Reactome,0,,11 (12),1,1,2,C00020;C00025,27 (27),0.0195,0.193,0.0964,0.264\n3-Methylglutaconic Aciduria Type I,SMPDB,1,IVD,30 (30),0.0543,1,1,C00025,58 (58),0.377,1,0.1,0.264\nValine_ Leucine and Isoleucine Degradation,SMPDB,1,IVD,30 (30),0.0543,1,1,C00025,58 (58),0.377,1,0.1,0.264\n2-Methyl-3-Hydroxybutryl CoA Dehydrogenase Deficiency,SMPDB,1,IVD,30 (30),0.0543,1,1,C00025,58 (58),0.377,1,0.1,0.264\nIsovaleric Aciduria,SMPDB,1,IVD,30 (30),0.0543,1,1,C00025,58 (58),0.377,1,0.1,0.264\n3-Methylcrotonyl Coa Carboxylase Deficiency Type I,SMPDB,1,IVD,30 (30),0.0543,1,1,C00025,58 (58),0.377,1,0.1,0.264\nPropionic Acidemia,SMPDB,1,IVD,30 (30),0.0543,1,1,C00025,58 (58),0.377,1,0.1,0.264\nMaple Syrup Urine Disease,SMPDB,1,IVD,30 (30),0.0543,1,1,C00025,58 (58),0.377,1,0.1,0.264\n3-Hydroxy-3-Methylglutaryl-CoA Lyase Deficiency,SMPDB,1,IVD,30 (30),0.0543,1,1,C00025,58 (58),0.377,1,0.1,0.264\nIsobutyryl-coa dehydrogenase deficiency,SMPDB,1,IVD,30 (30),0.0543,1,1,C00025,58 (58),0.377,1,0.1,0.264\n3-hydroxyisobutyric aciduria,SMPDB,1,IVD,30 (30),0.0543,1,1,C00025,58 (58),0.377,1,0.1,0.264\n3-hydroxyisobutyric acid dehydrogenase deficiency,SMPDB,1,IVD,30 (30),0.0543,1,1,C00025,58 (58),0.377,1,0.1,0.264\nIsovaleric acidemia,SMPDB,1,IVD,30 (30),0.0543,1,1,C00025,58 (58),0.377,1,0.1,0.264\nMethylmalonate Semialdehyde Dehydrogenase Deficiency,SMPDB,1,IVD,30 (30),0.0543,1,1,C00025,58 (58),0.377,1,0.1,0.264\nMethylmalonic Aciduria,SMPDB,1,IVD,30 (30),0.0543,1,1,C00025,58 (58),0.377,1,0.1,0.264\n3-Methylglutaconic Aciduria Type IV,SMPDB,1,IVD,30 (30),0.0543,1,1,C00025,58 (58),0.377,1,0.1,0.264\n3-Methylglutaconic Aciduria Type III,SMPDB,1,IVD,30 (30),0.0543,1,1,C00025,58 (58),0.377,1,0.1,0.264\nBeta-Ketothiolase Deficiency,SMPDB,1,IVD,30 (30),0.0543,1,1,C00025,58 (58),0.377,1,0.1,0.264\nPhenylalanine and Tyrosine Metabolism,SMPDB,0,,11 (11),1,1,2,C00020;C00025,28 (28),0.0209,0.202,0.102,0.264\nPhenylketonuria,SMPDB,0,,11 (11),1,1,2,C00020;C00025,28 (28),0.0209,0.202,0.102,0.264\nSelenoamino Acid Metabolism,SMPDB,0,,11 (12),1,1,2,C00020;C00041,28 (28),0.0209,0.202,0.102,0.264\nTyrosinemia Type 3 (TYRO3),SMPDB,0,,11 (11),1,1,2,C00020;C00025,28 (28),0.0209,0.202,0.102,0.264\nTyrosinemia Type 2 (or Richner-Hanhart syndrome),SMPDB,0,,11 (11),1,1,2,C00020;C00025,28 (28),0.0209,0.202,0.102,0.264\nCarbohydrate digestion and absorption - Homo sapiens (human),KEGG,0,,45 (45),1,1,2,C00031;C01835,27 (27),0.0195,0.193,0.0964,0.264\nGlycerolipid metabolism - Homo sapiens (human),KEGG,0,,55 (55),1,1,2,C00116;C00197,28 (28),0.0209,0.202,0.102,0.264\nPantothenate and CoA biosynthesis - Homo sapiens (human),KEGG,0,,17 (17),1,1,2,C00106;C00097,28 (28),0.0209,0.202,0.102,0.264\nPyrimidine metabolism,EHMN,0,,134 (139),1,1,3,C00020;C00106;C00025,77 (77),0.0233,0.219,0.111,0.282\nMetabolism of lipids and lipoproteins,Reactome,0,,497 (516),1,1,7,C00158;C00116;C00245;C00020;C00025;C00836;C00346,365 (447),0.0237,0.219,0.112,0.282\nL-kynurenine degradation,HumanCyc,0,,13 (15),1,1,2,C00041;C00025,30 (30),0.0238,0.219,0.113,0.282\nDNA-PK pathway in nonhomologous end joining,PID,1,XRCC6,13 (13),0.0239,1,0,,1 (1),1,1,0.113,0.282\nAntiviral mechanism by IFN-stimulated genes,Reactome,0,,40 (40),1,1,1,C00020,3 (4),0.024,0.219,0.114,0.282\nCleavage of the damaged pyrimidine ,Reactome,0,,6 (6),1,1,1,C00106,3 (8),0.024,0.219,0.114,0.282\nSulfur metabolism - Homo sapiens (human),KEGG,0,,9 (10),1,1,2,C00245;C00097,29 (29),0.0224,0.215,0.107,0.278\nActivation of Ca-permeable Kainate Receptor,Reactome,0,,12 (12),1,1,1,C00025,3 (3),0.024,0.219,0.114,0.282\nAMPK inhibits chREBP transcriptional activation activity,Reactome,0,,5 (5),1,1,1,C00020,3 (3),0.024,0.219,0.114,0.282\nActivation of Na-permeable Kainate Receptors,Reactome,0,,2 (2),1,1,1,C00025,3 (3),0.024,0.219,0.114,0.282\nActivation of PPARGC1A (PGC-1alpha) by phosphorylation,Reactome,0,,10 (10),1,1,1,C00020,3 (3),0.024,0.219,0.114,0.282\nProcessing and activation of SUMO,Reactome,0,,9 (10),1,1,1,C00020,3 (3),0.024,0.219,0.114,0.282\nActivation of AMPA receptors,Reactome,0,,2 (4),1,1,1,C00025,3 (3),0.024,0.219,0.114,0.282\nDepyrimidination,Reactome,0,,6 (6),1,1,1,C00106,3 (8),0.024,0.219,0.114,0.282\nSUMO is conjugated to E1 (UBA2:SAE1),Reactome,0,,5 (6),1,1,1,C00020,3 (3),0.024,0.219,0.114,0.282\nISG15 antiviral mechanism,Reactome,0,,40 (40),1,1,1,C00020,3 (4),0.024,0.219,0.114,0.282\nSUMOylation,Reactome,0,,9 (10),1,1,1,C00020,3 (3),0.024,0.219,0.114,0.282\nTryptophan metabolism,EHMN,0,,22 (22),1,1,3,C00020;C00041;C00025,78 (78),0.0241,0.219,0.114,0.282\nThe citric acid (TCA) cycle and respiratory electron transport,Wikipathways,0,,0 (0),1,1,2,C00158;C00989,37 (37),0.0352,0.283,0.0352,0.283\nSphingolipid de novo biosynthesis,Reactome,0,,32 (33),1,1,2,C00836;C00346,31 (31),0.0253,0.229,0.119,0.292\nPurine salvage,Reactome,0,,13 (14),1,1,2,C00020;C00130,31 (32),0.0253,0.229,0.119,0.292\nIRF3-mediated induction of type I IFN,Reactome,1,XRCC6,14 (14),0.0257,1,0,,5 (7),1,1,0.12,0.293\ntelomeres telomerase cellular aging and immortality,BioCarta,1,XRCC6,14 (15),0.0257,1,0,,3 (3),1,1,0.12,0.293\nEarly Phase of HIV Life Cycle,Reactome,1,XRCC6,14 (16),0.0257,1,0,,5 (5),1,1,0.12,0.293\nAmino acid conjugation,Wikipathways,0,,0 (0),1,1,1,C00245,5 (5),0.0397,0.3,0.0397,0.3\nEukaryotic Translation Elongation,Reactome,2,EEF1D;RPL9,138 (148),0.0266,1,0,,24 (25),1,1,0.123,0.301\nHuntington_s disease - Homo sapiens (human),KEGG,0,,190 (193),1,1,1,C00025,3 (3),0.024,0.219,0.114,0.282\nPropanoate metabolism,EHMN,0,,18 (18),1,1,2,C00158;C00020,33 (33),0.0285,0.255,0.13,0.316\nFatty acid_ triacylglycerol_ and ketone body metabolism,Reactome,0,,94 (96),1,1,3,C00158;C00020;C00116,85 (91),0.0301,0.26,0.136,0.325\nSTING mediated induction of host immune responses,Reactome,1,XRCC6,17 (17),0.0311,1,0,,7 (9),1,1,0.139,0.325\nNicotinate and Nicotinamide Metabolism,SMPDB,0,,14 (14),1,1,2,C00020;C00025,35 (35),0.0318,0.26,0.141,0.325\nputrescine biosynthesis I,HumanCyc,0,,1 (1),1,1,1,C00077,4 (4),0.0319,0.26,0.142,0.325\nMitochondrial biogenesis,Reactome,0,,26 (28),1,1,1,C00020,4 (4),0.0319,0.26,0.142,0.325\nButirosin and neomycin biosynthesis - Homo sapiens (human),KEGG,0,,5 (5),1,1,2,C00031;C00025,33 (33),0.0285,0.255,0.13,0.316\nPancreas Function,SMPDB,0,,6 (6),1,1,1,C00031,4 (4),0.0319,0.26,0.142,0.325\nhydrogen sulfide biosynthesis (trans-sulfuration),HumanCyc,0,,2 (3),1,1,1,C00097,4 (6),0.0319,0.26,0.142,0.325\nProcessive synthesis on the lagging strand,Reactome,0,,16 (16),1,1,1,C00020,4 (4),0.0319,0.26,0.142,0.325\nS-methyl-5_-thioadenosine degradation,HumanCyc,0,,1 (1),1,1,1,C00170,4 (4),0.0319,0.26,0.142,0.325\nIonotropic activity of Kainate Receptors,Reactome,0,,12 (12),1,1,1,C00025,4 (4),0.0319,0.26,0.142,0.325\nglutamate dependent acid resistance,HumanCyc,0,,3 (3),1,1,1,C00025,4 (4),0.0319,0.26,0.142,0.325\nRibosome biogenesis in eukaryotes - Homo sapiens (human),KEGG,1,REXO2,81 (84),0.14,1,0,,0 (0),1,1,0.14,1\naspartate biosynthesis,HumanCyc,0,,2 (2),1,1,1,C00025,4 (4),0.0319,0.26,0.142,0.325\nActivation of Kainate Receptors upon glutamate binding,Reactome,0,,30 (32),1,1,1,C00025,4 (4),0.0319,0.26,0.142,0.325\nAntigen processing: Ubiquitination & Proteasome degradation,Reactome,0,,163 (178),1,1,1,C00020,4 (4),0.0319,0.26,0.142,0.325\nRemoval of the Flap Intermediate,Reactome,0,,14 (14),1,1,1,C00020,4 (4),0.0319,0.26,0.142,0.325\nhistamine biosynthesis,HumanCyc,0,,1 (1),1,1,1,C00135,4 (4),0.0319,0.26,0.142,0.325\nType II diabetes mellitus,Wikipathways,0,,21 (21),1,1,1,C00031,4 (4),0.0319,0.26,0.142,0.325\nSodium-coupled sulphate_ di- and tri-carboxylate transporters,Reactome,0,,5 (6),1,1,1,C00158,4 (4),0.0319,0.26,0.142,0.325\nTCA Cycle Nutrient Utilization and Invasiveness of Ovarian Cancer,Wikipathways,0,,5 (5),1,1,1,C00031,4 (4),0.0319,0.26,0.142,0.325\nClass II GLUTs,Reactome,0,,4 (4),1,1,1,C00366,4 (4),0.0319,0.26,0.142,0.325\nregulators of bone mineralization,BioCarta,0,,11 (11),1,1,1,C00020,4 (4),0.0319,0.26,0.142,0.325\nno2-dependent il-12 pathway in nk cells,BioCarta,0,,8 (9),1,1,1,C00327,4 (4),0.0319,0.26,0.142,0.325\nCam-PDE 1 activation,Reactome,0,,4 (6),1,1,1,C00020,4 (4),0.0319,0.26,0.142,0.325\nglycine biosynthesis,HumanCyc,0,,2 (2),1,1,1,C00041,4 (4),0.0319,0.26,0.142,0.325\nsuperpathway of methionine degradation,HumanCyc,0,,19 (20),1,1,2,C00025;C00097,36 (36),0.0335,0.27,0.147,0.326\nButanoate metabolism,EHMN,0,,24 (24),1,1,2,C00020;C00025,36 (36),0.0335,0.27,0.147,0.326\nVitamin B3 (nicotinate and nicotinamide) metabolism,EHMN,0,,47 (47),1,1,2,C00020;C00025,36 (36),0.0335,0.27,0.147,0.326\nGalactose Metabolism,SMPDB,0,,13 (13),1,1,2,C00116;C00031,36 (36),0.0335,0.27,0.147,0.326\nGalactosemia,SMPDB,0,,13 (13),1,1,2,C00116;C00031,36 (36),0.0335,0.27,0.147,0.326\nPhase 1 - Functionalization of compounds,Reactome,1,ACSS2,73 (80),0.127,1,2,C00020;C00315,126 (151),0.27,1,0.15,0.326\nDefective CYP27A1 causes Cerebrotendinous xanthomatosis (CTX),Reactome,1,ACSS2,73 (80),0.127,1,2,C00020;C00315,126 (151),0.27,1,0.15,0.326\nDefective CYP1B1 causes Glaucoma,Reactome,1,ACSS2,73 (80),0.127,1,2,C00020;C00315,126 (151),0.27,1,0.15,0.326\nDefective CYP4F22 causes Ichthyosis_ congenital_ autosomal recessive 5 (ARCI5),Reactome,1,ACSS2,73 (80),0.127,1,2,C00020;C00315,126 (151),0.27,1,0.15,0.326\nDefective CYP11B2 causes Corticosterone methyloxidase 1 deficiency (CMO-1 deficiency),Reactome,1,ACSS2,73 (80),0.127,1,2,C00020;C00315,126 (151),0.27,1,0.15,0.326\nDefective CYP24A1 causes Hypercalcemia_ infantile (HCAI),Reactome,1,ACSS2,73 (80),0.127,1,2,C00020;C00315,126 (151),0.27,1,0.15,0.326\nDefective MAOA causes Brunner syndrome (BRUNS),Reactome,1,ACSS2,73 (80),0.127,1,2,C00020;C00315,126 (151),0.27,1,0.15,0.326\nDefective CYP7B1 causes Spastic paraplegia 5A_ autosomal recessive (SPG5A) and Congenital bile acid synthesis defect 3 (CBAS3),Reactome,1,ACSS2,73 (80),0.127,1,2,C00020;C00315,126 (151),0.27,1,0.15,0.326\nDefective FMO3 causes Trimethylaminuria (TMAU),Reactome,1,ACSS2,73 (80),0.127,1,2,C00020;C00315,126 (151),0.27,1,0.15,0.326\nDefective CYP21A2 causes Adrenal hyperplasia 3 (AH3),Reactome,1,ACSS2,73 (80),0.127,1,2,C00020;C00315,126 (151),0.27,1,0.15,0.326\nDefective CYP11A1 causes Adrenal insufficiency_ congenital_ with 46_XY sex reversal (AICSR),Reactome,1,ACSS2,73 (80),0.127,1,2,C00020;C00315,126 (151),0.27,1,0.15,0.326\nDefective CYP2R1 causes Rickets vitamin D-dependent 1B (VDDR1B),Reactome,1,ACSS2,73 (80),0.127,1,2,C00020;C00315,126 (151),0.27,1,0.15,0.326\nDefective CYP17A1 causes Adrenal hyperplasia 5 (AH5),Reactome,1,ACSS2,73 (80),0.127,1,2,C00020;C00315,126 (151),0.27,1,0.15,0.326\nDefective CYP11B1 causes Adrenal hyperplasia 4 (AH4),Reactome,1,ACSS2,73 (80),0.127,1,2,C00020;C00315,126 (151),0.27,1,0.15,0.326\nDefective CYP19A1 causes Aromatase excess syndrome (AEXS),Reactome,1,ACSS2,73 (80),0.127,1,2,C00020;C00315,126 (151),0.27,1,0.15,0.326\nDefective CYP26B1 causes Radiohumeral fusions with other skeletal and craniofacial anomalies (RHFCA),Reactome,1,ACSS2,73 (80),0.127,1,2,C00020;C00315,126 (151),0.27,1,0.15,0.326\nDefective CYP26C1 causes Focal facial dermal dysplasia 4 (FFDD4),Reactome,1,ACSS2,73 (80),0.127,1,2,C00020;C00315,126 (151),0.27,1,0.15,0.326\nDefective CYP27B1 causes Rickets vitamin D-dependent 1A (VDDR1A),Reactome,1,ACSS2,73 (80),0.127,1,2,C00020;C00315,126 (151),0.27,1,0.15,0.326\nDefective TBXAS1 causes Ghosal hematodiaphyseal dysplasia (GHDD),Reactome,1,ACSS2,73 (80),0.127,1,2,C00020;C00315,126 (151),0.27,1,0.15,0.326\nDefective CYP2U1 causes Spastic paraplegia 56_ autosomal recessive (SPG56),Reactome,1,ACSS2,73 (80),0.127,1,2,C00020;C00315,126 (151),0.27,1,0.15,0.326\nPentose phosphate pathway,EHMN,0,,29 (29),1,1,2,C00020;C00257,37 (37),0.0352,0.283,0.153,0.332\nGlycerophospholipid Biosynthetic Pathway,Wikipathways,0,,0 (0),1,1,1,C00116,6 (9),0.0475,0.336,0.0475,0.336\nTranslocation of GLUT4 to the Plasma Membrane,Wikipathways,0,,0 (0),1,1,1,C00020,6 (6),0.0475,0.336,0.0475,0.336\nNeurotransmitter uptake and Metabolism In Glial Cells,Wikipathways,0,,0 (0),1,1,1,C00025,6 (6),0.0475,0.336,0.0475,0.336\nDeadenylation-dependent mRNA decay,Wikipathways,0,,0 (0),1,1,1,C00020,6 (7),0.0475,0.336,0.0475,0.336\nMalonyl-coa decarboxylase deficiency,SMPDB,0,,14 (14),1,1,2,C00020;C00025,38 (38),0.037,0.292,0.159,0.339\nMalonic Aciduria,SMPDB,0,,14 (14),1,1,2,C00020;C00025,38 (38),0.037,0.292,0.159,0.339\nPropanoate Metabolism,SMPDB,0,,14 (14),1,1,2,C00020;C00025,38 (38),0.037,0.292,0.159,0.339\nMethylmalonic Aciduria Due to Cobalamin-Related Disorders,SMPDB,0,,14 (14),1,1,2,C00020;C00025,38 (38),0.037,0.292,0.159,0.339\nSelenoamino acid metabolism,EHMN,0,,35 (36),1,1,2,C00020;C00041,38 (38),0.037,0.292,0.159,0.339\nPyrimidine catabolism,Reactome,0,,12 (12),1,1,2,C00106;C00041,38 (39),0.037,0.292,0.159,0.339\nPyruvate metabolism and Citric Acid (TCA) cycle,Reactome,0,,44 (47),1,1,2,C00158;C00989,38 (38),0.037,0.292,0.159,0.339\nPI3K-Akt signaling pathway - Homo sapiens (human),KEGG,0,,335 (347),1,1,1,C00020,4 (4),0.0319,0.26,0.142,0.325\nValproic Acid Pathway_ Pharmacokinetics,PharmGKB,1,IVD,21 (21),0.0383,1,0,,1 (1),1,1,0.163,0.343\nDiseases of glycosylation,Reactome,2,SEC24C;SEC13,226 (230),0.065,1,1,C00025,109 (139),0.591,1,0.164,0.343\nSynthesis and interconversion of nucleotide di- and triphosphates,Reactome,0,,19 (19),1,1,2,C00020;C00025,39 (39),0.0388,0.3,0.165,0.343\nGABA synthesis,Reactome,0,,2 (2),1,1,1,C00025,5 (5),0.0397,0.3,0.168,0.343\nFacilitative Na+-independent glucose transporters,Reactome,0,,11 (12),1,1,1,C00366,5 (5),0.0397,0.3,0.168,0.343\nRegulation of AMPK activity via LKB1,Reactome,0,,14 (14),1,1,1,C00020,5 (5),0.0397,0.3,0.168,0.343\nGlibenclamide Action Pathway,SMPDB,0,,6 (6),1,1,1,C00031,5 (5),0.0397,0.3,0.168,0.343\nGliclazide Action Pathway,SMPDB,0,,6 (6),1,1,1,C00031,5 (5),0.0397,0.3,0.168,0.343\nRepaglinide Action Pathway,SMPDB,0,,6 (6),1,1,1,C00031,5 (5),0.0397,0.3,0.168,0.343\nNateglinide Action Pathway,SMPDB,0,,6 (6),1,1,1,C00031,5 (5),0.0397,0.3,0.168,0.343\nmRNA decay by 5_ to 3_ exoribonuclease,Reactome,0,,13 (15),1,1,1,C00020,5 (7),0.0397,0.3,0.168,0.343\nhypusine biosynthesis,HumanCyc,0,,2 (2),1,1,1,C00315,5 (5),0.0397,0.3,0.168,0.343\nPRPP biosynthesis,HumanCyc,0,,3 (3),1,1,1,C00020,5 (6),0.0397,0.3,0.168,0.343\nadenine and adenosine salvage II,HumanCyc,0,,1 (1),1,1,1,C00020,5 (5),0.0397,0.3,0.168,0.343\nthiamin salvage III,HumanCyc,0,,1 (1),1,1,1,C00020,5 (5),0.0397,0.3,0.168,0.343\nRemoval of the Flap Intermediate from the C-strand,Reactome,0,,10 (10),1,1,1,C00020,5 (5),0.0397,0.3,0.168,0.343\nRas activation uopn Ca2+ infux through NMDA receptor,Reactome,0,,18 (19),1,1,1,C00025,5 (5),0.0397,0.3,0.168,0.343\nUnblocking of NMDA receptor_ glutamate binding and activation,Reactome,0,,14 (17),1,1,1,C00025,5 (5),0.0397,0.3,0.168,0.343\nCREB phosphorylation through the activation of CaMKII,Reactome,0,,16 (17),1,1,1,C00025,5 (5),0.0397,0.3,0.168,0.343\nhypoxia-inducible factor in the cardivascular system,BioCarta,0,,15 (16),1,1,1,C00031,5 (5),0.0397,0.3,0.168,0.343\nnitric oxide signaling pathway,BioCarta,0,,18 (19),1,1,1,C00327,5 (5),0.0397,0.3,0.168,0.343\nFatty Acid Biosynthesis,Wikipathways,1,ACSS2,22 (22),0.0401,1,0,,4 (4),1,1,0.169,0.344\nGlycerophospholipid metabolism,EHMN,0,,137 (141),1,1,3,C00116;C00041;C00346,96 (96),0.0411,0.31,0.172,0.35\nDouble-Strand Break Repair,Reactome,1,XRCC6,23 (23),0.0419,1,0,,2 (2),1,1,0.175,0.355\nInsulin signaling pathway - Homo sapiens (human),KEGG,0,,136 (141),1,1,1,C00031,4 (4),0.0319,0.26,0.142,0.325\nGPCR ligand binding,Wikipathways,0,,0 (0),1,1,3,C00077;C00047;C00025,106 (125),0.0526,0.361,0.0526,0.361\nEnergy dependent regulation of mTOR by LKB1-AMPK,Wikipathways,0,,0 (0),1,1,1,C00020,7 (7),0.0551,0.361,0.0551,0.361\nSphingolipid metabolism,Wikipathways,0,,0 (0),1,1,1,C00836,7 (7),0.0551,0.361,0.0551,0.361\nbile acid biosynthesis_ neutral pathway,HumanCyc,0,,15 (15),1,1,2,C00020;C00245,42 (44),0.0444,0.334,0.183,0.369\nAminosugars metabolism,EHMN,0,,50 (51),1,1,2,C00041;C00025,43 (43),0.0464,0.336,0.189,0.372\nchondroitin sulfate degradation (metazoa),HumanCyc,0,,6 (7),1,1,3,C00379;C00116;C01835,101 (137),0.0466,0.336,0.19,0.372\nD-myo-inositol (1_4_5)-trisphosphate degradation,HumanCyc,0,,13 (13),1,1,1,C03546,6 (6),0.0475,0.336,0.192,0.372\nAstrocytic Glutamate-Glutamine Uptake And Metabolism,Reactome,0,,4 (4),1,1,1,C00025,6 (6),0.0475,0.336,0.192,0.372\nNeurotransmitter uptake and Metabolism In Glial Cells,Reactome,0,,4 (4),1,1,1,C00025,6 (6),0.0475,0.336,0.192,0.372\nα-tocopherol degradation,HumanCyc,0,,1 (1),1,1,1,C02477,6 (9),0.0475,0.336,0.192,0.372\nPDE3B signalling,Reactome,0,,2 (2),1,1,1,C00020,6 (6),0.0475,0.336,0.192,0.372\nRegulation of Rheb GTPase activity by AMPK,Reactome,0,,9 (10),1,1,1,C00020,6 (6),0.0475,0.336,0.192,0.372\nProtein processing in endoplasmic reticulum - Homo sapiens (human),KEGG,2,SEC24C;SEC13,167 (168),0.0378,1,0,,6 (6),1,1,0.162,0.343\nhomocarnosine biosynthesis,HumanCyc,0,,1 (1),1,1,1,C00135,6 (7),0.0475,0.336,0.192,0.372\nInhibition of HSL,Reactome,0,,2 (2),1,1,1,C00020,6 (6),0.0475,0.336,0.192,0.372\nDeadenylation-dependent mRNA decay,Reactome,0,,55 (58),1,1,1,C00020,6 (9),0.0475,0.336,0.192,0.372\nLagging Strand Synthesis,Reactome,0,,21 (21),1,1,1,C00020,6 (6),0.0475,0.336,0.192,0.372\nlipoate salvage,HumanCyc,0,,2 (2),1,1,1,C00020,6 (6),0.0475,0.336,0.192,0.372\nVEGFR1 specific signals,PID,0,,29 (31),1,1,1,C00327,6 (6),0.0475,0.336,0.192,0.372\nGlycogen Metabolism,Wikipathways,0,,36 (36),1,1,1,C00031,6 (6),0.0475,0.336,0.192,0.372\n5-Phosphoribose 1-diphosphate biosynthesis,Reactome,0,,3 (3),1,1,1,C00020,6 (6),0.0475,0.336,0.192,0.372\nvegf hypoxia and angiogenesis,BioCarta,0,,27 (31),1,1,1,C00327,6 (6),0.0475,0.336,0.192,0.372\nInsulin Signalling,SMPDB,0,,15 (17),1,1,1,C00031,6 (6),0.0475,0.336,0.192,0.372\nTranslocation of GLUT4 to the plasma membrane,Reactome,0,,34 (34),1,1,1,C00020,6 (6),0.0475,0.336,0.192,0.372\nDNA strand elongation,Reactome,0,,32 (32),1,1,1,C00020,6 (6),0.0475,0.336,0.192,0.372\nmRNA decay by 3_ to 5_ exoribonuclease,Reactome,0,,11 (11),1,1,1,C00020,6 (8),0.0475,0.336,0.192,0.372\nAxon guidance,Reactome,1,EVL,310 (332),0.443,1,1,C00025,14 (14),0.107,0.512,0.192,0.372\ndermatan sulfate degradation (metazoa),HumanCyc,0,,6 (7),1,1,3,C00379;C00116;C01835,102 (138),0.0478,0.338,0.193,0.373\nPhospholipid biosynthesis,Wikipathways,0,,0 (0),1,1,1,C00346,8 (8),0.0628,0.379,0.0628,0.379\nButanoate metabolism - Homo sapiens (human),KEGG,0,,26 (26),1,1,2,C00025;C00989,41 (41),0.0425,0.32,0.177,0.358\nType II diabetes mellitus - Homo sapiens (human),KEGG,0,,45 (48),1,1,1,C00031,6 (6),0.0475,0.336,0.192,0.372\nHistidine metabolism - Homo sapiens (human),KEGG,0,,23 (23),1,1,2,C00025;C00135,45 (45),0.0503,0.355,0.201,0.386\nTranslation,Reactome,2,EEF1D;RPL9,205 (217),0.0547,1,0,,28 (29),1,1,0.214,0.387\nRapoport-Luebering glycolytic shunt,HumanCyc,0,,4 (6),1,1,1,C00197,7 (7),0.0551,0.361,0.215,0.387\nConjugation of salicylate with glycine,Reactome,0,,7 (8),1,1,1,C00020,7 (8),0.0551,0.361,0.215,0.387\nEnergy dependent regulation of mTOR by LKB1-AMPK,Reactome,0,,16 (17),1,1,1,C00020,7 (7),0.0551,0.361,0.215,0.387\nGalactose metabolism - Homo sapiens (human),KEGG,0,,30 (30),1,1,2,C00116;C00031,45 (45),0.0503,0.355,0.201,0.386\nglycerol degradation,HumanCyc,0,,5 (5),1,1,1,C00116,7 (8),0.0551,0.361,0.215,0.387\nIminoglycinuria,SMPDB,0,,28 (33),1,1,1,C00097,7 (8),0.0551,0.361,0.215,0.387\nLysinuric Protein Intolerance,SMPDB,0,,28 (33),1,1,1,C00097,7 (8),0.0551,0.361,0.215,0.387\ncysteine biosynthesis/homocysteine degradation (trans-sulfuration),HumanCyc,0,,2 (3),1,1,1,C00097,7 (7),0.0551,0.361,0.215,0.387\nBlue diaper syndrome,SMPDB,0,,28 (33),1,1,1,C00097,7 (8),0.0551,0.361,0.215,0.387\nLysinuric protein intolerance (LPI),SMPDB,0,,28 (33),1,1,1,C00097,7 (8),0.0551,0.361,0.215,0.387\nIminoglycinuria,SMPDB,0,,28 (33),1,1,1,C00097,7 (8),0.0551,0.361,0.215,0.387\nCystinuria,SMPDB,0,,28 (33),1,1,1,C00097,7 (8),0.0551,0.361,0.215,0.387\nTorsemide Action Pathway,SMPDB,0,,28 (33),1,1,1,C00097,7 (9),0.0551,0.361,0.215,0.387\nBiotin Metabolism,SMPDB,0,,3 (3),1,1,1,C00047,7 (7),0.0551,0.361,0.215,0.387\ncarnosine biosynthesis,HumanCyc,0,,1 (1),1,1,1,C00135,7 (7),0.0551,0.361,0.215,0.387\nHartnup Disorder,SMPDB,0,,28 (33),1,1,1,C00097,7 (8),0.0551,0.361,0.215,0.387\nRenal Glucosuria,SMPDB,0,,28 (33),1,1,1,C00097,7 (8),0.0551,0.361,0.215,0.387\nBase-Excision Repair_ AP Site Formation,Reactome,0,,9 (10),1,1,1,C00106,7 (14),0.0551,0.361,0.215,0.387\nKidney Function,SMPDB,0,,28 (33),1,1,1,C00097,7 (8),0.0551,0.361,0.215,0.387\nMultiple carboxylase deficiency_ neonatal or early onset form,SMPDB,0,,3 (3),1,1,1,C00047,7 (7),0.0551,0.361,0.215,0.387\nglutamine biosynthesis,HumanCyc,0,,1 (1),1,1,1,C00025,7 (7),0.0551,0.361,0.215,0.387\nBiotinidase Deficiency,SMPDB,0,,3 (3),1,1,1,C00047,7 (7),0.0551,0.361,0.215,0.387\nadenine and adenosine salvage I,HumanCyc,0,,1 (1),1,1,1,C00020,7 (7),0.0551,0.361,0.215,0.387\nPropanoate metabolism - Homo sapiens (human),KEGG,1,ACSS2,28 (28),0.0508,1,0,,44 (44),1,1,0.202,0.387\nPolycystic Kidney Disease Pathway,Wikipathways,0,,16 (16),1,1,1,C00020,7 (8),0.0551,0.361,0.215,0.387\nArachidonate production from DAG,Reactome,0,,3 (3),1,1,1,C00116,7 (7),0.0551,0.361,0.215,0.387\nacetyl-CoA biosynthesis from citrate,HumanCyc,0,,1 (1),1,1,1,C00158,7 (7),0.0551,0.361,0.215,0.387\n4-hydroxy-2-nonenal detoxification,HumanCyc,0,,5 (5),1,1,1,C00025,7 (8),0.0551,0.361,0.215,0.387\nTriacylglyceride Synthesis,Wikipathways,0,,24 (24),1,1,1,C00116,7 (10),0.0551,0.361,0.215,0.387\nglutamate removal from folates,HumanCyc,0,,1 (1),1,1,1,C00025,7 (7),0.0551,0.361,0.215,0.387\nAMPK Signaling,Wikipathways,0,,65 (65),1,1,1,C00031,7 (7),0.0551,0.361,0.215,0.387\nEffects of Nitric Oxide,Wikipathways,0,,8 (8),1,1,1,C00327,7 (7),0.0551,0.361,0.215,0.387\nPolyol Pathway,Wikipathways,0,,4 (4),1,1,1,C00031,7 (7),0.0551,0.361,0.215,0.387\nCREB phosphorylation through the activation of Ras,Reactome,0,,28 (32),1,1,1,C00025,7 (7),0.0551,0.361,0.215,0.387\nGlucose Transporter Defect (SGLT2),SMPDB,0,,28 (33),1,1,1,C00097,7 (8),0.0551,0.361,0.215,0.387\nSignaling by Robo receptor,Reactome,1,EVL,31 (32),0.0561,1,0,,6 (6),1,1,0.218,0.391\nGalactose metabolism,EHMN,0,,44 (45),1,1,2,C00031;C00116,48 (48),0.0565,0.368,0.219,0.392\nProstaglandin formation from arachidonate,EHMN,0,,34 (34),1,1,2,C00020;C00116,48 (48),0.0565,0.368,0.219,0.392\nGPCR ligand binding,Reactome,0,,412 (454),1,1,3,C00077;C00025;C00047,110 (129),0.0575,0.374,0.222,0.395\nDefective ACTH causes Obesity and Pro-opiomelanocortinin deficiency (POMCD),Reactome,0,,412 (454),1,1,3,C00077;C00025;C00047,110 (129),0.0575,0.374,0.222,0.395\nLong-term potentiation - Homo sapiens (human),KEGG,0,,65 (67),1,1,1,C00025,7 (7),0.0551,0.361,0.215,0.387\nNicotine addiction - Homo sapiens (human),KEGG,0,,35 (40),1,1,1,C00025,7 (7),0.0551,0.361,0.215,0.387\npyrimidine ribonucleosides degradation,HumanCyc,0,,3 (3),1,1,1,C00106,8 (8),0.0628,0.379,0.237,0.395\nGlutamate Binding_ Activation of AMPA Receptors and Synaptic Plasticity,Reactome,0,,28 (31),1,1,1,C00025,8 (8),0.0628,0.379,0.237,0.395\nmalate-aspartate shuttle,HumanCyc,0,,5 (5),1,1,1,C00025,8 (8),0.0628,0.379,0.237,0.395\nHydroflumethiazide Action Pathway,SMPDB,0,,28 (33),1,1,1,C00097,8 (9),0.0628,0.379,0.237,0.395\nConjugation of phenylacetate with glutamine,Reactome,0,,2 (2),1,1,1,C00020,8 (8),0.0628,0.379,0.237,0.395\nEPHB-mediated forward signaling,Reactome,0,,42 (47),1,1,1,C00025,8 (8),0.0628,0.379,0.237,0.395\nmTOR signalling,Reactome,0,,26 (28),1,1,1,C00020,8 (8),0.0628,0.379,0.237,0.395\nEPH-Ephrin signaling,Reactome,0,,77 (83),1,1,1,C00025,8 (8),0.0628,0.379,0.237,0.395\nPolythiazide Action Pathway,SMPDB,0,,28 (33),1,1,1,C00097,8 (9),0.0628,0.379,0.237,0.395\nMethyclothiazide Action Pathway,SMPDB,0,,28 (33),1,1,1,C00097,8 (9),0.0628,0.379,0.237,0.395\nBumetanide Action Pathway,SMPDB,0,,28 (33),1,1,1,C00097,8 (9),0.0628,0.379,0.237,0.395\nEplerenone Action Pathway,SMPDB,0,,28 (33),1,1,1,C00097,8 (9),0.0628,0.379,0.237,0.395\nTriamterene Action Pathway,SMPDB,0,,28 (33),1,1,1,C00097,8 (9),0.0628,0.379,0.237,0.395\nAmiloride Action Pathway,SMPDB,0,,28 (33),1,1,1,C00097,8 (9),0.0628,0.379,0.237,0.395\nEthacrynic Acid Action Pathway,SMPDB,0,,28 (33),1,1,1,C00097,8 (9),0.0628,0.379,0.237,0.395\nQuinethazone Action Pathway,SMPDB,0,,28 (33),1,1,1,C00097,8 (9),0.0628,0.379,0.237,0.395\nChlorthalidone Action Pathway,SMPDB,0,,28 (33),1,1,1,C00097,8 (9),0.0628,0.379,0.237,0.395\nTrichlormethiazide Action Pathway,SMPDB,0,,28 (33),1,1,1,C00097,8 (9),0.0628,0.379,0.237,0.395\nFurosemide Action Pathway,SMPDB,0,,28 (33),1,1,1,C00097,8 (9),0.0628,0.379,0.237,0.395\nHomocysteine Degradation,SMPDB,0,,2 (3),1,1,1,C00097,8 (8),0.0628,0.379,0.237,0.395\nGamma-cystathionase deficiency (CTH),SMPDB,0,,2 (3),1,1,1,C00097,8 (8),0.0628,0.379,0.237,0.395\nHomocystinuria_ cystathionine beta-synthase deficiency,SMPDB,0,,2 (3),1,1,1,C00097,8 (8),0.0628,0.379,0.237,0.395\nHydrochlorothiazide Action Pathway,SMPDB,0,,28 (33),1,1,1,C00097,8 (9),0.0628,0.379,0.237,0.395\nCyclothiazide Action Pathway,SMPDB,0,,28 (33),1,1,1,C00097,8 (9),0.0628,0.379,0.237,0.395\nChlorothiazide Action Pathway,SMPDB,0,,28 (33),1,1,1,C00097,8 (9),0.0628,0.379,0.237,0.395\nRibosome - Homo sapiens (human),KEGG,1,RPL9,134 (135),0.222,1,0,,0 (0),1,1,0.222,1\nSHP2 signaling,PID,0,,57 (61),1,1,1,C00327,8 (8),0.0628,0.379,0.237,0.395\nPassive transport by Aquaporins,Reactome,0,,12 (15),1,1,1,C00116,8 (8),0.0628,0.379,0.237,0.395\ncreatine biosynthesis,HumanCyc,0,,2 (2),1,1,1,C00077,8 (8),0.0628,0.379,0.237,0.395\nEndohydrolysis of 1_4-alpha-D-glucosidic linkages in polysaccharides by alpha-amylase,EHMN,0,,6 (6),1,1,1,C01835,8 (8),0.0628,0.379,0.237,0.395\nIndapamide Action Pathway,SMPDB,0,,28 (33),1,1,1,C00097,8 (9),0.0628,0.379,0.237,0.395\nSignaling events mediated by VEGFR1 and VEGFR2,PID,0,,68 (75),1,1,1,C00327,8 (8),0.0628,0.379,0.237,0.395\nOrganic anion transport,Reactome,0,,5 (5),1,1,1,C00366,8 (8),0.0628,0.379,0.237,0.395\nBendroflumethiazide Action Pathway,SMPDB,0,,28 (33),1,1,1,C00097,8 (9),0.0628,0.379,0.237,0.395\ncorticosteroids and cardioprotection,BioCarta,0,,26 (28),1,1,1,C00327,8 (8),0.0628,0.379,0.237,0.395\nMetolazone Action Pathway,SMPDB,0,,28 (33),1,1,1,C00097,8 (9),0.0628,0.379,0.237,0.395\nSpironolactone Action Pathway,SMPDB,0,,28 (33),1,1,1,C00097,8 (9),0.0628,0.379,0.237,0.395\nSynthesis of bile acids and bile salts via 7alpha-hydroxycholesterol,Reactome,0,,24 (24),1,1,2,C00020;C00245,51 (51),0.0629,0.379,0.237,0.395\nFolate Metabolism,Wikipathways,0,,65 (66),1,1,2,C00031;C00097,51 (65),0.0629,0.379,0.237,0.395\nFatty acid biosynthesis - Homo sapiens (human),KEGG,0,,13 (13),1,1,2,C01571;C06423,50 (50),0.0608,0.379,0.231,0.395\nBase Excision Repair,Wikipathways,0,,0 (0),1,1,1,C00106,9 (13),0.0703,0.401,0.0703,0.401\nGeneration of second messenger molecules,Reactome,1,EVL,37 (43),0.0666,1,0,,6 (6),1,1,0.247,0.407\nArachidonic acid metabolism,EHMN,0,,96 (101),1,1,2,C00020;C00116,53 (53),0.0673,0.401,0.249,0.407\nselenocysteine biosynthesis,HumanCyc,0,,6 (6),1,1,1,C00020,9 (10),0.0703,0.401,0.257,0.407\nGlycine_ serine and threonine metabolism - Homo sapiens (human),KEGG,0,,39 (40),1,1,2,C00197;C00097,50 (50),0.0608,0.379,0.231,0.395\nAcyl chain remodeling of DAG and TAG,Reactome,0,,5 (5),1,1,1,C00116,9 (9),0.0703,0.401,0.257,0.407\nDegradation of GABA,Reactome,0,,2 (2),1,1,1,C00025,9 (9),0.0703,0.401,0.257,0.407\nUDP-N-acetyl-D-glucosamine biosynthesis II,HumanCyc,0,,4 (5),1,1,1,C00025,9 (11),0.0703,0.401,0.257,0.407\nγ-linolenate biosynthesis,HumanCyc,0,,14 (15),1,1,1,C00020,9 (9),0.0703,0.401,0.257,0.407\nPKB-mediated events,Reactome,0,,27 (29),1,1,1,C00020,9 (9),0.0703,0.401,0.257,0.407\n7-(3-amino-3-carboxypropyl)-wyosine biosynthesis,HumanCyc,0,,2 (2),1,1,1,C00170,9 (9),0.0703,0.401,0.257,0.407\nLactose Degradation,SMPDB,0,,11 (11),1,1,1,C00031,9 (9),0.0703,0.401,0.257,0.407\nLactose Intolerance,SMPDB,0,,11 (11),1,1,1,C00031,9 (9),0.0703,0.401,0.257,0.407\nThiamine Metabolism,SMPDB,0,,3 (4),1,1,1,C00020,9 (9),0.0703,0.401,0.257,0.407\n4-aminobutyrate degradation,HumanCyc,0,,2 (2),1,1,1,C00025,9 (9),0.0703,0.401,0.257,0.407\nglutamate biosynthesis/degradation,HumanCyc,0,,3 (3),1,1,1,C00025,9 (9),0.0703,0.401,0.257,0.407\nMismatch repair (MMR) directed by MSH2:MSH6 (MutSalpha),Reactome,0,,15 (15),1,1,1,C00020,9 (9),0.0703,0.401,0.257,0.407\nProcessive synthesis on the C-strand of the telomere,Reactome,0,,12 (12),1,1,1,C00020,9 (9),0.0703,0.401,0.257,0.407\nMismatch repair (MMR) directed by MSH2:MSH3 (MutSbeta),Reactome,0,,15 (15),1,1,1,C00020,9 (9),0.0703,0.401,0.257,0.407\nMismatch Repair,Reactome,0,,16 (16),1,1,1,C00020,9 (9),0.0703,0.401,0.257,0.407\nCysteine formation from homocysteine,Reactome,0,,2 (3),1,1,1,C00097,9 (9),0.0703,0.401,0.257,0.407\nwybutosine biosynthesis,HumanCyc,0,,3 (3),1,1,1,C00170,9 (9),0.0703,0.401,0.257,0.407\nPhenylacetate Metabolism,SMPDB,0,,3 (3),1,1,1,C00020,9 (9),0.0703,0.401,0.257,0.407\nL-dopa degradation,HumanCyc,0,,1 (1),1,1,1,C00025,9 (12),0.0703,0.401,0.257,0.407\nPlasma membrane estrogen receptor signaling,PID,0,,39 (42),1,1,1,C00327,9 (9),0.0703,0.401,0.257,0.407\ntRNA splicing,HumanCyc,0,,5 (5),1,1,1,C00020,9 (11),0.0703,0.401,0.257,0.407\nVitamin B1 (thiamin) metabolism,Reactome,0,,3 (4),1,1,1,C00020,9 (9),0.0703,0.401,0.257,0.407\npyrimidine deoxyribonucleosides degradation,HumanCyc,0,,4 (4),1,1,1,C00106,9 (10),0.0703,0.401,0.257,0.407\nPost NMDA receptor activation events,Reactome,0,,36 (40),1,1,1,C00025,9 (9),0.0703,0.401,0.257,0.407\nphosphatidylethanolamine biosynthesis II,HumanCyc,0,,7 (7),1,1,1,C00346,9 (12),0.0703,0.401,0.257,0.407\nCalmodulin induced events,Reactome,0,,26 (28),1,1,1,C00020,9 (9),0.0703,0.401,0.257,0.407\nCaM pathway,Reactome,0,,26 (28),1,1,1,C00020,9 (9),0.0703,0.401,0.257,0.407\nSphingolipid metabolism,Wikipathways,0,,0 (0),1,1,2,C00836;C00346,55 (58),0.0719,0.408,0.0719,0.408\nDevelopmental Biology,Reactome,1,EVL,426 (451),0.554,1,1,C00025,17 (18),0.129,0.59,0.26,0.411\nCocaine addiction - Homo sapiens (human),KEGG,0,,48 (50),1,1,1,C00025,8 (8),0.0628,0.379,0.237,0.395\nTyrosine metabolism - Homo sapiens (human),KEGG,1,ADH5,35 (35),0.0631,1,0,,77 (77),1,1,0.237,0.395\nBile acid biosynthesis,EHMN,0,,52 (54),1,1,2,C00020;C00245,55 (55),0.0719,0.408,0.261,0.411\nGlycerophospholipid biosynthesis,Wikipathways,0,,0 (0),1,1,2,C00116;C00346,56 (64),0.0741,0.42,0.0741,0.42\nSphingolipid metabolism,Reactome,0,,70 (72),1,1,2,C00836;C00346,56 (58),0.0741,0.42,0.267,0.42\nTransport of fatty acids,Reactome,0,,8 (8),1,1,1,C01571,10 (10),0.0779,0.426,0.277,0.424\nBase Excision Repair,Reactome,0,,20 (22),1,1,1,C00106,10 (17),0.0779,0.426,0.277,0.424\nGlycogen breakdown (glycogenolysis),Reactome,0,,16 (18),1,1,1,C00020,10 (10),0.0779,0.426,0.277,0.424\nLong-term depression - Homo sapiens (human),KEGG,0,,59 (60),1,1,1,C00025,9 (9),0.0703,0.401,0.257,0.407\nMalate-Aspartate Shuttle,SMPDB,0,,4 (4),1,1,1,C00025,10 (10),0.0779,0.426,0.277,0.424\nPyruvate metabolism - Homo sapiens (human),KEGG,1,ACSS2,40 (40),0.0718,1,0,,31 (31),1,1,0.261,0.411\nPutative anti-Inflammatory metabolites formation from EPA,EHMN,0,,41 (42),1,1,1,C00025,10 (10),0.0779,0.426,0.277,0.424\nasparagine degradation,HumanCyc,0,,5 (5),1,1,1,C00025,10 (10),0.0779,0.426,0.277,0.424\nInterconversion of 2-oxoglutarate and 2-hydroxyglutarate,Reactome,0,,3 (3),1,1,1,C00989,10 (10),0.0779,0.426,0.277,0.424\nPentose and glucuronate interconversions - Homo sapiens (human),KEGG,0,,36 (36),1,1,2,C00379;C00116,55 (55),0.0719,0.408,0.261,0.411\nmTOR signaling pathway - Homo sapiens (human),KEGG,0,,59 (60),1,1,1,C00020,10 (10),0.0779,0.426,0.277,0.424\nDifferentiation Pathway,Wikipathways,0,,44 (44),1,1,1,C00245,10 (13),0.0779,0.426,0.277,0.424\nFactors involved in megakaryocyte development and platelet production,Reactome,0,,115 (115),1,1,1,C00020,10 (11),0.0779,0.426,0.277,0.424\nAmyotrophic lateral sclerosis (ALS),Wikipathways,0,,33 (35),1,1,1,C00025,10 (10),0.0779,0.426,0.277,0.424\nAngiopoietin receptor Tie2-mediated signaling,PID,0,,48 (52),1,1,1,C00327,10 (10),0.0779,0.426,0.277,0.424\nuracil degradation,HumanCyc,0,,3 (3),1,1,1,C00106,10 (10),0.0779,0.426,0.277,0.424\nAmyotrophic lateral sclerosis (ALS) - Homo sapiens (human),KEGG,0,,47 (51),1,1,1,C00025,10 (10),0.0779,0.426,0.277,0.424\nVitamin B2 (riboflavin) metabolism,Reactome,0,,5 (5),1,1,1,C00020,10 (10),0.0779,0.426,0.277,0.424\nHIF1A and PPARG regulation of glycolysis,Wikipathways,0,,0 (0),1,1,1,C00031,10 (10),0.0779,0.426,0.0779,0.426\nSignaling by Type 1 Insulin-like Growth Factor 1 Receptor (IGF1R),Wikipathways,0,,0 (0),1,1,1,C00020,10 (10),0.0779,0.426,0.0779,0.426\nSynthesis of DNA,Wikipathways,0,,0 (0),1,1,1,C00020,10 (10),0.0779,0.426,0.0779,0.426\nFactors involved in megakaryocyte development and platelet production,Wikipathways,0,,0 (0),1,1,1,C00020,10 (11),0.0779,0.426,0.0779,0.426\nAlcoholism - Homo sapiens (human),KEGG,0,,175 (180),1,1,1,C00025,10 (10),0.0779,0.426,0.277,0.424\nGlycerophospholipid biosynthesis,Reactome,0,,92 (96),1,1,2,C00116;C00346,58 (63),0.0788,0.43,0.279,0.426\nTryptophan Metabolism,SMPDB,0,,19 (19),1,1,2,C00041;C00025,59 (60),0.0812,0.443,0.285,0.434\nAmphetamine addiction - Homo sapiens (human),KEGG,0,,65 (68),1,1,1,C00025,10 (10),0.0779,0.426,0.277,0.424\nSynthesis of bile acids and bile salts,Reactome,0,,27 (27),1,1,2,C00020;C00245,60 (73),0.0836,0.45,0.291,0.438\nUTP and CTP dephosphorylation II,HumanCyc,0,,4 (4),1,1,1,C00025,11 (11),0.0853,0.45,0.295,0.438\nPAOs oxidise polyamines to amines,Reactome,0,,2 (2),1,1,1,C00315,11 (11),0.0853,0.45,0.295,0.438\nNitric oxide stimulates guanylate cyclase,Reactome,0,,22 (25),1,1,1,C00327,11 (11),0.0853,0.45,0.295,0.438\nSynthesis of UDP-N-acetyl-glucosamine,Reactome,0,,4 (5),1,1,1,C00025,11 (11),0.0853,0.45,0.295,0.438\nD-Arginine and D-ornithine metabolism - Homo sapiens (human),KEGG,0,,1 (1),1,1,1,C00077,10 (10),0.0779,0.426,0.277,0.424\nsuperpathway of D-myo-inositol (1_4_5)-trisphosphate metabolism,HumanCyc,0,,19 (20),1,1,1,C03546,11 (11),0.0853,0.45,0.295,0.438\nUTP and CTP de novo biosynthesis,HumanCyc,0,,13 (13),1,1,1,C00025,11 (11),0.0853,0.45,0.295,0.438\nDARPP-32 events,Reactome,0,,26 (27),1,1,1,C00020,11 (11),0.0853,0.45,0.295,0.438\nFatty acid degradation - Homo sapiens (human),KEGG,1,ADH5,44 (44),0.0787,1,0,,50 (50),1,1,0.279,0.426\nVitamin B1 (thiamin) metabolism,EHMN,0,,3 (4),1,1,1,C00020,11 (11),0.0853,0.45,0.295,0.438\nVitamin H (biotin) metabolism,EHMN,0,,6 (6),1,1,1,C00047,11 (11),0.0853,0.45,0.295,0.438\nHerpes simplex infection - Homo sapiens (human),KEGG,1,EEF1D,179 (186),0.285,1,0,,0 (0),1,1,0.285,1\nbiotin-carboxyl carrier protein assembly,HumanCyc,0,,3 (3),1,1,1,C00020,11 (11),0.0853,0.45,0.295,0.438\nHormone-sensitive lipase (HSL)-mediated triacylglycerol hydrolysis,Reactome,0,,12 (12),1,1,1,C00116,11 (11),0.0853,0.45,0.295,0.438\nActivation of NMDA receptor upon glutamate binding and postsynaptic events,Reactome,0,,38 (44),1,1,1,C00025,11 (11),0.0853,0.45,0.295,0.438\nβ-alanine degradation,HumanCyc,0,,2 (2),1,1,1,C00025,11 (11),0.0853,0.45,0.295,0.438\nion channels and their functional role in vascular endothelium,BioCarta,0,,44 (47),1,1,1,C00327,11 (11),0.0853,0.45,0.295,0.438\nCytosolic sensors of pathogen-associated DNA ,Reactome,1,XRCC6,49 (49),0.0872,1,0,,9 (14),1,1,0.3,0.444\nIRS-related events triggered by IGF1R,Reactome,0,,87 (93),1,1,1,C00020,12 (12),0.0927,0.469,0.313,0.447\nSignaling by Type 1 Insulin-like Growth Factor 1 Receptor (IGF1R),Reactome,0,,90 (96),1,1,1,C00020,12 (12),0.0927,0.469,0.313,0.447\nPLC-gamma1 signalling,Reactome,0,,33 (35),1,1,1,C00020,12 (12),0.0927,0.469,0.313,0.447\nEGFR interacts with phospholipase C-gamma,Reactome,0,,33 (35),1,1,1,C00020,12 (12),0.0927,0.469,0.313,0.447\nThromboxane A2 receptor signaling,PID,0,,53 (58),1,1,1,C00327,12 (12),0.0927,0.469,0.313,0.447\nD-glucuronate degradation,HumanCyc,0,,2 (2),1,1,1,C00379,12 (13),0.0927,0.469,0.313,0.447\nadenine and adenosine salvage III,HumanCyc,0,,3 (3),1,1,1,C00130,12 (12),0.0927,0.469,0.313,0.447\nIGF1R signaling cascade,Reactome,0,,90 (96),1,1,1,C00020,12 (12),0.0927,0.469,0.313,0.447\nPLCG1 events in ERBB2 signaling,Reactome,0,,34 (36),1,1,1,C00020,12 (12),0.0927,0.469,0.313,0.447\nInsulin receptor signalling cascade,Reactome,0,,89 (96),1,1,1,C00020,12 (12),0.0927,0.469,0.313,0.447\nPI3K Cascade,Reactome,0,,66 (70),1,1,1,C00020,12 (12),0.0927,0.469,0.313,0.447\nIRS-mediated signalling,Reactome,0,,83 (89),1,1,1,C00020,12 (12),0.0927,0.469,0.313,0.447\nValine_ leucine and isoleucine degradation - Homo sapiens (human),KEGG,1,IVD,46 (46),0.0821,1,0,,40 (40),1,1,0.287,0.437\nFamotidine Action Pathway,SMPDB,0,,10 (11),1,1,1,C00025,12 (13),0.0927,0.469,0.313,0.447\nphytol degradation,HumanCyc,0,,2 (2),1,1,1,C00020,12 (14),0.0927,0.469,0.313,0.447\nCa-dependent events,Reactome,0,,28 (30),1,1,1,C00020,12 (12),0.0927,0.469,0.313,0.447\nLipoate metabolism,EHMN,0,,7 (7),1,1,1,C00047,12 (12),0.0927,0.469,0.313,0.447\nMono-unsaturated fatty acid beta-oxidation,EHMN,0,,21 (22),1,1,1,C00020,12 (12),0.0927,0.469,0.313,0.447\nIRS-related events,Reactome,0,,85 (92),1,1,1,C00020,12 (12),0.0927,0.469,0.313,0.447\nGap junction - Homo sapiens (human),KEGG,0,,87 (89),1,1,1,C00025,11 (11),0.0853,0.45,0.295,0.438\nDAG and IP3 signaling,Reactome,0,,31 (33),1,1,1,C00020,12 (12),0.0927,0.469,0.313,0.447\nRespiratory electron transport,Reactome,0,,86 (92),1,1,1,C00020,12 (14),0.0927,0.469,0.313,0.447\nEffects of PIP2 hydrolysis,Reactome,0,,25 (25),1,1,1,C00116,12 (12),0.0927,0.469,0.313,0.447\nSphingolipid Metabolism,Wikipathways,0,,19 (19),1,1,1,C00836,12 (12),0.0927,0.469,0.313,0.447\nspermine and spermidine degradation I,HumanCyc,0,,5 (5),1,1,1,C00315,12 (13),0.0927,0.469,0.313,0.447\nEndothelin Pathways,Wikipathways,0,,30 (30),1,1,1,C00327,12 (12),0.0927,0.469,0.313,0.447\nTransport of vitamins_ nucleosides_ and related molecules,Reactome,0,,39 (39),1,1,2,C01571;C00106,64 (64),0.0933,0.471,0.315,0.449\nEffects of PIP2 hydrolysis,Wikipathways,0,,0 (0),1,1,1,C00116,11 (12),0.0853,0.45,0.0853,0.45\nSignaling by Insulin receptor,Wikipathways,0,,0 (0),1,1,1,C00020,11 (11),0.0853,0.45,0.0853,0.45\nTelomere Maintenance,Wikipathways,0,,0 (0),1,1,1,C00020,11 (11),0.0853,0.45,0.0853,0.45\nDAG and IP3 signaling,Wikipathways,0,,0 (0),1,1,1,C00020,11 (11),0.0853,0.45,0.0853,0.45\nBile acid and bile salt metabolism,Reactome,0,,35 (36),1,1,2,C00020;C00245,65 (78),0.0958,0.483,0.321,0.456\nPhospholipid metabolism,Reactome,0,,144 (148),1,1,2,C00116;C00346,66 (71),0.0983,0.491,0.326,0.461\ntryptophan degradation to 2-amino-3-carboxymuconate semialdehyde,HumanCyc,0,,7 (9),1,1,1,C00041,13 (13),0.1,0.491,0.33,0.461\nCircadian entrainment - Homo sapiens (human),KEGG,0,,91 (97),1,1,1,C00025,11 (11),0.0853,0.45,0.295,0.438\nCDP-diacylglycerol biosynthesis,HumanCyc,0,,22 (23),1,1,1,C00116,13 (19),0.1,0.491,0.33,0.461\nSignaling by Insulin receptor,Reactome,0,,113 (120),1,1,1,C00020,13 (13),0.1,0.491,0.33,0.461\ntyrosine degradation,HumanCyc,0,,5 (5),1,1,1,C00025,13 (13),0.1,0.491,0.33,0.461\nLamivudine Metabolism Pathway,SMPDB,0,,18 (18),1,1,1,C00346,13 (13),0.1,0.491,0.33,0.461\nGABA shunt,HumanCyc,0,,8 (8),1,1,1,C00025,13 (13),0.1,0.491,0.33,0.461\nMethionine salvage pathway,Reactome,0,,6 (6),1,1,1,C00170,13 (17),0.1,0.491,0.33,0.461\nVitamin C metabolism,EHMN,0,,3 (4),1,1,1,C02477,13 (13),0.1,0.491,0.33,0.461\nSynthesis of DNA,Reactome,0,,53 (54),1,1,1,C00020,13 (13),0.1,0.491,0.33,0.461\nInterconversion of polyamines,Reactome,0,,3 (3),1,1,1,C00315,13 (13),0.1,0.491,0.33,0.461\nfolate polyglutamylation,HumanCyc,0,,6 (6),1,1,1,C00025,13 (13),0.1,0.491,0.33,0.461\nSynthesis of PG,Reactome,0,,9 (9),1,1,1,C00116,13 (13),0.1,0.491,0.33,0.461\nOrganic anion transporters,Reactome,0,,8 (8),1,1,1,C00025,13 (13),0.1,0.491,0.33,0.461\nTransmission across Chemical Synapses,Reactome,1,GLS,195 (213),0.306,1,1,C00025,51 (51),0.34,1,0.34,0.471\nCoregulation of Androgen receptor activity,PID,1,XRCC6,60 (63),0.106,1,0,,2 (2),1,1,0.343,0.471\nSynthesis of IP2_ IP_ and Ins in the cytosol,Reactome,0,,11 (11),1,1,1,C03546,14 (14),0.107,0.512,0.347,0.471\nglutathione-mediated detoxification,HumanCyc,0,,24 (24),1,1,1,C00025,14 (14),0.107,0.512,0.347,0.471\nAflatoxin activation and detoxification,Reactome,0,,22 (25),1,1,1,C00025,14 (25),0.107,0.512,0.347,0.471\nDefective ACY1 causes encephalopathy,Reactome,0,,22 (25),1,1,1,C00025,14 (25),0.107,0.512,0.347,0.471\nDigestion of dietary carbohydrate,Reactome,0,,10 (10),1,1,1,C01835,14 (14),0.107,0.512,0.347,0.471\neicosapentaenoate biosynthesis,HumanCyc,0,,14 (15),1,1,1,C00020,14 (17),0.107,0.512,0.347,0.471\nTelomere C-strand (Lagging Strand) Synthesis,Reactome,0,,23 (23),1,1,1,C00020,14 (14),0.107,0.512,0.347,0.471\nVitamin B2 (riboflavin) metabolism,EHMN,0,,5 (5),1,1,1,C00020,14 (14),0.107,0.512,0.347,0.471\nAquaporin-mediated transport,Reactome,0,,45 (51),1,1,1,C00116,14 (14),0.107,0.512,0.347,0.471\nProlactin signaling pathway - Homo sapiens (human),KEGG,0,,66 (72),1,1,1,C00031,11 (11),0.0853,0.45,0.295,0.438\nExtension of Telomeres,Reactome,0,,30 (31),1,1,1,C00020,14 (14),0.107,0.512,0.347,0.471\nTelomere Maintenance,Reactome,0,,81 (83),1,1,1,C00020,14 (14),0.107,0.512,0.347,0.471\nDNA Replication,Reactome,0,,58 (59),1,1,1,C00020,14 (14),0.107,0.512,0.347,0.471\nGlucuronidation,Wikipathways,0,,26 (26),1,1,1,C00031,14 (14),0.107,0.512,0.347,0.471\n4-hydroxyproline degradation,HumanCyc,0,,4 (4),1,1,1,C00025,14 (17),0.107,0.512,0.347,0.471\npyrimidine ribonucleosides degradation,HumanCyc,0,,6 (6),1,1,1,C00106,14 (14),0.107,0.512,0.347,0.471\npurine ribonucleosides degradation to ribose-1-phosphate,HumanCyc,0,,3 (3),1,1,1,C00385,14 (15),0.107,0.512,0.347,0.471\nInterferon Signaling,Reactome,0,,74 (79),1,1,1,C00020,14 (15),0.107,0.512,0.347,0.471\nSynaptic vesicle cycle - Homo sapiens (human),KEGG,0,,62 (63),1,1,1,C00025,12 (12),0.0927,0.469,0.313,0.447\nProteasome Degradation,Wikipathways,1,PSMD9,64 (64),0.112,1,0,,1 (1),1,1,0.358,0.485\nGlyoxylate metabolism,Reactome,0,,5 (5),1,1,1,C00041,15 (15),0.115,0.54,0.363,0.487\nUTP and CTP dephosphorylation I,HumanCyc,0,,3 (3),1,1,1,C00025,15 (15),0.115,0.54,0.363,0.487\nMorphine addiction - Homo sapiens (human),KEGG,0,,85 (93),1,1,1,C00020,12 (12),0.0927,0.469,0.313,0.447\nChromosome Maintenance,Reactome,0,,109 (111),1,1,1,C00020,15 (15),0.115,0.54,0.363,0.487\nInsulin secretion - Homo sapiens (human),KEGG,0,,85 (86),1,1,1,C00031,13 (13),0.1,0.491,0.33,0.461\nPhospholipase C-mediated cascade,Reactome,0,,54 (56),1,1,1,C00020,15 (15),0.115,0.54,0.363,0.487\nserotonin degradation,HumanCyc,0,,14 (15),1,1,1,C00097,15 (20),0.115,0.54,0.363,0.487\nPTM- gamma carboxylation_ hypusine formation and arylsulfatase activation,Wikipathways,0,,0 (0),1,1,1,C00315,13 (13),0.1,0.491,0.1,0.491\nGABA synthesis_ release_ reuptake and degradation,Wikipathways,0,,0 (0),1,1,1,C00025,13 (13),0.1,0.491,0.1,0.491\nCell Cycle_ Mitotic,Reactome,1,SEC13,430 (443),0.558,1,1,C00020,29 (29),0.21,0.847,0.368,0.493\ncGMP-PKG signaling pathway - Homo sapiens (human),KEGG,0,,163 (167),1,1,1,C00020,14 (14),0.107,0.512,0.347,0.471\nTCR signaling,Reactome,1,EVL,67 (74),0.117,1,0,,11 (11),1,1,0.369,0.493\nRegulation of Telomerase,PID,1,XRCC6,68 (71),0.119,1,0,,1 (1),1,1,0.372,0.497\nGABA synthesis_ release_ reuptake and degradation,Reactome,0,,20 (21),1,1,1,C00025,16 (16),0.122,0.566,0.378,0.501\nVEGFR2 mediated vascular permeability,Reactome,0,,28 (29),1,1,1,C00327,16 (16),0.122,0.566,0.378,0.501\nThyroxine biosynthesis,Reactome,0,,8 (8),1,1,1,C00041,16 (16),0.122,0.566,0.378,0.501\nisoleucine degradation,HumanCyc,0,,12 (12),1,1,1,C00025,16 (16),0.122,0.566,0.378,0.501\nOmega-3 fatty acid metabolism,EHMN,0,,18 (19),1,1,1,C00020,16 (16),0.122,0.566,0.378,0.501\n(S)-reticuline biosynthesis,HumanCyc,0,,1 (1),1,1,1,C00025,16 (16),0.122,0.566,0.378,0.501\nS Phase,Wikipathways,0,,0 (0),1,1,1,C00020,14 (14),0.107,0.512,0.107,0.512\nRetinol metabolism - Homo sapiens (human),KEGG,1,ADH5,62 (64),0.109,1,0,,24 (24),1,1,0.351,0.476\nPhagosomal maturation (early endosomal stage),Reactome,0,,33 (34),1,1,1,C00327,17 (22),0.129,0.59,0.393,0.513\nfatty acid α-oxidation,HumanCyc,0,,5 (5),1,1,1,C00020,17 (20),0.129,0.59,0.393,0.513\nstearate biosynthesis,HumanCyc,0,,13 (13),1,1,1,C00020,17 (17),0.129,0.59,0.393,0.513\nSynthesis of bile acids and bile salts via 24-hydroxycholesterol,Reactome,0,,14 (14),1,1,1,C00020,17 (28),0.129,0.59,0.393,0.513\ncysteine biosynthesis,HumanCyc,0,,8 (9),1,1,1,C00097,17 (17),0.129,0.59,0.393,0.513\nSynthesis of PE,Reactome,0,,13 (14),1,1,1,C00346,17 (18),0.129,0.59,0.393,0.513\nMetabolism of folate and pterines,Reactome,0,,10 (11),1,1,1,C00025,17 (20),0.129,0.59,0.393,0.513\nS Phase,Reactome,0,,78 (79),1,1,1,C00020,17 (17),0.129,0.59,0.393,0.513\nDigestion of dietary lipid,Reactome,0,,5 (6),1,1,1,C00116,17 (17),0.129,0.59,0.393,0.513\nfatty acid α-oxidation III,HumanCyc,0,,3 (3),1,1,1,C00020,17 (23),0.129,0.59,0.393,0.513\nCell Cycle,Reactome,1,SEC13,477 (492),0.596,1,1,C00020,31 (31),0.223,0.892,0.401,0.523\nParkinson_s disease - Homo sapiens (human),KEGG,0,,141 (143),1,1,1,C00020,15 (15),0.115,0.54,0.363,0.487\nNeurotransmitter Receptor Binding And Downstream Transmission In The Postsynaptic Cell,Reactome,0,,133 (149),1,1,1,C00025,18 (18),0.136,0.616,0.407,0.527\nTransport of nucleosides and free purine and pyrimidine bases across the plasma membrane,Reactome,0,,7 (7),1,1,1,C00106,18 (18),0.136,0.616,0.407,0.527\nPLC beta mediated events,Reactome,0,,43 (45),1,1,1,C00020,18 (18),0.136,0.616,0.407,0.527\nAmine Oxidase reactions,Reactome,0,,4 (4),1,1,1,C00315,18 (18),0.136,0.616,0.407,0.527\nG-protein mediated events,Reactome,0,,44 (46),1,1,1,C00020,18 (18),0.136,0.616,0.407,0.527\nAlanine and aspartate metabolism,Wikipathways,0,,12 (12),1,1,1,C00158,18 (18),0.136,0.616,0.407,0.527\nNeuronal System,Reactome,1,GLS,274 (294),0.403,1,1,C00025,51 (51),0.34,1,0.409,0.529\nHIF-1 signaling pathway - Homo sapiens (human),KEGG,0,,104 (106),1,1,1,C00031,15 (15),0.115,0.54,0.363,0.487\nDrug metabolism - cytochrome P450 - Homo sapiens (human),KEGG,1,ADH5,67 (68),0.117,1,0,,88 (88),1,1,0.369,0.493\nGLUT-1 deficiency syndrome,SMPDB,0,,11 (11),1,1,1,C00031,19 (19),0.143,0.636,0.421,0.535\nCongenital disorder of glycosylation CDG-IId,SMPDB,0,,11 (11),1,1,1,C00031,19 (19),0.143,0.636,0.421,0.535\nLactose Synthesis,SMPDB,0,,11 (11),1,1,1,C00031,19 (19),0.143,0.636,0.421,0.535\nMitochondrial Beta-Oxidation of Short Chain Saturated Fatty Acids,SMPDB,0,,8 (8),1,1,1,C00020,19 (19),0.143,0.636,0.421,0.535\nShort-chain 3-hydroxyacyl-CoA dehydrogenase deficiency (SCHAD),SMPDB,0,,8 (8),1,1,1,C00020,19 (19),0.143,0.636,0.421,0.535\nRiboflavin Metabolism,SMPDB,0,,5 (5),1,1,1,C00020,19 (19),0.143,0.636,0.421,0.535\nButyrate Metabolism,SMPDB,0,,8 (8),1,1,1,C00020,19 (19),0.143,0.636,0.421,0.535\nDi-unsaturated fatty acid beta-oxidation,EHMN,0,,23 (24),1,1,1,C00020,19 (19),0.143,0.636,0.421,0.535\nUMP biosynthesis,HumanCyc,0,,2 (2),1,1,1,C00025,19 (20),0.143,0.636,0.421,0.535\nCytokine Signaling in Immune system,Reactome,0,,182 (198),1,1,1,C00020,19 (22),0.143,0.636,0.421,0.535\ninosine-5_-phosphate biosynthesis,HumanCyc,0,,3 (4),1,1,1,C00130,19 (19),0.143,0.636,0.421,0.535\nClass I MHC mediated antigen processing & presentation,Wikipathways,0,,0 (0),1,1,1,C00020,15 (15),0.115,0.54,0.115,0.54\nMetabolism of xenobiotics by cytochrome P450 - Homo sapiens (human),KEGG,1,ADH5,73 (74),0.127,1,0,,121 (121),1,1,0.39,0.513\nvaline degradation,HumanCyc,0,,13 (13),1,1,1,C00025,20 (20),0.15,0.653,0.434,0.542\nCarnitine Synthesis,SMPDB,0,,5 (5),1,1,1,C00047,20 (20),0.15,0.653,0.434,0.542\nRespiratory electron transport_ ATP synthesis by chemiosmotic coupling_ and heat production by uncoupling proteins.,Reactome,0,,107 (113),1,1,1,C00020,20 (22),0.15,0.653,0.434,0.542\nDAP12 interactions,Reactome,0,,178 (189),1,1,1,C00020,20 (20),0.15,0.653,0.434,0.542\n5-aminoimidazole ribonucleotide biosynthesis,HumanCyc,0,,3 (3),1,1,1,C00025,20 (20),0.15,0.653,0.434,0.542\nTrans-sulfuration and one carbon metabolism,Wikipathways,0,,31 (31),1,1,1,C00097,20 (20),0.15,0.653,0.434,0.542\nSynthesis of very long-chain fatty acyl-CoAs,Reactome,0,,17 (18),1,1,1,C00020,20 (23),0.15,0.653,0.434,0.542\nDownstream signaling of activated FGFR,Reactome,0,,145 (155),1,1,1,C00020,20 (20),0.15,0.653,0.434,0.542\nSignaling by FGFR,Reactome,0,,158 (168),1,1,1,C00020,20 (20),0.15,0.653,0.434,0.542\nDAP12 signaling,Reactome,0,,160 (171),1,1,1,C00020,20 (20),0.15,0.653,0.434,0.542\nDownstream signal transduction,Reactome,0,,157 (168),1,1,1,C00020,20 (20),0.15,0.653,0.434,0.542\nAlpha-oxidation of phytanate,Reactome,0,,4 (4),1,1,1,C00020,20 (23),0.15,0.653,0.434,0.542\nSignaling by ERBB2,Reactome,0,,159 (169),1,1,1,C00020,20 (20),0.15,0.653,0.434,0.542\neNOS activation,Reactome,0,,9 (9),1,1,1,C00327,20 (20),0.15,0.653,0.434,0.542\nChemical carcinogenesis - Homo sapiens (human),KEGG,1,ADH5,78 (80),0.135,1,0,,99 (99),1,1,0.406,0.527\nSignaling by FGFR in disease,Reactome,0,,173 (184),1,1,1,C00020,21 (27),0.157,0.677,0.447,0.554\nSynthesis of Leukotrienes (LT) and Eoxins (EX),Reactome,0,,24 (27),1,1,1,C00025,21 (28),0.157,0.677,0.447,0.554\nAbnormal metabolism in phenylketonuria,Reactome,0,,6 (7),1,1,1,C00041,21 (21),0.157,0.677,0.447,0.554\nSignaling by EGFR,Reactome,0,,175 (185),1,1,1,C00020,21 (21),0.157,0.677,0.447,0.554\nPyrimidine salvage reactions,Reactome,0,,10 (10),1,1,1,C00106,21 (21),0.157,0.677,0.447,0.554\ntryptophan degradation,HumanCyc,0,,9 (11),1,1,1,C00041,22 (22),0.164,0.694,0.46,0.56\nNGF signalling via TRKA from the plasma membrane,Reactome,0,,199 (211),1,1,1,C00020,22 (22),0.164,0.694,0.46,0.56\nFamilial lipoprotein lipase deficiency,SMPDB,0,,13 (13),1,1,1,C00116,22 (23),0.164,0.694,0.46,0.56\nsuperpathway of pyrimidine ribonucleotides de novo biosynthesis,HumanCyc,0,,15 (15),1,1,1,C00025,22 (23),0.164,0.694,0.46,0.56\nGlycerolipid Metabolism,SMPDB,0,,13 (13),1,1,1,C00116,22 (23),0.164,0.694,0.46,0.56\nGlycerol Kinase Deficiency,SMPDB,0,,13 (13),1,1,1,C00116,22 (23),0.164,0.694,0.46,0.56\nD-glyceric acidura,SMPDB,0,,13 (13),1,1,1,C00116,22 (23),0.164,0.694,0.46,0.56\nPhytanic acid peroxisomal oxidation,EHMN,0,,16 (17),1,1,1,C00020,22 (22),0.164,0.694,0.46,0.56\nTCA cycle,HumanCyc,0,,19 (22),1,1,1,C00158,22 (23),0.164,0.694,0.46,0.56\nLinoleic acid (LA) metabolism,Reactome,0,,7 (7),1,1,1,C00020,22 (24),0.164,0.694,0.46,0.56\nOpioid Signalling,Reactome,0,,78 (83),1,1,1,C00020,22 (22),0.164,0.694,0.46,0.56\nfatty acid activation,HumanCyc,0,,8 (9),1,1,1,C00020,22 (38),0.164,0.694,0.46,0.56\nSignaling by Overexpressed Wild-Type EGFR in Cancer,Reactome,0,,175 (185),1,1,1,C00020,22 (25),0.164,0.694,0.46,0.56\nInositol Phosphate Metabolism,SMPDB,0,,13 (14),1,1,1,C03546,22 (23),0.164,0.694,0.46,0.56\nTCA Cycle and PDHc,Wikipathways,0,,0 (0),1,1,1,C00158,16 (16),0.122,0.566,0.122,0.566\nFc epsilon receptor (FCERI) signaling,Wikipathways,0,,0 (0),1,1,1,C00020,16 (16),0.122,0.566,0.122,0.566\nNeurotransmitter Receptor Binding And Downstream Transmission In The Postsynaptic Cell,Wikipathways,0,,0 (0),1,1,1,C00025,16 (16),0.122,0.566,0.122,0.566\nTCA Cycle,Wikipathways,0,,17 (17),1,1,1,C00158,23 (24),0.17,0.722,0.472,0.574\nOmega-6 fatty acid metabolism,EHMN,0,,30 (31),1,1,1,C00020,23 (23),0.17,0.722,0.472,0.574\nResolution of Sister Chromatid Cohesion,Reactome,1,SEC13,101 (102),0.172,1,0,,6 (6),1,1,0.474,0.576\nPlatelet homeostasis,Reactome,0,,73 (79),1,1,1,C00327,24 (24),0.177,0.742,0.484,0.582\nGlycolysis,SMPDB,0,,15 (15),1,1,1,C00031,24 (24),0.177,0.742,0.484,0.582\nGlycogenosis_ Type VII. Tarui disease,SMPDB,0,,15 (15),1,1,1,C00031,24 (24),0.177,0.742,0.484,0.582\nMercaptopurine Metabolism Pathway,SMPDB,0,,15 (15),1,1,1,C00020,24 (24),0.177,0.742,0.484,0.582\nLinoleate metabolism,EHMN,0,,70 (76),1,1,1,C01601,24 (24),0.177,0.742,0.484,0.582\nSignaling by EGFRvIII in Cancer,Reactome,0,,177 (187),1,1,1,C00020,24 (26),0.177,0.742,0.484,0.582\nFanconi-bickel syndrome,SMPDB,0,,15 (15),1,1,1,C00031,24 (24),0.177,0.742,0.484,0.582\nSignaling by PDGF,Reactome,0,,178 (191),1,1,1,C00020,24 (24),0.177,0.742,0.484,0.582\nSignalling by NGF,Reactome,0,,279 (297),1,1,1,C00020,25 (25),0.184,0.767,0.495,0.594\nMitochondrial Beta-Oxidation of Medium Chain Saturated Fatty Acids,SMPDB,0,,8 (8),1,1,1,C00020,25 (25),0.184,0.767,0.495,0.594\nMitochondrial Beta-Oxidation of Long Chain Saturated Fatty Acids,SMPDB,0,,10 (10),1,1,1,C00020,25 (26),0.184,0.767,0.495,0.594\nMitotic Prometaphase,Reactome,1,SEC13,110 (112),0.186,1,0,,6 (6),1,1,0.498,0.597\nVEGFA-VEGFR2 Pathway,Reactome,0,,104 (108),1,1,1,C00327,26 (26),0.19,0.792,0.506,0.605\nSignaling by VEGF,Reactome,0,,112 (117),1,1,1,C00327,26 (26),0.19,0.792,0.506,0.605\nBile secretion - Homo sapiens (human),KEGG,0,,72 (72),1,1,3,C00031;C00366;C00315,162 (162),0.14,0.635,0.416,0.535\nNitrogen metabolism - Homo sapiens (human),KEGG,0,,17 (17),1,1,1,C00025,19 (19),0.143,0.636,0.421,0.535\nLeukotriene metabolism,EHMN,0,,100 (105),1,1,1,C00020,27 (27),0.197,0.813,0.517,0.613\nG alpha (s) signalling events,Reactome,0,,117 (129),1,1,1,C00020,27 (27),0.197,0.813,0.517,0.613\nCitric acid cycle (TCA cycle),Reactome,0,,19 (22),1,1,1,C00158,27 (27),0.197,0.813,0.517,0.613\nSignaling by EGFR in Cancer,Reactome,0,,177 (187),1,1,1,C00020,27 (34),0.197,0.813,0.517,0.613\nSignaling by Ligand-Responsive EGFR Variants in Cancer,Reactome,0,,177 (187),1,1,1,C00020,27 (34),0.197,0.813,0.517,0.613\nRespiratory electron transport_ ATP synthesis by chemiosmotic coupling_ and heat production by uncoupling proteins.,Wikipathways,0,,0 (0),1,1,1,C00020,18 (20),0.136,0.616,0.136,0.616\nSeparation of Sister Chromatids,Reactome,1,SEC13,119 (126),0.199,1,0,,2 (2),1,1,0.521,0.617\nMicroRNAs in cancer - Homo sapiens (human),KEGG,1,GLS,291 (297),0.422,1,0,,0 (0),1,1,0.422,1\nFolate malabsorption_ hereditary,SMPDB,0,,14 (14),1,1,1,C00025,28 (28),0.203,0.83,0.527,0.618\nCitrate cycle (TCA cycle) - Homo sapiens (human),KEGG,0,,30 (30),1,1,1,C00158,20 (20),0.15,0.653,0.434,0.542\nPentose Phosphate Pathway,SMPDB,0,,14 (14),1,1,1,C00020,28 (28),0.203,0.83,0.527,0.618\nHomocystinuria due to defect of N(5_10)-methylene THF deficiency,SMPDB,0,,14 (14),1,1,1,C00025,28 (28),0.203,0.83,0.527,0.618\nFolate Metabolism,SMPDB,0,,14 (14),1,1,1,C00025,28 (28),0.203,0.83,0.527,0.618\nGlucose-6-phosphate dehydrogenase deficiency,SMPDB,0,,14 (14),1,1,1,C00020,28 (28),0.203,0.83,0.527,0.618\nRibose-5-phosphate isomerase deficiency,SMPDB,0,,14 (14),1,1,1,C00020,28 (28),0.203,0.83,0.527,0.618\nTransaldolase deficiency,SMPDB,0,,14 (14),1,1,1,C00020,28 (28),0.203,0.83,0.527,0.618\nInositol phosphate metabolism,Reactome,0,,46 (47),1,1,1,C03546,29 (37),0.21,0.847,0.538,0.627\nLatent infection of Homo sapiens with Mycobacterium tuberculosis,Reactome,0,,33 (34),1,1,1,C00327,29 (39),0.21,0.847,0.538,0.627\nMethotrexate Action Pathway,SMPDB,0,,14 (14),1,1,1,C00025,29 (29),0.21,0.847,0.538,0.627\nalpha-linolenic acid (ALA) metabolism,Reactome,0,,12 (12),1,1,1,C00020,29 (32),0.21,0.847,0.538,0.627\nPyrimidine biosynthesis,Reactome,0,,6 (6),1,1,1,C00025,29 (29),0.21,0.847,0.538,0.627\nMitotic Anaphase,Reactome,1,SEC13,130 (137),0.216,1,0,,4 (4),1,1,0.547,0.635\nEukaryotic Translation Termination,Reactome,1,RPL9,130 (139),0.216,1,0,,5 (6),1,1,0.547,0.635\nCaffeine metabolism - Homo sapiens (human),KEGG,0,,5 (5),1,1,1,C00385,21 (21),0.157,0.677,0.447,0.554\nInositol Metabolism,SMPDB,0,,21 (22),1,1,1,C03546,30 (30),0.216,0.87,0.548,0.635\nIntegration of energy metabolism,Reactome,0,,86 (89),1,1,1,C00020,30 (31),0.216,0.87,0.548,0.635\nMitotic Metaphase and Anaphase,Reactome,1,SEC13,131 (138),0.217,1,0,,4 (4),1,1,0.549,0.635\nPeptide chain elongation,Reactome,1,RPL9,131 (141),0.217,1,0,,24 (25),1,1,0.549,0.635\nPhosphatidylinositol signaling system - Homo sapiens (human),KEGG,0,,81 (81),1,1,1,C03546,27 (27),0.197,0.813,0.517,0.613\nLipid digestion_ mobilization_ and transport,Reactome,0,,50 (53),1,1,1,C00116,31 (32),0.223,0.892,0.557,0.641\nNonsense Mediated Decay (NMD) independent of the Exon Junction Complex (EJC),Reactome,1,RPL9,135 (144),0.223,1,0,,2 (2),1,1,0.558,0.641\nInnate Immune System,Reactome,1,XRCC6,579 (607),0.669,1,1,C00020,50 (65),0.335,1,0.559,0.641\nSjogren Larsson Syndrome,SMPDB,0,,16 (17),1,1,1,C00025,32 (32),0.229,0.901,0.567,0.641\nSialuria or French Type Sialuria,SMPDB,0,,16 (17),1,1,1,C00025,32 (32),0.229,0.901,0.567,0.641\nAmino Sugar Metabolism,SMPDB,0,,16 (17),1,1,1,C00025,32 (32),0.229,0.901,0.567,0.641\nPyruvate dehydrogenase deficiency (E2),SMPDB,0,,21 (21),1,1,1,C00158,32 (32),0.229,0.901,0.567,0.641\n2-ketoglutarate dehydrogenase complex deficiency,SMPDB,0,,21 (21),1,1,1,C00158,32 (32),0.229,0.901,0.567,0.641\nMitochondrial complex II deficiency,SMPDB,0,,21 (21),1,1,1,C00158,32 (32),0.229,0.901,0.567,0.641\nFumarase deficiency,SMPDB,0,,21 (21),1,1,1,C00158,32 (32),0.229,0.901,0.567,0.641\nCongenital lactic acidosis,SMPDB,0,,21 (21),1,1,1,C00158,32 (32),0.229,0.901,0.567,0.641\nCitric Acid Cycle,SMPDB,0,,21 (21),1,1,1,C00158,32 (32),0.229,0.901,0.567,0.641\nTay-Sachs Disease,SMPDB,0,,16 (17),1,1,1,C00025,32 (32),0.229,0.901,0.567,0.641\nSalla Disease/Infantile Sialic Acid Storage Disease,SMPDB,0,,16 (17),1,1,1,C00025,32 (32),0.229,0.901,0.567,0.641\nPyruvate dehydrogenase deficiency (E3),SMPDB,0,,21 (21),1,1,1,C00158,32 (32),0.229,0.901,0.567,0.641\nMetabolism of nitric oxide,Reactome,0,,19 (20),1,1,1,C00327,32 (35),0.229,0.901,0.567,0.641\nG(M2)-Gangliosidosis: Variant B_ Tay-sachs disease,SMPDB,0,,16 (17),1,1,1,C00025,32 (32),0.229,0.901,0.567,0.641\nPlatelet activation_ signaling and aggregation,Reactome,0,,212 (231),1,1,1,C00116,32 (32),0.229,0.901,0.567,0.641\neNOS activation and regulation,Reactome,0,,19 (20),1,1,1,C00327,32 (35),0.229,0.901,0.567,0.641\nHIV Life Cycle,Reactome,1,XRCC6,142 (147),0.233,1,0,,14 (14),1,1,0.573,0.647\nsuperpathway of pyrimidine deoxyribonucleotides de novo biosynthesis,HumanCyc,0,,22 (22),1,1,1,C00025,33 (34),0.235,0.924,0.576,0.65\nGlycogenosis_ Type IA. Von gierke disease,SMPDB,0,,22 (22),1,1,1,C00031,34 (34),0.242,0.938,0.585,0.653\nGlycogenosis_ Type IC,SMPDB,0,,22 (22),1,1,1,C00031,34 (34),0.242,0.938,0.585,0.653\nTriosephosphate isomerase,SMPDB,0,,22 (22),1,1,1,C00031,34 (34),0.242,0.938,0.585,0.653\nFructose-1_6-diphosphatase deficiency,SMPDB,0,,22 (22),1,1,1,C00031,34 (34),0.242,0.938,0.585,0.653\nFructose and mannose metabolism,EHMN,0,,21 (22),1,1,1,C00031,34 (34),0.242,0.938,0.585,0.653\nGluconeogenesis,SMPDB,0,,22 (22),1,1,1,C00031,34 (34),0.242,0.938,0.585,0.653\nGlycogen Storage Disease Type 1A (GSD1A) or Von Gierke Disease,SMPDB,0,,22 (22),1,1,1,C00031,34 (34),0.242,0.938,0.585,0.653\nPhosphoenolpyruvate carboxykinase deficiency 1 (PEPCK1),SMPDB,0,,22 (22),1,1,1,C00031,34 (34),0.242,0.938,0.585,0.653\nGlycogenosis_ Type IB,SMPDB,0,,22 (22),1,1,1,C00031,34 (34),0.242,0.938,0.585,0.653\nNonsense Mediated Decay (NMD) enhanced by the Exon Junction Complex (EJC),Reactome,1,RPL9,148 (159),0.242,1,0,,4 (4),1,1,0.585,0.653\nNonsense-Mediated Decay (NMD),Reactome,1,RPL9,148 (159),0.242,1,0,,4 (4),1,1,0.585,0.653\nAbnormal metabolism in phenylketonuria,Wikipathways,0,,0 (0),1,1,1,C00041,20 (21),0.15,0.653,0.15,0.653\nCytosolic sulfonation of small molecules,Reactome,0,,19 (21),1,1,1,C00020,35 (44),0.248,0.959,0.593,0.66\nSelenocompound metabolism - Homo sapiens (human),KEGG,0,,17 (17),1,1,1,C00041,27 (27),0.197,0.813,0.517,0.613\nVitamin B9 (folate) metabolism,EHMN,0,,28 (29),1,1,1,C00025,35 (35),0.248,0.959,0.593,0.66\nProcessing of Capped Intron-Containing Pre-mRNA,Reactome,1,HNRNPF,155 (162),0.252,1,0,,1 (2),1,1,0.599,0.665\nTCA cycle,EHMN,0,,30 (34),1,1,1,C00158,36 (36),0.254,0.98,0.602,0.668\nL13a-mediated translational silencing of Ceruloplasmin expression,Reactome,1,RPL9,160 (169),0.259,1,0,,1 (1),1,1,0.609,0.674\n3_ -UTR-mediated translational regulation,Reactome,1,RPL9,160 (169),0.259,1,0,,1 (1),1,1,0.609,0.674\nAmine-derived hormones,Reactome,0,,16 (16),1,1,1,C00041,37 (38),0.26,1,0.61,0.674\nalpha-linolenic (omega3) and linoleic (omega6) acid metabolism,Reactome,0,,12 (12),1,1,1,C00020,37 (42),0.26,1,0.61,0.674\nGTP hydrolysis and joining of the 60S ribosomal subunit,Reactome,1,RPL9,161 (170),0.26,1,0,,3 (4),1,1,0.611,0.674\nLysosome - Homo sapiens (human),KEGG,1,NAPSA,120 (122),0.201,1,0,,4 (4),1,1,0.523,0.618\nNeurotransmitter Release Cycle,Wikipathways,0,,0 (0),1,1,1,C00025,21 (21),0.157,0.677,0.157,0.677\nOpioid Signalling,Wikipathways,0,,0 (0),1,1,1,C00020,21 (21),0.157,0.677,0.157,0.677\nBiotin metabolism - Homo sapiens (human),KEGG,0,,3 (3),1,1,1,C00047,28 (28),0.203,0.83,0.527,0.618\nCap-dependent Translation Initiation,Reactome,1,RPL9,167 (177),0.269,1,0,,5 (6),1,1,0.622,0.683\nEukaryotic Translation Initiation,Reactome,1,RPL9,167 (177),0.269,1,0,,5 (6),1,1,0.622,0.683\nThiamine metabolism - Homo sapiens (human),KEGG,0,,3 (4),1,1,1,C00097,30 (30),0.216,0.87,0.548,0.635\nOxidative phosphorylation - Homo sapiens (human),KEGG,1,LHPP,132 (133),0.219,1,0,,16 (16),1,1,0.551,0.637\nPeroxisomal lipid metabolism,Reactome,0,,21 (21),1,1,1,C00020,42 (55),0.29,1,0.648,0.71\nHIV Infection,Reactome,1,XRCC6,183 (190),0.29,1,0,,16 (16),1,1,0.649,0.71\nLong chain acyl-CoA dehydrogenase deficiency (LCAD),SMPDB,0,,14 (14),1,1,1,C00020,43 (43),0.295,1,0.655,0.71\nCarnitine palmitoyl transferase deficiency (II),SMPDB,0,,14 (14),1,1,1,C00020,43 (43),0.295,1,0.655,0.71\nVery-long-chain acyl coa dehydrogenase deficiency (VLCAD),SMPDB,0,,14 (14),1,1,1,C00020,43 (43),0.295,1,0.655,0.71\nMedium chain acyl-coa dehydrogenase deficiency (MCAD),SMPDB,0,,14 (14),1,1,1,C00020,43 (43),0.295,1,0.655,0.71\nShort Chain Acyl CoA Dehydrogenase Deficiency (SCAD Deficiency),SMPDB,0,,14 (14),1,1,1,C00020,43 (43),0.295,1,0.655,0.71\nFatty acid Metabolism,SMPDB,0,,14 (14),1,1,1,C00020,43 (43),0.295,1,0.655,0.71\nGlutaric Aciduria Type I,SMPDB,0,,14 (14),1,1,1,C00020,43 (43),0.295,1,0.655,0.71\nCarnitine palmitoyl transferase deficiency (I),SMPDB,0,,14 (14),1,1,1,C00020,43 (43),0.295,1,0.655,0.71\nTrifunctional protein deficiency,SMPDB,0,,14 (14),1,1,1,C00020,43 (43),0.295,1,0.655,0.71\nEthylmalonic Encephalopathy,SMPDB,0,,14 (14),1,1,1,C00020,43 (43),0.295,1,0.655,0.71\nLysine biosynthesis - Homo sapiens (human),KEGG,0,,2 (2),1,1,1,C00047,35 (35),0.248,0.959,0.593,0.66\nRNA transport - Homo sapiens (human),KEGG,1,SEC13,162 (165),0.262,1,0,,3 (3),1,1,0.612,0.675\nVitamin digestion and absorption - Homo sapiens (human),KEGG,0,,24 (24),1,1,1,C02477,38 (38),0.266,1,0.618,0.681\nSynthesis of substrates in N-glycan biosythesis,Reactome,0,,52 (54),1,1,1,C00025,47 (55),0.318,1,0.682,0.736\nPlatelet homeostasis,Wikipathways,0,,0 (0),1,1,1,C00327,24 (24),0.177,0.742,0.177,0.742\nNeuroactive ligand-receptor interaction - Homo sapiens (human),KEGG,0,,240 (275),1,1,2,C00245;C00025,128 (128),0.276,1,0.632,0.693\nN-Glycan biosynthesis,EHMN,0,,43 (43),1,1,1,C00031,51 (51),0.34,1,0.707,0.76\nOrganelle biogenesis and maintenance,Reactome,0,,293 (307),1,1,1,C00020,52 (53),0.345,1,0.713,0.764\ncAMP signaling pathway - Homo sapiens (human),KEGG,0,,189 (200),1,1,1,C00020,40 (40),0.278,1,0.634,0.695\nInositol phosphate metabolism - Homo sapiens (human),KEGG,0,,60 (61),1,1,1,C03546,44 (44),0.301,1,0.662,0.716\nPyrimidine Metabolism,SMPDB,0,,23 (23),1,1,1,C00106,57 (57),0.372,1,0.74,0.789\nUMP Synthase Deiciency (Orotic Aciduria),SMPDB,0,,23 (23),1,1,1,C00106,57 (57),0.372,1,0.74,0.789\nMNGIE (Mitochondrial Neurogastrointestinal Encephalopathy),SMPDB,0,,23 (23),1,1,1,C00106,57 (57),0.372,1,0.74,0.789\nBeta Ureidopropionase Deficiency,SMPDB,0,,23 (23),1,1,1,C00106,57 (57),0.372,1,0.74,0.789\nDihydropyrimidinase Deficiency,SMPDB,0,,23 (23),1,1,1,C00106,57 (57),0.372,1,0.74,0.789\nDe novo fatty acid biosynthesis,EHMN,0,,44 (44),1,1,1,C00020,59 (59),0.382,1,0.75,0.799\nM Phase,Reactome,1,SEC13,260 (269),0.387,1,0,,18 (18),1,1,0.754,0.803\n27-Hydroxylase Deficiency,SMPDB,0,,17 (17),1,1,1,C00245,61 (61),0.392,1,0.759,0.803\nCongenital Bile Acid Synthesis Defect Type II,SMPDB,0,,17 (17),1,1,1,C00245,61 (61),0.392,1,0.759,0.803\nFamilial Hypercholanemia (FHCA),SMPDB,0,,17 (17),1,1,1,C00245,61 (61),0.392,1,0.759,0.803\nCongenital Bile Acid Synthesis Defect Type III,SMPDB,0,,17 (17),1,1,1,C00245,61 (61),0.392,1,0.759,0.803\nZellweger Syndrome,SMPDB,0,,17 (17),1,1,1,C00245,61 (61),0.392,1,0.759,0.803\nBile Acid Biosynthesis,SMPDB,0,,17 (17),1,1,1,C00245,61 (61),0.392,1,0.759,0.803\nCerebrotendinous Xanthomatosis (CTX),SMPDB,0,,17 (17),1,1,1,C00245,61 (61),0.392,1,0.759,0.803\nBiosynthesis of the N-glycan precursor (dolichol lipid-linked oligosaccharide_ LLO) and transfer to a nascent protein,Reactome,0,,68 (70),1,1,1,C00025,62 (70),0.397,1,0.764,0.803\nTyrosinemia Type I,SMPDB,0,,18 (18),1,1,1,C00025,65 (67),0.412,1,0.777,0.803\nTyrosinemia_ transient_ of the newborn,SMPDB,0,,18 (18),1,1,1,C00025,65 (67),0.412,1,0.777,0.803\nDopamine beta-hydroxylase deficiency,SMPDB,0,,18 (18),1,1,1,C00025,65 (67),0.412,1,0.777,0.803\nTyrosine Metabolism,SMPDB,0,,18 (18),1,1,1,C00025,65 (67),0.412,1,0.777,0.803\nAlkaptonuria,SMPDB,0,,18 (18),1,1,1,C00025,65 (67),0.412,1,0.777,0.803\nMonoamine oxidase-a deficiency (MAO-A),SMPDB,0,,18 (18),1,1,1,C00025,65 (67),0.412,1,0.777,0.803\nHawkinsinuria,SMPDB,0,,18 (18),1,1,1,C00025,65 (67),0.412,1,0.777,0.803\nCyanoamino acid metabolism - Homo sapiens (human),KEGG,0,,7 (7),1,1,1,C00097,45 (45),0.307,1,0.669,0.723\nG alpha (i) signalling events,Reactome,0,,220 (242),1,1,1,C00025,67 (83),0.421,1,0.785,0.803\nLeukotriene C4 Synthesis Deficiency,SMPDB,0,,30 (30),1,1,1,C00025,69 (69),0.431,1,0.794,0.803\nAntrafenine Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,69 (69),0.431,1,0.794,0.803\nLumiracoxib Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,69 (70),0.431,1,0.794,0.803\nNepafenac Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,69 (70),0.431,1,0.794,0.803\nTenoxicam Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,69 (69),0.431,1,0.794,0.803\nArachidonic Acid Metabolism,SMPDB,0,,30 (30),1,1,1,C00025,69 (69),0.431,1,0.794,0.803\nRofecoxib Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,69 (69),0.431,1,0.794,0.803\nTrisalicylate-choline Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,69 (69),0.431,1,0.794,0.803\nLornoxicam Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,69 (69),0.431,1,0.794,0.803\nSalsalate Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,69 (69),0.431,1,0.794,0.803\nNabumetone Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,69 (70),0.431,1,0.794,0.803\nKetoprofen Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,70 (70),0.435,1,0.797,0.803\nAcetylsalicylic Acid Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,70 (70),0.435,1,0.797,0.803\nDiflunisal Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,70 (70),0.435,1,0.797,0.803\nAcetaminophen Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,70 (70),0.435,1,0.797,0.803\nSulindac Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,70 (70),0.435,1,0.797,0.803\nDiclofenac Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,70 (70),0.435,1,0.797,0.803\nKetorolac Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,70 (70),0.435,1,0.797,0.803\nNaproxen Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,70 (70),0.435,1,0.797,0.803\nEtoricoxib Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,70 (70),0.435,1,0.797,0.803\nCarprofen Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,70 (70),0.435,1,0.797,0.803\nFlurbiprofen Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,70 (70),0.435,1,0.797,0.803\nAntipyrine Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,70 (70),0.435,1,0.797,0.803\nMagnesium salicylate Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,70 (70),0.435,1,0.797,0.803\nPhenylbutazone Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,70 (70),0.435,1,0.797,0.803\nTiaprofenic Acid Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,70 (70),0.435,1,0.797,0.803\nTolmetin Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,70 (70),0.435,1,0.797,0.803\nSalicylic Acid Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,70 (70),0.435,1,0.797,0.803\nSalicylate-sodium Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,70 (70),0.435,1,0.797,0.803\nOxaprozin Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,70 (70),0.435,1,0.797,0.803\nValdecoxib Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,70 (70),0.435,1,0.797,0.803\nMeloxicam Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,70 (70),0.435,1,0.797,0.803\nSuprofen Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,70 (70),0.435,1,0.797,0.803\nBromfenac Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,70 (70),0.435,1,0.797,0.803\nMefenamic Acid Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,70 (70),0.435,1,0.797,0.803\nPiroxicam Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,70 (70),0.435,1,0.797,0.803\nEtodolac Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,70 (70),0.435,1,0.797,0.803\nFenoprofen Action Pathway,SMPDB,0,,30 (30),1,1,1,C00025,70 (70),0.435,1,0.797,0.803\nIndomethacin Action Pathway,SMPDB,0,,31 (31),1,1,1,C00025,70 (70),0.435,1,0.797,0.803\nPrimary bile acid biosynthesis - Homo sapiens (human),KEGG,0,,17 (17),1,1,1,C00245,47 (47),0.318,1,0.682,0.736\nCelecoxib Action Pathway,SMPDB,0,,36 (36),1,1,1,C00025,73 (73),0.449,1,0.809,0.813\nIbuprofen Action Pathway,SMPDB,0,,49 (50),1,1,1,C00025,74 (74),0.454,1,0.812,0.815\nLatent infection of Homo sapiens with Mycobacterium tuberculosis,Wikipathways,0,,0 (0),1,1,1,C00327,28 (40),0.203,0.83,0.203,0.83\nLipid digestion_ mobilization_ and transport,Wikipathways,0,,0 (0),1,1,1,C00116,28 (29),0.203,0.83,0.203,0.83\nArachidonic acid metabolism,Reactome,0,,54 (57),1,1,1,C00025,80 (102),0.48,1,0.832,0.835\nInositol phosphate metabolism,Wikipathways,0,,0 (0),1,1,1,C03546,29 (35),0.21,0.847,0.21,0.847\nIntegration of energy metabolism,Wikipathways,0,,0 (0),1,1,1,C00020,29 (31),0.21,0.847,0.21,0.847\nStarch and sucrose metabolism - Homo sapiens (human),KEGG,0,,56 (56),1,1,1,C00031,51 (51),0.34,1,0.707,0.76\nMetabolism of nitric oxide,Wikipathways,0,,0 (0),1,1,1,C00327,31 (35),0.223,0.892,0.223,0.892\nGlycerophospholipid metabolism - Homo sapiens (human),KEGG,0,,92 (93),1,1,1,C00346,52 (52),0.345,1,0.713,0.764\nLysine degradation - Homo sapiens (human),KEGG,0,,51 (51),1,1,1,C00047,52 (52),0.345,1,0.713,0.764\nPhase 1 - Functionalization of compounds,Wikipathways,0,,0 (0),1,1,2,C00020;C00315,115 (137),0.237,0.93,0.237,0.93\nalpha-linolenic (omega3) and linoleic (omega6) acid metabolism,Wikipathways,0,,0 (0),1,1,1,C00020,36 (41),0.254,0.98,0.254,0.98\nSignaling events mediated by HDAC Class III,PID,2,XRCC6;ACSS2,39 (39),0.00233,1,0,,0 (0),1,1,0.00233,1\nProcessing of DNA ends prior to end rejoining,Reactome,1,XRCC6,4 (4),0.00741,1,0,,0 (0),1,1,0.00741,1\nNon-homologous end joining,Wikipathways,1,XRCC6,6 (6),0.0111,1,0,,0 (0),1,1,0.0111,1\n2-LTR circle formation,Reactome,1,XRCC6,7 (7),0.0129,1,0,,0 (0),1,1,0.0129,1\nIntegration of provirus,Reactome,1,XRCC6,10 (11),0.0184,1,0,,0 (0),1,1,0.0184,1\nPyrimidine metabolism - Homo sapiens (human),KEGG,0,,103 (105),1,1,1,C00106,66 (66),0.417,1,0.781,0.803\nIfosfamide Pathway_ Pharmacodynamics,PharmGKB,1,ADH5,14 (14),0.0257,1,0,,0 (0),1,1,0.0257,1\nCyclophosphamide Pathway_ Pharmacodynamics,PharmGKB,1,ADH5,17 (17),0.0311,1,0,,0 (0),1,1,0.0311,1\nBARD1 signaling events,PID,1,XRCC6,29 (29),0.0525,1,0,,0 (0),1,1,0.0525,1\nTranslation Factors,Wikipathways,1,EEF1D,49 (50),0.0872,1,0,,0 (0),1,1,0.0872,1\nParkin-Ubiquitin Proteasomal System pathway,Wikipathways,1,PSMD9,72 (73),0.126,1,0,,0 (0),1,1,0.126,1\nPhenylalanine metabolism - Homo sapiens (human),KEGG,0,,18 (18),1,1,1,C00180,72 (72),0.445,1,0.805,0.81\nCytoplasmic Ribosomal Proteins,Wikipathways,1,RPL9,88 (88),0.151,1,0,,0 (0),1,1,0.151,1\nmRNA Splicing - Major Pathway,Reactome,1,HNRNPF,124 (131),0.207,1,0,,0 (0),1,1,0.207,1\nmRNA Splicing,Reactome,1,HNRNPF,124 (131),0.207,1,0,,0 (0),1,1,0.207,1\nUbiquinone and other terpenoid-quinone biosynthesis - Homo sapiens (human),KEGG,0,,10 (10),1,1,1,C02477,90 (90),0.521,1,0.861,0.863\nFormation of a pool of free 40S subunits,Reactome,1,RPL9,142 (151),0.233,1,0,,0 (0),1,1,0.233,1\nAndrogenReceptor,NetPath,1,XRCC6,145 (149),0.238,1,0,,0 (0),1,1,0.238,1\nSRP-dependent cotranslational protein targeting to membrane,Reactome,1,RPL9,154 (164),0.25,1,0,,0 (1),1,1,0.25,1\nAmino sugar and nucleotide sugar metabolism - Homo sapiens (human),KEGG,0,,45 (46),1,1,1,C00031,105 (105),0.577,1,0.894,0.895\nPeroxisomal lipid metabolism,Wikipathways,0,,0 (0),1,1,1,C00020,42 (55),0.29,1,0.29,1\nTCR,NetPath,1,EVL,239 (251),0.362,1,0,,0 (0),1,1,0.362,1\nBile acid and bile salt metabolism,Wikipathways,0,,0 (0),1,1,1,C00020,56 (70),0.367,1,0.367,1\nAsparagine N-linked glycosylation,Wikipathways,0,,0 (0),1,1,1,C00025,61 (64),0.392,1,0.392,1\nArachidonic acid metabolism,Wikipathways,0,,0 (0),1,1,1,C00025,63 (78),0.402,1,0.402,1\nPorphyrin and chlorophyll metabolism - Homo sapiens (human),KEGG,0,,41 (41),1,1,1,C00025,124 (124),0.639,1,0.925,0.925\nmiR-targeted genes in muscle cell - TarBase,Wikipathways,1,ABHD10,398 (398),0.529,1,0,,0 (0),1,1,0.529,1\nmiR-targeted genes in lymphocytes - TarBase,Wikipathways,1,ABHD10,482 (482),0.6,1,0,,0 (0),1,1,0.6,1\n"
},
{
"path": "Demos/Data Analysis Workflow/results/statistical_results.csv",
"content": "\"\",\"ID\",\"description\",\"type\",\"healthy.mean.....std.dev\",\"sick.mean.....std.dev\",\"mean.sick_mean.healthy\",\"group_p.values\",\"group_adjusted.p.values\",\"group_q.values\"\n\"1\",\"C00379\",\"xylitol\",\"metabolite\",\"579 +/- 300\",\"880 +/- 410\",1.52,0.00328545050468262,0.0361722723945709,0.0159161803445209\n\"2\",\"C00385\",\"xanthine\",\"metabolite\",\"574 +/- 350\",\"1670 +/- 1500\",2.91,0.00039090476261604,0.013030158753868,0.00607284567246352\n\"3\",\"C00105 \",\"uridine-5'-monophosphate\",\"metabolite\",\"120 +/- 55\",\"131 +/- 87\",1.08,0.610200483328374,0.729045221914476,0.357776810754231\n\"4\",\"C00299\",\"uridine\",\"metabolite\",\"1990 +/- 1100\",\"1550 +/- 1300\",0.78,0.18894602157576,0.374150537773782,0.171821768581724\n\"5\",\"C00366\",\"uric acid\",\"metabolite\",\"2960 +/- 2100\",\"4150 +/- 1900\",1.4,0.0366106802463671,0.124104000835143,0.0592287600595077\n\"6\",\"C00086\",\"urea\",\"metabolite\",\"104000 +/- 42000\",\"98900 +/- 38000\",0.95,0.669390202042164,0.773861505251056,0.379317299747421\n\"7\",\"C00106\",\"uracil\",\"metabolite\",\"16300 +/- 11000\",\"24700 +/- 17000\",1.52,0.0334733584315987,0.123975401598514,0.0574215010905176\n\"8\",\"C00082\",\"tyrosine\",\"metabolite\",\"37200 +/- 16000\",\"42700 +/- 17000\",1.15,0.225792163316927,0.40320029163737,0.190812415121345\n\"9\",\"C00078\",\"tryptophan\",\"metabolite\",\"8010 +/- 5800\",\"7120 +/- 3400\",0.89,0.502255888686614,0.643647776650811,0.314383605053178\n\"10\",\"C01083\",\"trehalose\",\"metabolite\",\"3040 +/- 3000\",\"6020 +/- 7200\",1.98,0.0536152999524518,0.167444393629659,0.0757129572671398\n\"11\",\"C01157\",\"trans-4-hydroxyproline\",\"metabolite\",\"394 +/- 120\",\"467 +/- 170\",1.18,0.0758355243406092,0.204856156963675,0.0948887416739869\n\"12\",\"C02477\",\"tocopherol alpha\",\"metabolite\",\"1160 +/- 1400\",\"2600 +/- 1200\",2.24,0.000150503421423003,0.00771455795526333,0.0036324624978498\n\"13\",\"C00178\",\"thymine\",\"metabolite\",\"148 +/- 42\",\"159 +/- 72\",1.08,0.474287088571268,0.628097012342116,0.302166723515868\n\"14\",\"C00188\",\"threonine\",\"metabolite\",\"32400 +/- 9600\",\"34800 +/- 8700\",1.08,0.331102583126561,0.50167058049479,0.239100214453653\n\"15\",\"C01620\",\"threonic acid\",\"metabolite\",\"6560 +/- 2500\",\"6920 +/- 4500\",1.06,0.713870260961445,0.803136066730948,0.394577053665743\n\"16\",\"C00245\",\"taurine\",\"metabolite\",\"93000 +/- 29000\",\"125000 +/- 59000\",1.34,0.0142252181612132,0.0812869609212182,0.0387942844063903\n\"17\",\"D09007\",\"tagatose\",\"metabolite\",\"163 +/- 61\",\"108 +/- 56\",0.66,0.00110565990755587,0.023396520705865,0.0101120604656123\n\"18\",\"C01530\",\"stearic acid\",\"metabolite\",\"246000 +/- 110000\",\"219000 +/- 150000\",0.89,0.466703534304084,0.622271379072112,0.29877880335116\n\"19\",\"C00315\",\"spermidine\",\"metabolite\",\"276 +/- 100\",\"189 +/- 110\",0.68,0.00376483482897061,0.0361722723945709,0.016878366812324\n\"20\",\"C00794\",\"sorbitol\",\"metabolite\",\"454 +/- 470\",\"535 +/- 730\",1.18,0.6284298127158,0.73073234036721,0.364568519605175\n\"21\",\"C00493\",\"shikimic acid\",\"metabolite\",\"292 +/- 130\",\"306 +/- 180\",1.05,0.736754854822454,0.81695201055171,0.402139384789744\n\"22\",\"C00121\",\"ribose\",\"metabolite\",\"726 +/- 620\",\"698 +/- 440\",0.96,0.85143234827253,0.883370899047943,0.4373573640815\n\"23\",\"C00013\",\"pyrophosphate\",\"metabolite\",\"441 +/- 240\",\"461 +/- 260\",1.05,0.766763163297035,0.833205495903235,0.411774108279692\n\"24\",\"C00134\",\"putrescine\",\"metabolite\",\"384 +/- 280\",\"423 +/- 290\",1.1,0.619398259889562,0.729045221914476,0.36122167938282\n\"25\",\"C02067\",\"pseudo uridine\",\"metabolite\",\"925 +/- 300\",\"886 +/- 280\",0.96,0.619688438627305,0.729045221914476,0.361329759571424\n\"26\",\"C00148\",\"proline\",\"metabolite\",\"68200 +/- 38000\",\"94500 +/- 49000\",1.39,0.0311610632409306,0.120715763058434,0.0559433595293094\n\"27\",\"C00408\",\"pipecolic acid\",\"metabolite\",\"224 +/- 50\",\"235 +/- 74\",1.05,0.509346910367233,0.643647776650811,0.317413317953474\n\"28\",\"C00346\",\"phosphoethanolamine\",\"metabolite\",\"13500 +/- 7700\",\"21200 +/- 14000\",1.57,0.013411529593671,0.0810371067971812,0.0376724937670257\n\"29\",\"C00166\",\"phenylpyruvic acid\",\"metabolite\",\"396 +/- 120\",\"433 +/- 97\",1.09,0.223180529519009,0.40320029163737,0.189555230532812\n\"30\",\"C01601\",\"pelargonic acid\",\"metabolite\",\"18800 +/- 8300\",\"13900 +/- 6700\",0.74,0.0210762888444018,0.100363280211437,0.0474777261697044\n\"31\",\"C00864\",\"pantothenic acid\",\"metabolite\",\"1200 +/- 900\",\"975 +/- 340\",0.81,0.222687705090258,0.40320029163737,0.189316573834548\n\"32\",\"C00249\",\"palmitic acid\",\"metabolite\",\"40000 +/- 13000\",\"36300 +/- 19000\",0.91,0.410550185349807,0.570208590763622,0.272630754320155\n\"33\",\"C01879\",\"oxoproline\",\"metabolite\",\"96200 +/- 18000\",\"110000 +/- 33000\",1.15,0.0579236291551109,0.169178754049596,0.0793887501329705\n\"34\",\"C00295\",\"orotic acid\",\"metabolite\",\"186 +/- 140\",\"211 +/- 210\",1.13,0.612593773617059,0.729045221914476,0.358676747543567\n\"35\",\"C00077\",\"ornithine\",\"metabolite\",\"7300 +/- 2900\",\"3910 +/- 2100\",0.54,9.43331694404056e-06,0.00188666338880811,0.000900412012641018\n\"36\",\"D01924\",\"octadecanol\",\"metabolite\",\"1620 +/- 5100\",\"514 +/- 380\",0.32,0.266215262893444,0.451212309988888,0.210985145434877\n\"37\",\"C06424\",\"myristic acid\",\"metabolite\",\"2310 +/- 520\",\"2080 +/- 880\",0.9,0.257023581181974,0.443144105486162,0.206552985715321\n\"38\",\"C00073\",\"methionine\",\"metabolite\",\"9880 +/- 4900\",\"11800 +/- 6100\",1.19,0.216498165456674,0.40320029163737,0.186280079508244\n\"39\",\"C00159\",\"mannose\",\"metabolite\",\"3420 +/- 2000\",\"3290 +/- 2500\",0.96,0.830774157508609,0.871885795193779,0.431322821154691\n\"40\",\"C01835\",\"maltotriose\",\"metabolite\",\"3290 +/- 2200\",\"7370 +/- 9700\",2.24,0.0376626546976765,0.125542182325588,0.059915133632047\n\"41\",\"C00208\",\"maltose\",\"metabolite\",\"2390 +/- 2200\",\"4590 +/- 5400\",1.92,0.0544194279296391,0.167444393629659,0.0764158574242376\n\"42\",\"C07272\",\"maleimide\",\"metabolite\",\"626 +/- 330\",\"644 +/- 370\",1.03,0.856869772076505,0.883370899047943,0.438924478848032\n\"43\",\"C00047\",\"lysine\",\"metabolite\",\"24900 +/- 25000\",\"10700 +/- 12000\",0.43,0.00996149512009512,0.071153536572108,0.0320594353178956\n\"44\",\"C01595\",\"linoleic acid\",\"metabolite\",\"1210 +/- 750\",\"1310 +/- 600\",1.08,0.615589596542029,0.729045221914476,0.3597997033252\n\"45\",\"C00123\",\"leucine\",\"metabolite\",\"107000 +/- 47000\",\"125000 +/- 66000\",1.16,0.265781453888122,0.451212309988888,0.210778720302186\n\"46\",\"C02679 \",\"lauric acid\",\"metabolite\",\"8490 +/- 3500\",\"7000 +/- 2700\",0.82,0.086433883256494,0.217685164150831,0.10340021807679\n\"47\",\"C00328\",\"kynurenine\",\"metabolite\",\"826 +/- 620\",\"759 +/- 740\",0.92,0.718806779724198,0.803136066730948,0.39622449068396\n\"48\",\"C03546\",\"inositol-4-monophosphate\",\"metabolite\",\"302 +/- 130\",\"218 +/- 120\",0.72,0.0164080411102474,0.0886921141094456,0.041912480929005\n\"49\",\"C00137\",\"inositol myo-\",\"metabolite\",\"260000 +/- 85000\",\"260000 +/- 120000\",1,0.991599588771298,0.991599588771298,0.475146821165374\n\"50\",\"C00130\",\"inosine 5'-monophosphate\",\"metabolite\",\"199 +/- 340\",\"625 +/- 790\",3.14,0.0130565042625477,0.0810371067971812,0.0371616684439078\n\"51\",\"C00294\",\"inosine\",\"metabolite\",\"36000 +/- 21000\",\"39500 +/- 18000\",1.1,0.505192752792119,0.643647776650811,0.315641667685943\n\"52\",\"C02043\",\"indole-3-lactate\",\"metabolite\",\"243 +/- 98\",\"293 +/- 180\",1.21,0.203395727334973,0.391145629490332,0.179603606846582\n\"53\",\"C16526\",\"icosenoic acid\",\"metabolite\",\"663 +/- 260\",\"632 +/- 340\",0.95,0.7120111208497,0.803136066730948,0.393954280279788\n\"54\",\"C00262\",\"hypoxanthine\",\"metabolite\",\"45000 +/- 18000\",\"54100 +/- 23000\",1.2,0.113813847243337,0.258667834643947,0.123278102847589\n\"55\",\"C00192\",\"hydroxylamine\",\"metabolite\",\"7640 +/- 2900\",\"6050 +/- 4000\",0.79,0.100469887508766,0.239214017878015,0.113797249879282\n\"56\",\"C00135\",\"histidine\",\"metabolite\",\"6550 +/- 3400\",\"4720 +/- 1700\",0.72,0.0157996448519427,0.087775804733015,0.0410838025555293\n\"57\",\"C00160\",\"glycolic acid\",\"metabolite\",\"1360 +/- 390\",\"1250 +/- 580\",0.92,0.399296554607556,0.562085005266816,0.267264577851748\n\"58\",\"C00037\",\"glycine\",\"metabolite\",\"338000 +/- 64000\",\"373000 +/- 1e+05\",1.11,0.129084938984622,0.283703162603566,0.134305825956803\n\"59\",\"C00093\",\"glycerol-alpha-phosphate\",\"metabolite\",\"7840 +/- 10000\",\"9120 +/- 6100\",1.16,0.580341330408203,0.707733329766101,0.346332919078033\n\"60\",\"C05401\",\"glycerol-3-galactoside\",\"metabolite\",\"361 +/- 370\",\"695 +/- 1200\",1.93,0.161118623692168,0.332203347818903,0.155447545885591\n\"61\",\"C00116\",\"glycerol\",\"metabolite\",\"22900 +/- 7600\",\"27700 +/- 8200\",1.21,0.0270207871502094,0.116240884373239,0.0529215876871668\n\"62\",\"C00064\",\"glutamine\",\"metabolite\",\"11600 +/- 3000\",\"11800 +/- 3800\",1.02,0.770715083710493,0.833205495903235,0.41301985397975\n\"63\",\"C00025\",\"glutamic acid\",\"metabolite\",\"72900 +/- 18000\",\"96200 +/- 30000\",1.32,0.00111252948326934,0.023396520705865,0.0101348246975382\n\"64\",\"C00191\",\"glucuronic acid\",\"metabolite\",\"229 +/- 110\",\"207 +/- 110\",0.9,0.477353729380008,0.628097012342116,0.303527454411203\n\"65\",\"C00031\",\"glucose\",\"metabolite\",\"335000 +/- 2e+05\",\"144000 +/- 140000\",0.43,0.000154291159105267,0.00771455795526333,0.00365064633329483\n\"66\",\"C00257\",\"gluconic acid\",\"metabolite\",\"502 +/- 580\",\"199 +/- 120\",0.4,0.0105176944711578,0.072535823939019,0.0330724274491998\n\"67\",\"C00446\",\"galactose-6-phosphate\",\"metabolite\",\"221 +/- 110\",\"197 +/- 160\",0.89,0.516061954477097,0.643647776650811,0.320257808963803\n\"68\",\"C00122\",\"fumaric acid\",\"metabolite\",\"4470 +/- 1900\",\"4250 +/- 2100\",0.95,0.686705360754177,0.784806126576202,0.385348155828769\n\"69\",\"C01018\",\"fucose+ rhamnose\",\"metabolite\",\"1150 +/- 730\",\"2500 +/- 2000\",2.17,0.00175499678084269,0.024873584628788,0.0116847020661663\n\"70\",\"C01094\",\"fructose 1 phosphate\",\"metabolite\",\"168 +/- 74\",\"159 +/- 88\",0.94,0.675649390425982,0.776608494742508,0.381510979950102\n\"71\",\"C00095\",\"fructose\",\"metabolite\",\"685 +/- 800\",\"934 +/- 1100\",1.36,0.34938837100958,0.525396046630947,0.246143144304717\n\"72\",\"C00836\",\"dihydrosphingosine\",\"metabolite\",\"341 +/- 150\",\"234 +/- 210\",0.69,0.0360026114833942,0.124104000835143,0.0588947333343749\n\"73\",\"C00112\",\"cytidine-5'-diphosphate\",\"metabolite\",\"5290 +/- 3600\",\"5310 +/- 3900\",1,0.984486499102861,0.989433667440062,0.473351796665577\n\"74\",\"C00097\",\"cysteine\",\"metabolite\",\"5160 +/- 2500\",\"8460 +/- 3400\",1.64,0.00015078430404477,0.00771455795526333,0.0036338359417622\n\"75\",\"C00791\",\"creatinine\",\"metabolite\",\"10800 +/- 4200\",\"15500 +/- 6600\",1.44,0.00288081460529995,0.0347878155376343,0.0149745227503144\n\"76\",\"C00327\",\"citrulline\",\"metabolite\",\"597 +/- 240\",\"401 +/- 180\",0.67,0.00162923691829092,0.024873584628788,0.0114506409842931\n\"77\",\"C00158\",\"citric acid\",\"metabolite\",\"3640 +/- 2000\",\"1930 +/- 1400\",0.53,0.000579756071214233,0.0165644591775495,0.00760320297361104\n\"78\",\"C00187\",\"cholesterol\",\"metabolite\",\"132000 +/- 47000\",\"126000 +/- 48000\",0.95,0.62590607396611,0.73073234036721,0.363636828781049\n\"79\",\"C06423\",\"caprylic acid\",\"metabolite\",\"1470 +/- 530\",\"1080 +/- 490\",0.74,0.00734854914898753,0.0565273011460579,0.026584412159079\n\"80\",\"C01571\",\"capric acid\",\"metabolite\",\"2800 +/- 830\",\"2240 +/- 1100\",0.8,0.0363759041820177,0.124104000835143,0.0591006690983508\n\"81\",\"C06555\",\"biuret\",\"metabolite\",\"269 +/- 180\",\"150 +/- 66\",0.56,0.00185532094508146,0.024873584628788,0.0118544519881803\n\"82\",\"C00180\",\"benzoic acid\",\"metabolite\",\"12200 +/- 3100\",\"10000 +/- 4400\",0.83,0.0468593104881019,0.148759715835244,0.0694773959805803\n\"83\",\"C08281\",\"behenic acid\",\"metabolite\",\"990 +/- 460\",\"821 +/- 430\",0.83,0.168264576786352,0.339928437952225,0.159839930977894\n\"84\",\"C00152\",\"asparagine\",\"metabolite\",\"6100 +/- 1800\",\"7320 +/- 3300\",1.2,0.096381547589217,0.235076945339554,0.110881902747376\n\"85\",\"C06425\",\"arachidic acid\",\"metabolite\",\"3880 +/- 2400\",\"3350 +/- 2200\",0.87,0.401890778765773,0.562085005266816,0.268423912079844\n\"86\",\"C00026\",\"alpha ketoglutaric acid\",\"metabolite\",\"114 +/- 68\",\"109 +/- 81\",0.95,0.782516107944086,0.836915623469611,0.416708630314735\n\"87\",\"C00041\",\"alanine\",\"metabolite\",\"213000 +/- 59000\",\"258000 +/- 86000\",1.21,0.0296848111802146,0.119912537411498,0.0549254772257551\n\"88\",\"C06104\",\"adipic acid\",\"metabolite\",\"926 +/- 680\",\"591 +/- 370\",0.64,0.029795109415341,0.119912537411498,0.0550036789067433\n\"89\",\"C00020\",\"adenosine-5-phosphate\",\"metabolite\",\"1300 +/- 1500\",\"3020 +/- 2800\",2.32,0.00683740192348928,0.0546992153879142,0.0253505504669233\n\"90\",\"C00212\",\"adenosine\",\"metabolite\",\"2690 +/- 3600\",\"2650 +/- 2100\",0.98,0.954861765806219,0.973547556674085,0.465741880978428\n\"91\",\"C05659\",\"5-methoxytryptamine\",\"metabolite\",\"204 +/- 120\",\"164 +/- 100\",0.8,0.195677615445374,0.383681598912498,0.175504023097903\n\"92\",\"C00170\",\"5'-deoxy-5'-methylthioadenosine\",\"metabolite\",\"216 +/- 83\",\"355 +/- 160\",1.64,0.000200405517131727,0.0080162206852691,0.00382574943883209\n\"93\",\"C00431\",\"5-aminovaleric acid\",\"metabolite\",\"451 +/- 140\",\"497 +/- 200\",1.1,0.32482727779475,0.495919508083587,0.236601251638133\n\"94\",\"C00989\",\"4-hydroxybutyric acid\",\"metabolite\",\"209 +/- 42\",\"282 +/- 110\",1.35,0.00295696432069891,0.0347878155376343,0.0151620939094918\n\"95\",\"C00197\",\"3-phosphoglycerate\",\"metabolite\",\"1030 +/- 660\",\"510 +/- 490\",0.49,0.0018655188471591,0.024873584628788,0.0118709434497229\n\"96\",\"C05145\",\"3-aminoisobutyric acid\",\"metabolite\",\"1770 +/- 540\",\"1600 +/- 830\",0.9,0.35445072268183,0.529030929375865,0.248032559609213\n\"97\",\"C02112\",\"2-monoolein\",\"metabolite\",\"326 +/- 170\",\"341 +/- 190\",1.04,0.776081010597322,0.834495710319702,0.414702908509561\n\"98\",\"C00233\",\"2-ketoisocaproic acid\",\"metabolite\",\"487 +/- 270\",\"358 +/- 240\",0.74,0.071356058130456,0.195496049672482,0.0910515660134888\n\"99\",\"C00956\",\"2-aminoadipic acid\",\"metabolite\",\"200 +/- 130\",\"207 +/- 220\",1.03,0.893520660904468,0.916431447081505,0.449264059145774\n\"100\",\"C07326\",\"1,5-anhydroglucitol\",\"metabolite\",\"7390 +/- 4300\",\"5940 +/- 3600\",0.8,0.185357340547488,0.370714681094977,0.169816580435001\n\"101\",\"SPTAN1\",\"spectrin, alpha, non-erythrocytic 1\",\"protein\",\"5.29e+09 +/- 3.4e+09\",\"3.77e+09 +/- 2.2e+09\",0.71,0.0586164101090191,0.169178754049596,0.0799597140126616\n\"102\",\"CFH\",\"complement factor H\",\"protein\",\"1.85e+08 +/- 2e+08\",\"2.08e+08 +/- 2.6e+08\",1.12,0.715899490472791,0.803136066730948,0.395255344181119\n\"103\",\"VPS13C\",\"vacuolar protein sorting 13 homolog C (S. cerevisiae)\",\"protein\",\"17800000 +/- 1.7e+07\",\"24500000 +/- 2.5e+07\",1.37,0.252402218773571,0.438960380475775,0.204277371609485\n\"104\",\"XRCC6\",\"X-ray repair complementing defective repair in Chinese hamster cells 6\",\"protein\",\"2.51e+08 +/- 3.4e+08\",\"7.33e+08 +/- 9.1e+08\",2.92,0.0130794769787721,0.0810371067971812,0.0371951322316488\n\"105\",\"APOA1\",\"apolipoprotein A-I\",\"protein\",\"4.58e+09 +/- 4.5e+09\",\"4.01e+09 +/- 4e+09\",0.87,0.616485245952436,0.729045221914476,0.360134665799367\n\"106\",\"SUPT16H\",\"suppressor of Ty 16 homolog (S. cerevisiae)\",\"protein\",\"11800000 +/- 3.6e+07\",\"2.4e+07 +/- 4.5e+07\",2.04,0.277201453442009,0.458494327452479,0.216124608658543\n\"107\",\"ACTBL2\",\"actin, beta-like 2\",\"protein\",\"6.72e+09 +/- 5.7e+09\",\"7.99e+09 +/- 6.7e+09\",1.19,0.454902619414127,0.613690977390211,0.293440873249496\n\"108\",\"HPR\",\"haptoglobin-related protein\",\"protein\",\"1.84e+09 +/- 2.2e+09\",\"1.81e+09 +/- 2.3e+09\",0.98,0.958944343323973,0.973547556674085,0.466803641085138\n\"109\",\"APOE\",\"apolipoprotein E\",\"protein\",\"5.29e+08 +/- 4.3e+08\",\"4.25e+08 +/- 3.2e+08\",0.8,0.315379656348093,0.495919508083587,0.232756239848219\n\"110\",\"MAOB\",\"monoamine oxidase B\",\"protein\",\"2.44e+08 +/- 1.5e+08\",\"2.57e+08 +/- 2e+08\",1.05,0.7984770882671,0.849443710922447,0.421624593129142\n\"111\",\"DMD\",\"dystrophin\",\"protein\",\"5030000 +/- 6800000\",\"6690000 +/- 1.1e+07\",1.33,0.501339399716578,0.643647776650811,0.313990061771896\n\"112\",\"EEF1D\",\"eukaryotic translation elongation factor 1 delta (guanine nucleotide exchange protein)\",\"protein\",\"2.12e+08 +/- 2.6e+08\",\"5.18e+08 +/- 5.7e+08\",2.45,0.0137763081555208,0.0810371067971812,0.0381836082789274\n\"113\",\"GLS\",\"glutaminase\",\"protein\",\"13900000 +/- 3.1e+07\",\"83700000 +/- 1.5e+08\",6.04,0.0255208160350274,0.113425849044566,0.0516866006445412\n\"114\",\"NAPSA\",\"napsin A aspartic peptidase\",\"protein\",\"3.33e+08 +/- 3.2e+08\",\"1.37e+09 +/- 2.2e+09\",4.11,0.0200583572485543,0.0987772806835311,0.0463772386731902\n\"115\",\"EIF3L\",\"eukaryotic translation initiation factor 3, subunit L\",\"protein\",\"23500000 +/- 2.7e+07\",\"58400000 +/- 1e+08\",2.49,0.0843549281697272,0.216294687614685,0.101790075381712\n\"116\",\"LTBP4\",\"latent transforming growth factor beta binding protein 4\",\"protein\",\"4540000 +/- 7100000\",\"10200000 +/- 1.9e+07\",2.24,0.158695968958725,0.330616601997343,0.153927039640461\n\"117\",\"CNP\",\"2',3'-cyclic nucleotide 3' phosphodiesterase\",\"protein\",\"35700000 +/- 6.4e+07\",\"49700000 +/- 6.3e+07\",1.39,0.423472170536489,0.584099545567571,0.278819205711635\n\"118\",\"ANXA7\",\"annexin A7\",\"protein\",\"81800000 +/- 7.5e+07\",\"1.54e+08 +/- 1.7e+08\",1.88,0.0434724943620194,0.140233852780708,0.0661110685594147\n\"119\",\"ADH5\",\"alcohol dehydrogenase 5 (class III), chi polypeptide\",\"protein\",\"1.18e+08 +/- 1.1e+08\",\"2.33e+08 +/- 2.2e+08\",1.98,0.0184761354568517,0.0947494125992395,0.0445464424868661\n\"120\",\"GNA11\",\"guanine nucleotide binding protein (G protein), alpha 11 (Gq class)\",\"protein\",\"66400000 +/- 6.7e+07\",\"61500000 +/- 8.3e+07\",0.93,0.813525418806749,0.860873459054761,0.426184167722323\n\"121\",\"DDX21\",\"DEAD (Asp-Glu-Ala-Asp) box helicase 21\",\"protein\",\"6410000 +/- 2e+07\",\"19700000 +/- 5.3e+07\",3.07,0.228400677979968,0.404248987575164,0.192055584005662\n\"122\",\"TAP2\",\"transporter 2, ATP-binding cassette, sub-family B (MDR/TAP)\",\"protein\",\"19500000 +/- 2.5e+07\",\"38200000 +/- 6.5e+07\",1.96,0.167519784098847,0.339928437952225,0.159388461599096\n\"123\",\"SLFN5\",\"schlafen family member 5\",\"protein\",\"10100000 +/- 1.4e+07\",\"2.1e+07 +/- 2.3e+07\",2.07,0.0429044717342592,0.140233852780708,0.0655294786176391\n\"124\",\"HTRA1\",\"HtrA serine peptidase 1\",\"protein\",\"1.29e+08 +/- 1.1e+08\",\"1.35e+08 +/- 1.3e+08\",1.05,0.856207828723303,0.883370899047943,0.438734167620614\n\"125\",\"ANKFY1\",\"ankyrin repeat and FYVE domain containing 1\",\"protein\",\"9590000 +/- 1e+07\",\"11600000 +/- 1.5e+07\",1.21,0.566022044749419,0.694505576379655,0.340698937782297\n\"126\",\"HNRNPF\",\"heterogeneous nuclear ribonucleoprotein F\",\"protein\",\"1.59e+08 +/- 2.4e+08\",\"4.12e+08 +/- 4.2e+08\",2.59,0.00853303001599452,0.0632076297481076,0.0292256876657171\n\"127\",\"PC\",\"pyruvate carboxylase\",\"protein\",\"1170000 +/- 4700000\",\"8050000 +/- 2e+07\",6.88,0.0909259330204405,0.22450847659368,0.106845812702126\n\"128\",\"SEC24C\",\"SEC24 family member C\",\"protein\",\"14500000 +/- 2.2e+07\",\"38500000 +/- 4.8e+07\",2.66,0.0223397627775472,0.103905873383941,0.0487673830313929\n\"129\",\"IVD\",\"isovaleryl-CoA dehydrogenase\",\"protein\",\"1.1e+07 +/- 1.4e+07\",\"58900000 +/- 7.1e+07\",5.34,0.00116982603529325,0.023396520705865,0.0103179316536522\n\"130\",\"ENTPD1\",\"ectonucleoside triphosphate diphosphohydrolase 1\",\"protein\",\"50800000 +/- 5.3e+07\",\"40100000 +/- 6.5e+07\",0.79,0.506399141816642,0.643647776650811,0.316157111023143\n\"131\",\"ACSS2\",\"acyl-CoA synthetase short-chain family member 2\",\"protein\",\"698000 +/- 2e+06\",\"12700000 +/- 2.8e+07\",18.25,0.0318359195600991,0.120715763058434,0.0563887944164092\n\"132\",\"API5\",\"apoptosis inhibitor 5\",\"protein\",\"16700000 +/- 3.2e+07\",\"39900000 +/- 4.2e+07\",2.39,0.0254517405839864,0.113425849044566,0.0516276981564353\n\"133\",\"RPL9\",\"ribosomal protein L9\",\"protein\",\"96200000 +/- 1e+08\",\"1.88e+08 +/- 1.8e+08\",1.96,0.0273166078277111,0.116240884373239,0.0531556044202828\n\"134\",\"GLOD4\",\"glyoxalase domain containing 4\",\"protein\",\"24500000 +/- 4.5e+07\",\"1.24e+08 +/- 1.4e+08\",5.06,0.00129696068640331,0.0235811033891511,0.0106853821389526\n\"135\",\"ABHD10\",\"abhydrolase domain containing 10\",\"protein\",\"2530000 +/- 6700000\",\"20100000 +/- 4.2e+07\",7.93,0.0351529101300627,0.124104000835143,0.0584152922366447\n\"136\",\"F11R\",\"F11 receptor\",\"protein\",\"48500000 +/- 6.5e+07\",\"3.2e+07 +/- 2.9e+07\",0.66,0.230645819902185,0.404641789302078,0.193115703662602\n\"137\",\"STXBP1\",\"syntaxin binding protein 1\",\"protein\",\"4300000 +/- 6900000\",\"3570000 +/- 1e+07\",0.83,0.759900165465411,0.831117224108447,0.409598105735211\n\"138\",\"FER1L6\",\"fer-1-like family member 6\",\"protein\",\"0 +/- 0\",\"47200 +/- 250000\",Inf,0.321941348172835,0.495919508083587,0.235437415083026\n\"139\",\"SCYL1\",\"SCY1-like 1 (S. cerevisiae)\",\"protein\",\"749000 +/- 2700000\",\"3240000 +/- 7200000\",4.32,0.0996681227865923,0.239214017878015,0.113232652513425\n\"140\",\"GBP2\",\"guanylate binding protein 2, interferon-inducible\",\"protein\",\"7090000 +/- 1.2e+07\",\"53900000 +/- 1.6e+08\",7.6,0.131333557323075,0.285507733311033,0.13586363143613\n\"141\",\"PSMD7\",\"proteasome (prosome, macropain) 26S subunit, non-ATPase, 7\",\"protein\",\"67400000 +/- 1.6e+08\",\"96200000 +/- 1.7e+08\",1.43,0.521637686952684,0.64399714438603,0.322601734946892\n\"142\",\"GMDS\",\"GDP-mannose 4,6-dehydratase\",\"protein\",\"89100 +/- 460000\",\"5580000 +/- 1.5e+07\",62.63,0.0592125639173586,0.169178754049596,0.0804467515819778\n\"143\",\"SIAE\",\"sialic acid acetylesterase\",\"protein\",\"67800 +/- 350000\",\"6710000 +/- 1.9e+07\",98.86,0.0827911528953708,0.215041955572392,0.100560375510433\n\"144\",\"TXNL1\",\"thioredoxin-like 1\",\"protein\",\"12400000 +/- 2.2e+07\",\"46100000 +/- 5.4e+07\",3.73,0.00380982884819193,0.0361722723945709,0.0169613598162995\n\"145\",\"EPS8\",\"epidermal growth factor receptor pathway substrate 8\",\"protein\",\"411000 +/- 1300000\",\"6240000 +/- 1.1e+07\",15.2,0.0110649381689454,0.0737662544596362,0.0340247204561482\n\"146\",\"U2SURP\",\"U2 snRNP-associated SURP domain containing\",\"protein\",\"2420000 +/- 4700000\",\"4410000 +/- 8100000\",1.83,0.275552916683402,0.458494327452479,0.215364074736382\n\"147\",\"AGO2\",\"argonaute RISC catalytic component 2\",\"protein\",\"2490000 +/- 6700000\",\"4990000 +/- 7900000\",2.01,0.213894769544741,0.40320029163737,0.184980842044265\n\"148\",\"DTX3L\",\"deltex 3 like, E3 ubiquitin ligase\",\"protein\",\"4520000 +/- 4900000\",\"7510000 +/- 1.7e+07\",1.66,0.379738830717982,0.542484043882832,0.258361323092\n\"149\",\"EVL\",\"Enah/Vasp-like\",\"protein\",\"6110000 +/- 8600000\",\"21900000 +/- 3.7e+07\",3.59,0.0349413798824399,0.124104000835143,0.0582935673014051\n\"150\",\"SEC13\",\"SEC13 homolog (S. cerevisiae)\",\"protein\",\"20100000 +/- 3.4e+07\",\"65200000 +/- 7e+07\",3.24,0.0039789499634028,0.0361722723945709,0.0172632536263772\n\"151\",\"TPPP3\",\"tubulin polymerization-promoting protein family member 3\",\"protein\",\"7290000 +/- 1.4e+07\",\"41500000 +/- 1.1e+08\",5.69,0.104161112556844,0.245084970721987,0.116353003680353\n\"152\",\"TUBB1\",\"tubulin, beta 1 class VI\",\"protein\",\"2.23e+08 +/- 2.3e+08\",\"3.67e+08 +/- 4.5e+08\",1.65,0.142154797337148,0.30099559436685,0.14311779504771\n\"153\",\"PDLIM7\",\"PDZ and LIM domain 7 (enigma)\",\"protein\",\"74800000 +/- 6.1e+07\",\"1.24e+08 +/- 2.5e+08\",1.66,0.322412476757539,0.495919508083587,0.235628047305912\n\"154\",\"AGO1\",\"argonaute RISC catalytic component 1\",\"protein\",\"4770000 +/- 1.6e+07\",\"4880000 +/- 7500000\",1.02,0.976205125939598,0.986065783777372,0.47124641934114\n\"155\",\"UBQLN4\",\"ubiquilin 4\",\"protein\",\"3640000 +/- 1.4e+07\",\"6290000 +/- 1.1e+07\",1.73,0.443755508306818,0.603748990893631,0.288323499649177\n\"156\",\"ITGA11\",\"integrin, alpha 11\",\"protein\",\"1210000 +/- 5800000\",\"2520000 +/- 8800000\",2.09,0.518136460203903,0.643647776650811,0.321131781932626\n\"157\",\"SERPINA7\",\"serpin peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 7\",\"protein\",\"5810000 +/- 8400000\",\"5230000 +/- 1.1e+07\",0.9,0.832650934410059,0.871885795193779,0.431876394209847\n\"158\",\"CDK5RAP3\",\"CDK5 regulatory subunit associated protein 3\",\"protein\",\"630000 +/- 2300000\",\"1780000 +/- 3e+06\",2.83,0.117414150306826,0.263852023161407,0.125923645215935\n\"159\",\"PTGR2\",\"prostaglandin reductase 2\",\"protein\",\"244000 +/- 840000\",\"1970000 +/- 4600000\",8.09,0.0584244912949333,0.169178754049596,0.0798020813087872\n\"160\",\"EIF5A2\",\"eukaryotic translation initiation factor 5A2\",\"protein\",\"54300000 +/- 7.6e+07\",\"1.47e+08 +/- 1.9e+08\",2.71,0.0202493425401239,0.0987772806835311,0.0465881184407656\n\"161\",\"PSMD9\",\"proteasome (prosome, macropain) 26S subunit, non-ATPase, 9\",\"protein\",\"5810000 +/- 1.4e+07\",\"53100000 +/- 8.5e+07\",9.15,0.00626073521190854,0.0521727934325711,0.0238827121997089\n\"162\",\"ZW10\",\"zw10 kinetochore protein\",\"protein\",\"1340000 +/- 2100000\",\"8930000 +/- 3e+07\",6.68,0.198806126705078,0.386031313990442,0.177181208532096\n\"163\",\"KDM1A\",\"lysine (K)-specific demethylase 1A\",\"protein\",\"204000 +/- 820000\",\"489000 +/- 1400000\",2.4,0.368802231815649,0.530650693259926,0.253253580666222\n\"164\",\"REXO2\",\"RNA exonuclease 2\",\"protein\",\"1140000 +/- 2400000\",\"6780000 +/- 9400000\",5.95,0.00392053842648044,0.0361722723945709,0.0171607380039347\n\"165\",\"ERMP1\",\"endoplasmic reticulum metallopeptidase 1\",\"protein\",\"1730000 +/- 4100000\",\"2670000 +/- 6300000\",1.54,0.517582740222708,0.643647776650811,0.320898724006608\n\"166\",\"TMEM214\",\"transmembrane protein 214\",\"protein\",\"2540000 +/- 5500000\",\"7630000 +/- 2.1e+07\",3.01,0.224018049584567,0.40320029163737,0.189959771820575\n\"167\",\"PGK2\",\"phosphoglycerate kinase 2\",\"protein\",\"2.17e+08 +/- 3.7e+08\",\"6.67e+08 +/- 8.7e+08\",3.08,0.0169915871846209,0.0894294062348471,0.0426802746769151\n\"168\",\"SEC63\",\"SEC63 homolog (S. cerevisiae)\",\"protein\",\"1220000 +/- 2900000\",\"3e+06 +/- 3900000\",2.47,0.0615434310577959,0.173361777627594,0.0823138220429128\n\"169\",\"COG4\",\"component of oligomeric golgi complex 4\",\"protein\",\"182000 +/- 870000\",\"545000 +/- 2e+06\",3,0.390745104820936,0.554248375632533,0.263407465561433\n\"170\",\"BPHL\",\"biphenyl hydrolase-like (serine hydrolase)\",\"protein\",\"956000 +/- 3500000\",\"1680000 +/- 3600000\",1.76,0.457199778155707,0.613690977390211,0.294486312064147\n\"171\",\"MYZAP\",\"myocardial zonula adherens protein\",\"protein\",\"1170000 +/- 2200000\",\"404000 +/- 1100000\",0.34,0.109209836856161,0.25105709622106,0.11981725337821\n\"172\",\"SCAF4\",\"SR-related CTD-associated factor 4\",\"protein\",\"6450000 +/- 1.7e+07\",\"11600000 +/- 1.7e+07\",1.79,0.283915703124914,0.465435578893302,0.219184240026175\n\"173\",\"PARP10\",\"poly (ADP-ribose) polymerase family, member 10\",\"protein\",\"0 +/- 0\",\"913000 +/- 4300000\",Inf,0.27738906810875,0.458494327452479,0.216210927736388\n\"174\",\"PPIL1\",\"peptidylprolyl isomerase (cyclophilin)-like 1\",\"protein\",\"2710000 +/- 1e+07\",\"4740000 +/- 8e+06\",1.75,0.426736678632089,0.584570792646698,0.280365981074687\n\"175\",\"CPSF3\",\"cleavage and polyadenylation specific factor 3, 73kDa\",\"protein\",\"1050000 +/- 3400000\",\"3630000 +/- 6700000\",3.45,0.081050035926648,0.213289568228021,0.0991720420151246\n\"176\",\"DHX38\",\"DEAH (Asp-Glu-Ala-His) box polypeptide 38\",\"protein\",\"1130000 +/- 2200000\",\"2280000 +/- 5100000\",2.02,0.288725813352769,0.469472867240275,0.221339457787999\n\"177\",\"FYN\",\"FYN proto-oncogene, Src family tyrosine kinase\",\"protein\",\"31400000 +/- 6.7e+07\",\"7880000 +/- 9700000\",0.25,0.0768210588613783,0.204856156963675,0.0957130535770883\n\"178\",\"AP3B2\",\"adaptor-related protein complex 3, beta 2 subunit\",\"protein\",\"206000 +/- 650000\",\"1450000 +/- 5200000\",7.03,0.221232390852693,0.40320029163737,0.188609161250918\n\"179\",\"MRTO4\",\"mRNA turnover 4 homolog (S. cerevisiae)\",\"protein\",\"425000 +/- 1600000\",\"6200000 +/- 2e+07\",14.61,0.142972907324254,0.30099559436685,0.143650452183286\n\"180\",\"DUT\",\"deoxyuridine triphosphatase\",\"protein\",\"1860000 +/- 5100000\",\"8170000 +/- 1.6e+07\",4.4,0.0560335429212322,0.169178754049596,0.0778031581468442\n\"181\",\"PDCD6\",\"programmed cell death 6\",\"protein\",\"18700000 +/- 2.1e+07\",\"49100000 +/- 6.8e+07\",2.63,0.0299781343528746,0.119912537411498,0.0551326589686327\n\"182\",\"LHPP\",\"phospholysine phosphohistidine inorganic pyrophosphate phosphatase\",\"protein\",\"8140000 +/- 1.1e+07\",\"38200000 +/- 5.2e+07\",4.7,0.00464695818720693,0.0404083320626689,0.0192849173921797\n\"183\",\"ARMCX3\",\"armadillo repeat containing, X-linked 3\",\"protein\",\"20200 +/- 110000\",\"2830000 +/- 8900000\",139.8,0.105890385832605,0.246256711238617,0.117526264784731\n\"184\",\"WDR33\",\"WD repeat domain 33\",\"protein\",\"0 +/- 0\",\"297000 +/- 1500000\",Inf,0.321941348172834,0.495919508083587,0.235437415083026\n\"185\",\"CPA6\",\"carboxypeptidase A6\",\"protein\",\"3310000 +/- 1.4e+07\",\"2220000 +/- 1.2e+07\",0.67,0.760472260059229,0.831117224108447,0.40978011063697\n\"186\",\"WDR82\",\"WD repeat domain 82\",\"protein\",\"317000 +/- 1200000\",\"1410000 +/- 3600000\",4.45,0.140038375407462,0.30099559436685,0.141729764384562\n\"187\",\"IGFN1\",\"immunoglobulin-like and fibronectin type III domain containing 1\",\"protein\",\"231000 +/- 850000\",\"110000 +/- 460000\",0.47,0.517772301943548,0.643647776650811,0.320978527583109\n\"188\",\"MEMO1\",\"mediator of cell motility 1\",\"protein\",\"186000 +/- 950000\",\"1900000 +/- 5e+06\",10.24,0.0870740656603324,0.217685164150831,0.103890465007097\n\"189\",\"HRSP12\",\"heat-responsive protein 12\",\"protein\",\"33400 +/- 170000\",\"1040000 +/- 4100000\",31.2,0.205620752278862,0.391658575769262,0.180762004992734\n\"190\",\"NT5DC1\",\"5'-nucleotidase domain containing 1\",\"protein\",\"280000 +/- 810000\",\"3240000 +/- 8e+06\",11.55,0.0628775387710606,0.174659829919613,0.0833565806801436\n\"191\",\"TRIM21\",\"tripartite motif containing 21\",\"protein\",\"129000 +/- 670000\",\"4e+06 +/- 2e+07\",30.99,0.308827237385239,0.495919508083587,0.230029273261299\n\"192\",\"PTPRK\",\"protein tyrosine phosphatase, receptor type, K\",\"protein\",\"701000 +/- 3e+06\",\"162000 +/- 680000\",0.23,0.36553148555418,0.530650693259926,0.252080934181609\n\"193\",\"KDM3B\",\"lysine (K)-specific demethylase 3B\",\"protein\",\"1420000 +/- 7200000\",\"148000 +/- 440000\",0.1,0.365428424559181,0.530650693259926,0.252043821164736\n\"194\",\"BNIP1\",\"BCL2/adenovirus E1B 19kDa interacting protein 1\",\"protein\",\"108000 +/- 560000\",\"177000 +/- 920000\",1.64,0.739341569549298,0.81695201055171,0.402982310572385\n\"195\",\"PCLO\",\"piccolo presynaptic cytomatrix protein\",\"protein\",\"742000 +/- 2800000\",\"235000 +/- 710000\",0.32,0.367711772521732,0.530650693259926,0.25286373427761\n\"196\",\"F13B\",\"coagulation factor XIII, B polypeptide\",\"protein\",\"25300 +/- 130000\",\"0 +/- 0\",0,0.321941348172834,0.495919508083587,0.235437415083026\n\"197\",\"ANKRD28\",\"ankyrin repeat domain 28\",\"protein\",\"48800 +/- 190000\",\"462000 +/- 950000\",9.46,0.031989677210485,0.120715763058434,0.0564886159975311\n\"198\",\"MATN3\",\"matrilin 3\",\"protein\",\"0 +/- 0\",\"23900 +/- 120000\",Inf,0.321941348172835,0.495919508083587,0.235437415083026\n\"199\",\"USP34\",\"ubiquitin specific peptidase 34\",\"protein\",\"2980000 +/- 9300000\",\"149000 +/- 770000\",0.05,0.122564845706434,0.272366323792075,0.129685739762852\n\"200\",\"DCAF7\",\"DDB1 and CUL4 associated factor 7\",\"protein\",\"599000 +/- 3e+06\",\"1320000 +/- 2800000\",2.21,0.36265855374751,0.530650693259926,0.251042609848482\n"
},
{
"path": "Demos/Data Analysis Workflow/results/statistical_results_sig.csv",
"content": ",ID,description,type,healthy.mean.....std.dev,sick.mean.....std.dev,mean.sick_mean.healthy,group_p.values,group_adjusted.p.values,group_q.values\n35,C00077,ornithine,metabolite,7300 +/- 2900,3910 +/- 2100,0.54,9.43E-06,0.001886663,0.000900412\n12,C02477,tocopherol alpha,metabolite,1160 +/- 1400,2600 +/- 1200,2.24,0.000150503,0.007714558,0.003632462\n74,C00097,cysteine,metabolite,5160 +/- 2500,8460 +/- 3400,1.64,0.000150784,0.007714558,0.003633836\n65,C00031,glucose,metabolite,335000 +/- 2e+05,144000 +/- 140000,0.43,0.000154291,0.007714558,0.003650646\n92,C00170,5'-deoxy-5'-methylthioadenosine,metabolite,216 +/- 83,355 +/- 160,1.64,0.000200406,0.008016221,0.003825749\n2,C00385,xanthine,metabolite,574 +/- 350,1670 +/- 1500,2.91,0.000390905,0.013030159,0.006072846\n77,C00158,citric acid,metabolite,3640 +/- 2000,1930 +/- 1400,0.53,0.000579756,0.016564459,0.007603203\n17,D09007,tagatose,metabolite,163 +/- 61,108 +/- 56,0.66,0.00110566,0.023396521,0.01011206\n63,C00025,glutamic acid,metabolite,72900 +/- 18000,96200 +/- 30000,1.32,0.001112529,0.023396521,0.010134825\n129,IVD,isovaleryl-CoA dehydrogenase,protein,1.1e+07 +/- 1.4e+07,58900000 +/- 7.1e+07,5.34,0.001169826,0.023396521,0.010317932\n134,GLOD4,glyoxalase domain containing 4,protein,24500000 +/- 4.5e+07,1.24e+08 +/- 1.4e+08,5.06,0.001296961,0.023581103,0.010685382\n76,C00327,citrulline,metabolite,597 +/- 240,401 +/- 180,0.67,0.001629237,0.024873585,0.011450641\n69,C01018,fucose+ rhamnose,metabolite,1150 +/- 730,2500 +/- 2000,2.17,0.001754997,0.024873585,0.011684702\n81,C06555,biuret,metabolite,269 +/- 180,150 +/- 66,0.56,0.001855321,0.024873585,0.011854452\n95,C00197,3-phosphoglycerate,metabolite,1030 +/- 660,510 +/- 490,0.49,0.001865519,0.024873585,0.011870943\n75,C00791,creatinine,metabolite,10800 +/- 4200,15500 +/- 6600,1.44,0.002880815,0.034787816,0.014974523\n94,C00989,4-hydroxybutyric acid,metabolite,209 +/- 42,282 +/- 110,1.35,0.002956964,0.034787816,0.015162094\n1,C00379,xylitol,metabolite,579 +/- 300,880 +/- 410,1.52,0.003285451,0.036172272,0.01591618\n19,C00315,spermidine,metabolite,276 +/- 100,189 +/- 110,0.68,0.003764835,0.036172272,0.016878367\n144,TXNL1,thioredoxin-like 1,protein,12400000 +/- 2.2e+07,46100000 +/- 5.4e+07,3.73,0.003809829,0.036172272,0.01696136\n164,REXO2,RNA exonuclease 2,protein,1140000 +/- 2400000,6780000 +/- 9400000,5.95,0.003920538,0.036172272,0.017160738\n150,SEC13,SEC13 homolog (S. cerevisiae),protein,20100000 +/- 3.4e+07,65200000 +/- 7e+07,3.24,0.00397895,0.036172272,0.017263254\n182,LHPP,phospholysine phosphohistidine inorganic pyrophosphate phosphatase,protein,8140000 +/- 1.1e+07,38200000 +/- 5.2e+07,4.7,0.004646958,0.040408332,0.019284917\n161,PSMD9,\"proteasome (prosome, macropain) 26S subunit, non-ATPase, 9\",protein,5810000 +/- 1.4e+07,53100000 +/- 8.5e+07,9.15,0.006260735,0.052172793,0.023882712\n89,C00020,adenosine-5-phosphate,metabolite,1300 +/- 1500,3020 +/- 2800,2.32,0.006837402,0.054699215,0.02535055\n79,C06423,caprylic acid,metabolite,1470 +/- 530,1080 +/- 490,0.74,0.007348549,0.056527301,0.026584412\n126,HNRNPF,heterogeneous nuclear ribonucleoprotein F,protein,1.59e+08 +/- 2.4e+08,4.12e+08 +/- 4.2e+08,2.59,0.00853303,0.06320763,0.029225688\n43,C00047,lysine,metabolite,24900 +/- 25000,10700 +/- 12000,0.43,0.009961495,0.071153537,0.032059435\n66,C00257,gluconic acid,metabolite,502 +/- 580,199 +/- 120,0.4,0.010517694,0.072535824,0.033072427\n145,EPS8,epidermal growth factor receptor pathway substrate 8,protein,411000 +/- 1300000,6240000 +/- 1.1e+07,15.2,0.011064938,0.073766254,0.03402472\n50,C00130,inosine 5'-monophosphate,metabolite,199 +/- 340,625 +/- 790,3.14,0.013056504,0.081037107,0.037161668\n104,XRCC6,X-ray repair complementing defective repair in Chinese hamster cells 6,protein,2.51e+08 +/- 3.4e+08,7.33e+08 +/- 9.1e+08,2.92,0.013079477,0.081037107,0.037195132\n28,C00346,phosphoethanolamine,metabolite,13500 +/- 7700,21200 +/- 14000,1.57,0.01341153,0.081037107,0.037672494\n112,EEF1D,eukaryotic translation elongation factor 1 delta (guanine nucleotide exchange protein),protein,2.12e+08 +/- 2.6e+08,5.18e+08 +/- 5.7e+08,2.45,0.013776308,0.081037107,0.038183608\n16,C00245,taurine,metabolite,93000 +/- 29000,125000 +/- 59000,1.34,0.014225218,0.081286961,0.038794284\n56,C00135,histidine,metabolite,6550 +/- 3400,4720 +/- 1700,0.72,0.015799645,0.087775805,0.041083803\n48,C03546,inositol-4-monophosphate,metabolite,302 +/- 130,218 +/- 120,0.72,0.016408041,0.088692114,0.041912481\n167,PGK2,phosphoglycerate kinase 2,protein,2.17e+08 +/- 3.7e+08,6.67e+08 +/- 8.7e+08,3.08,0.016991587,0.089429406,0.042680275\n119,ADH5,\"alcohol dehydrogenase 5 (class III), chi polypeptide\",protein,1.18e+08 +/- 1.1e+08,2.33e+08 +/- 2.2e+08,1.98,0.018476135,0.094749413,0.044546442\n114,NAPSA,napsin A aspartic peptidase,protein,3.33e+08 +/- 3.2e+08,1.37e+09 +/- 2.2e+09,4.11,0.020058357,0.098777281,0.046377239\n160,EIF5A2,eukaryotic translation initiation factor 5A2,protein,54300000 +/- 7.6e+07,1.47e+08 +/- 1.9e+08,2.71,0.020249343,0.098777281,0.046588118\n30,C01601,pelargonic acid,metabolite,18800 +/- 8300,13900 +/- 6700,0.74,0.021076289,0.10036328,0.047477726\n128,SEC24C,SEC24 family member C,protein,14500000 +/- 2.2e+07,38500000 +/- 4.8e+07,2.66,0.022339763,0.103905873,0.048767383\n132,API5,apoptosis inhibitor 5,protein,16700000 +/- 3.2e+07,39900000 +/- 4.2e+07,2.39,0.025451741,0.113425849,0.051627698\n113,GLS,glutaminase,protein,13900000 +/- 3.1e+07,83700000 +/- 1.5e+08,6.04,0.025520816,0.113425849,0.051686601\n61,C00116,glycerol,metabolite,22900 +/- 7600,27700 +/- 8200,1.21,0.027020787,0.116240884,0.052921588\n133,RPL9,ribosomal protein L9,protein,96200000 +/- 1e+08,1.88e+08 +/- 1.8e+08,1.96,0.027316608,0.116240884,0.053155604\n87,C00041,alanine,metabolite,213000 +/- 59000,258000 +/- 86000,1.21,0.029684811,0.119912537,0.054925477\n88,C06104,adipic acid,metabolite,926 +/- 680,591 +/- 370,0.64,0.029795109,0.119912537,0.055003679\n181,PDCD6,programmed cell death 6,protein,18700000 +/- 2.1e+07,49100000 +/- 6.8e+07,2.63,0.029978134,0.119912537,0.055132659\n26,C00148,proline,metabolite,68200 +/- 38000,94500 +/- 49000,1.39,0.031161063,0.120715763,0.05594336\n131,ACSS2,acyl-CoA synthetase short-chain family member 2,protein,698000 +/- 2e+06,12700000 +/- 2.8e+07,18.25,0.03183592,0.120715763,0.056388794\n197,ANKRD28,ankyrin repeat domain 28,protein,48800 +/- 190000,462000 +/- 950000,9.46,0.031989677,0.120715763,0.056488616\n7,C00106,uracil,metabolite,16300 +/- 11000,24700 +/- 17000,1.52,0.033473358,0.123975402,0.057421501\n149,EVL,Enah/Vasp-like,protein,6110000 +/- 8600000,21900000 +/- 3.7e+07,3.59,0.03494138,0.124104001,0.058293567\n135,ABHD10,abhydrolase domain containing 10,protein,2530000 +/- 6700000,20100000 +/- 4.2e+07,7.93,0.03515291,0.124104001,0.058415292\n72,C00836,dihydrosphingosine,metabolite,341 +/- 150,234 +/- 210,0.69,0.036002611,0.124104001,0.058894733\n80,C01571,capric acid,metabolite,2800 +/- 830,2240 +/- 1100,0.8,0.036375904,0.124104001,0.059100669\n5,C00366,uric acid,metabolite,2960 +/- 2100,4150 +/- 1900,1.4,0.03661068,0.124104001,0.05922876\n40,C01835,maltotriose,metabolite,3290 +/- 2200,7370 +/- 9700,2.24,0.037662655,0.125542182,0.059915134\n123,SLFN5,schlafen family member 5,protein,10100000 +/- 1.4e+07,2.1e+07 +/- 2.3e+07,2.07,0.042904472,0.140233853,0.065529479\n118,ANXA7,annexin A7,protein,81800000 +/- 7.5e+07,1.54e+08 +/- 1.7e+08,1.88,0.043472494,0.140233853,0.066111069\n82,C00180,benzoic acid,metabolite,12200 +/- 3100,10000 +/- 4400,0.83,0.04685931,0.148759716,0.069477396\n"
},
{
"path": "Demos/Mapped Network From Data (Biochemical and Structural)/data.csv",
"content": "ID,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92\r\n1,0,0.000234,0,0.00329,0,0.367,0,0.000706,0.000564,0.0874,0.000425,0.00094,0,0.000645,0.000178,0.000341,0,0,0,0.00072,0.00463,0.00276,0,0,0.00339,0,0.00228,0,0,0.000324,0.000102,0.00196,0.000937,0,0,0,0.000153,0.000778,0.00069,0.00573,0.000863,0,0.00169,0,0.000237,0.000306,0,0,0,0.0504,0,0.021,0.000732,0.00177,0,0.00292,0.0017,0,0.00107,0.00816,0.0509,0.0065,0.00309,0,0,0.00275,0.00387,0,0.00423,0.00128,0,0,0.0023,0.056,0.000795,0,0.00049,0.000767,0.00154,0.00143,0,0,0.00272,0.00849,0.000809,0.00101,0,0.00077,0,0,0,0.000241\r\n2,0,0.000228,0,0.00388,0,0.213,0,0.000698,0.000952,0.0542,0.000268,0.00417,0,0.000308,0,0.000227,0.0000927,0,0,0.0009,0.00521,0.00207,0,0,0.00519,0,0.00481,0,0,0.000108,0,0.000399,0.00103,0.000395,0,0,0,0.00392,0.000599,0.00697,0.00123,0,0.00183,0,0,0.000112,0,0,0.0000789,0.00621,0,0.0314,0,0,0,0.001597,0.00753,0,0.00269,0.0055,0.00509,0.00713,0.00424,0,0,0.00144,0.00464,0,0.00324,0.00431,0,0,0.0341,0.118,0,0.00119,0.0000928,0,0.00324,0.0013,0,0,0.00401,0.00993,0.000777,0.000544,0.000745,0.000755,0,0.00292,0.009,0\r\n3,0,0,0,0,0,0,0,0,0,0.0579,0,0,0,0,0,0.000143,0,0,0,0.00702,0.0506,0,0.000369,0.000273,0.00102,0,0,0,0,0,0,0.00297,0,0,0.00253,0,0,0,0.0015,0.054,0.00277,0.00183,0,0.0521,0.000407,0,0,0,0,0.14,0,0,0.00286,0,0,0,0.00596,0,0,0.0267,0,0.0667,0,0,0.0718,0,0,0,0,0.00306,0,0.00136,0.00503,0,0,0,0,0,0.000531,0,0,0,0.00227,0,0.00113,0,0,0,0.00102,0.00591,0,0.000539\r\n4,0,0.000286,0.00021,0.00254,0,0.241,0,0.001324,0.000476,0.0558,0.000661,0.00354,0,0.000458,0.000119,0.000327,0,0.0005,0,0.00132,0.00449,0.00364,0,0,0.00462,0,0.00247,0.0009,0,0.000558,0,0.000703,0.00112,0,0,0,0,0.00222,0.0005,0.00655,0.0014,0.00083,0.00121,0,0.000141,0.000566,0,0,0.0000752,0.0205,0,0.0522,0.000147,0,0,0.0055,0,0.000943,0,0.00385,0.0075,0.0351,0.00477,0,0,0.00145,0.00397,0,0.00585,0.00271,0,0,0.0068,0.0681,0,0.000414,0.0013,0,0.00184,0.00137,0,0,0.00379,0.0521,0.00161,0.00523,0.00208,0.0563,0.000546,0.00185,0,0.000243\r\n5,0,0,0,0.00492,0,0.183,0,0.000236,0.000658,0.0438,0,0.00108,0,0,0.0000805,0.000417,0,0,0,0.00102,0.004,0.00128,0,0,0.00386,0,0.00131,0,0,0.0000882,0,0.000234,0.000869,0,0,0,0,0,0.00047,0.00556,0.000703,0,0.00137,0,0.000349,0.000115,0,0,0.000112,0.0678,0,0.0436,0.00026,0.000683,0.000443,0.001761,0,0.00562,0.00519,0.00117,0.00086,0,0.00759,0.00436,0,0.00367,0.00178,0.0039,0.00338,0.00317,0,0.0000806,0.00189,0.0808,0.000728,0.000766,0.0000743,0,0.00375,0.00057,0,0,0.00111,0.00871,0.00107,0.000838,0,0,0.000582,0.0508,0,0.005793\r\n6,0,0.00054,0.00014,0.00228,0,0.177,0,0.0014,0.000678,0.0574,0.000538,0.00586,0,0.000423,0.000121,0.000387,0.0000662,0.000113,0,0.00121,0.00543,0.00338,0,0,0.00411,0.00015,0.00225,0,0,0.000444,0.0000665,0.000744,0.00128,0,0,0.0005,0.000155,0.05946,0.00096,0.00901,0.00161,0.00115,0.00149,0.00107,0.000167,0.000253,0.00044,0.00119,0.0001,0.0512,0.00336,0.0298,0.000509,0.000613,0,0.00452,0.00704,0,0.0045,0.00637,0.0552,0.00708,0.00639,0,0,0.00218,0.00224,0,0.00292,0.00258,0.0000765,0.000262,0.00391,0.0906,0.00162,0.000417,0.00116,0,0.00851,0.00192,0,0.0504,0.00649,0.0547,0.00123,0.00488,0.000299,0.00287,0.000413,0.00218,0,0.000247\r\n7,0,0,0,0.00449,0,0.281,0,0.000187,0.000644,0.0375,0,0.00359,0,0,0,0,0,0,0,0.000595,0.00345,0.00148,0,0,0.00287,0,0.00232,0,0,0,0,0.000495,0,0,0,0,0,0.000916,0.0004,0.00457,0.00106,0,0.00108,0,0,0,0,0,0,0.00317,0,0.0511,0,0.000707,0,0.000846,0.00133,0,0.00341,0.00318,0,0.00228,0.00296,0,0,0.00752,0.00113,0,0.0053,0,0,0,0.00211,0.00373,0,0,0,0,0.00381,0.000503,0,0,0.00398,0.0081,0.00108,0.000849,0,0,0,0.0584,0,0.00293\r\n8,0,0,0,0.05,0,0.297,0,0.000199,0,0.0284,0.000146,0,0,0.000291,0.000254,0.000668,0,0,0,0.00147,0.00987,0.00248,0.000309,0,0.00594,0,0,0,0,0,0.000273,0.00122,0.00164,0,0,0,0,0,0.00088,0.0508,0.00263,0.00221,0.00158,0,0.000535,0,0,0.00117,0.000261,0.00364,0,0.0339,0.00167,0,0,0.000781,0.00955,0,0,0.05576,0,0.0053,0.00578,0,0,0.00259,0,0.00102,0.0083,0.0014,0,0,0.00262,0.0524,0.00119,0,0,0,0.00696,0.00149,0,0,0.00369,0.009,0.00162,0.0014,0,0,0.00142,0.0041,0,0\r\n9,0,0,0,0.00671,0,0.266,0,0.000531,0.0015,0.0212,0,0.00198,0,0.000398,0.000177,0.00046,0.00314,0,0,0.00058,0.00657,0.00239,0.0000936,0,0.00623,0,0.00224,0,0.00109,0.000138,0,0.000432,0.00131,0.00127,0.000815,0,0,0,0.00109,0.00857,0.00141,0,0.00224,0,0.000396,0,0,0,0.00159,0.0256,0,0.0532,0.00905,0,0,0.000789,0.00169,0,0,0.00587,0.00139,0.00502,0.00482,0,0,0,0.00235,0,0.00571,0.00363,0,0.000174,0.00314,0.0758,0.00378,0.000707,0.000145,0,0.00611,0.00123,0,0,0,0.0572,0.00183,0.00227,0.00075,0.00222,0.000501,0,0,0\r\n10,0,0,0,0.0212,0,0.136,0.00164,0,0,0.0507,0,0,0.000296,0.000395,0.000226,0.000678,0,0,0,0.00505,0.0503,0.00217,0.000537,0,0.00674,0,0,0,0,0,0.000349,0.00129,0.0027,0,0.00284,0,0.000545,0,0.00101,0.0592,0.00298,0.00288,0.00155,0,0.00125,0,0,0,0.000319,0.0207,0,0.0551,0.00736,0,0,0.0013,0,0.00128,0.05677,0.05803,0,0.00374,0.00719,0,0,0.00363,0,0,0.00873,0.00297,0,0.000262,0.00498,0.00225,0.00517,0,0,0,0.00665,0.00456,0,0,0.00328,0.0537,0.00386,0.00258,0,0,0.00116,0.00511,0,0.00201\r\n11,0,0.000483,0,0.00701,0,0.231,0,0.000437,0.000907,0.0327,0,0.00343,0,0.000355,0.000087,0.000442,0.000338,0.00021,0,0.0006,0.00441,0.0018,0,0,0.00392,0,0.00319,0,0,0,0,0.00234,0.000939,0,0.000573,0,0,0,0.000934,0.0074,0.00155,0.00146,0.00143,0.000569,0,0.000214,0,0.00118,0.000141,0.00277,0,0.0389,0,0,0,0.00242,0.00432,0,0,0.00685,0.00106,0.00504,0.00526,0,0,0,0.00435,0,0.00672,0.00239,0,0,0.00692,0.0419,0,0.00118,0,0,0.00824,0.000535,0,0,0.0041,0.0537,0.00188,0.00225,0,0.000726,0.000339,0.000372,0,0\r\n12,0,0.000315,0,0.00109,0,0.173,0,0.0005,0.000921,0.0266,0,0.00326,0,0.000358,0,0.000246,0,0,0,0.000383,0.00344,0.00137,0,0,0.0032,0,0.00506,0,0,0.000364,0,0.00179,0.00189,0,0,0,0,0.00387,0.00088,0.00666,0.00111,0,0.001,0,0,0.000201,0.000573,0,0.000288,0.00229,0,0.0336,0,0,0,0.00454,0.00408,0,0,0.00648,0.00606,0.00532,0.00506,0,0,0.00152,0.00565,0,0.00459,0.0029,0.0000784,0,0.00734,0.102,0,0.00121,0.0012,0,0.00835,0.000508,0,0,0.00371,0.0541,0.00165,0.00152,0.00101,0.00884,0.000393,0.000716,0,0\r\n13,0,0,0,0.00841,0,0.0295,0,0,0,0.0462,0.000246,0,0,0.000601,0.000277,0.00155,0,0,0,0.00335,0.0576,0.00303,0.000504,0,0.0502,0,0,0,0.00226,0,0.000314,0.00875,0.00641,0,0.00167,0,0,0,0.00171,0.0582,0.00416,0.00921,0.00486,0,0.00122,0.000166,0,0.000449,0,0.0248,0,0.0356,0.00245,0.00205,0,0,0.0558,0,0,0.02941,0,0.148,0.0539,0,0,0.00318,0,0.0025,0.0515,0.00416,0,0.000794,0.00478,0,0.0028,0.00277,0,0.00138,0.00949,0.00254,0,0,0.00547,0.000883,0.00399,0.000512,0.000216,0,0.00314,0.00171,0.00575,0.000631\r\n14,0,0.000302,0.000181,0.00212,0,0.222,0,0.00137,0.000826,0.0238,0.000604,0.00538,0,0.000152,0.000243,0.000595,0.000266,0,0,0.00127,0.00603,0.00183,0,0,0.00583,0,0.00219,0,0.000958,0.000421,0,0.000393,0.0015,0.00246,0.000647,0,0,0.00488,0.000526,0.00652,0.00135,0.000697,0.00202,0.000357,0.000206,0,0.00198,0,0.000313,0.00477,0,0.0819,0,0.00115,0.000439,0.003083,0,0.00742,0,0.00715,0.00957,0.00396,0.00731,0,0,0.00178,0.00339,0,0.00303,0.00096,0,0.00026,0.00166,0.0793,0,0.000657,0.000961,0.00352,0.00678,0.000456,0,0,0.00375,0.0504,0.00104,0.00485,0,0.000361,0.000814,0.00599,0,0.0000897\r\n15,0,0,0.0000798,0.00332,0,0.273,0,0.000419,0.000531,0.0459,0.000136,0.000911,0,0.000133,0.000268,0.00038,0.000264,0,0,0.00157,0.0075,0.00176,0,0,0.00713,0,0.00394,0,0,0.0000941,0.000116,0.000672,0.00247,0.000427,0.000421,0,0.000321,0.000334,0.000718,0.00832,0.00104,0,0.0016,0,0.0000729,0.0000907,0.000314,0,0.00104,0.00348,0,0.034,0,0,0,0.000736,0.0021,0.00242,0,0.004688,0.000967,0.00471,0.00549,0,0.00708,0.00192,0.00236,0,0.00396,0.00166,0,0,0.0019,0.101,0,0.00116,0.000241,0,0.00512,0.000709,0,0,0.00336,0.0507,0.00119,0.00167,0,0.00118,0.0007,0.004,0,0.00113\r\n16,0,0,0,0.00319,0,0.156,0,0.000155,0.000428,0.0248,0,0.00121,0,0.000183,0,0.000244,0.00734,0,0,0.000671,0.00448,0.00155,0,0,0.00394,0,0.00306,0,0,0.000111,0.000869,0,0.00127,0,0,0,0,0,0,0.0061,0.00116,0.00103,0.00102,0,0.00013,0,0,0,0.000225,0.00288,0,0.032,0.000618,0.000791,0,0.000988,0.00398,0,0,0.00878,0.00146,0.00491,0.00393,0,0,0.00155,0.00245,0,0.00784,0.00237,0,0,0.00551,0.186,0.000721,0.00129,0,0,0.00742,0.000664,0,0,0.00273,0.0568,0.00207,0.000562,0,0,0.000946,0.00549,0,0.000246\r\n17,0,0.000391,0.000237,0.0017,0.0000597,0.208,0,0.00265,0.000712,0.0383,0.002494,0.00818,0,0.000521,0.0000546,0.000255,0.00063,0.000364,0,0.000366,0.00331,0.00363,0,0,0.00228,0,0.00372,0.00049,0.000628,0.000571,0,0.000737,0.00117,0.00194,0,0,0,0.00381,0.0006,0.00512,0.00112,0,0.00114,0,0,0.000144,0,0,0,0.0036,0,0.0859,0,0.000803,0,0.00446,0,0,0,0.00847,0.0579,0.171,0.00407,0,0,0,0.00495,0.000375,0.00506,0.0017,0,0.000103,0.00375,0.00701,0,0,0.00107,0,0.00165,0.00043,0,0.000327,0.001,0.00923,0.000939,0.000671,0.000462,0.00531,0.000287,0,0,0\r\n18,0,0,0,0.00656,0,0.191,0,0.0000732,0.000197,0.0435,0,0.00131,0,0,0,0,0,0,0,0.0009,0.00239,0.00137,0,0,0.00154,0,0.000693,0,0,0,0,0.000975,0.000579,0,0,0,0,0,0.0001,0.00312,0.000558,0,0.000506,0.000372,0,0,0,0,0,0.0501,0,0.0398,0.000597,0,0,0.000293,0.00274,0,0,0.00479,0,0.279,0.00294,0,0,0,0.00129,0,0.00275,0.00138,0,0,0.00407,0.053,0,0,0,0,0.00189,0.00119,0,0,0.00444,0.00377,0.00079,0.000679,0.000197,0,0,0,0,0\r\n19,0,0.000283,0,0.00442,0,0.269,0,0.000139,0.000445,0.0546,0,0.00087,0,0.00023,0.00034,0.00104,0,0,0,0.0018,0.00611,0.00133,0,0,0.00433,0,0.00267,0,0,0,0,0.000492,0.00111,0,0,0,0,0,0.0005,0.00562,0.000933,0,0.00156,0.000419,0.000285,0,0,0,0,0.09172,0,0.0588,0.00246,0,0,0.00146,0.0523,0,0,0.00483,0.000635,0.0593,0.0035,0,0,0.00318,0.00106,0,0.00441,0.00355,0,0,0.000645,0.0306,0.00272,0.000373,0.000127,0,0.00324,0.000607,0,0,0.00217,0.00798,0.000924,0.000932,0,0.00483,0.00033,0.00771,0,0.010296\r\n20,0,0,0,0.00968,0,0.359,0,0,0.000516,0.0559,0,0.000496,0,0,0,0,0,0,0,0,0.00262,0.00119,0,0,0.00198,0,0.000799,0,0,0,0,0.000779,0,0,0,0,0,0,0,0.00412,0,0,0,0.000803,0,0,0,0,0,0.00351,0,0.0531,0,0,0,0,0.00226,0,0,0.00263,0,0.00388,0.00171,0,0,0,0.00129,0,0.00289,0.00149,0,0,0.00161,0.00227,0,0,0,0,0.0013,0.00025,0,0,0.00231,0.00428,0.000701,0,0.00115,0,0,0,0,0\r\n21,0,0.000167,0,0.00618,0,0.174,0,0.000389,0.000869,0.028,0,0.00164,0,0,0.000208,0.000969,0,0,0,0.00111,0.00771,0.00207,0.000168,0,0.00586,0,0.0022,0,0.00109,0.000117,0.0000989,0.00158,0.00202,0,0.000704,0,0,0,0.000629,0.00975,0.00122,0,0.00236,0.000549,0.00056,0,0,0,0,0.00865,0,0.102,0.00196,0,0,0.001028,0,0.000847,0.0015,0.05178,0,0.0386,0.00766,0,0,0.00193,0.00169,0,0.00445,0.00831,0,0,0.0299,0.0513,0.00309,0,0,0,0.00909,0.00115,0,0,0.00612,0.0527,0.00164,0.00066,0.000581,0,0.00111,0.0521,0,0.00143\r\n22,0,0,0,0.00413,0,0.463,0,0.000206,0.000892,0.0452,0,0.00131,0,0.000309,0,0,0,0,0,0.0004,0.00282,0.000954,0,0,0.00204,0,0.00888,0,0,0.000131,0,0.000432,0,0.00086,0,0,0,0,0,0.00257,0.000128,0,0.000378,0,0,0,0,0,0,0.00521,0,0.0201,0.00086,0,0,0.001016,0,0,0,0.00135,0.0022,0.00228,0.000765,0,0,0,0.00368,0,0.0012,0.00151,0,0,0.00047,0.0226,0.000601,0,0.0000855,0,0.00102,0,0,0,0.000597,0.00278,0.000312,0,0.000172,0,0,0,0,0\r\n23,0,0,0,0.00127,0,0.23,0,0.000657,0.000529,0.0414,0.000125,0.00111,0,0.000164,0.000228,0.000324,0,0,0,0.0012,0.00445,0.00189,0,0,0.00351,0,0.00219,0,0,0.000212,0,0.000508,0.00149,0,0,0,0,0.00505,0.000458,0.0076,0.00166,0,0.00156,0,0.000122,0.000131,0,0,0,0.00779,0,0.0418,0.000279,0.00083,0,0.0028,0.00554,0,0.00335,0.00549,0.0057,0.0497,0.00373,0,0,0.0033,0.00171,0.00125,0.0518,0.00234,0,0.000239,0.0226,0.0883,0,0.000725,0.000504,0,0.00343,0.000701,0,0,0.00717,0.0519,0.00118,0.001,0.000223,0.00107,0.000299,0.00216,0,0.0018\r\n24,0.000214,0,0,0.00406,0,0.182,0,0,0.000533,0.0376,0,0.000901,0,0,0,0.000284,0,0,0,0.001,0.00347,0.000911,0,0,0.00318,0,0.00136,0,0,0,0,0.000549,0.000639,0,0,0,0,0,0.000295,0.00221,0.000794,0,0.00115,0.000485,0,0,0,0,0,0.0535,0,0.0515,0.000943,0,0,0.000275,0.00115,0,0,0,0,0,0.00167,0,0,0.0527,0.000826,0,0,0.00473,0,0,0.00332,0.108,0.00159,0,0,0,0.0027,0,0,0,0,0.0506,0.00142,0,0.00104,0.00182,0,0.00263,0,0.00395\r\n25,0,0,0,0.00179,0,0.281,0,0.000594,0.000846,0.0283,0.000259,0.00137,0,0.00103,0,0,0.000237,0,0,0.00046,0.00327,0.00184,0,0,0.00265,0,0.00467,0,0,0.000251,0,0.000243,0.00195,0.000277,0,0,0,0.00167,0,0.00506,0.000693,0,0.000569,0,0,0,0.000473,0,0.000562,0.00609,0,0.0244,0.000262,0.000353,0,0.00201,0.00747,0,0,0.003467,0.00291,0.00765,0.00267,0,0,0,0.00273,0,0.00276,0.00228,0,0,0.00643,0.144,0,0,0.000221,0,0.00149,0.000239,0,0,0.00189,0.0078,0.000882,0,0.000642,0.00374,0,0,0,0\r\n26,0,0.000265,0,0.00803,0,0.204,0,0.000356,0.00123,0.0699,0.000236,0.000956,0,0.000326,0.0000747,0.000605,0,0,0,0.00043,0.00594,0.002,0,0,0.00519,0,0.00183,0.0008,0,0.0000862,0,0.000319,0.000673,0,0,0,0,0,0.0008,0.00722,0.00117,0.002264,0.00258,0,0.000055,0.000705,0,0,0.00016,0.00407,0,0.028,0.000692,0.000721,0,0.00256,0,0.0078,0,0.0097,0.00146,0.00774,0.00676,0,0,0.00227,0.00364,0,0.00424,0.00235,0,0.000144,0.0025,0.0628,0.000877,0.00267,0.000153,0,0.0516,0.00167,0,0,0.000671,0.0541,0.00208,0.00326,0,0,0.000565,0.04,0,0.00311\r\n27,0,0.000154,0,0.00661,0,0.199,0,0.000747,0.00114,0.0707,0.000163,0.00272,0,0.00112,0.000113,0.000282,0.0000812,0,0,0.00352,0.0556,0.00259,0.0003,0.0014,0.00769,0,0.00423,0,0.00115,0.000196,0.000118,0.000841,0.00092,0.000546,0.000847,0,0,0.000487,0.000775,0.0503,0.00286,0.00165,0.00187,0,0.000349,0.000299,0,0,0.000261,0.00602,0,0.0615,0.00129,0.0011,0.000359,0.003775,0,0.00144,0,0.00737,0.00415,0.00906,0.00978,0,0,0.00155,0.00674,0,0.00412,0.00335,0.000161,0,0.00306,0.055,0.00378,0.00131,0.000243,0,0.00744,0.00542,0,0,0.0579,0.0566,0.00184,0.00265,0.000251,0.000777,0.000807,0.00143,0,0\r\n28,0,0,0,0.00492,0,0.27,0,0.000201,0.00107,0.053,0,0.000926,0,0.000201,0.0000984,0,0.000145,0,0,0.000734,0.00428,0.00208,0,0,0.0046,0,0.00176,0,0.000992,0.000134,0,0.000446,0.00146,0,0,0,0,0,0.00065,0.00721,0.00115,0,0.00134,0,0.000136,0.000321,0,0,0.000169,0.00412,0,0.0358,0.000386,0.000436,0,0.00116,0.00249,0,0.00246,0.007122,0.00103,0.159,0.00398,0,0,0.00945,0.00255,0,0.0051,0.00159,0,0.000232,0.00194,0.00523,0.000487,0,0.0000907,0,0.00353,0.000541,0,0,0.0041,0.0503,0.00101,0.00384,0.00048,0.000781,0.000271,0.00123,0,0.0018\r\n29,0,0.000354,0,0.00658,0,0.171,0,0.000214,0.000763,0.0565,0,0.00316,0,0.000248,0.000269,0.000707,0.000206,0,0,0.0021,0.00574,0.00235,0.000179,0,0.00496,0,0.00179,0,0.000787,0,0.000194,0.000493,0.00157,0,0,0,0,0.000555,0.000534,0.00969,0.00172,0,0.00227,0.000575,0.00018,0.000205,0,0,0,0.0557,0,0.1,0.000821,0.000987,0,0.000667,0.0219,0.00149,0,0.007512,0.00303,0.00973,0.00466,0,0,0.00475,0.00315,0,0.00601,0.00422,0,0,0.00764,0.0513,0.00117,0,0.000116,0,0.00866,0.000977,0,0,0.0054,0.0584,0.00226,0.00324,0.000731,0,0.000446,0.0508,0,0.00297\r\n30,0,0.000117,0.000302,0.00283,0,0.301,0,0.0015266,0.000822,0.0288,0.001063,0.00222,0,0.000448,0.000908,0.000979,0.000734,0.000189,0,0.00157,0.00855,0.00325,0,0,0.00609,0.000064,0.00341,0,0.000709,0.000393,0.000111,0.00147,0.003,0,0,0,0,0.00218,0.000648,0.00871,0.0014,0,0.00192,0.000375,0.000277,0.000217,0.00064,0.0015,0.000266,0.0549,0,0.0306,0.0005,0.000897,0,0.00276,0.0502,0,0,0.00551,0.0509,0.00836,0.00338,0,0,0.00157,0.00257,0,0.00539,0.00258,0,0.000601,0.0037,0.0245,0.000563,0.000392,0.000552,0,0.00433,0.00181,0,0,0.0013,0.0513,0.00113,0.00289,0,0.00592,0.00042,0.000362,0,0\r\n31,0,0,0,0.0501,0,0.354,0,0.000107,0.00056,0.0236,0,0.00195,0,0.000222,0,0,0,0,0,0.00039,0.00228,0.00186,0,0,0.00199,0,0.00169,0,0,0,0,0.000478,0.000401,0,0,0,0,0,0.000381,0.00477,0,0,0.000601,0,0,0,0,0,0,0.00765,0,0.0593,0.00093,0.000783,0,0.001654,0.00158,0,0,0.003025,0,0.00824,0.00244,0,0,0.00118,0.00107,0.000935,0.003,0,0,0,0.00167,0.00665,0,0,0,0,0.00209,0.00074,0,0,0.00164,0.00387,0.000659,0,0,0,0,0.00025,0,0\r\n32,0,0,0,0.00676,0,0.237,0,0.000274,0.000929,0.043,0,0.00128,0,0.00029,0.000168,0.000599,0,0,0,0.000675,0.00444,0.00253,0,0,0.00401,0,0.00265,0,0,0,0,0.00153,0.00115,0,0,0,0.000206,0,0.000813,0.00762,0.00122,0,0.00152,0,0.00046,0,0,0.00125,0.000173,0.00612,0,0.0651,0,0.00224,0,0.000606,0.00529,0,0,0.00722,0.00018,0.00496,0.00425,0,0,0.00181,0.00219,0,0.00639,0.00517,0,0,0.0033,0.104,0,0.000853,0,0,0.00506,0.00069,0,0,0.00611,0.00994,0.00129,0.00092,0.00239,0.000906,0.000345,0,0,0\r\n33,0,0,0,0.00564,0,0.239,0,0.000174,0.000588,0.0321,0,0.0014,0,0,0,0.000247,0,0,0,0.00113,0.00435,0.0015,0,0,0.00381,0,0.00217,0,0,0,0,0.000431,0.000707,0,0,0,0,0,0.0005,0.00646,0.00104,0,0.000834,0.000736,0,0,0,0,0.000117,0.00978,0,0.0333,0,0,0,0.000206,0.00329,0,0.00114,0.00303,0,0.00454,0.00307,0,0,0.00176,0.00167,0,0.00644,0.00257,0,0,0.00163,0.116,0,0.000308,0,0,0.00539,0.000727,0,0,0.00488,0.0506,0.00137,0.000872,0.00325,0.00256,0,0.00107,0.000295,0\r\n34,0,0,0,0.00493,0,0.384,0,0.000221,0.000445,0.0582,0,0.000837,0,0,0,0,0,0,0,0,0.000938,0.000644,0,0,0.000695,0,0.000766,0,0,0,0,0,0,0,0,0,0,0.000691,0,0.00128,0,0,0,0,0,0,0,0,0,0.000638,0,0.0097,0,0,0,0.000495,0.000651,0,0,0.000926,0,0.00249,0,0,0,0,0.000751,0,0.000695,0,0,0,0,0.0997,0,0,0,0,0.000726,0,0.000766,0,0,0.00168,0.00015,0,0,0,0,0,0,0\r\n35,0,0,0,0.00818,0,0.286,0,0.000202,0.000427,0.0568,0,0.00183,0,0,0,0,0,0,0,0,0.00223,0.0014,0,0,0.00183,0,0.00221,0,0,0,0,0,0.000418,0,0,0,0,0,0,0.00359,0.000571,0.00182,0.0004,0,0,0,0,0,0,0.00642,0,0.0382,0.000433,0.00139,0,0.000329,0.000436,0,0,0.00335,0,0.0531,0.00404,0,0,0,0.00122,0,0.00288,0,0,0,0.00197,0.00429,0,0,0,0,0.00708,0.000458,0,0,0.0019,0.00383,0.000668,0.0004,0,0,0,0,0,0\r\n36,0,0,0,0.00117,0,0.141,0,0.000151,0.000289,0.00501,0,0.000397,0,0,0.0000811,0,0,0,0,0.00052,0.00423,0.000857,0,0,0.00353,0,0.00206,0,0,0,0,0.000198,0.00175,0,0,0,0,0,0,0.00462,0,0,0.00113,0.000325,0,0,0,0,0,0.0566,0,0.0476,0.000534,0,0,0.000507,0.00233,0,0,0.00375,0.00041,0.0755,0.00431,0,0,0.00124,0.00179,0,0.00618,0.00118,0,0.000205,0.00188,0.206,0.000504,0.00074,0,0,0.00365,0.000812,0,0,0.000763,0.00996,0.00113,0.00148,0,0.00185,0.00055,0.0064,0,0.00082\r\n37,0.000266,0,0,0,0,0,0,0,0,0.00551,0,0,0,0,0,0,0,0,0.000821,0.00326,0.0274,0,0.00108,0,0,0,0,0,0,0,0.0005,0.00215,0,0,0.00345,0,0,0,0.00326,0.0267,0.00789,0.0097,0,0.0568,0,0,0,0.0016,0,0.000911,0,0,0.00175,0.00141,0,0,0.0201,0.0516,0,0.127,0,0.0217,0,0.00778,0,0.00581,0,0,0,0.00342,0,0.00397,0.03,0,0,0.00533,0,0,0.00143,0,0,0,0.00751,0,0,0,0,0,0.00294,0,0,0\r\n38,0,0,0,0.00644,0,0.176,0,0.000172,0.000748,0.0759,0,0.00214,0,0.000474,0.000183,0.000378,0.000365,0,0,0.000887,0.00662,0.00241,0,0,0.00486,0,0.00356,0,0,0.000127,0,0.000828,0.00115,0,0,0,0,0,0.001,0.00983,0.00128,0,0.00169,0.00065,0,0.000265,0,0,0,0.0046,0,0.0438,0,0.000946,0,0.00264,0.0056,0,0,0.00994,0.00214,0.0088,0.00571,0,0,0.00208,0.0509,0.000342,0.00446,0.00355,0,0,0.00327,0.0253,0,0.00234,0,0,0.00337,0.000954,0,0,0.00702,0.059,0.00262,0.00117,0,0,0.00113,0.00163,0,0.000305\r\n39,0,0,0,0.00994,0,0.265,0,0,0.000332,0.0321,0,0.00119,0,0,0,0,0,0,0,0,0.00189,0.000941,0,0,0.00168,0,0.000406,0,0,0,0,0.00057,0,0,0,0,0,0,0,0.0032,0.000428,0,0.000403,0.000237,0,0.000209,0,0,0,0.00194,0,0.0341,0,0,0,0,0,0.00077,0,0.003878,0,0.0919,0.00215,0,0,0,0.000813,0,0.00235,0.00115,0,0,0.00206,0.036,0.000332,0,0,0,0.00171,0,0,0,0.00494,0.00219,0.000405,0.000571,0,0,0,0.00209,0,0\r\n40,0,0,0,0.00679,0,0.23,0,0,0.000376,0.0544,0,0.000633,0,0,0.000193,0.000227,0,0,0,0.0004,0.00371,0.00121,0,0,0.00351,0,0.00164,0,0,0,0,0.000887,0.000523,0,0,0,0,0,0.000558,0.00538,0.000757,0,0.00112,0.000532,0,0,0,0,0,0.0447,0,0.0201,0.00393,0,0.00632,0.000329,0.00139,0,0,0.0028,0,0.00265,0.00274,0,0,0,0.00144,0,0.00422,0,0,0,0.000919,0.0292,0.0042,0,0,0,0.05,0.000685,0,0,0.00246,0.00746,0.000854,0.00195,0,0.00086,0,0.00476,0,0\r\n41,0,0,0,0.00693,0,0.267,0,0.00108,0.00044,0.027,0.00027,0.00307,0,0,0,0,0,0,0,0,0.00241,0.00302,0,0,0.00222,0,0.00138,0,0,0,0,0.000173,0,0,0.000351,0,0,0,0.00048,0.00311,0.000577,0,0.000406,0,0,0,0,0,0,0.0504,0,0.0246,0,0.00076,0,0.000852,0,0,0,0.00245,0.0013,0,0.00218,0,0,0,0.00082,0,0.00249,0.00262,0,0,0.00207,0.081,0.000357,0,0,0,0.00284,0,0,0,0.00178,0.00427,0.000877,0,0,0,0,0.000599,0,0.00201\r\n42,0,0,0,0.0228,0,0.257,0,0,0.000132,0.0621,0,0.00101,0,0,0,0.000339,0,0,0,0,0.00376,0.00109,0,0,0.00218,0,0,0.000567,0,0,0,0.00141,0.000459,0,0,0,0,0,0.00055,0.0044,0.000964,0,0.000591,0.00132,0,0,0,0,0,0.00769,0,0.069,0.000146,0,0,0,0.00108,0,0,0.00765,0,0.00819,0,0,0.0583,0,0.00101,0,0.00423,0.00369,0,0,0.0563,0.0514,0,0.000903,0,0.000298,0,0.000407,0,0,0.00213,0.00236,0.000971,0,0.000561,0,0,0.00365,0,0.00053\r\n"
},
{
"path": "Demos/Mapped Network From Data (Biochemical and Structural)/edge.list.csv",
"content": "\"\",\"source\",\"target\",\"value\",\"type\"\r\n\"1\",8,12,1,\"KEGG\"\r\n\"2\",11,12,1,\"KEGG\"\r\n\"3\",3,26,1,\"KEGG\"\r\n\"4\",8,26,1,\"KEGG\"\r\n\"5\",7,50,1,\"KEGG\"\r\n\"6\",12,50,1,\"KEGG\"\r\n\"7\",43,52,1,\"KEGG\"\r\n\"8\",30,56,1,\"KEGG\"\r\n\"9\",8,77,1,\"KEGG\"\r\n\"10\",26,82,1,\"KEGG\"\r\n\"11\",58,90,1,\"KEGG\"\r\n\"12\",60,90,1,\"KEGG\"\r\n\"13\",60,92,1,\"KEGG\"\r\n\"213\",3,5,0.829268292682927,\"Tanimoto\"\r\n\"423\",3,8,0.825,\"Tanimoto\"\r\n\"424\",5,8,0.702127659574468,\"Tanimoto\"\r\n\"633\",3,11,0.739130434782609,\"Tanimoto\"\r\n\"634\",5,11,0.775510204081633,\"Tanimoto\"\r\n\"637\",8,11,0.847826086956522,\"Tanimoto\"\r\n\"703\",3,12,0.794117647058823,\"Tanimoto\"\r\n\"842\",2,14,0.727272727272727,\"Tanimoto\"\r\n\"923\",14,15,0.794871794871795,\"Tanimoto\"\r\n\"993\",14,16,0.794871794871795,\"Tanimoto\"\r\n\"994\",15,16,0.897435897435897,\"Tanimoto\"\r\n\"1053\",3,17,0.75,\"Tanimoto\"\r\n\"1054\",5,17,0.790697674418605,\"Tanimoto\"\r\n\"1123\",3,18,0.82051282051282,\"Tanimoto\"\r\n\"1127\",8,18,0.853658536585366,\"Tanimoto\"\r\n\"1130\",11,18,0.729166666666667,\"Tanimoto\"\r\n\"1131\",12,18,0.72972972972973,\"Tanimoto\"\r\n\"1191\",1,19,0.884615384615385,\"Tanimoto\"\r\n\"1261\",1,20,0.785714285714286,\"Tanimoto\"\r\n\"1274\",15,20,0.72972972972973,\"Tanimoto\"\r\n\"1275\",16,20,0.72972972972973,\"Tanimoto\"\r\n\"1278\",19,20,0.892857142857143,\"Tanimoto\"\r\n\"1331\",1,21,0.785714285714286,\"Tanimoto\"\r\n\"1344\",15,21,0.72972972972973,\"Tanimoto\"\r\n\"1345\",16,21,0.72972972972973,\"Tanimoto\"\r\n\"1348\",19,21,0.892857142857143,\"Tanimoto\"\r\n\"1349\",20,21,1,\"Tanimoto\"\r\n\"1483\",14,25,0.846153846153846,\"Tanimoto\"\r\n\"1484\",15,25,0.948717948717949,\"Tanimoto\"\r\n\"1485\",16,25,0.948717948717949,\"Tanimoto\"\r\n\"1543\",3,26,0.825,\"Tanimoto\"\r\n\"1544\",5,26,0.702127659574468,\"Tanimoto\"\r\n\"1547\",8,26,0.733333333333333,\"Tanimoto\"\r\n\"1557\",18,26,0.727272727272727,\"Tanimoto\"\r\n\"1693\",14,28,0.846153846153846,\"Tanimoto\"\r\n\"1694\",15,28,0.948717948717949,\"Tanimoto\"\r\n\"1695\",16,28,0.948717948717949,\"Tanimoto\"\r\n\"1702\",25,28,1,\"Tanimoto\"\r\n\"1762\",13,29,0.702702702702703,\"Tanimoto\"\r\n\"1832\",13,29,0.702702702702703,\"Tanimoto\"\r\n\"1846\",29,29,1,\"Tanimoto\"\r\n\"1966\",7,32,0.808080808080808,\"Tanimoto\"\r\n\"2184\",15,36,0.755555555555556,\"Tanimoto\"\r\n\"2185\",16,36,0.755555555555556,\"Tanimoto\"\r\n\"2192\",25,36,0.8,\"Tanimoto\"\r\n\"2195\",28,36,0.8,\"Tanimoto\"\r\n\"2313\",3,38,0.702127659574468,\"Tanimoto\"\r\n\"2314\",5,38,0.74,\"Tanimoto\"\r\n\"2381\",1,39,0.709677419354839,\"Tanimoto\"\r\n\"2398\",19,39,0.806451612903226,\"Tanimoto\"\r\n\"2399\",20,39,0.9,\"Tanimoto\"\r\n\"2400\",21,39,0.9,\"Tanimoto\"\r\n\"2413\",37,39,0.75,\"Tanimoto\"\r\n\"2451\",1,40,0.709677419354839,\"Tanimoto\"\r\n\"2468\",19,40,0.806451612903226,\"Tanimoto\"\r\n\"2469\",20,40,0.9,\"Tanimoto\"\r\n\"2470\",21,40,0.9,\"Tanimoto\"\r\n\"2483\",37,40,0.75,\"Tanimoto\"\r\n\"2485\",39,40,1,\"Tanimoto\"\r\n\"2551\",35,42,0.716666666666667,\"Tanimoto\"\r\n\"2553\",37,42,0.826086956521739,\"Tanimoto\"\r\n\"2689\",32,45,0.776859504132231,\"Tanimoto\"\r\n\"2731\",1,46,0.709677419354839,\"Tanimoto\"\r\n\"2749\",20,46,0.727272727272727,\"Tanimoto\"\r\n\"2750\",21,46,0.727272727272727,\"Tanimoto\"\r\n\"2821\",22,47,0.774193548387097,\"Tanimoto\"\r\n\"2834\",38,47,0.811320754716981,\"Tanimoto\"\r\n\"2968\",30,50,0.732142857142857,\"Tanimoto\"\r\n\"3013\",3,51,0.717391304347826,\"Tanimoto\"\r\n\"3014\",5,51,0.755102040816326,\"Tanimoto\"\r\n\"3044\",38,51,0.895833333333333,\"Tanimoto\"\r\n\"3051\",47,51,0.9,\"Tanimoto\"\r\n\"3102\",25,52,0.7,\"Tanimoto\"\r\n\"3105\",28,52,0.7,\"Tanimoto\"\r\n\"3106\",29,52,0.74468085106383,\"Tanimoto\"\r\n\"3107\",29,52,0.74468085106383,\"Tanimoto\"\r\n\"3156\",7,53,0.724770642201835,\"Tanimoto\"\r\n\"3179\",32,53,0.858490566037736,\"Tanimoto\"\r\n\"3189\",45,53,0.75,\"Tanimoto\"\r\n\"3248\",30,56,0.834862385321101,\"Tanimoto\"\r\n\"3249\",32,56,0.845454545454545,\"Tanimoto\"\r\n\"3266\",53,56,0.752066115702479,\"Tanimoto\"\r\n\"3323\",37,57,0.8,\"Tanimoto\"\r\n\"3325\",39,57,0.731707317073171,\"Tanimoto\"\r\n\"3326\",40,57,0.731707317073171,\"Tanimoto\"\r\n\"3327\",42,57,0.931818181818182,\"Tanimoto\"\r\n\"3393\",37,58,0.883720930232558,\"Tanimoto\"\r\n\"3395\",39,58,0.731707317073171,\"Tanimoto\"\r\n\"3396\",40,58,0.731707317073171,\"Tanimoto\"\r\n\"3397\",42,58,0.847826086956522,\"Tanimoto\"\r\n\"3408\",57,58,0.906976744186046,\"Tanimoto\"\r\n\"3461\",35,59,0.728813559322034,\"Tanimoto\"\r\n\"3463\",37,59,0.844444444444444,\"Tanimoto\"\r\n\"3467\",42,59,0.977272727272727,\"Tanimoto\"\r\n\"3478\",57,59,0.953488372093023,\"Tanimoto\"\r\n\"3479\",58,59,0.866666666666667,\"Tanimoto\"\r\n\"3531\",35,60,0.728813559322034,\"Tanimoto\"\r\n\"3533\",37,60,0.844444444444444,\"Tanimoto\"\r\n\"3537\",42,60,0.977272727272727,\"Tanimoto\"\r\n\"3548\",57,60,0.953488372093023,\"Tanimoto\"\r\n\"3549\",58,60,0.866666666666667,\"Tanimoto\"\r\n\"3550\",59,60,1,\"Tanimoto\"\r\n\"3692\",61,61,1,\"Tanimoto\"\r\n\"3728\",19,62,0.735294117647059,\"Tanimoto\"\r\n\"3729\",20,62,0.818181818181818,\"Tanimoto\"\r\n\"3730\",21,62,0.818181818181818,\"Tanimoto\"\r\n\"3745\",39,62,0.909090909090909,\"Tanimoto\"\r\n\"3746\",40,62,0.909090909090909,\"Tanimoto\"\r\n\"3747\",42,62,0.75,\"Tanimoto\"\r\n\"3758\",57,62,0.804878048780488,\"Tanimoto\"\r\n\"3759\",58,62,0.804878048780488,\"Tanimoto\"\r\n\"3760\",59,62,0.767441860465116,\"Tanimoto\"\r\n\"3761\",60,62,0.767441860465116,\"Tanimoto\"\r\n\"3798\",19,64,0.735294117647059,\"Tanimoto\"\r\n\"3799\",20,64,0.818181818181818,\"Tanimoto\"\r\n\"3800\",21,64,0.818181818181818,\"Tanimoto\"\r\n\"3815\",39,64,0.909090909090909,\"Tanimoto\"\r\n\"3816\",40,64,0.909090909090909,\"Tanimoto\"\r\n\"3817\",42,64,0.75,\"Tanimoto\"\r\n\"3828\",57,64,0.804878048780488,\"Tanimoto\"\r\n\"3829\",58,64,0.804878048780488,\"Tanimoto\"\r\n\"3830\",59,64,0.767441860465116,\"Tanimoto\"\r\n\"3831\",60,64,0.767441860465116,\"Tanimoto\"\r\n\"3834\",62,64,1,\"Tanimoto\"\r\n\"3868\",19,66,0.735294117647059,\"Tanimoto\"\r\n\"3869\",20,66,0.818181818181818,\"Tanimoto\"\r\n\"3870\",21,66,0.818181818181818,\"Tanimoto\"\r\n\"3885\",39,66,0.909090909090909,\"Tanimoto\"\r\n\"3886\",40,66,0.909090909090909,\"Tanimoto\"\r\n\"3887\",42,66,0.75,\"Tanimoto\"\r\n\"3898\",57,66,0.804878048780488,\"Tanimoto\"\r\n\"3899\",58,66,0.804878048780488,\"Tanimoto\"\r\n\"3900\",59,66,0.767441860465116,\"Tanimoto\"\r\n\"3901\",60,66,0.767441860465116,\"Tanimoto\"\r\n\"3904\",62,66,1,\"Tanimoto\"\r\n\"3905\",64,66,1,\"Tanimoto\"\r\n\"3934\",15,68,0.755102040816326,\"Tanimoto\"\r\n\"3935\",16,68,0.755102040816326,\"Tanimoto\"\r\n\"3942\",25,68,0.795918367346939,\"Tanimoto\"\r\n\"3945\",28,68,0.795918367346939,\"Tanimoto\"\r\n\"3951\",35,68,0.830508474576271,\"Tanimoto\"\r\n\"3952\",36,68,0.784313725490196,\"Tanimoto\"\r\n\"3965\",52,68,0.727272727272727,\"Tanimoto\"\r\n\"4004\",15,69,0.755102040816326,\"Tanimoto\"\r\n\"4005\",16,69,0.755102040816326,\"Tanimoto\"\r\n\"4012\",25,69,0.795918367346939,\"Tanimoto\"\r\n\"4015\",28,69,0.795918367346939,\"Tanimoto\"\r\n\"4021\",35,69,0.830508474576271,\"Tanimoto\"\r\n\"4022\",36,69,0.784313725490196,\"Tanimoto\"\r\n\"4035\",52,69,0.727272727272727,\"Tanimoto\"\r\n\"4047\",68,69,1,\"Tanimoto\"\r\n\"4103\",50,71,0.723076923076923,\"Tanimoto\"\r\n\"4402\",77,78,0.844919786096257,\"Tanimoto\"\r\n\"4453\",50,80,0.792307692307692,\"Tanimoto\"\r\n\"4457\",56,80,0.77037037037037,\"Tanimoto\"\r\n\"4483\",3,82,0.75,\"Tanimoto\"\r\n\"4484\",5,82,0.714285714285714,\"Tanimoto\"\r\n\"4497\",18,82,0.702127659574468,\"Tanimoto\"\r\n\"4503\",26,82,0.863636363636364,\"Tanimoto\"\r\n\"4653\",37,85,1,\"Tanimoto\"\r\n\"4655\",39,85,0.75,\"Tanimoto\"\r\n\"4656\",40,85,0.75,\"Tanimoto\"\r\n\"4657\",42,85,0.826086956521739,\"Tanimoto\"\r\n\"4668\",57,85,0.8,\"Tanimoto\"\r\n\"4669\",58,85,0.883720930232558,\"Tanimoto\"\r\n\"4670\",59,85,0.844444444444444,\"Tanimoto\"\r\n\"4671\",60,85,0.844444444444444,\"Tanimoto\"\r\n\"4793\",37,90,0.8,\"Tanimoto\"\r\n\"4797\",42,90,0.843137254901961,\"Tanimoto\"\r\n\"4808\",57,90,0.82,\"Tanimoto\"\r\n\"4809\",58,90,0.82,\"Tanimoto\"\r\n\"4810\",59,90,0.86,\"Tanimoto\"\r\n\"4811\",60,90,0.86,\"Tanimoto\"\r\n\"4827\",85,90,0.8,\"Tanimoto\"\r\n\"4863\",37,92,0.730769230769231,\"Tanimoto\"\r\n\"4867\",42,92,0.843137254901961,\"Tanimoto\"\r\n\"4878\",57,92,0.82,\"Tanimoto\"\r\n\"4879\",58,92,0.75,\"Tanimoto\"\r\n\"4880\",59,92,0.86,\"Tanimoto\"\r\n\"4881\",60,92,0.86,\"Tanimoto\"\r\n\"4897\",85,92,0.730769230769231,\"Tanimoto\"\r\n\"4899\",90,92,0.923076923076923,\"Tanimoto\"\r\n\"4831\",1,92,0.431372549019608,\"Tanimoto weak\"\r\n\"4832\",2,92,0.321428571428571,\"Tanimoto weak\"\r\n\"4840\",11,92,0.333333333333333,\"Tanimoto weak\"\r\n\"4842\",13,92,0.327586206896552,\"Tanimoto weak\"\r\n\"4843\",14,92,0.406779661016949,\"Tanimoto weak\"\r\n\"4844\",15,92,0.474576271186441,\"Tanimoto weak\"\r\n\"4845\",16,92,0.474576271186441,\"Tanimoto weak\"\r\n\"4848\",19,92,0.490196078431373,\"Tanimoto weak\"\r\n\"4849\",20,92,0.54,\"Tanimoto weak\"\r\n\"4850\",21,92,0.54,\"Tanimoto weak\"\r\n\"4852\",25,92,0.459016393442623,\"Tanimoto weak\"\r\n\"4855\",28,92,0.459016393442623,\"Tanimoto weak\"\r\n\"4857\",29,92,0.34375,\"Tanimoto weak\"\r\n\"4861\",35,92,0.651515151515151,\"Tanimoto weak\"\r\n\"4862\",36,92,0.46031746031746,\"Tanimoto weak\"\r\n\"4865\",39,92,0.6,\"Tanimoto weak\"\r\n\"4866\",40,92,0.6,\"Tanimoto weak\"\r\n\"4870\",46,92,0.428571428571429,\"Tanimoto weak\"\r\n\"4875\",52,92,0.391304347826087,\"Tanimoto weak\"\r\n\"4884\",62,92,0.66,\"Tanimoto weak\"\r\n\"4885\",64,92,0.66,\"Tanimoto weak\"\r\n\"4886\",66,92,0.66,\"Tanimoto weak\"\r\n\"4887\",68,92,0.523076923076923,\"Tanimoto weak\"\r\n\"4888\",69,92,0.523076923076923,\"Tanimoto weak\"\r\n\"4890\",72,92,0.530120481927711,\"Tanimoto weak\"\r\n\"4896\",84,92,0.333333333333333,\"Tanimoto weak\"\r\n\"4898\",87,92,0.310126582278481,\"Tanimoto weak\"\r\n\"4828\",87,90,0.30188679245283,\"Tanimoto weak\"\r\n"
},
{
"path": "Demos/Mapped Network From Data (Biochemical and Structural)/network code.r",
"content": "setwd(\"C:\\\\Users\\\\D\\\\Dropbox\\\\Software\\\\TeachingDemos\\\\Demos\\\\Mapped Network From Data (Biochemical and Structural)\")\nsetwd(\"C:\\\\Users\\\\Node\\\\Dropbox\\\\Software\\\\TeachingDemos\\\\Demos\\\\Mapped Network From Data (Biochemical and Structural)\")\nsetwd(\"C:\\\\Users\\\\dgrapov\\\\Dropbox\\\\Software\\\\TeachingDemos\\\\Demos\\\\Mapped Network From Data (Biochemical and Structural)\")\nsource(\"http://pastebin.com/raw.php?i=1Bs7G5ds\")\n#source devium \nsource(\"http://pastebin.com/raw.php?i=UyDBTA57\") #\n#save()\n# load objects\ntmp.data<-read.csv(\"data.csv\",row.names=1)\nvar.meta<-read.csv(\"variable info.csv\")\nsample.meta<-read.csv(\"sample info.csv\")\n\n# Calculate Network Connections\n#---------------------------------\n#identify required Pubchem CIDs and KEGG IDs\nCIDS<-fixln(var.meta$PubChem)\nKEGG<-fixlc(var.meta$KEGG)\n\n#get KEGG RPAIRS\n# load reaction DB and return all reactions of type main\nreaction.DB<-get.KEGG.pairs(type=\"main\")\nkegg.edges<-get.Reaction.pairs(KEGG,reaction.DB,index.translation.DB=NULL,parallel=FALSE,translate=FALSE)\n\n#create shared index to allow merging with other edge identifiers\nindex<-KEGG\nedge.names<-data.frame(index, network.id = c(1:length(index)))\nkegg.edges<-data.frame(make.edge.list.index(edge.names,kegg.edges))\n\n#get structural similarity edges based on Tanimoto >0.7 \ntanimoto.edges<-CID.to.tanimoto(cids=CIDS, cut.off = 0, parallel=FALSE) #return all possible connections\n\n#create shared index\nindex<-CIDS\nedge.names<-data.frame(index, network.id = c(1:length(index)))\ntmp<-make.edge.list.index(edge.names,tanimoto.edges)\ntanimoto.edges[,1:2]<-tmp\n\n#prepare tanimoto edges for merge with KEGG\ntanimoto.edges$type<-\"Tanimoto\"\n\n#merge the biochemical and structural similarity edge lists\nkegg.edges$value<-1 # give arbitrary weight, here the max tanimoto can take\nkegg.edges$type<-\"KEGG\" # set type to identify between KEGG and tanimoto\n\nfinal.edge.list<-rbind(kegg.edges,tanimoto.edges) #note duplicated edges maybe prioritized diffrently in various orders \n#write.csv(final.edge.list,file=\"edge.list.csv\") # uncomment to save the file\n\n#render network for preview\n{\nedge.list<-clean.edgeList(data=final.edge.list)\ntmp.edge.list<-edge.list[fixln(edge.list$value)>=0.7,]\nggplot2.network(tmp.edge.list,edge.color.var=\"type\", bezier=FALSE,node.size=3, node.names=fixlc(var.meta$Name2),node.label.size = 3)\n\n#removing unconnected nodes and getting names right\ntmp.edge.list$source<-paste(\" \",tmp.edge.list$source,sep=\"\")\ntmp.edge.list$target<-paste(\" \",tmp.edge.list$target,sep=\"\")\n\nggplot2.network(tmp.edge.list,edge.color.var=\"type\", bezier=FALSE,node.size=3, node.names=node.names,node.label.size = 3)\n\n#create node attributes\nid<-unique(unlist(tmp.edge.list[,1:2]))\nnode.names<-data.frame(fixlc(var.meta$Name2)[fixln(gsub(\" \",\"\",id))])#[order(id,decreasing=TRUE)]\n\nfct<-rep(c(1:2),length.out=nrow(node.names))\nlen<-length(unique(fct))\nnode.data<-data.frame(name=node.names[,],color=rainbow(len)[fct],size=seq(1,6,by=.5)[fct])\nrownames(node.data)<-unique(unlist(tmp.edge.list[,1:2]))\n\nggplot2.network(edge.list=tmp.edge.list,edge.color.var=\"type\", bezier=FALSE,node.size=3, \nnode.data=node.data,node.names=node.names,\nmax.edge.thickness = 1,node.label.size = 3)\n\n\n#create function to remove duplicated edges and self edges\n# from an edgelist with diffrent types controling heirarchy of existence\nedge.list<-data.frame(data.frame(source=c(3,2,3,4),target=c(3,2,1,3)))\nedge.list$type<-c(\"a\",\"b\",\"b\",\"b\")\nedge.list$extra<-c(1:4)\nclean.edgeList(data=edge.list)\n\nclean.edgeList<-function(source=\"source\",target=\"target\",type=\"type\", data=edge.list){\n \n library(igraph)\n #remove self edges else if all self passed will cause an error\n el<-data[,c(source,target)]\n self<-el[,1]==el[,2]\n el<-el[!self,]\n tmp.data<-as.data.frame(as.matrix(data)[!self,])\n lel<-split(el,tmp.data$type)\n \n el.res<-do.call(\"rbind\",lapply(1:length(lel),function(i){\n nodes<-matrix(sort(unique(matrix(as.matrix(lel[[i]]),,1))),,1)\n g<-graph.data.frame(lel[[i]],directed=FALSE,vertices=nodes)\n g.adj<-get.adjacency(g,sparse=FALSE,type=\"upper\")\n g.adj[g.adj>0]<-1\n adj<-graph.adjacency(g.adj,mode=\"upper\",diag=FALSE,add.rownames=\"code\")\n get.edgelist(adj)\n }))\n \n ids<-unique(join.columns(el.res)) \n tmp<-data.frame(el,tmp.data[,!colnames(tmp.data)%in%c(source,target)])\n rownames(tmp)<-make.unique(join.columns(tmp[,1:2])) \n flip<-!ids%in%rownames(tmp) \n ids[flip]<-unique(join.columns(el.res[,2:1])) \n return(tmp[ids,]) \n \n}\n\n\n\n\n#over lay paths and nodes as separate images?\n.theme<- theme(\n\t\t\taxis.line = element_blank(), \n\t\t\taxis.ticks = element_blank(),\n\t\t\taxis.title.x = element_blank(), \n\t\t\tpanel.background = element_blank(), \n\t\t\tplot.background = element_blank(),\n\t\t\tpanel.grid = element_blank(),\n\t\t\taxis.text.x = element_blank(),\n\t\t\taxis.text.y = element_blank(),\n\t\t\taxis.title.x = element_blank(),\n\t\t\taxis.title.y = element_blank(),\n\t\t\tlegend.key = element_blank()\n\t\t )\n\t\t \nvis<-data.frame(x=1:2,y=3:4)\np<-ggplot(vis, aes(x=x,y=y))\np+geom_line()+.theme\npng(file =\"layer1.png\", pointsize=1,width=600,height=600, bg = \"transparent\")\np+geom_line()+.theme\ndev.off()\npng(file =\"layer2.png\", pointsize=1,width=600,height=600, bg = \"transparent\")\np+geom_point(color=\"red\",size=2)+.theme\ndev.off()\n\n\n\nlibrary(png)\ni1 <- readPNG(\"layer1.png\", native=FALSE)\ni2 <- readPNG(\"layer2.png\", native=FALSE)\n\nghostize <- function(r, alpha=0.5)\n matrix(adjustcolor(rgb(r[,,1],r[,,2],r[,,3],r[,,4]), alpha.f=alpha), nrow=dim(r)[1])\n\ngrid.newpage()\ngrid.rect(gp=gpar(fill=\"white\"))\ngrid.raster(i1)\ngrid.raster(i2)\n\nlibrary(png)\nimg <- readPNG(\"layer1.png\")\nr = as.raster(img[,,1:3])\nr[img[,,4] == 0] = \"white\"\n\nplot(1:2,type=\"n\")\nrasterImage(r,1,1,2,2)\n\nN <- 1000 # Warning: slow\nd <- data.frame(x1=rnorm(N),\n x2=rnorm(N, 0.8, 0.9),\n y=rnorm(N, 0.8, 0.2),\n z=rnorm(N, 0.2, 0.4))\n\nv <- with(d, dataViewport(c(x1,x2),c(y, z)))\n\npng(\"layer1.png\", bg=\"transparent\")\nwith(d, grid.points(x1,y, vp=v,default=\"native\",pch=\".\",gp=gpar(col=\"blue\")))\ndev.off()\npng(\"layer2.png\", bg=\"transparent\")\nwith(d, grid.points(x2,z, vp=v,default=\"native\",pch=\".\",gp=gpar(col=\"red\")))\ndev.off()\n\nlibrary(png)\ni1 <- readPNG(\"layer1.png\", native=FALSE)\ni2 <- readPNG(\"layer2.png\", native=FALSE)\n\nghostize <- function(r, alpha=0.5)\n matrix(adjustcolor(rgb(r[,,1],r[,,2],r[,,3],r[,,4]), alpha.f=alpha), nrow=dim(r)[1])\n\ngrid.newpage()\ngrid.rect(gp=gpar(fill=\"white\"))\ngrid.raster(ghostize(i1))\ngrid.raster(ghostize(i2))\n\n\n#function to plot network in ggplot 2\n#ggplot based network drawing fxn\n\nggplot2.network<-function(edge.list, edge.color.var = NULL, edge.color = NULL, directed = FALSE,\n\t\t\t\t\t\tnode.data=NULL, node.color = NULL, node.names=NULL, show.names = TRUE, node.shape=15,\n\t\t\t\t\t\tbezier = FALSE, node.size = 7,node.label.size = 5, max.edge.thickness = 2, color.scale=NULL,fill.scale=NULL, group.bounds=NULL){\n\t# edge list = 2 column data.frame representing source and target. \n\t# \tColumns over 2 will be sorted with edgelist and can be segment mapped to color transparency and width\n\t# edge.color.var = name of variable in edge list to use to color\n\t# edge.color = color for each level of object edge.color.var\n\t# directed = logical, if FALSE edge will be transposed and duplicated making undirected\n\t# node.color = colors for nodes, need to take into account node name ordering\n\t# show.names = can be supplied names for nodes, TRUE = network index, FALSE = nothing\n\t# node names should be a 2 column matrix with edge IDs and mapped names\n\t\n\t#should have a global node attributes (names, color, size, etc) object form which colnames are used for various mappings\n\t\n\tlibrary(network) # as.network\n\tlibrary(sna) # layouts\n\tlibrary(ggplot2)\n\tlibrary(Hmisc) # bezier edges\n\t\n\t# Function to generate paths between each connected node (very slow when transparent!)\n\t# adapted from : https://gist.github.com/dsparks/4331058\n\tedgeMaker <- function(whichRow, len = 100, curved = TRUE){\n\t fromC <- layoutCoordinates[adjacencyList[whichRow, 1], ] # Origin\n\t toC <- layoutCoordinates[adjacencyList[whichRow, 2], ] # Terminus\n\t \n\t # Add curve:\n\t graphCenter <- colMeans(layoutCoordinates) # Center of the overall graph\n\t bezierMid <- c(fromC[1], toC[2]) # A midpoint, for bended edges\n\t distance1 <- sum((graphCenter - bezierMid)^2)\n\t if(distance1 < sum((graphCenter - c(toC[1], fromC[2]))^2)){\n\t\tbezierMid <- c(toC[1], fromC[2])\n\t\t} # To select the best Bezier midpoint\n\t bezierMid <- (fromC + toC + bezierMid) / 3 # Moderate the Bezier midpoint\n\t if(curved == FALSE){bezierMid <- (fromC + toC) / 2} # Remove the curve\n\t \n\t edge <- data.frame(bezier(c(fromC[1], bezierMid[1], toC[1]), # Generate\n\t\t\t\t\t\t\t\tc(fromC[2], bezierMid[2], toC[2]), # X & y\n\t\t\t\t\t\t\t\tevaluation = len)) # Bezier path coordinates\n\t edge$Sequence <- 1:len # For size and colour weighting in plot\n\t edge$Group <- paste(adjacencyList[whichRow, 1:2], collapse = \">\")\n\t if(ncol(adjacencyList)>2){\n\t\t\ttmp<-data.frame(matrix(as.matrix(adjacencyList[whichRow, -c(1,2),drop=FALSE]),nrow = nrow(edge), ncol=ncol(adjacencyList)-2, byrow=TRUE))\n\t\t\tcolnames(tmp)<-colnames(adjacencyList)[-c(1:2)]\n\t\t\tedge$extra<-tmp\n\t\t\tedge<-do.call(\"cbind\",edge)\n\t\t\tcolnames(edge)<-gsub(\"extra.\",\"\",colnames(edge))\n\t\t}\n\t return(edge)\n\t }\n\t \n #straight edges\n\tedgeMaker2<-function(whichRow){\n\t fromC <- layoutCoordinates[adjacencyList[whichRow, 1], ] # Origin\n\t toC <- layoutCoordinates[adjacencyList[whichRow, 2], ] # Terminus\n\t \n\t edge <- data.frame(c(fromC[1], toC[1]), c(fromC[2] ,toC[2])) # Generate\n\t\t\t\t\t\t\t\t # X & ) # Bezier path coordinates\n\t edge$Sequence <- 1 # For size and colour weighting in plot\n\t edge$Group <- paste(adjacencyList[whichRow, 1:2], collapse = \">\")\n\t #get other info if supplied with edge list\n\t if(ncol(adjacencyList)>2){\n\t\t\ttmp<-data.frame(matrix(as.matrix(adjacencyList[whichRow, -c(1,2),drop=FALSE]),nrow = nrow(edge), ncol=ncol(adjacencyList)-2, byrow=TRUE))\n\t\t\tcolnames(tmp)<-colnames(adjacencyList)[-c(1:2)]\n\t\t\tedge$extra<-tmp\n\t\t\tedge<-do.call(\"cbind\",edge)\n\t\t\tcolnames(edge)<-gsub(\"extra.\",\"\",colnames(edge))\n\t\t}\n\t colnames(edge)[1:2]<-c(\"x\",\"y\")\n\t return(edge)\n\t }\n\t\n\t# adding transposed source target edges to make undirected bezier curves\n\tif (bezier == TRUE) {\n\t\tif(all(!directed)) { is.rev<-rep(TRUE, nrow(edge.list)) } else { is.rev<-directed==TRUE }\n\t\trev.edge.list<-data.frame(rbind(as.matrix(edge.list[,1:2]),as.matrix(edge.list[is.rev,2:1]))) # need matrix else no reordering of columns?\n\t} else{ \n\t\trev.edge.list<-edge.list[,1:2,drop=FALSE]\n\t}\n\t#extra info (separate now, later recombine)\n\tinfo<-edge.list[,-c(1:2)]\n\t\n\t#getting layout and making sure edge list ids are in the same order\n\tg<-as.network(rev.edge.list[,1:2],matrix.type = \"edgelist\") # \n\t\n\t#layout\n\tnode.layout<-gplot.layout.fruchtermanreingold(g[,], layout.par = NULL)\n\t\n\tn.edge.list<-as.matrix.network.edgelist(g)\n\tdimnames(node.layout)<-list(rownames(g[,]),c(\"x\",\"y\"))\n\t\n\t#preparing for edge path\n\tlayoutCoordinates<-node.layout\n\tadjacencyList<-data.frame(n.edge.list,info)\n\n\tif (bezier == TRUE) {\n\t\tallEdges <- lapply(1:nrow(adjacencyList), edgeMaker, len = 500, curved = TRUE)\n\t\tallEdges <- do.call(rbind, allEdges) # a fine-grained path ^, with bend ^\n\t } else {\n\t\t#straight edges using same controls(faster)\n\t\tallEdges <- lapply(1:nrow(adjacencyList), edgeMaker2)\n\t\tallEdges <- do.call(rbind, allEdges)\n\t}\n\tallEdges$neg.Sequence<- - allEdges$Sequence\n\n\t#Edge Attributes\n\t#-------------------\n\t#set default plotting variables\n\t# Edge colors\n\tedge.guide = TRUE\n\t\n\tif(is.null(edge.color.var)){edge.list$edge.color.var<-1;edge.color.var<-\"edge.color.var\";edge.guide = FALSE}\n\t\n\tif(is.null(edge.color)){\n\t\t\tedge.color<-rainbow(nlevels(as.factor(with (edge.list, get(edge.color.var)))))\n\t} \n\t\n\t#Node Attributes\n\t#-------------------\n\tnode.obj<-tryCatch(data.frame(layoutCoordinates,node.data[rownames(node.layout),]), error=function(e){data.frame(layoutCoordinates)})\n\t#set defaults\n\tdefault<-factor(rep(1,nrow(node.obj)))\n\t# could match input column to those below here\n\tattribute<-c(\"size\",\"color\",\"shape\")\n\tfor(i in 1:length(attribute)){if(is.null(node.obj[[attribute[i]]])){node.obj[[attribute[i]]]<-default} }#else {node.obj[[attribute[i]]]<-factor(node.obj[[attribute[i]]])}}\n\t\n\t#default input\n\t#color\n\tif(is.null(node.color)){\n\t\t\tnode.color<-rainbow(length(unique(node.obj$color)))\n\t\t\tcol.scale<-scale_color_manual(values=node.color)\n\t}\n\t\n\t\n\t#size\n\tif(is.null(node.size)){\n\t\tnode.size<-seq(3,7,length.out=length(unique(node.obj$size)))\n\t\t\n\t} \n\t#shape\n\tif(is.null(node.shape)){\n\t\tnode.shape<-rep(c(15:18),length.out=length(unique(node.obj$shape)))\n\t} \n\t\t\n\tnode.points<-geom_point(data = node.obj, aes(x = x, y = y, color=color, size=size),shape=15,show_guide = TRUE) #,shape=shape\n\t\n\t\t# # testing\n\t\t# zp1+node.points\n\t\t\n\t\t# zp1 <- ggplot() # Pretty simple plot code\n\t# # bezier edges\t\n\t# zp1 <- zp1 + geom_path(data=allEdges,aes_string(x = \"x\", y = \"y\", group = \"Group\", # Edges with gradient\n\t\t\t\t\t\t\t # colour = edge.color.var, size = \"neg.Sequence\")) # and tap\n\t\t\n\t\t# zp1<-zp1+geom_point(data = node.obj, aes(x = x, y = y, color=color,size=size, shape=shape,fill=color),shape=21)\n\t\t# col.scale#,size=node.size,show_guide = FALSE)\n\t\n #labels\n\tif(is.null(node.obj$name)){\n\t\tnode.obj$name<-rownames(node.obj)\n\t} \n\tif(show.names==FALSE){node.obj$name<-rep(\"\",nrow(node.layout))} #nothing\n\t\n\tnode.labels<-geom_text(data = node.obj, aes(x = x, y = y-.2, label = name), size = node.label.size)\t# node names\n\t\n\tpolygons<-NULL\n# \t#add grouping vis\n# \t#Hoettellings T2 ellipse\n# \tpolygons<-NULL\n# \tif(group.bounds==\"ellipse\"){\t\t\n# \t\tell<-get.ellipse.coords(cbind(x=node.obj$x,y=node.obj$y), group=node.obj$group)# group visualization via \n# \t\tpolygons<-if(is.null(color)){\n# \t\t\t\tgeom_polygon(data=data.frame(ell$coords),aes(x=x,y=y), fill=\"gray\", color=\"gray\",linetype=2,alpha=g.alpha, show_guide = FALSE) \n# \t\t\t} else {\n# \t\t\t\tgeom_polygon(data=data.frame(ell$coords),aes(x=x,y=y, fill=group),linetype=2,alpha=g.alpha, show_guide = FALSE) \n# \t\t\t}\n# \t}\n# \t\n# \n# \tif(group.bounds==\"polygon\"){\n# \t\tell<-get.polygon.coords(data.frame(tmp.obj),tmp$color)# group visualization via \n# \t\tpolygons<-if(is.null(color)){\n# \t\t\t\tgeom_polygon(data=data.frame(ell),aes(x=x,y=y), fill=\"gray\", color=\"gray\",linetype=2,alpha=g.alpha, show_guide = FALSE) \n# \t\t\t} else {\n# \t\t\t\tgeom_polygon(data=data.frame(ell),aes(x=x,y=y, fill=group),linetype=2,alpha=g.alpha, show_guide = FALSE) \n# \t\t\t}\n# \t}\n\t\n\t#set up for plotting\n\t#theme \n\tnew_theme_empty <- theme_bw()\n\tnew_theme_empty$line <- element_blank()\n\tnew_theme_empty$rect <- element_blank()\n\tnew_theme_empty$strip.text <- element_blank()\n\tnew_theme_empty$axis.text <- element_blank()\n\tnew_theme_empty$plot.title <- element_blank()\n\tnew_theme_empty$axis.title <- element_blank()\n\tnew_theme_empty$plot.margin <- structure(c(0, 0, -1, -1), unit = \"lines\", valid.unit = 3L, class = \"unit\")\n new_theme_empty$legend.text <-element_text( size = 20)\n\tnew_theme_empty$legend.title <-element_text(size = 20 ) \n\t\n\t\t \n\t\n\t# # node names (set above)\n\t# if(length(show.names) == attr(n.edge.list,\"vnames\")) { node.names <- show.names} \n\t# if (show.names) { node.names<-attr(n.edge.list,\"vnames\") } \n\t# if(!show.names){node.names<-rep(\"\",nrow(node.layout))}\n\t#make plot\n\tzp1 <- ggplot() # Pretty simple plot code\n\t#area\n\tzp1<-zp1 + polygons\n\t#edges\t\n\tzp1 <- zp1 + geom_path(data=allEdges,aes_string(x = \"x\", y = \"y\", group = \"Group\", # Edges with gradient\n\t\t\t\t\t\t\tcolour = edge.color.var),size=max.edge.thickness) # and taper # Customize taper\t\t\t\t\t \n\t#nodes\t\n\tzp1 <- zp1 + node.points + node.labels\n\t# node.obj<-data.frame(layoutCoordinates, color = as.factor(node.color), shape=as.factor(node.shape))\t\n\t# zp1 <- zp1 + geom_point(data = node.obj, aes(x = x, y = y, fill=color, shape=shape), size = node.size, colour = \"black\", show_guide = node.guide)# Add\n\t# zp1<-zp1 + scale_fill_manual(values=fixlc(node.obj$color)) + scale_shape_manual(values =fixln(node.obj$shape))\n\t# zp1<-zp1 + geom_text(data = data.frame(layoutCoordinates, label = node.names), aes(x = x, y = y-.2, label = label), size = node.label.size)\t# node names\n\tzp1 <- zp1 + scale_colour_manual(values = c(node.color,edge.color)\n\t)\n\t# zp1 <- zp1 + scale_size(range = c(1/100, max.edge.thickness), guide = \"none\") #edge thickness\n\tzp1 <-zp1 + guides(color = guide_legend(override.aes = list (size = 3))) + labs(color='Edge Type')\t\n\t# Customize gradient \n\tzp1 <- zp1 + new_theme_empty # Clean up plot\n\tprint(zp1)\n}\n\n}\n\n#create Node attribute mappings\n#---------------------------------\n# color \t= direction or fold change or O-PLS-DA absolute loading\n# size \t\t= log fold change or absolute value of O-PLS-DA loading on LV 1\n# border \t= O-PLS-DA VIP >=1\n# shape\t\t= chemical class\n\n# calculate summary statistics (fold change and p-value) for main hypothesis in study\n{\n# set main factor to sample.meta$group \ntest.data<-log(tmp.data+1)#test shifted log transformed data\np.values<-multi.t.test(data=test.data, factor=sample.meta$group,paired=FALSE,progress=TRUE)\nfold.change<-calc.FC(data=tmp.data,factor=sample.meta$group,denom=levels(sample.meta$group)[2],sig.figs=1,log=FALSE)\n#fold change will need scaling and has some problems with 0 values \nfold.change[fold.change==\"Inf\"]<-0\n#create summary table\n\nstats<-data.frame(name=fixlc(var.meta$Name2),p.values,fold.change)\nwrite.csv(stats,file=\"statistics.csv\")\n}\n\n# do PCA \n{\npca.data<-tmp.data\n\n#set PCA args\npca.inputs<-list()\npca.inputs$pca.algorithm<-\"svd\"\npca.inputs$pca.components<-4\npca.inputs$pca.center<-TRUE\npca.inputs$pca.scaling<-\"uv\"\npca.inputs$pca.data<-pca.data\npca.inputs$pca.cv<-\"q2\"\n\n#calculate model\nres<-devium.pca.calculate(pca.inputs,return=\"list\",plot=FALSE) # need to recreate model in another format to plot\n\n#plot scores by type\nresults<-\"scores\"#\"biplot\"#\"scores\",\"loadings\",\"biplot\")\"screeplot\"\ncolor<-data.frame(group=sample.meta$group)\nxaxis<-1\nyaxis=2\ngroup.bounds=\"ellipse\"\nplot.PCA(pca=res,results=results,yaxis=yaxis,xaxis=xaxis,size=4,color=color, label=T, legend.name = NULL,font.size=1.75,group.bounds,alpha=.75)\n# samples 37 and 13 maybe outliers\n}\n\n#O-PLS-DA\n{\n#calculate preliminary model\ncomp<-3\nocomp<-2\n\npls.y<-data.frame(group=as.numeric(sample.meta$group))\nscaled.data<-data.frame(scale(pca.data,center=TRUE,scale=TRUE))\nmods1<-make.OSC.PLS.model(pls.y=pls.y,pls.data=scaled.data,comp=comp,OSC.comp=ocomp,validation = \"LOO\",method=\"oscorespls\",cv.scale=T)\nplot.OSC.results(mods1,plot=\"scores\",groups=color)\nfinal<-get.OSC.model(obj=mods1,OSC.comp=ocomp)\n\n#collect results for mapping\nnode.obj<-data.frame()\n\n# plot scores\nplot.PLS(obj=final,results=\"scores\",color=color,group.bounds=\"ellipse\")\n\n\n#carry out feature selection\n\n#feature selection\nobj<-final\ntype<-\"quantile\"#\"number\"\ntop<-0.9\np.value=0.05\nFDR=FALSE\nseparate=FALSE\n.scores<-obj$scores[,]\n.loadings<-obj$loadings[,]\t\nselected.features<-PLS.feature.select(pls.data=scaled.data,pls.scores=.scores[,1],pls.loadings=.loadings[,1],pls.weight=.loadings[,1],\n\t\t\t\tp.value=p.value, FDR=FDR,cut.type=type,top=top,separate=separate,type=\"spearman\",make.plot=FALSE)\nselected.features<-selected.features[,c(1,3,7)]\nselected.features$VIP<-as.matrix(obj$VIP[,1,drop=F])\n}\n\n#map node attributes\n{\ntmp<-list()\ntmp$ID<-1:length(selected.features$VIP)\ntmp$color<-ifelse(fold.change>=1,\"up\",\"down\")\ntmp$size<-selected.features$VIP\ntmp$selected<-selected.features$VIP>=1\ntmp$name<-fixlc(var.meta$Name2)\n\n\nnode.attributes<-data.frame(do.call(\"cbind\",tmp))\ncolnames(node.attributes)<-names(tmp)\n\n#get db info for analytes\nDB<-IDEOMgetR()\ninfo<-enrichR.IDEOM(id=CIDS, from=\"PubChem CID\",IDEOM.DB=DB)\ntmp<-fixlc(info$Map)\ntmp[is.na(tmp)]<-\"other\"\n\nnode.attributes$class<-tmp\n\nwrite.csv(node.attributes,file=\"node.attributes.csv\")\n\n}"
},
{
"path": "Demos/Mapped Network From Data (Biochemical and Structural)/node.attributes.csv",
"content": "ID,color,size,selected,name,class\r\n1,down,1.299818383,TRUE,Propylene glycol,Lipid Metabolism\r\n2,up,1.733627798,TRUE,Glycolic acid,Nucleotide Metabolism\r\n3,up,0.906013278,FALSE,L-Alanine,other\r\n4,down,0.252883372,FALSE,,other\r\n5,down,0.788061117,FALSE,L-Valine,Amino Acid Metabolism\r\n6,down,1.234377918,TRUE,Urea,Amino Acid Metabolism\r\n7,down,0.790245582,FALSE,Benzoic acid,other\r\n8,up,1.098129963,TRUE,L-Serine,Amino Acid Metabolism\r\n9,down,0.634914989,FALSE,Ethanolamine,Amino Acid Metabolism\r\n10,up,0.659531582,FALSE,Phosphoric acid,Energy Metabolism\r\n11,up,0.962800591,FALSE,L-Threonine,Amino Acid Metabolism\r\n12,up,1.266322545,TRUE,Glycine,Xenobiotics Biodegradation and Metabolism\r\n13,down,0.790245582,FALSE,Succinic acid,other\r\n14,up,0.521221946,FALSE,Glyceric acid,Carbohydrate Metabolism\r\n15,up,0.546569114,FALSE,\"2,4-Dihydroxybutanoic acid\",other\r\n16,up,1.93931343,TRUE,\"(S)-3,4-Dihydroxybutyric acid\",other\r\n17,up,0.908627549,FALSE,3-Aminoisobutanoic acid,other\r\n18,up,1.101488745,TRUE,Aminomalonic acid,\r\n19,down,1.486559547,TRUE,\"(2R*,3R*)-1,2,3-Butanetriol\",other\r\n20,up,1.286588241,TRUE,D-Threitol,other\r\n21,up,0.990993538,FALSE,Erythritol,other\r\n22,up,1.375844298,TRUE,Pyroglutamic acid,Amino Acid Metabolism\r\n23,up,0.872181606,FALSE,,other\r\n24,down,0.645797177,FALSE,,other\r\n25,up,1.247175218,TRUE,Erythronic acid,other\r\n26,up,0.563686189,FALSE,L-Cysteine,Amino Acid Metabolism\r\n27,down,0.538705285,FALSE,Creatinine,Amino Acid Metabolism\r\n28,up,0.457205694,FALSE,Threonic acid,other\r\n29,up,0.713549876,FALSE,Oxoglutaric acid,Carbohydrate Metabolism\r\n30,up,1.345520899,TRUE,L-Phenylalanine,other\r\n31,up,0.908828556,FALSE,,other\r\n32,up,1.45892177,TRUE,p-Hydroxyphenylacetic acid,Amino Acid Metabolism\r\n33,up,1.68358479,TRUE,,other\r\n34,up,1.037666091,TRUE,Taurine,Lipid Metabolism\r\n35,up,1.19678404,TRUE,Pectin,\r\n36,down,0.842423667,FALSE,Arabinose,Amino Acid Metabolism\r\n37,up,1.087402931,TRUE,D-Ribose,Carbohydrate Metabolism\r\n38,up,0.961460011,FALSE,L-Lysine,Carbohydrate Metabolism\r\n39,up,1.148532611,TRUE,D-Xylitol,Amino Acid Metabolism\r\n40,up,1.296995751,TRUE,D-Arabitol,other\r\n41,up,1.116369463,TRUE,Ribitol,other\r\n42,up,0.75269885,FALSE,L-Fucose,Carbohydrate Metabolism\r\n43,up,1.851909456,TRUE,cis-Aconitic acid,other\r\n44,down,0.981482424,FALSE,D-Ribose,Carbohydrate Metabolism\r\n45,up,2.048365046,TRUE,Homovanillic acid,Amino Acid Metabolism\r\n46,down,0.440987815,FALSE,Glycerol 3-phosphate,Lipid Metabolism\r\n47,up,0.721778091,FALSE,L-Glutamine,Amino Acid Metabolism\r\n48,down,0.227845964,FALSE,,other\r\n49,up,1.033067548,TRUE,Hypoxanthine,Carbohydrate Metabolism\r\n50,up,0.76290555,FALSE,Hippuric acid,Amino Acid Metabolism\r\n51,down,0.842423667,FALSE,Ornithine,Amino Acid Metabolism\r\n52,up,0.661796848,FALSE,Citric acid,other\r\n53,up,1.027152014,TRUE,3-(3-Hydroxyphenyl)-3-hydroxypropanoic acid,other\r\n54,down,0.864456119,FALSE,,other\r\n55,down,0.683583279,FALSE,Quinic acid,other\r\n56,up,1.299131713,TRUE,L-Tyrosine,other\r\n57,up,0.380070114,FALSE,L-Sorbose,\r\n58,down,1.206093428,TRUE,D-Fructose,Carbohydrate Metabolism\r\n59,up,0.900822861,FALSE,D-Galactose,Xenobiotics Drugs etc\r\n60,up,0.793094738,FALSE,D-Glucose,other\r\n61,up,0.949160549,FALSE,L-Histidine,other\r\n62,up,0.608357131,FALSE,Mannitol,Amino Acid Metabolism\r\n63,up,1.406634949,TRUE,,other\r\n64,down,0.474463577,FALSE,Sorbitol,Carbohydrate Metabolism\r\n65,up,0.878501982,FALSE,,other\r\n66,down,0.688496547,FALSE,Galactitol,Carbohydrate Metabolism\r\n67,down,0.620119883,FALSE,,other\r\n68,up,0.767222179,FALSE,Gulonic acid,other\r\n69,up,0.353143318,FALSE,Gluconic acid,Carbohydrate Metabolism\r\n70,up,0.58165814,FALSE,,other\r\n71,up,0.469785178,FALSE,Kynurenic acid,Amino Acid Metabolism\r\n72,down,1.156206268,TRUE,Beta-N-Acetylglucosamine,\r\n73,down,0.480199886,FALSE,,other\r\n74,down,0.583574412,FALSE,Uric acid,Nucleotide Metabolism\r\n75,up,0.798486399,FALSE,,other\r\n76,down,0.596285854,FALSE,,other\r\n77,up,1.527657596,TRUE,L-Tryptophan,Carbohydrate Metabolism\r\n78,up,0.998177161,FALSE,5-Hydroxyindoleacetic acid,Amino Acid Metabolism\r\n79,down,0.663749299,FALSE,,other\r\n80,up,1.134615214,TRUE,4-Hydroxyhippuric acid,other\r\n81,down,0.705675506,FALSE,,other\r\n82,down,0.847647221,FALSE,L-Cystine,other\r\n83,down,0.812004248,FALSE,,other\r\n84,up,0.427448727,FALSE,Pseudouridine,Nucleotide Metabolism\r\n85,up,1.540686424,TRUE,Arabinofuranose,\r\n86,up,0.905064018,FALSE,,other\r\n87,down,0.347224178,FALSE,Acetaminophen glucuronide,Carbohydrate Metabolism\r\n88,up,0.817887212,FALSE,Hydroxyproline dipeptide 4-TMS,other\r\n89,up,1.54246661,TRUE,Xylobiose,other\r\n90,up,0.62092751,FALSE,Sucrose,Amino Acid Metabolism\r\n91,up,0.979388644,FALSE,,other\r\n92,up,0.54023557,FALSE,Alpha-Lactose,\r\n"
},
{
"path": "Demos/Mapped Network From Data (Biochemical and Structural)/sample info.csv",
"content": "ID,sample,class,group\r\n1,A019,2,A\r\n2,A021,,A\r\n3,A023,3,A\r\n4,A029,2,A\r\n5,A031,3,A\r\n6,A037,1,A\r\n7,A040,2,A\r\n8,A042,3,A\r\n9,A051,,A\r\n10,A053,2,A\r\n11,A054,1,A\r\n12,A055,2,A\r\n13,A057,3,A\r\n14,A061,3,A\r\n15,A063,3,A\r\n16,A065,1,A\r\n17,A067,,A\r\n18,A069,3,A\r\n19,A071,3,A\r\n20,A073,2,A\r\n21,A077,3,A\r\n22,C020,0,C\r\n23,C022,0,C\r\n24,C024,0,C\r\n25,C030,0,C\r\n26,C032,0,C\r\n27,C038,0,C\r\n28,C041,0,C\r\n29,C043,0,C\r\n30,C052,0,C\r\n31,C054,0,C\r\n32,C056,0,C\r\n33,C058,0,C\r\n34,C060,0,C\r\n35,C062,0,C\r\n36,C064,0,C\r\n37,C066,0,C\r\n38,C068,0,C\r\n39,C070,0,C\r\n40,C072,0,C\r\n41,C074,0,C\r\n42,C078,0,C\r\n"
},
{
"path": "Demos/Mapped Network From Data (Biochemical and Structural)/statistics.csv",
"content": "\"\",\"name\",\"t.test_p.value\",\"t.test_adjusted.p.value\",\"t.test_q.value\",\"FC.A.C\"\r\n\"X1\",\"Propylene glycol\",0.164881412830391,0.619253978472208,0.28788774182491,0\r\n\"X2\",\"Glycolic acid\",0.0198760115978473,0.619253978472208,0.28788774182491,3.6\r\n\"X3\",\"L-Alanine\",0.253768829006344,0.619253978472208,0.28788774182491,2.8\r\n\"X4\",\"\",0.805517644591721,0.871854391793393,0.405320273715117,0.9\r\n\"X5\",\"L-Valine\",0.329256577171709,0.619253978472208,0.28788774182491,0\r\n\"X6\",\"Urea\",0.263544795092355,0.619253978472208,0.28788774182491,0.9\r\n\"X7\",\"Benzoic acid\",0.329256577171709,0.619253978472208,0.28788774182491,0\r\n\"X8\",\"L-Serine\",0.205624719892993,0.619253978472208,0.28788774182491,1.7\r\n\"X9\",\"Ethanolamine\",0.585937463130809,0.792738920706389,0.368539929774745,0.9\r\n\"X10\",\"Phosphoric acid\",0.939932342558587,0.96206209129645,0.447257333157555,1\r\n\"X11\",\"L-Threonine\",0.231750325772512,0.619253978472208,0.28788774182491,2.6\r\n\"X12\",\"Glycine\",0.123976364637514,0.619253978472208,0.28788774182491,1.6\r\n\"X13\",\"Succinic acid\",0.329256577171709,0.619253978472208,0.28788774182491,0\r\n\"X14\",\"Glyceric acid\",0.728385365088196,0.84824624795081,0.394344978511868,1.1\r\n\"X15\",\"2,4-Dihydroxybutanoic acid\",0.768515517290104,0.849001881328481,0.394696268280376,1.1\r\n\"X16\",\"(S)-3,4-Dihydroxybutyric acid\",0.0497484671907041,0.619253978472208,0.28788774182491,1.9\r\n\"X17\",\"3-Aminoisobutanoic acid\",0.198594350833866,0.619253978472208,0.28788774182491,6.9\r\n\"X18\",\"Aminomalonic acid\",0.139796023200647,0.619253978472208,0.28788774182491,6.3\r\n\"X19\",\"(2R*,3R*)-1,2,3-Butanetriol\",0.329256577171709,0.619253978472208,0.28788774182491,0\r\n\"X20\",\"D-Threitol\",0.148967215292983,0.619253978472208,0.28788774182491,1.7\r\n\"X21\",\"Erythritol\",0.348730848839896,0.619253978472208,0.28788774182491,1.6\r\n\"X22\",\"Pyroglutamic acid\",0.20655427500011,0.619253978472208,0.28788774182491,1.2\r\n\"X23\",\"\",0.760260252307975,0.849001881328481,0.394696268280376,1.3\r\n\"X24\",\"\",0.438150043451881,0.639838158691636,0.297457213102767,0.2\r\n\"X25\",\"Erythronic acid\",0.189274253243826,0.619253978472208,0.28788774182491,1.9\r\n\"X26\",\"L-Cysteine\",0.60227083056255,0.793505619284379,0.368896363693543,2.3\r\n\"X27\",\"Creatinine\",0.76296861396891,0.849001881328481,0.394696268280376,0.9\r\n\"X28\",\"Threonic acid\",0.986866731047075,0.986866731047075,0.458788872675808,1\r\n\"X29\",\"Oxoglutaric acid\",0.463394223837754,0.666129196766771,0.309679770962398,1.7\r\n\"X30\",\"L-Phenylalanine\",0.093398637603731,0.619253978472208,0.28788774182491,2.2\r\n\"X31\",\"\",0.254758495997756,0.619253978472208,0.28788774182491,2.4\r\n\"X32\",\"p-Hydroxyphenylacetic acid\",0.102648972831718,0.619253978472208,0.28788774182491,2.1\r\n\"X33\",\"\",0.103344825685762,0.619253978472208,0.28788774182491,1.7\r\n\"X34\",\"Taurine\",0.165062457353588,0.619253978472208,0.28788774182491,3.9\r\n\"X35\",\"Pectin\",0.294850444096769,0.619253978472208,0.28788774182491,2.2\r\n\"X36\",\"Arabinose\",0.329256577171709,0.619253978472208,0.28788774182491,0\r\n\"X37\",\"D-Ribose\",0.158874692105942,0.619253978472208,0.28788774182491,5.7\r\n\"X38\",\"L-Lysine\",0.251540076711914,0.619253978472208,0.28788774182491,7.5\r\n\"X39\",\"D-Xylitol\",0.429398587022026,0.637172096871394,0.296217775741559,1.3\r\n\"X40\",\"D-Arabitol\",0.157959973710896,0.619253978472208,0.28788774182491,1.8\r\n\"X41\",\"Ribitol\",0.604761035100845,0.793505619284379,0.368896363693543,1.2\r\n\"X42\",\"L-Fucose\",0.670189357605648,0.825695968188865,0.383861478455554,1.4\r\n\"X43\",\"cis-Aconitic acid\",0.110228853838941,0.619253978472208,0.28788774182491,1.4\r\n\"X44\",\"D-Ribose\",0.941147698007397,0.96206209129645,0.447257333157555,0.9\r\n\"X45\",\"Homovanillic acid\",0.0210598598317868,0.619253978472208,0.28788774182491,3.8\r\n\"X46\",\"Glycerol 3-phosphate\",0.862835879992945,0.912424148958056,0.42418092892695,0.9\r\n\"X47\",\"L-Glutamine\",0.327213751112235,0.619253978472208,0.28788774182491,3\r\n\"X48\",\"\",0.907768597469626,0.949030806445518,0.441199161067862,0.9\r\n\"X49\",\"Hypoxanthine\",0.151292577387652,0.619253978472208,0.28788774182491,2.7\r\n\"X50\",\"Hippuric acid\",0.334740288033347,0.619253978472208,0.28788774182491,1.5\r\n\"X51\",\"Ornithine\",0.329256577171709,0.619253978472208,0.28788774182491,0\r\n\"X52\",\"Citric acid\",0.386365212751301,0.619253978472208,0.28788774182491,1.2\r\n\"X53\",\"3-(3-Hydroxyphenyl)-3-hydroxypropanoic acid\",0.168655111865478,0.619253978472208,0.28788774182491,2.2\r\n\"X54\",\"\",0.293404566764687,0.619253978472208,0.28788774182491,0.7\r\n\"X55\",\"Quinic acid\",0.37146956942578,0.619253978472208,0.28788774182491,0.1\r\n\"X56\",\"L-Tyrosine\",0.130286001429568,0.619253978472208,0.28788774182491,1.6\r\n\"X57\",\"L-Sorbose\",0.728005123544874,0.84824624795081,0.394344978511868,1.2\r\n\"X58\",\"D-Fructose\",0.40687608994573,0.619253978472208,0.28788774182491,0.3\r\n\"X59\",\"D-Galactose\",0.23954691170069,0.619253978472208,0.28788774182491,10.8\r\n\"X60\",\"D-Glucose\",0.527272928664007,0.724016558762518,0.33659128466195,1.4\r\n\"X61\",\"L-Histidine\",0.231586291516813,0.619253978472208,0.28788774182491,2.6\r\n\"X62\",\"Mannitol\",0.410592311813094,0.619253978472208,0.28788774182491,1.6\r\n\"X63\",\"\",0.132368316110587,0.619253978472208,0.28788774182491,2.2\r\n\"X64\",\"Sorbitol\",0.704670638158063,0.84824624795081,0.394344978511868,0.6\r\n\"X65\",\"\",0.826738024894663,0.884417421980337,0.411160756807241,1.4\r\n\"X66\",\"Galactitol\",0.362332614944998,0.619253978472208,0.28788774182491,0.5\r\n\"X67\",\"\",0.403383482031547,0.619253978472208,0.28788774182491,0.5\r\n\"X68\",\"Gulonic acid\",0.283817109684353,0.619253978472208,0.28788774182491,3.1\r\n\"X69\",\"Gluconic acid\",0.756449180307697,0.849001881328481,0.394696268280376,1.2\r\n\"X70\",\"\",0.47195325607601,0.667995377830661,0.310547348194029,1.2\r\n\"X71\",\"Kynurenic acid\",0.974993982358594,0.985708201944952,0.458250278918411,1\r\n\"X72\",\"Beta-N-Acetylglucosamine\",0.625895495671477,0.793505619284379,0.368896363693543,0.6\r\n\"X73\",\"\",0.775175630778178,0.849001881328481,0.394696268280376,0.9\r\n\"X74\",\"Uric acid\",0.723484270841933,0.84824624795081,0.394344978511868,0.9\r\n\"X75\",\"\",0.364447095492839,0.619253978472208,0.28788774182491,1.6\r\n\"X76\",\"\",0.617439420550046,0.793505619284379,0.368896363693543,0.8\r\n\"X77\",\"L-Tryptophan\",0.0463971338065384,0.619253978472208,0.28788774182491,3.5\r\n\"X78\",\"5-Hydroxyindoleacetic acid\",0.168074250813843,0.619253978472208,0.28788774182491,19\r\n\"X79\",\"\",0.373720389297531,0.619253978472208,0.28788774182491,0.6\r\n\"X80\",\"4-Hydroxyhippuric acid\",0.356128215300218,0.619253978472208,0.28788774182491,1.4\r\n\"X81\",\"\",0.329256577171709,0.619253978472208,0.28788774182491,0\r\n\"X82\",\"L-Cystine\",0.325981442466079,0.619253978472208,0.28788774182491,0\r\n\"X83\",\"\",0.391209618293358,0.619253978472208,0.28788774182491,0.6\r\n\"X84\",\"Pseudouridine\",0.673121713197445,0.825695968188865,0.383861478455554,1.1\r\n\"X85\",\"Arabinofuranose\",0.0979348834661696,0.619253978472208,0.28788774182491,1.4\r\n\"X86\",\"\",0.307117130523681,0.619253978472208,0.28788774182491,1.4\r\n\"X87\",\"Acetaminophen glucuronide\",0.629629458779996,0.793505619284379,0.368896363693543,0.8\r\n\"X88\",\"Hydroxyproline dipeptide 4-TMS\",0.269089443348433,0.619253978472208,0.28788774182491,4.1\r\n\"X89\",\"Xylobiose\",0.192736164387755,0.619253978472208,0.28788774182491,1.8\r\n\"X90\",\"Sucrose\",0.393758561766684,0.619253978472208,0.28788774182491,1.8\r\n\"X91\",\"\",0.181333607978092,0.619253978472208,0.28788774182491,50\r\n\"X92\",\"Alpha-Lactose\",0.511447992874468,0.712927505218956,0.331436045140505,1.5\r\n"
},
{
"path": "Demos/Mapped Network From Data (Biochemical and Structural)/variable info.csv",
"content": "ID,name,Name2,HMDB,PubChem,KEGG\r\n1,1-2-Propandiol,Propylene glycol,HMDB01881,1030,C00583\r\n2,Glycolic acid,Glycolic acid,HMDB00115,757,C00160\r\n3,Alanine 2-TMS,L-Alanine,HMDB00161,5950,C00041\r\n4,S,,,,\r\n5,Valine 2-TMS,L-Valine,HMDB00883,6287,C00183\r\n6,Urea,Urea,HMDB00294,1176,C00086\r\n7,Benzoic acid,Benzoic acid,HMDB01870,243,C00180\r\n8,Serine,L-Serine,HMDB00187,5951,C00065\r\n9,Ethanolamine,Ethanolamine,HMDB00149,700,C00189\r\n10,Phosphate,Phosphoric acid,HMDB02142,1004,C00009\r\n11,Threonine,L-Threonine,HMDB00167,6288,C00188\r\n12,Glycine,Glycine,HMDB00123,750,C00037\r\n13,Succinic acid,Succinic acid,HMDB00254,1110,C00042\r\n14,2-3-Dihydroxybutyric acid,Glyceric acid,HMDB00139,439194,C00258\r\n15,2-4-Dihydroxybutyric acid,\"2,4-Dihydroxybutanoic acid\",HMDB00360,192742,\r\n16,3-4-Dihydroxybutyric acid,\"(S)-3,4-Dihydroxybutyric acid\",HMDB00337,150929,\r\n17,3-Aminoisobutyric acid 3-TMS,3-Aminoisobutanoic acid,HMDB03911,64956,C05145\r\n18,Aminomalonic acid,Aminomalonic acid,HMDB01147,100714,C00872\r\n19,1-2-3-Butanetriol,\"(2R*,3R*)-1,2,3-Butanetriol\",HMDB34778,20497,\r\n20,Threitol,D-Threitol,HMDB04136,169019,C16884\r\n21,Erythritol,Erythritol,HMDB02994,222285,C00503\r\n22,Pyroglutamic acid,Pyroglutamic acid,HMDB00267,7405,C01879\r\n23,1-Deoxypentitol,,,,\r\n24,Erythropentose-2-deoxy A- 2-Deoxy-ribose,,,,\r\n25,Erythronic or threonic acid,Erythronic acid,HMDB00613,2781043,\r\n26,Cysteine 3-TMS,L-Cysteine,HMDB00574,5862,C00097\r\n27,Creatinine 3-TMS,Creatinine,HMDB00562,588,C00791\r\n28,2-3-4-Trihydroxybutyric acid (Threonic or erythronic),Threonic acid,HMDB00943,151152,C01620\r\n29,2-Ketoglutaric acid,Oxoglutaric acid,HMDB00208,51,C00026\r\n30,Phenylalanine 2-TMS,L-Phenylalanine,HMDB00159,6140,C00079\r\n31,3-Deoxypentitol,,,,\r\n32,4-Hydroxyphenylacetic acid,p-Hydroxyphenylacetic acid,HMDB00020,127,C00642\r\n33,3-4-5-Trihydroxypentanoic acid,,,,\r\n34,Taurine 3-TMS,Taurine,HMDB00251,1123,C00245\r\n35,Lyxose A,Pectin,HMDB03402,441476,C08348\r\n36,Arabinose,Arabinose,HMDB29942,66308,\r\n37,Ribose A,D-Ribose,HMDB00283,5779,C00121\r\n38,Lysine,L-Lysine,HMDB00182,5962,C00047\r\n39,Xylitol,D-Xylitol,HMDB02917,6912,C00379\r\n40,Arabitol,D-Arabitol,HMDB00568,827,C01904\r\n41,Ribitol (Adonitol),Ribitol,HMDB00508, ,C00474\r\n42,Fucose (6-Deoxygalactose),L-Fucose,HMDB00174,17106,C01019\r\n43,cis-Aconitic acid,cis-Aconitic acid,HMDB00072,643757,C00417\r\n44,Ribose 4-TMS,D-Ribose,HMDB00283,5779,C00121\r\n45,Homovanillic acid,Homovanillic acid,HMDB00118,1738,C05582\r\n46,Glycerol-3-phosphate,Glycerol 3-phosphate,HMDB00126,439162,C00093\r\n47,Glutamine 3-TMS,L-Glutamine,HMDB00641,5961,C00064\r\n48,2-2-Dimethyl-3-hydroxybutyric acid,,,,\r\n49,Hypoxanthine,Hypoxanthine,HMDB00157,790,C00262\r\n50,Hippuric acid,Hippuric acid,HMDB00714,464,C01586\r\n51,Ornithine 3-TMS,Ornithine,HMDB00214,6262,C00077\r\n52,Citric acid,Citric acid,HMDB00094,311,C00158\r\n53,3-(3-Hydroxyphenyl)-3-hydroxypropionic acid 3-TMS,3-(3-Hydroxyphenyl)-3-hydroxypropanoic acid,HMDB02643,102959,\r\n54,Glucoheptonic acid 1-4-lactone,,,,\r\n55,Quinic acid,Quinic acid,HMDB03072, ,C06746\r\n56,Tyrosine,L-Tyrosine,HMDB00158,6057,C00082\r\n57,Sorbose,L-Sorbose,HMDB01266,441484,C08356\r\n58,Fructose,D-Fructose,HMDB00660,439709,C02336\r\n59,Galactose,D-Galactose,HMDB00143,439357,C00984\r\n60,Glucose,D-Glucose,HMDB00122,5793,C00031\r\n61,Histidine 3-TMS,L-Histidine,HMDB00177,6274,C00135\r\n62,Mannitol,Mannitol,HMDB00765,6251,C00392\r\n63,2-3-4-5-Tetrahydroxypentanoic acid 1-4-lactone,,,,\r\n64,Sorbitol,Sorbitol,HMDB00247,5780,C00794\r\n65,Isoascorbic acid,,,,\r\n66,Galactitol,Galactitol,HMDB00107,11850,C01697\r\n67,2-O-Glycerol-galactopyranoside 6-TMS,,,,\r\n68,Gulonic acid,Gulonic acid,HMDB03290,152304,C00800\r\n69,Gluconic acid,Gluconic acid,HMDB00625,10690,C00257\r\n70,Inositol-like,,,,\r\n71,Kynurenic acid,Kynurenic acid,HMDB00715,3845,C01717\r\n72,N-Acetylglucosamine,Beta-N-Acetylglucosamine,HMDB00803,24139,C03878\r\n73,Inositol,,,,\r\n74,Uric acid,Uric acid,HMDB00289,1175,C00366\r\n75,Glycine- N-(4-Hydroxybenzoyl) 2-TMS deriv.,,,,\r\n76,E,,,,\r\n77,Tryptophan 2-TMS,L-Tryptophan,HMDB00929,6305,C00078\r\n78,5-Hydroxyindole-3-acetic acid (5-HIAA),5-Hydroxyindoleacetic acid,HMDB00763,1826,C05635\r\n79,C,,,,\r\n80,Glycine- N-(4-Hydroxybenzoyl) 2-TMS,4-Hydroxyhippuric acid,HMDB13678,151012,\r\n81,F,,,,\r\n82,Cystine 4-TMS,L-Cystine,HMDB00192,67678,C00491\r\n83,2-O-Glycerol-galactopyranoside 6-TMS,,,,\r\n84,Pseudouridine,Pseudouridine,HMDB00767,15047,C02067\r\n85,Arabinofuranose- 1-2-3-4-OTMS,Arabinofuranose,HMDB12325,440921,C06115\r\n86,A,,,,\r\n87,Paracetamol glucuronide,Acetaminophen glucuronide,HMDB10316,83944,\r\n88,Hydroxyproline dipeptide 4-TMS,Hydroxyproline dipeptide 4-TMS, , , \r\n89,Xylobiose-6-TMS,Xylobiose,HMDB29894, ,C01630\r\n90,Sucrose,Sucrose,HMDB00258,5988,C00089\r\n91,B,,,,\r\n92,Lactose,Alpha-Lactose,HMDB00186,84571,C00243\r\n"
},
{
"path": "Demos/Pathway Analysis/.xml",
"content": "\r\n"
},
{
"path": "Demos/Pathway Analysis/KEGG Pathway Enrichment.Rmd",
"content": "Visualization of KEGG Pathway Enrichment\r\n========================================================\r\n\r\nThe [KEGG](http://www.genome.jp/kegg/) database is a useful repository of biochemical domain knowledge.\r\nThe following is an example of how to map changes in genes, proteins and metabolites on an organism specific basis to KEGG defined [biochemical pathways](http://www.genome.jp/kegg/pathway.html).\r\nFor this example we will use the R packages [pathview](http://bioconductor.org/packages/release/bioc/html/pathview.html), [KEGGREST](http://bioconductor.org/packages/2.12/bioc/html/KEGGREST.html) and [KEGGgraph](http://www.bioconductor.org/packages/release/bioc/html/KEGGgraph.html).\r\n\r\n\r\nTo generate a pathway enrichment visualization we minimally need a list of fold changes in biochemical components of interest (e.g. genes, proteins, metabolites). \r\nWe need to supply gene/protein and metabolite/compound fold changes separately as data.frames with rownames specifying database identifiers (see [pathview](http://pathview.r-forge.r-project.org/) for possible options). \r\nThis can be generated based on the output from statistical analysis used to identify any significantly differentially expressed species. \r\n\r\nLets create gene and metabolite data for a demonstration.\r\n```{r,message=FALSE,warning=FALSE}\r\nlibrary(pathview)\r\nmetabolite.data<- data.frame(FC=sim.mol.data(mol.type=\"cpd\", nmol=3000))\r\n```\r\nLets take a look at the format of the metabolite inputs. \r\n```{r}\r\nhead(metabolite.data)\r\n```\r\nNotice we have a single column data frame containing log fold changes with KEGG identifiers for the rownames. Next lets load the example gene data. For genes we can specify the organism of interest using the ```r 'species'``` argument. We can check for available organisms using the commands below.\r\n```{r}\r\ndata(korg)\r\nhead(korg)\r\n```\r\nWe can use the columns ```r 'scientific.name'``` or ```r 'common.name'``` to search for the ```r \"kegg.code\"``` for our organism of interest. Here is an example of how we can do this for *arabidopsis thaliana*.\r\n```{r}\r\norganism<-\"arabidopsis thaliana\"\r\nmatches<-unlist(sapply(1:ncol(korg),function(i) {agrep(organism,korg[,i])}))\r\n(kegg.code<-korg[matches,1,drop=F])\r\n```\r\n\r\n```{r}\r\n#load gene data\r\ngene.data<- data.frame(FC=sim.mol.data(mol.type=\"gene\", nmol=3000,species=kegg.code))\r\n```\r\nLets take a look at the gene data.\r\n```{r}\r\nhead(gene.data)\r\n```\r\nLooking at the rownames we can see that the identifiers are specific for arabidopsis thaliana. Using real data it is possible that we will need to translate identifiers to match the type used in pathview. Here are the different identifiers which can be supplied for metabolites.\r\n```{r}\r\ndata(cpd.simtypes)\r\ncpd.simtypes\r\n```\r\nHere are some possible options for gene identifiers.\r\n```{r}\r\ndata(gene.idtype.list)\r\ngene.idtype.list\r\n```\r\nWe can optionally simulate data with with other identifiers by selecting the appropriate identifier type for the argument `id.type` in the function ```r \"sim.mol.data\"``` .\r\n \r\nIn the example data above we have the the logarithm of fold changes for `r nrow(metabolite.data)` metabolites and `r nrow(gene.data)` genes. Now that we have the data we can select some pathway of interest to map the fold changes to. \r\nWe could get this information from a pathway enrichment analysis. We would do this by testing if the significantly differential expressed metabolites or genes are enriched for some specific KEGG pathways of interest. Here are some tools to conduct enrichment analysis for genes and metabolites.\r\n\r\n* [MBrole](http://csbg.cnb.csic.es/mbrole/) (metabolites)\r\n* [MetaboAnalyst](http://www.metaboanalyst.ca/MetaboAnalyst/faces/Home.jsp) (metabolites)\r\n* [David](http://david.abcc.ncifcrf.gov/) (genes)\r\n* [IMPaLA](http://impala.molgen.mpg.de/) (genes and metabolites) \r\n\r\nFor the example below we will randomly select some KEGG pathways. \r\nWe can use the R package [KEGGREST](http://bioconductor.org/packages/2.12/bioc/html/KEGGREST.html) to get all KEGG pathway identifiers for ```r organism ```.\r\n\r\n```{r,message=FALSE,warning=FALSE}\r\n#get names of pathways to visualize\r\nlibrary(KEGGREST)\r\npathways<-keggList(\"pathway\" ,kegg.code)\r\nhead(pathways)\r\n```\r\nLets visualize changes in our genes and metabolites for ```r pathways[2] ``` which has the KEGG id ```r names(pathways)[2] ``` .\r\n```{r,message=FALSE,warning=FALSE}\r\nlibrary(pathview)\r\nmap<-gsub(\"path:\",\"\",names(pathways)[2]) # remove 'path:'\r\npv.out <- pathview(gene.data = gene.data, cpd.data = metabolite.data, gene.idtype = \"KEGG\",\r\npathway.id = map, species = kegg.code, out.suffix = map,\r\nkeys.align = \"y\", kegg.native = T, match.data=T, key.pos = \"topright\")\r\nplot.name<-paste(map,map,\"png\",sep=\".\")\r\n```\r\nIf everything went as planned this generated a file named ```r plot.name``` mapped KEGG pathway.\r\n\r\nWe can take a look at the mappings made to this pathway.\r\n```{r}\r\nhead(pv.out)\r\n```\r\n\r\n\r\nWe can also display changes in genes/proteins and metabolites for multiple comparisons. To do this lets create some more artificial data and this time lets get protein information. \r\n```{r}\r\nmetabolite.data2<- sim.mol.data(mol.type=\"cpd\", nmol=3000,nexp=2)\r\nhead(metabolite.data2)\r\ngene.data2<- sim.mol.data(mol.type=\"gene\", nmol=3000,nexp=2,id.type=\"UNIPROT\") \r\nhead(gene.data2)\r\n```\r\nNotice UNIPROT IDs do not map to ```r organism ```. We can check the available mappings by looking at the database of identifiers for ```r organism ``` org.At.tair.db. Lets instead map changes to a human pathway or KEGG code \"hsa\". \r\nTo do this we need to make sure we supply the correct ```r 'species' ``` and ```r 'gene.idtype'``` arguments. Next lets map changes in proteins and metabolites for the two comparisons to the pathway .\r\n\r\n```{r, message=FALSE,warning=FALSE}\r\nmap<-gsub(\"path:ath\",\"\",names(pathways)[1]) # remove 'path:ath'\r\npv.out <- pathview(gene.data = gene.data2, cpd.data = metabolite.data2, gene.idtype = \"UNIPROT\",\r\npathway.id = map, species = \"hsa\", out.suffix = map,\r\nkeys.align = \"y\", kegg.native = T, match.data=T, key.pos = \"topright\")\r\nplot.name<-paste(map,map,\"multi\",\"png\",sep=\".\")\r\n```\r\nThis should have generated an image file named ```r plot.name```.\r\n\r\n\r\nWe could also map metabolites on a large scale using `pathway.id= '01100'`.\r\n\r\n\r\n```{r}\r\nsessionInfo()\r\n```\r\n© Dmitry Grapov (2014) \r\n"
},
{
"path": "Demos/Pathway Analysis/KEGG Pathway Enrichment.html",
"content": "\n\n\n\n\n\n![\"\"/](\"http://pathview.r-forge.r-project.org/images/hsa01100.sim.cpd.png\")
\nThe KEGG database is a useful repository of biochemical domain knowledge.\nThe following is an example of how to map changes in genes, proteins and metabolites on an organism specific basis to KEGG defined biochemical pathways.\nFor this example we will use the R packages pathview, KEGGREST and KEGGgraph.
To generate a pathway enrichment visualization we minimally need a list of fold changes in biochemical components of interest (e.g. genes, proteins, metabolites). \nWe need to supply gene/protein and metabolite/compound fold changes separately as data.frames with rownames specifying database identifiers (see pathview for possible options). \nThis can be generated based on the output from statistical analysis used to identify any significantly differentially expressed species.
\n\nLets create gene and metabolite data for a demonstration.
\n\nlibrary(pathview)\nmetabolite.data <- data.frame(FC = sim.mol.data(mol.type = "cpd", nmol = 3000))\nLets take a look at the format of the metabolite inputs.
\n\nhead(metabolite.data)\n## FC\n## C02787 -1.15260\n## C08521 0.46416\n## C01043 0.72893\n## C11496 0.41062\n## C07111 -1.46115\n## C00031 -0.01891\nNotice we have a single column data frame containing log fold changes with KEGG identifiers for the rownames. Next lets load the example gene data. For genes we can specify the organism of interest using the species argument. We can check for available organisms using the commands below.
data(korg)\nhead(korg)\n## kegg.code scientific.name common.name \n## [1,] "hsa" "Homo sapiens" "human" \n## [2,] "ptr" "Pan troglodytes" "chimpanzee" \n## [3,] "pps" "Pan paniscus" "bonobo" \n## [4,] "ggo" "Gorilla gorilla gorilla" "western lowland gorilla"\n## [5,] "pon" "Pongo abelii" "Sumatran orangutan" \n## [6,] "mcc" "Macaca mulatta" "rhesus monkey" \n## entrez.gnodes kegg.geneid ncbi.geneid\n## [1,] "1" "100" "100" \n## [2,] "1" "100533953" "100533953"\n## [3,] "1" "100967419" "100967419"\n## [4,] "1" "101123859" "101123859"\n## [5,] "1" "100169736" "100169736"\n## [6,] "1" "100301991" "100301991"\nWe can use the columns scientific.name or common.name to search for the kegg.code for our organism of interest. Here is an example of how we can do this for arabidopsis thaliana.
organism <- "arabidopsis thaliana"\nmatches <- unlist(sapply(1:ncol(korg), function(i) {\n agrep(organism, korg[, i])\n}))\n(kegg.code <- korg[matches, 1, drop = F])\n## kegg.code\n## [1,] "ath"\n# load gene data\ngene.data <- data.frame(FC = sim.mol.data(mol.type = "gene", nmol = 3000, species = kegg.code))\nLets take a look at the gene data.
\n\nhead(gene.data)\n## FC\n## AT2G21190 -1.15260\n## AT1G80990 0.46416\n## AT3G48550 0.72893\n## AT1G15410 0.41062\n## AT3G15220 -1.46115\n## AT3G18980 -0.01891\nLooking at the rownames we can see that the identifiers are specific for arabidopsis thaliana. Using real data it is possible that we will need to translate identifiers to match the type used in pathview. Here are the different identifiers which can be supplied for metabolites.
\n\ndata(cpd.simtypes)\ncpd.simtypes\n## [1] "Beilstein Registry Number" "CAS Registry Number" \n## [3] "ChEMBL COMPOUND" "KEGG COMPOUND accession" \n## [5] "KEGG DRUG accession" "Patent accession" \n## [7] "PubMed citation"\nHere are some possible options for gene identifiers.
\n\ndata(gene.idtype.list)\ngene.idtype.list\n## [1] "SYMBOL" "GENENAME" "ENSEMBL" "ENSEMBLPROT" \n## [5] "PROSITE" "UNIGENE" "UNIPROT" "ACCNUM" \n## [9] "ENSEMBLTRANS" "REFSEQ"\nWe can optionally simulate data with with other identifiers by selecting the appropriate identifier type for the argument id.type in the function sim.mol.data .
In the example data above we have the the logarithm of fold changes for 3000 metabolites and 3000 genes. Now that we have the data we can select some pathway of interest to map the fold changes to. \nWe could get this information from a pathway enrichment analysis. We would do this by testing if the significantly differential expressed metabolites or genes are enriched for some specific KEGG pathways of interest. Here are some tools to conduct enrichment analysis for genes and metabolites.
\n\nFor the example below we will randomly select some KEGG pathways. \nWe can use the R package KEGGREST to get all KEGG pathway identifiers for arabidopsis thaliana.
# get names of pathways to visualize\nlibrary(KEGGREST)\npathways <- keggList("pathway", kegg.code)\nhead(pathways)\n## path:ath00010 \n## "Glycolysis / Gluconeogenesis - Arabidopsis thaliana (thale cress)" \n## path:ath00020 \n## "Citrate cycle (TCA cycle) - Arabidopsis thaliana (thale cress)" \n## path:ath00030 \n## "Pentose phosphate pathway - Arabidopsis thaliana (thale cress)" \n## path:ath00040 \n## "Pentose and glucuronate interconversions - Arabidopsis thaliana (thale cress)" \n## path:ath00051 \n## "Fructose and mannose metabolism - Arabidopsis thaliana (thale cress)" \n## path:ath00052 \n## "Galactose metabolism - Arabidopsis thaliana (thale cress)"\nLets visualize changes in our genes and metabolites for Citrate cycle (TCA cycle) - Arabidopsis thaliana (thale cress) which has the KEGG id path:ath00020 .
library(pathview)\nmap <- gsub("path:", "", names(pathways)[2]) # remove 'path:'\npv.out <- pathview(gene.data = gene.data, cpd.data = metabolite.data, gene.idtype = "KEGG", \n pathway.id = map, species = kegg.code, out.suffix = map, keys.align = "y", \n kegg.native = T, match.data = T, key.pos = "topright")\nplot.name <- paste(map, map, "png", sep = ".")\nIf everything went as planned this generated a file named ath00020.ath00020.png mapped KEGG pathway.\n\nWe can take a look at the mappings made to this pathway.
head(pv.out)\n## $plot.data.gene\n## kegg.names labels type x y width height FC mol.col\n## 29 AT1G48030 mtLPD1 gene 467 618 46 17 NA #FFFFFF\n## 30 AT3G55410 AT3G55410 gene 661 574 46 17 NA #FFFFFF\n## 31 AT3G55410 AT3G55410 gene 530 575 46 17 NA #FFFFFF\n## 32 AT4G26910 AT4G26910 gene 403 574 46 17 NA #FFFFFF\n## 33 AT2G20420 AT2G20420 gene 260 574 46 17 NA #FFFFFF\n## 34 AT2G20420 AT2G20420 gene 260 555 46 17 NA #FFFFFF\n## 35 AT1G54340 ICDH gene 718 505 46 17 NA #FFFFFF\n## 36 AT5G03290 IDH-IV gene 766 458 46 17 -0.4385 #5FDF5F\n## 37 AT1G54340 ICDH gene 718 400 46 17 NA #FFFFFF\n## 38 AT1G10670 ACLA-3 gene 434 355 46 17 -1.1941 #00FF00\n## 39 AT2G47510 FUM1 gene 191 436 46 17 NA #FFFFFF\n## 40 AT2G05710 ACO3 gene 670 345 46 17 NA #FFFFFF\n## 41 AT2G05710 ACO3 gene 571 345 46 17 NA #FFFFFF\n## 42 AT2G42790 CSY3 gene 434 334 46 17 NA #FFFFFF\n## 43 AT1G04410 AT1G04410 gene 253 344 46 17 NA #FFFFFF\n## 77 AT1G30120 PDH-E1_ALPHA gene 686 241 46 17 0.6117 #EF3030\n## 79 AT1G30120 PDH-E1_ALPHA gene 582 241 46 17 0.6117 #EF3030\n## 82 AT1G48030 mtLPD1 gene 529 285 46 17 NA #FFFFFF\n## 84 AT1G34430 EMB3003 gene 464 242 46 17 NA #FFFFFF\n## 91 AT4G37870 PCK1 gene 371 138 46 17 NA #FFFFFF\n## 97 AT5G66760 SDH1-2 gene 191 530 46 17 1.1329 #FF0000\n## \n## $plot.data.cpd\n## kegg.names labels type x y width height FC mol.col\n## 58 C00022 C00022 compound 737 241 8 8 0.21082 #CECE8F\n## 59 C00122 C00122 compound 190 481 8 8 NA #FFFFFF\n## 60 C00036 C00036 compound 320 344 8 8 1.42760 #FFFF00\n## 61 C05379 C05379 compound 718 453 8 8 0.76991 #EFEF30\n## 62 C00024 C00024 compound 400 241 8 8 NA #FFFFFF\n## 63 C00149 C00149 compound 190 393 8 8 1.93744 #FFFF00\n## 64 C00311 C00311 compound 718 344 8 8 -1.33400 #0000FF\n## 65 C00417 C00417 compound 621 344 8 8 -0.82811 #0000FF\n## 66 C00042 C00042 compound 190 574 8 8 1.28575 #FFFF00\n## 67 C00158 C00158 compound 522 344 8 8 NA #FFFFFF\n## 68 C15972 C15972 compound 522 617 8 8 NA #FFFFFF\n## 69 C00068 C00068 compound 589 530 8 8 0.01167 #BEBEBE\n## 70 C16254 C16254 compound 462 573 8 8 NA #FFFFFF\n## 71 C15973 C15973 compound 408 617 8 8 NA #FFFFFF\n## 72 C00091 C00091 compound 334 574 8 8 -1.97370 #0000FF\n## 73 C00026 C00026 compound 718 573 8 8 1.10432 #FFFF00\n## 74 C05381 C05381 compound 593 573 8 8 NA #FFFFFF\n## 76 C05125 C05125 compound 631 241 8 8 NA #FFFFFF\n## 78 C00068 C00068 compound 631 198 8 8 0.01167 #BEBEBE\n## 80 C15972 C15972 compound 580 284 8 8 NA #FFFFFF\n## 83 C16255 C16255 compound 517 241 8 8 NA #FFFFFF\n## 93 C15973 C15973 compound 478 284 8 8 NA #FFFFFF\n## 94 C00074 C00074 compound 471 127 8 8 NA #FFFFFF\nWe can also display changes in genes/proteins and metabolites for multiple comparisons. To do this lets create some more artificial data and this time lets get protein information.
\n\nmetabolite.data2 <- sim.mol.data(mol.type = "cpd", nmol = 3000, nexp = 2)\nhead(metabolite.data2)\n## exp1 exp2\n## C02787 -1.15260 1.2914\n## C08521 0.46416 0.6706\n## C01043 0.72893 -0.3065\n## C11496 0.41062 -2.1996\n## C07111 -1.46115 -0.3476\n## C00031 -0.01891 0.7219\ngene.data2 <- sim.mol.data(mol.type = "gene", nmol = 3000, nexp = 2, id.type = "UNIPROT")\nhead(gene.data2)\n## exp1 exp2\n## E9PDR7 -1.15260 1.2914\n## Q9UBV2 0.46416 0.6706\n## Q9BQ95 0.72893 -0.3065\n## P24310 0.41062 -2.1996\n## Q8N5I0 -1.46115 -0.3476\n## Q9Y6J0 -0.01891 0.7219\nNotice UNIPROT IDs do not map to arabidopsis thaliana. We can check the available mappings by looking at the database of identifiers for arabidopsis thaliana org.At.tair.db. Lets instead map changes to a human pathway or KEGG code “hsa”. \nTo do this we need to make sure we supply the correct species and gene.idtype arguments. Next lets map changes in proteins and metabolites for the two comparisons to the pathway .
map <- gsub("path:ath", "", names(pathways)[1]) # remove 'path:ath'\npv.out <- pathview(gene.data = gene.data2, cpd.data = metabolite.data2, gene.idtype = "UNIPROT", \n pathway.id = map, species = "hsa", out.suffix = map, keys.align = "y", kegg.native = T, \n match.data = T, key.pos = "topright")\nplot.name <- paste(map, map, "multi", "png", sep = ".")\nThis should have generated an image file named 00010.00010.multi.png.\n
We could also map metabolites on a large scale using pathway.id= '01100'.\n
sessionInfo()\n## R version 3.0.1 (2013-05-16)\n## Platform: i386-w64-mingw32/i386 (32-bit)\n## \n## locale:\n## [1] LC_COLLATE=English_United States.1252 \n## [2] LC_CTYPE=English_United States.1252 \n## [3] LC_MONETARY=English_United States.1252\n## [4] LC_NUMERIC=C \n## [5] LC_TIME=English_United States.1252 \n## \n## attached base packages:\n## [1] parallel stats graphics grDevices utils datasets methods \n## [8] base \n## \n## other attached packages:\n## [1] KEGGREST_1.2.2 pathview_1.2.4 org.Hs.eg.db_2.9.0 \n## [4] RSQLite_0.11.3 DBI_0.2-7 AnnotationDbi_1.24.0\n## [7] Biobase_2.22.0 BiocGenerics_0.8.0 KEGGgraph_1.16.0 \n## [10] graph_1.40.1 XML_3.96-1.1 knitr_1.5 \n## [13] rcom_2.2-5 rscproxy_2.0-5 \n## \n## loaded via a namespace (and not attached):\n## [1] Biostrings_2.30.1 evaluate_0.5.3 formatR_0.10 \n## [4] grid_3.0.1 httr_0.3 IRanges_1.20.7 \n## [7] png_0.1-7 RCurl_1.95-4.1 Rgraphviz_2.6.0 \n## [10] stats4_3.0.1 stringr_0.6.2 tools_3.0.1 \n## [13] XVector_0.2.0\n© Dmitry Grapov (2014)
\r\n"
},
{
"path": "Demos/Pathway Analysis/ath00020.xml",
"content": "\r\n\r\n\r\nSee here for more O-PLS methods or try PLS.
\n\nsource("http://pastebin.com/raw.php?i=UyDBTA57") # source Devium\nO-PLS-DA demo using Iris data\nThe goal is to predict the species of the flower based on four physical properties.
\n\nThis demonstration is focused on O-PLS-DA model:
\n\ndata(iris)\ntmp.data<-iris[,-5]\ntmp.group<-iris[,5] # species\ntmp.y<-matrix(as.numeric(tmp.group),ncol=1) # make numeric matrix\nThe data will be split into 1/3 test and 2/3 training sets.\nThe training data will be used for:
\n\nThe hold out set or the test data will be used to estimate the externally validated OOB.
\n\nGenerate external test set using the duplex or kennard stone method.
\n\ntrain.test.index.main=test.train.split(nrow(tmp.data),n=1,strata=tmp.group,split.type="duplex",data=tmp.data)\ntrain.id<-train.test.index.main=="train"\n\n#partition data to get the trainning set\ntmp.data<-tmp.data[train.id,]\ntmp.group<-tmp.group[train.id]\ntmp.y<-tmp.y[train.id,]\n\n#the variables could be scaled now, or done internally in the model for each CV split (leave-one-out)\n#scaled.data<-data.frame(scale(tmp.data,center=TRUE, scale=TRUE)) \nscaled.data<-tmp.data\nCompare a 2 latent variable (LV) PLS-DA and 2 LV with one orthogonal LV (OLV) O-PLS-DA model.
\n\nmods<-make.OSC.PLS.model(tmp.y,pls.data=scaled.data,comp=2,OSC.comp=1, validation = "LOO",method="oscorespls", cv.scale=TRUE, progress=FALSE)\n#extract model\nfinal<-get.OSC.model(obj=mods,OSC.comp=1)\n#view out-of-bag error for cross-validation splits\nplot.OSC.results(mods,plot="RMSEP",groups=tmp.group)\n
Ideally we want to select the simplest models with lowest root mean squared error of prediction (RMSEP), which in this case is calculated based on leave-one-one cross-validation.
\n\nThe 1 LV and 1 orthogonal LV (O-PLS-DA) model has similar error to a simple 2 LV PLS-DA model.
\n\nNext we can also compare the change in scores with the addition of the OLV.
\n\nplot.OSC.results(mods,plot="scores",groups=tmp.group)\n \nNon-overlapping scores for each species could signify a well fit model, but we need to carry out some further validations to be sure.
plot.PLS.results(obj=final,plot="scores",groups=tmp.group)\n \nIdeally the within species variance should be maximally orthogonal to the between species variance (our goal to maximize)in this case we see this represented by the vertical spread of the three species scores.
Next we can compare our model fit to random chance. We can do this using permutation testing by generating models for a randomly permuted species label (Y). We will generate 50 permuted models and compare their performance statics to our model. We also first want to generate a pseudo-training/test split for our training data in order to correctly simulate the RMSEP. The permuted models will be fit using the pseudo-training data and then used to predict the species label for the pseudo-test set.
\n\ntrain.test.index=test.train.split(nrow(scaled.data),n=100,strata=as.factor(tmp.y)) # strata controls if the species are sampled from equally\npermuted.stats<-permute.OSC.PLS(data=scaled.data,y=as.matrix(tmp.y),n=50,ncomp=2,osc.comp=1, progress=FALSE,train.test.index=train.test.index)\n#look how our model compares to random chance\nOSC.validate.model(model=mods,perm=permuted.stats)\n## Error: non-numeric argument to binary operator\nNext we can estimate the OOB error within the training set by conducting model training and testing. This is done using the pseudo-training/test split we generated for the model permutations.
\n\ntrain.stats<-OSC.PLS.train.test(pls.data = scaled.data,pls.y = tmp.y,train.test.index ,comp=2,OSC.comp=1,cv.scale=TRUE, progress=FALSE)\nNow we can compare the distributions for our models' performance statistics to their respective permuted distributions.
\n\nOSC.validate.model(model=mods,perm=permuted.stats,train=train.stats)\n## Xvar Q2 RMSEP\n## model 99.58 ± 0.178 0.8883 ± 0.0537 0.2342 ± 0.0516\n## permuted model 99.29 ± 0.457 -0.06332 ± 0.0444 0.8438 ± 0.0566\n## p-value 8.437e-05 1.183e-121 3.731e-77\nThis suggests that we have a strong model (far better than random chance) which capable of correctly predicting the species of the flower.\n
\n\nFinally we want to estimate the true estimate of the OOB error by predicting the species labels for the test set we excluded from our data before we even started modeling.
\n\nThe idea is that the test set was never involved in any of our modeling decisions (pretreatment, feature selection, etc) and is the most honest estimate of our models predictive performance.
\n\n#reset data\nscaled.data<-iris[,-5]\ntmp.group<-iris[,5] \ntmp.y<-matrix(as.numeric(tmp.group),ncol=1) \n\n#make predictions for the test set\nmods<-make.OSC.PLS.model(tmp.y,pls.data=scaled.data,comp=2,OSC.comp=1, validation = "LOO",\nmethod="oscorespls", cv.scale=TRUE, progress=FALSE,train.test.index=train.test.index.main)\n\n#get the true (actual) and predicted values\n#round them to integers to represent discreet species labels\nplot.data=data.frame(predicted = round(mods$predicted.Y[[2]][,1],0),actual= mods$test.y)\n#note these are numeric but we would prefer to interpret classification of species a class\nplot.data$predicted<-factor(plot.data$predicted,labels=levels(iris[,5]),levels=1:3)\nplot.data$actual<-factor(plot.data$actual,labels=levels(iris[,5]),levels=1:3)\n\ntable(plot.data)\n## actual\n## predicted setosa versicolor virginica\n## setosa 17 0 0\n## versicolor 0 16 1\n## virginica 0 1 16\nBased on the similarity between virginica and versicolor species' physical properties we expect this to be the most difficult classification to get correct.
\n\npairs(iris[,-5],pch=21,bg=rainbow(nlevels(iris[,5]),alpha=.75)[iris[,5]],upper.panel=NULL,cex=2)\npar(xpd=TRUE)\nlegend(.75,1,levels(iris[,5]),fill=rainbow(nlevels(iris[,5]),alpha=.75),bty="n")\n
O-PLS-DA becomes very useful when there are many variables. Model loadings on LV 1 (x-axis) can be used to linearly rank variables with respect to explaining differences in samples which are represented by the model scores. This approach is useful for reducing multidimensional comparisons (e.g. multiple class comparisons, genotype/treatment/time point) to single dimensional ranking of each variable representing it's weight for explaining a given hypothesis (Y) and the goodness of the answer depends on the strength of the produced model.
\n\n\n\n\n\n" }, { "path": "Demos/Predictive Modeling/Iris O-PLS-DA/O-PLS modeling of Iris data.md", "content": "Orthogonal Signal Correction Partial Least Squares (O-PLS) Discriminant Analysis (O-PLS-DA)\n========================================================\n\nSee here for more [O-PLS](https://raw.github.com/dgrapov/devium/master/R/Devium%20PLS%20%20and%20OPLS.r) methods or try [PLS](http://cran.r-project.org/web/packages/pls/index.html).\n\n\n\n```r\nsource(\"http://pastebin.com/raw.php?i=UyDBTA57\") # source Devium\n```\n\nO-PLS-DA demo using Iris data\nThe goal is to predict the species of the flower based on four physical properties.\n\nThis demonstration is focused on O-PLS-DA model: \n- [data pretreatment and preparation](#prep) \n- [model optimization](#oplsda)\n- [permutation testing](#perm)\n- [internal cross-validation](#intOOB)\n- [external cross-validation](#extOOB)\n\n\n\n## Preparation for modeling\n\n```r\ndata(iris)\ntmp.data<-iris[,-5]\ntmp.group<-iris[,5] # species\ntmp.y<-matrix(as.numeric(tmp.group),ncol=1) # make numeric matrix\n```\n\nThe data will be split into 1/3 test and 2/3 training sets.\nThe training data will be used for:\n\n- model optimization\n- permutation testing\n- internally cross-validated estimate of training and out-of-bag error (OOB)\n\nThe hold out set or the test data will be used to estimate the externally validated OOB.\n\nGenerate external test set using the duplex or kennard stone method.\n\n```r\ntrain.test.index.main=test.train.split(nrow(tmp.data),n=1,strata=tmp.group,split.type=\"duplex\",data=tmp.data)\ntrain.id<-train.test.index.main==\"train\"\n\n#partition data to get the trainning set\ntmp.data<-tmp.data[train.id,]\ntmp.group<-tmp.group[train.id]\ntmp.y<-tmp.y[train.id,]\n\n#the variables could be scaled now, or done internally in the model for each CV split (leave-one-out)\n#scaled.data<-data.frame(scale(tmp.data,center=TRUE, scale=TRUE)) \nscaled.data<-tmp.data\n```\n\n\n## Train O-PLS-DA model \nCompare a 2 latent variable (LV) PLS-DA and 2 LV with one orthogonal LV (OLV) O-PLS-DA model.\n\n```r\nmods<-make.OSC.PLS.model(tmp.y,pls.data=scaled.data,comp=2,OSC.comp=1, validation = \"LOO\",method=\"oscorespls\", cv.scale=TRUE, progress=FALSE)\n#extract model\nfinal<-get.OSC.model(obj=mods,OSC.comp=1)\n#view out-of-bag error for cross-validation splits\nplot.OSC.results(mods,plot=\"RMSEP\",groups=tmp.group)\n```\n\n \n\nIdeally we want to select the simplest models with lowest root mean squared error of prediction (RMSEP), which in this case is calculated based on leave-one-one cross-validation. \n\nThe 1 LV and 1 orthogonal LV (O-PLS-DA) model has similar error to a simple 2 LV PLS-DA model.\n\nNext we can also compare the change in scores with the addition of the OLV. \n\n```r\nplot.OSC.results(mods,plot=\"scores\",groups=tmp.group)\n```\n\n \nNon-overlapping scores for each species could signify a well fit model, but we need to carry out some further validations to be sure.\n\n```r\nplot.PLS.results(obj=final,plot=\"scores\",groups=tmp.group)\n```\n\n \nIdeally the within species variance should be maximally orthogonal to the between species variance (our goal to maximize)in this case we see this represented by the vertical spread of the three species scores.\n\n\n# Permutation Testing\nNext we can compare our model fit to random chance. We can do this using permutation testing by generating models for a randomly permuted species label (Y). We will generate 50 permuted models and compare their performance statics to our model. We also first want to generate a pseudo-training/test split for our training data in order to correctly simulate the RMSEP. The permuted models will be fit using the pseudo-training data and then used to predict the species label for the pseudo-test set.\n\n```r\ntrain.test.index=test.train.split(nrow(scaled.data),n=100,strata=as.factor(tmp.y)) # strata controls if the species are sampled from equally\npermuted.stats<-permute.OSC.PLS(data=scaled.data,y=as.matrix(tmp.y),n=50,ncomp=2,osc.comp=1, progress=FALSE,train.test.index=train.test.index)\n#look how our model compares to random chance\nOSC.validate.model(model=mods,perm=permuted.stats)\n```\n\n```\n## Error: non-numeric argument to binary operator\n```\n\n- Q2 represents the in-bag or error for the training data \n- Xvar the variance in the variables (X) explained or captured in the model\n- RMSEP is the out-of-bag error (OOB)\n- The p-values are from a single-sample t-Test comparing our models \n performance parameters (single values) to their respective permuted distributions \n\n\n# Internal (training set) Cross-validation\nNext we can estimate the OOB error within the training set by conducting model training and testing. This is done using the pseudo-training/test split we generated for the model permutations. \n\n\n```r\ntrain.stats<-OSC.PLS.train.test(pls.data = scaled.data,pls.y = tmp.y,train.test.index ,comp=2,OSC.comp=1,cv.scale=TRUE, progress=FALSE)\n```\n\nNow we can compare the distributions for our models' performance statistics to their respective permuted distributions.\n\n\n```r\nOSC.validate.model(model=mods,perm=permuted.stats,train=train.stats)\n```\n\n```\n## Xvar Q2 RMSEP\n## model 99.58 ± 0.178 0.8883 ± 0.0537 0.2342 ± 0.0516\n## permuted model 99.29 ± 0.457 -0.06332 ± 0.0444 0.8438 ± 0.0566\n## p-value 8.437e-05 1.183e-121 3.731e-77\n```\n\nThis suggests that we have a strong model (far better than random chance) which capable of correctly predicting the species of the flower.\n\n# Model testing\nFinally we want to estimate the true estimate of the OOB error by predicting the species labels for the test set we excluded from our data before we even started modeling.\n\nThe idea is that the test set was never involved in any of our modeling decisions (pretreatment, feature selection, etc) and is the most honest estimate of our models predictive performance.\n\n```r\n#reset data\nscaled.data<-iris[,-5]\ntmp.group<-iris[,5] \ntmp.y<-matrix(as.numeric(tmp.group),ncol=1) \n\n#make predictions for the test set\nmods<-make.OSC.PLS.model(tmp.y,pls.data=scaled.data,comp=2,OSC.comp=1, validation = \"LOO\",\nmethod=\"oscorespls\", cv.scale=TRUE, progress=FALSE,train.test.index=train.test.index.main)\n\n#get the true (actual) and predicted values\n#round them to integers to represent discreet species labels\nplot.data=data.frame(predicted = round(mods$predicted.Y[[2]][,1],0),actual= mods$test.y)\n#note these are numeric but we would prefer to interpret classification of species a class\nplot.data$predicted<-factor(plot.data$predicted,labels=levels(iris[,5]),levels=1:3)\nplot.data$actual<-factor(plot.data$actual,labels=levels(iris[,5]),levels=1:3)\n\ntable(plot.data)\n```\n\n```\n## actual\n## predicted setosa versicolor virginica\n## setosa 17 0 0\n## versicolor 0 16 1\n## virginica 0 1 16\n```\n\nBased on the similarity between virginica and versicolor species' physical properties we expect this to be the most difficult classification to get correct.\n\n\n```r\npairs(iris[,-5],pch=21,bg=rainbow(nlevels(iris[,5]),alpha=.75)[iris[,5]],upper.panel=NULL,cex=2)\npar(xpd=TRUE)\nlegend(.75,1,levels(iris[,5]),fill=rainbow(nlevels(iris[,5]),alpha=.75),bty=\"n\")\n```\n\n \n\nO-PLS-DA becomes very useful when there are many variables. Model loadings on LV 1 (x-axis) can be used to linearly rank variables with respect to explaining differences in samples which are represented by the model scores. This approach is useful for reducing multidimensional comparisons (e.g. multiple class comparisons, genotype/treatment/time point) to single dimensional ranking of each variable representing it's weight for explaining a given hypothesis (Y) and the goodness of the answer depends on the strength of the produced model. \n\n\n\n\n" }, { "path": "Demos/Predictive Modeling/Iris O-PLS-DA/O-PLS_modeling_of_Iris_data.html", "content": "\n\n\n\n\n\n\n\n\n\n\n\nSee here for more O-PLS methods or try PLS.
\nsource("http://pastebin.com/raw.php?i=UyDBTA57") # source DeviumO-PLS-DA demo using Iris data The goal is to predict the species of the flower based on four physical properties.
\nThis demonstration is focused on O-PLS-DA model: - data pretreatment and preparation - model optimization - permutation testing - internal cross-validation - external cross-validation - Comparison of O-PLS-DA and Random Forest classification (updated 11/7/15)
\n\ndata(iris)\ntmp.data<-iris[,-5]\ntmp.group<-iris[,5] # species\ntmp.y<-matrix(as.numeric(tmp.group),ncol=1) # make numeric matrixThe data will be split into 1/3 test and 2/3 training sets. The training data will be used for:
\nThe hold out set or the test data will be used to estimate the externally validated OOB.
\nGenerate external test set using the duplex or kennard stone method.
\ntrain.test.index.main=test.train.split(nrow(tmp.data),n=1,strata=tmp.group,split.type="duplex",data=tmp.data)\ntrain.id<-train.test.index.main=="train"\n\n#partition data to get the trainning set\ntmp.data<-tmp.data[train.id,]\ntmp.group<-tmp.group[train.id]\ntmp.y<-tmp.y[train.id,]\n\n#the variables could be scaled now, or done internally in the model for each CV split (leave-one-out)\n#scaled.data<-data.frame(scale(tmp.data,center=TRUE, scale=TRUE)) \nscaled.data<-tmp.data## Train O-PLS-DA model Compare a 2 latent variable (LV) PLS-DA and 2 LV with one orthogonal LV (OLV) O-PLS-DA model.
\nmods<-make.OSC.PLS.model(tmp.y,pls.data=scaled.data,comp=2,OSC.comp=1, validation = "LOO",method="oscorespls", cv.scale=TRUE, progress=FALSE)\n#extract model\nfinal<-get.OSC.model(obj=mods,OSC.comp=1)\n#view out-of-bag error for cross-validation splits\nplot.OSC.results(mods,plot="RMSEP",groups=tmp.group)Ideally we want to select the simplest models with lowest root mean squared error of prediction (RMSEP), which in this case is calculated based on leave-one-one cross-validation.
\nThe 1 LV and 1 orthogonal LV (O-PLS-DA) model has similar error to a simple 2 LV PLS-DA model.
\nNext we can also compare the change in scores with the addition of the OLV.
\nplot.OSC.results(mods,plot="scores",groups=tmp.group) Non-overlapping scores for each species could signify a well fit model, but we need to carry out some further validations to be sure.
# plot.PLS.results(obj=final,plot="scores",groups=tmp.group)\nplot.PLS(obj=final, results = "scores", color=data.frame(species=tmp.group), group.bounds="ellipse",g.alpha=.5, label=FALSE) Ideally the within species variance should be maximally orthogonal to the between species variance (our goal to maximize)in this case we see this represented by the vertical spread of the three species scores.
# Permutation Testing Next we can compare our model fit to random chance. We can do this using permutation testing by generating models for a randomly permuted species label (Y). We will generate 50 permuted models and compare their performance statics to our model. We also first want to generate a pseudo-training/test split for our training data in order to correctly simulate the RMSEP. The permuted models will be fit using the pseudo-training data and then used to predict the species label for the pseudo-test set.
\ntrain.test.index<- test.train.split(nrow(scaled.data),n=100,strata=as.factor(tmp.y)) # strata controls if the species are sampled from equally\npermuted.stats<- permute.OSC.PLS(data=scaled.data,y=as.matrix(tmp.y),n=50,ncomp=2,osc.comp=1, progress=FALSE,train.test.index=train.test.index)\n#look how our model compares to random chance\nOSC.validate.model(model=final,perm=permuted.stats)## RX2 Q2 RMSEP\n## model 99.54 0.9226 0.2271\n## permuted model 99.41 +/- 0.455 -0.06364 +/- 0.0361 0.8436 +/- 0.0468\n## p-value 0.05058 2.664e-72 8.228e-57# Internal (training set) Cross-validation Next we can estimate the OOB error within the training set by conducting model training and testing. This is done using the pseudo-training/test split we generated for the model permutations.
\ntrain.stats<-OSC.PLS.train.test(pls.data = scaled.data,pls.y = tmp.y,train.test.index ,comp=2,OSC.comp=1,cv.scale=TRUE, progress=FALSE)Now we can compare the distributions for our models’ performance statistics to their respective permuted distributions.
\nOSC.validate.model(model=mods,perm=permuted.stats,train=train.stats)## Xvar Q2 RMSEP\n## model 99.59 +/- 0.35 0.9007 +/- 0.0436 0.2248 +/- 0.0209\n## permuted model 99.41 +/- 0.455 -0.06364 +/- 0.0361 0.8436 +/- 0.0468\n## p-value 0.01923 1.184e-132 1.331e-64This suggests that we have a strong model (far better than random chance) which capable of correctly predicting the species of the flower. # Model testing Finally we want to estimate the true estimate of the OOB error by predicting the species labels for the test set we excluded from our data before we even started modeling.
\nThe idea is that the test set was never involved in any of our modeling decisions (pretreatment, feature selection, etc) and is the most honest estimate of our models predictive performance.
\n#reset data\nscaled.data<-iris[,-5]\ntmp.group<-iris[,5] \ntmp.y<-matrix(as.numeric(tmp.group),ncol=1) \n\n#make predictions for the test set\nmods<-make.OSC.PLS.model(tmp.y,pls.data=scaled.data,comp=2,OSC.comp=1, validation = "LOO",\nmethod="oscorespls", cv.scale=TRUE, progress=FALSE,train.test.index=train.test.index.main)\n\n#get the true (actual) and predicted values\n#round them to integers to represent discreet species labels\nplot.data=data.frame(predicted = round(mods$predicted.Y[[2]][,1],0),actual= mods$test.y)\n#note these are numeric but we would prefer to interpret classification of species a class\nplot.data$predicted<-factor(plot.data$predicted,labels=levels(iris[,5]),levels=1:3)\nplot.data$actual<-factor(plot.data$actual,labels=levels(iris[,5]),levels=1:3)\n\ntable(plot.data)## actual\n## predicted setosa versicolor virginica\n## setosa 17 0 0\n## versicolor 0 15 0\n## virginica 0 2 17Based on the similarity between virginica and versicolor species’ physical properties we expect this to be the most difficult classification to get correct.
\npairs(iris[,-5],pch=21,bg=rainbow(nlevels(iris[,5]),alpha=.75)[iris[,5]],upper.panel=NULL,cex=2)\npar(xpd=TRUE)\nlegend(.75,1,levels(iris[,5]),fill=rainbow(nlevels(iris[,5]),alpha=.75),bty="n")O-PLS-DA becomes very useful when there are many variables. Model loadings on LV 1 (x-axis) can be used to linearly rank variables with respect to explaining differences in samples which are represented by the model scores. This approach is useful for reducing multidimensional comparisons (e.g. multiple class comparisons, genotype/treatment/time point) to single dimensional ranking of each variable representing it’s weight for explaining a given hypothesis (Y) and the goodness of the answer depends on the strength of the produced model.
\n#### Lets see if we can build a model to discriminate between verginica and versicolor and compare performance between O-PLS-DA and Random Forest (RF). #### Predict classes using O-PLS-DA Lets split the data into train (2/3) test (1/3) sets and compare O-PLS-DA to Random Forest classification performance.
\nlibrary(dplyr)\nlibrary(caret)\n# set up data \ndata<-iris %>% filter(Species != "setosa") %>% droplevels()\n#make numeric\ngroup<-data$Species %>% as.numeric()\n\n#convert to (horrible idea "train", "test")\nset.seed(106)\ninTraining <- createDataPartition(group, p = 2/3, list = FALSE)\ntrain.index<-rep("test",nrow(data)) \ntrain.index[inTraining]<-"train"\n\nmods<-make.OSC.PLS.model(group,\n pls.data=data %>% select(-Species),\n train.test.index = train.index,\n comp=2,OSC.comp=1, validation = "LOO",\n method="oscorespls", cv.scale=TRUE, progress=FALSE)\n#extract model\nfinal<-get.OSC.model(obj=mods,OSC.comp=1)\n\n#get performance for test set\npred<-mods$predicted.Y[[2]] %>% matrix() %>%\n#predictions need to be made descreet\n round(0)\n#constrained to original bounds\npred[pred<0]<-1\npred[pred>2]<-2\nobs<-mods$test.y\n#back convert from numeric\nobs<-obs %>% factor(.,labels=c("versicolor","verginica"))\npred<-pred %>% factor(.,labels=c("versicolor","verginica"))\n\ntable(pred,obs)## obs\n## pred versicolor verginica\n## versicolor 15 0\n## verginica 1 16#PCA to visualize test and train samples\nargs<-list( pca.data = data,\n pca.algorithm = "svd",\n pca.components = 2,\n pca.center = FALSE, #need to fix requirement...\n pca.scaling = "none"\n )\n\n#calculate and view scree plot \nres<-devium.pca.calculate(args,return="list",plot=TRUE)#set up plot\n#show groups\ngroup<-data$Species \ngroup<-join.columns(data.frame(group,train.index),"_")\n#id misclassified samples\nid<-seq_along(1:nrow(data))\nid<-id[!id %in% inTraining]\n#mis\nmiss<-id[obs != pred]\ntmp<-rep("_",length(group))\ntmp[miss]<-"wrong"\ngroup<-join.columns(data.frame(group,tmp),"_") %>% gsub("__","",.)\n\np.args<-list( \n pca = res,\n results = "scores",\n color = data.frame(group),\n label=FALSE,\n group.bounds ="none",\n size=5\n )\n\ndo.call("plot.PCA",p.args)caret library.#create a classification model using random forests\n#generate training/test set\ngroup<-data$Species\n\ntrain.data <- data[ inTraining,]\ntest.data <- data[-inTraining,]\ntrain.y <- group[ inTraining] %>% droplevels()\ntest.y <- group[ -inTraining] %>% droplevels()\n\n#set cross-validation parameters\nfitControl <- trainControl(\n method = "repeatedcv",\n number = 3,\n ## repeated ten times\n repeats = 3,\n classProbs = TRUE,\n summaryFunction = twoClassSummary\n )\n\n\n#fit model to the training data\nset.seed(825)\nfit<- train(train.y ~ ., data = train.data,\n method = "rf",\n trControl = fitControl,\n metric = "ROC",\n tuneLength = 3 \n )\n\n#fit to the trainning data\nprint(fit)## Random Forest \n## \n## 68 samples\n## 4 predictor\n## 2 classes: 'versicolor', 'virginica' \n## \n## No pre-processing\n## Resampling: Cross-Validated (3 fold, repeated 3 times) \n## \n## Summary of sample sizes: 46, 46, 44, 45, 46, 45, ... \n## \n## Resampling results across tuning parameters:\n## \n## mtry ROC Sens Spec ROC SD Sens SD Spec SD\n## 2 1 1 1 0 0 0 \n## 3 1 1 1 0 0 0 \n## 5 1 1 1 0 0 0 \n## \n## ROC was used to select the optimal model using the largest value.\n## The final value used for the model was mtry = 2.#predict RF\n#predict the test set\npred<-predict(fit,newdata=test.data)\nprob<-predict(fit,newdata=test.data,type="prob")\nobs<-test.y\ntable(pred,obs)## obs\n## pred versicolor virginica\n## versicolor 16 0\n## virginica 0 16#twoClassSummary(data=data.frame(obs,pred,prob),lev=levels(pred))Load data set, and set model x and y.
\n#load library now to prevent loading messages\nSys.setenv(ALLOW_WGCNA_THREADS=1)\nsuppressPackageStartupMessages(library(WGCNA)) # so annoying## ==========================================================================\n## *\n## * Package WGCNA 1.41.1 loaded.\n## *\n## * Important note: It appears that your system supports multi-threading,\n## * but it is not enabled within WGCNA in R. \n## * To allow multi-threading within WGCNA with all available cores, use \n## *\n## * allowWGCNAThreads()\n## *\n## * within R. Use disableWGCNAThreads() to disable threading if necessary.\n## * Alternatively, set the following environment variable on your system:\n## *\n## * ALLOW_WGCNA_THREADS=<number_of_processors>\n## *\n## * for example \n## *\n## * ALLOW_WGCNA_THREADS=8\n## *\n## * To set the environment variable in linux bash shell, type \n## *\n## * export ALLOW_WGCNA_THREADS=8\n## *\n## * before running R. Other operating systems or shells will\n## * have a similar command to achieve the same aim.\n## *\n## ==========================================================================#data\ndata(mtcars)\n\n#X\npls.data<-mtcars[,-1]\n#y, mpg\npls.y<-mtcars[,1,drop=F]\n\n#make model\nopls.results<-make.OSC.PLS.model(pls.y,pls.data,\n comp=2,\n OSC.comp=1, \n validation = "LOO", \n cv.scale = TRUE,\n train.test.index=NULL,\n progress=FALSE) Get 2 latent variables (LVs) and 1 orthogonal LV model stats.
\n#extra results as some LV and OSC and print model stats\nfinal.opls.results<-get.OSC.model(obj=opls.results,OSC.comp=1) \n(opls.model.text<-data.frame("Xvar"=c(0,round(cumsum(final.opls.results$Xvar)*100,2)),"Q2"=final.opls.results$Q2,"RMSEP"= final.opls.results$RMSEP) )## Xvar Q2 RMSEP\n## 0.00 -0.06555671 6.123385\n## Comp 1 76.67 0.52660903 4.076258\n## Comp 2 88.06 0.74269548 3.004066Predict mpg values for held out car data and calculate test error (RMSEP).
\n#train/test index 2/3 train and 1/3 test\ntrain.test.index <- test.train.split(nrow(pls.data), n = 1) \n\n#fit model \nmods<-make.OSC.PLS.model(pls.y,pls.data,\n comp=2,\n OSC.comp=1, \n validation = "LOO", \n cv.scale = TRUE,\n train.test.index=train.test.index,\n progress=FALSE) \n\n#view predictions for test data\nfinal.opls.results2<-get.OSC.model(obj=mods,OSC.comp=1) \nfitted<-final.opls.results2$predicted.Y\n(RMSEP<-(.MSEP(actual=pls.y[train.test.index=="test",],pred=fitted))^.5)## [1] 3.511172Carry out 100 rounds of training and testing cross-validation and get model performance summary.
\n#train/test index 100 rounds\ntrain.test.index <- test.train.split(nrow(pls.data), n = 100) \nmulti.train.test<-OSC.PLS.train.test(pls.data = pls.data, pls.y = pls.y, train.test.index, comp = mods$total.LVs[1], OSC.comp = max(mods$OSC.LVs), cv.scale = mods$model.description$cv.scale, progress = FALSE) # ...\nmulti.train.test$summary## Xvar Q2 RMSEP \n## [1,] "81.01 +/- 8.35" "0.5406 +/- 0.158" "3.641 +/- 2.34"Carry out permutation testing and calculate random chance statistics (null model).
\nmulti.permute<-permute.OSC.PLS.train.test(pls.data = pls.data, pls.y = pls.y, perm.n = 100, comp = mods$total.LVs[1], OSC.comp=max(mods$OSC.LVs), progress = FALSE, train.test.index = train.test.index)Compare model statistical distrubutions to permuted model performance and calculate proportion of times real model was better then permuted model as a p-value.
\n#compare actual to permuted model performance\n(model.validation<-OSC.validate.model(model = mods, perm = multi.permute, train = multi.train.test,test="perm.test"))## Xvar Q2 RMSEP\n## model 81.01 +/- 8.35 0.5406 +/- 0.158 3.641 +/- 2.34\n## permuted model 99.93 +/- 0.316 -0.137 +/- 0.131 6.375 +/- 1.3\n## p-value 1 0.009901 0.05941Carry out a single round of feature selection select top 4 features and plot results.
\n#feature selection\nopts<-PLS.feature.select(pls.data,pls.scores=final.opls.results$scores[,][,1,drop=F],pls.loadings=final.opls.results$loadings[,][,1,drop=F],pls.weight=final.opls.results$loadings[,][,1,drop=F],plot=FALSE,p.value=0.1,FDR=TRUE,cut.type="number",top=4,separate=FALSE)\n# make s-plot plus\nplot.S.plot(obj=opts,return="all")## NULLCalculate and compare performance statistics for included and excluded feature models.
\noptim<-optimize.OPLS.feature.select(model=opls.results,feature.subset=opts$combined.selection,permute=TRUE,train.test.index,progress=FALSE,test="perm.test") \ncbind(model=c(rep(c("model","permuted","p-value"),2),"p.value"),optim$summary)## model model Xvar Q2 RMSEP\n## 1 model selected 98.78 +/- 1.67 0.6627 +/- 0.0698 3.617 +/- 0.764\n## 2 permuted selected 100 +/- 0 -0.1503 +/- 0.075 6.477 +/- 1.07\n## 3 p-value selected 1 0.009901 0.0198\n## 4 model excluded 62.28 +/- 10.7 0.6126 +/- 0.0746 3.453 +/- 0.71\n## 5 permuted excluded 98.42 +/- 4.47 -0.1165 +/- 0.176 6.603 +/- 1.34\n## 6 p-value excluded 1 0.009901 0.0198\n## 7 p.value comparison 0.009901 0.2178 0.6931Get model stats for decreasing number of model variables using full model loadings calculated above as a gradient.
\n#optimize model feature selections \nfilter<-seq(3,ncol(pls.data)-3) # number of variables to keep\nres<-multi.OPLS.feature.select(model=opls.results,filter=filter,plot=FALSE,OPLSDA=TRUE,train.test.index=train.test.index, test="perm.test", progress=FALSE) # use full model without training split as input\nplot.multi.OPLS.feature.select(res,objects=c("RMSEP","Q2")) # view resultsbest.OPLS.features(res)[,1:5] # extract best model## type vars model Xvar Q2\n## 9 model 7 included 92.582 0.4200007set.seed(1234)\ndata<-matrix(rnorm(10000,0,1),nrow=100, ncol=100)\nsimple.y<-matrix(rep(1:2,50),,1)\ncomplex.y<-matrix(sample(1:2,400,replace=T),,4)\n#scale data \nscaled.data<-data.frame(scale(data,scale=T,center=T)) \n\ncomp<-ocomp<-5 # maximum number of latent variables (LVs)\npls.y<-simple.y\n\nmods1<-OSC.correction(progress=FALSE,pls.y=pls.y,pls.data=scaled.data,comp=comp,OSC.comp=ocomp,validation = "LOO",method="oscorespls",cv.scale=T)\nplot.OSC.results(obj=mods1,plot="RMSEP",groups=group)\n
#create factor to visualize groups\ngroup<-factor(join.columns(pls.y))#visualize levels of y \nplot.OSC.results(obj=mods1,plot="scores",groups=group)\n
#create factor to visualize groups\nplot.OSC.results(obj=mods1,plot="loadings")\n
#fit 1:limit LV/OLV models to overview optimal LV and OLV\noptimal.model<-optimize.OPLS(max.LV=comp, # max LV\n tolerance =0.01, #tolerance for accepting higher error models but which are simpler\n pls.y=pls.y,pls.data=scaled.data, # y and data\n validation = "LOO",method="oscorespls",cv.scale=F,# see pls for theses options\n progress=FALSE) # see pls for theses options\noptimal.model\n## $best\n## RMSEP.1 RMSEP.2 LV OLV pls.y delta.tmp.min.\n## 9 0.4724 0.4704 2 2 1 0\n## \n## $LV\n## [1] 2\n## \n## $OLV\n## [1] 2\ntolerance is used to accept higher RMSEP but simpler models.mods1<-OSC.correction(progress=FALSE,pls.y=pls.y,pls.data=scaled.data,comp=optimal.model$LV,OSC.comp=optimal.model$OLV,validation = "LOO",method="oscorespls",cv.scale=T)\nfinal<-get.OSC.model(obj=mods1,OSC.comp=optimal.model$OLV) \n## Error: 'names' attribute [1] must be the same length as the vector [0]\ngroup<-factor(join.columns(pls.y))#visualize levels of y \nplot.PLS.results(obj=final,plot="scores",groups=group)\n## Error: object 'final' not found\n# make exploratory model to determine orthogonal LV (OLV) number \ncomp<-6 # maximum number of latent variables (LVs)\npls.y<-complex.y\n#fit 1:limit LV/OLV models to overview optimal LV and OLV\noptimal.model<-optimize.OPLS(max.LV=comp, # max LV\n tolerance =0.01, #tolerance for accepting higher error models but which are simpler\n pls.y=pls.y,pls.data=scaled.data, # y and data\n validation = "LOO",method="oscorespls",cv.scale=F,# see pls for theses options\n progress=FALSE) # see pls for theses options\noptimal.model\n## $best\n## RMSEP.1 RMSEP.2 LV OLV pls.y delta.tmp.min.\n## 54 0.4541 0.4528 5 2 1 5.140e-03\n## 78 0.4531 0.4521 5 3 1 4.435e-03\n## 102 0.4487 0.4476 5 4 1 0.000e+00\n## 126 0.4488 0.4478 5 5 1 1.326e-04\n## 30 0.4541 0.4528 2 2 2 3.125e-03\n## 50 0.4506 0.4497 4 2 2 6.253e-05\n## 70 0.4520 0.4511 4 3 2 1.458e-03\n## 90 0.4506 0.4496 4 4 2 0.000e+00\n## 14 0.4703 0.4693 1 1 3 2.804e-03\n## 75 0.4692 0.4676 4 3 3 1.105e-03\n## 95 0.4680 0.4665 4 4 3 0.000e+00\n## 66 0.4778 0.4763 5 2 3 9.788e-03\n## 114 0.4726 0.4711 5 4 3 4.601e-03\n## 138 0.4700 0.4686 5 5 3 2.048e-03\n## 16 0.4911 0.4910 1 1 4 0.000e+00\n## 24 0.4989 0.4981 2 1 4 7.131e-03\n## 36 0.4995 0.4987 2 2 4 7.718e-03\n## \n## $LV\n## [1] 5\n## \n## $OLV\n## [1] 4\nmods1<-OSC.correction(progress=FALSE,pls.y=pls.y,pls.data=scaled.data,comp=optimal.model$LV,OSC.comp=optimal.model$OLV,validation = "LOO",method="oscorespls",cv.scale=T)\nfinal<-get.OSC.model(obj=mods1,OSC.comp=optimal.model$OLV) # get all model information\n## Error: 'names' attribute [1] must be the same length as the vector [0]\n#view model scores\ngroup<-factor(join.columns(pls.y))#visualize levels of y \nplot.PLS.results(obj=final,plot="scores",groups=group)\n## Error: object 'final' not found\npls.y<-matrix(as.numeric(as.factor(join.columns(complex.y))),,1) # create numeric representation\n#fit 1:limit LV/OLV models to overview optimal LV and OLV\noptimal.model<-optimize.OPLS(max.LV=comp, # max LV\n tolerance =0.01, #tolerance for accepting higher error models but which are simpler\n pls.y=pls.y,pls.data=scaled.data, # y and data\n validation = "LOO",method="oscorespls",cv.scale=F,# see pls for theses options\n progress=FALSE) \noptimal.model\n## $best\n## RMSEP.1 RMSEP.2 LV OLV pls.y delta.tmp.min.\n## 4 4.103 4.09 1 1 1 0\n## \n## $LV\n## [1] 1\n## \n## $OLV\n## [1] 1\n# currently single LV models will cause an error so limit LV minimium to 2\nif(optimal.model$LV==1){optimal.model$LV<-2}\nmods1<-OSC.correction(progress=FALSE,pls.y=pls.y,pls.data=scaled.data,comp=optimal.model$LV,OSC.comp=optimal.model$OLV,validation = "LOO",method="oscorespls",cv.scale=T)\nfinal<-get.OSC.model(obj=mods1,OSC.comp=optimal.model$OLV) # get all model information\n## Error: 'names' attribute [1] must be the same length as the vector [0]\ngroup<-factor(join.columns(pls.y))#visualize levels of y \nplot.PLS.results(obj=final,plot="scores",groups=group) \n## Error: object 'final' not found\n# this is a comment R doesn't interpret this\n10 # works\n## [1] 10\n# a # remove comment and try to type a\nR knows what 10 is, it is a number which has been defined in the R environment. However a has not been defined and R throws an error when trying to print it to the console.
"a" # works\n## [1] "a"\na <- 10 # assignment\nb <- a + 1 # using a pre assigned variable\nWhen we give a we define that this object is as a character vector “a” which R understands how to print to screen. We can assign 10 to something R knows about like for instance 10. Having done this we can now use 10 as a variable to do whatever we want.
a + b #addition\n## [1] 21\na - b #subtraction\n## [1] -1\na * b #multiplication\n## [1] 110\na/b #division\n## [1] 0.9091\na^b #exponentiation\n## [1] 1e+11\na%/%b #integer division\n## [1] 0\na%%b #modulo (remainder) b%%a makes more sense\n## [1] 10\na == b #is a equal to b\n## [1] FALSE\na > b # a is greater than b, use < for less than\n## [1] FALSE\na >= b # a is greater than or equal to\n## [1] FALSE\n! #not\na!=b # is a equal to be? could also do !a==b \n## [1] FALSE\n#& #and\n# | #or\n# && #sequential and\n# || #sequential or\nx <- 1:10 # note ':' creates a sequence\nx != a # notice how the shorter a is recycled\n## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE\n(id <- !x >= a) # notice we assigned the results to id and use () to print this\n## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE FALSE\nx > a | x < b # check if any criteria specified is TRUE\n## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\nx > a & x < b # check if all criteria specified is TRUE\n## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE\na[1] # get the first 'unit' of a\n## [1] 10\na[2] # trying to reference something which doesn't exist, NA stands for missing value\n## [1] NA\nx[2] # this works because x has length >=2\n## [1] 2\nx[c(1, 2, 6:8)] # can use c() to get many specific elements\n## [1] 1 2 6 7 8\nx[id] # we can also use a logical to get our object\n## [1] 1 2 3 4 5 6 7 8 9\nstr(x) #structure, x is an integer vector (one dimension)\n## int [1:10] 1 2 3 4 5 6 7 8 9 10\nclass(id) # is logical\n## [1] "logical"\nlength(x) # length\n## [1] 10\ndata(iris) # we use a function named data to load the iris data\nstr(iris) # structure\n## 'data.frame': 150 obs. of 5 variables:\n## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...\n## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...\n## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...\n## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...\n## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...\n# notice we have 2 dimensions now, rows and columns now subset the object as\n# object[rows,columns]\niris[1:5, 3:4]\n## Petal.Length Petal.Width\n## 1 1.4 0.2\n## 2 1.4 0.2\n## 3 1.3 0.2\n## 4 1.5 0.2\n## 5 1.4 0.2\nspecies <- iris$Species # in data.frames and list we can also reference columns with '$'\nspecies <- iris[, "Species"] # or by name\nplot(iris[, 1:2]) # plot the first 2 columns\n
help(plot) #see what other arguments plot could take\nplot(iris[, 1], species)\n
plot(iris[, 1] ~ species) # plot is different because species is factor and formula notation '~' is used\n
plot(iris[, 1] ~ species, col = c("red", "green", "blue")) # add a color\n
plot(iris[, 1] ~ species, col = c("red", "green", "blue"), ylab = colnames(iris)[1]) # and label, use function colnames() to get column names and take the first columns name\n
plot(iris) # because we gave the whole data frame R calls pairs() and creates a scatterplot matrix\n
color <- c("red", "green", "blue")[species] # we can use the factor to subset our colors to create a color for each point\nplot(iris, pch = 21, bg = color, main = "My Awesome Plot!") # here we give custom point shape 'pch', border 'col' and inner color 'bg' as well as a title 'main'\n
mean(iris) # we want to get the mean but giving the whole data.frame with the factor does not make sense?\n## [1] NA\nmean(iris[, 1]) # this works\n## [1] 5.843\nmean(iris[, "Species"]) # this was the issue, it is not numeric (we could coerce 'as.numeric' but why?)\n## [1] NA\nmean(iris[iris$Species == "setosa", 1]) # here we subset the rows to only get values for the species setosa and return the mean for column 1 for this group\n## [1] 5.006\napply(iris[iris$Species == "setosa", 1:4], 2, mean) #here we use function apply to for each column use the function mean (see help(apply)) \n## Sepal.Length Sepal.Width Petal.Length Petal.Width \n## 5.006 3.428 1.462 0.246\n# we also removed species because we know mean wont work on this and also\n# causes bad behaviour for other columns\nbig.l <- split(iris[, 1:4], iris$Species) # create a list holding a data.frame for each level of the species\n(res <- lapply(big.l, apply, 2, mean)) # get means for each species and variable\n## $setosa\n## Sepal.Length Sepal.Width Petal.Length Petal.Width \n## 5.006 3.428 1.462 0.246 \n## \n## $versicolor\n## Sepal.Length Sepal.Width Petal.Length Petal.Width \n## 5.936 2.770 4.260 1.326 \n## \n## $virginica\n## Sepal.Length Sepal.Width Petal.Length Petal.Width \n## 6.588 2.974 5.552 2.026\ndata.frame(res) # get results combined\n## setosa versicolor virginica\n## Sepal.Length 5.006 5.936 6.588\n## Sepal.Width 3.428 2.770 2.974\n## Petal.Length 1.462 4.260 5.552\n## Petal.Width 0.246 1.326 2.026\nt(data.frame(res)) # transpose results \n## Sepal.Length Sepal.Width Petal.Length Petal.Width\n## setosa 5.006 3.428 1.462 0.246\n## versicolor 5.936 2.770 4.260 1.326\n## virginica 6.588 2.974 5.552 2.026\nmean.sd <- function(x) {\n paste0(round(c(mean(x), sd(x)), 1), collapse = " +/- ") # our custom function\n\n}\nt(sapply(big.l, apply, 2, mean.sd))\n## Sepal.Length Sepal.Width Petal.Length Petal.Width \n## setosa "5 +/- 0.4" "3.4 +/- 0.4" "1.5 +/- 0.2" "0.2 +/- 0.1"\n## versicolor "5.9 +/- 0.5" "2.8 +/- 0.3" "4.3 +/- 0.5" "1.3 +/- 0.2"\n## virginica "6.6 +/- 0.6" "3 +/- 0.3" "5.6 +/- 0.6" "2 +/- 0.3"\nCovariate adjustment is a widely used approach in statistical data analysis to improve the power of tests on independent variables. In this context, covariate adjustment plays an integral role the Analysis of Covariance (ANCOVA). However the reader should be warned that the valid application of ANCOVA makes a few assuptions. For a well written discussion of this topic the reader is directed Misunderstanding Analysis od Covariance.
\n\nCovariate adjustment can also be a useful approach in data pre-processing in the context of multivariate modeling. The following is an example application of covariate adjustment using a linear model. The function covar.adjustment part of the Devium tool set is used to carry out covariate adjustment on the famous Iris data set.
\n\nHere are the major steps involved
\n\nsource("http://pastebin.com/raw.php?i=UyDBTA57") # source Devium\ndata(iris)\nplot(data.frame(iris[, !colnames(iris) %in% "Species"]), pch = 21, bg = rainbow(nlevels(iris$Species), \n alpha = 0.75)[iris$Species], cex = 2)\n
Note the difference in the relationship between Sepal.width and Sepal.length for different species of Iris. We may want to adjust all flower measurements to model this relationship independnet of species. However be warned this may be an invalid assumption (see Introduction).
\n\n\n\nfactor <- iris$Species\nformula <- "factor"\ndata <- iris[, !colnames(iris) %in% "Species"]\nadj.iris <- covar.adjustment(data, formula)\nThe adjustment is done by creating a linear model for each variable and Species labels. The “Species” adjusted data is then the residuals from this model.
\n\n\n\nplot(as.data.frame(adj.iris), pch = 21, bg = rainbow(nlevels(iris$Species), \n alpha = 0.75)[iris$Species], cex = 2)\n
Now all the differences in the relationships among variables due to different species is removed (which may make no sense).\nWe can check this assumption on a multivariate basis using principal components analysis (PCA) and partial least squared discriminant analysis (O-PLS-DA).
\n\nCompare the PCA scores between the raw and covariate adjusted data.
\n\n# set PCA defaults\npca.inputs <- list()\npca.inputs$pca.algorithm <- "svd"\npca.inputs$pca.components <- 2\npca.inputs$pca.center <- TRUE\npca.inputs$pca.scaling <- "uv"\npca.inputs$pca.cv <- "q2"\n\n# calculate raw data PCA\npca.inputs$pca.data <- data\npca.raw <- devium.pca.calculate(pca.inputs, return = "list", plot = FALSE)\n\n# adjusted data PCA\npca.inputs$pca.data <- adj.iris\npca.adj <- devium.pca.calculate(pca.inputs, return = "list", plot = FALSE)\nRaw data
\n\n# plot.PCA(pca.raw, results =\n# c('screeplot'),size=3,color=data.frame(species=factor), label=FALSE)\nplot.PCA(pca.raw, results = c("scores"), size = 3, color = data.frame(species = factor), \n label = FALSE)\n
Adjusted data
\n\n# plot.PCA(pca.adj, results =\n# c('screeplot'),size=3,color=data.frame(species=factor), label=FALSE)\nplot.PCA(pca.adj, results = c("scores"), size = 3, color = data.frame(species = factor), \n label = FALSE)\n
Comparison of the raw and adjusted scores first two principal components shows similarities in samples given all the variables. Proximity or distance between diffrent species scores can be directly translated to similarity or dissimilarity in measured values.
\n\nA similair example can be shown using a supervised projection method, orthogonal partial least squares discriminant analysis, which attempts to maximize the diffrences between species. Next we will fit O-PLS-DA models to discriminate species using the raw and adjusted data and overview these models error for species classfication and scores.
\n\n\ngroups <- data.frame(species = factor)\npls.y <- data.frame(as.numeric(groups[, 1]))\n\ndata <- data\nscaled.data1 <- data.frame(scale(data, center = TRUE, scale = TRUE))\nscaled.data2 <- data.frame(scale(adj.iris, center = TRUE, scale = TRUE))\n# make models\nmods1 <- make.OSC.PLS.model(pls.y, pls.data = scaled.data1, comp = 2, OSC.comp = 1, \n validation = "LOO", method = "oscorespls", cv.scale = TRUE, return.obj = "stats", \n progress = FALSE)\nmods2 <- make.OSC.PLS.model(pls.y, pls.data = scaled.data2, comp = 2, OSC.comp = 1, \n validation = "LOO", method = "oscorespls", cv.scale = TRUE, return.obj = "stats", \n progress = FALSE)\nView the error for the classfication of raw and adjusted data.
\n\nplot.OSC.results(mods1, plot = "RMSEP", groups = groups)\n
plot.OSC.results(mods2, plot = "RMSEP", groups = groups)\n
Now we see that after removing the variance in measurements due to diffrences between species our error of prediction gets very high, and is interstingly worse than what we would expect by random chance (RMSEP=0.5). Next we can compare the O-PLS-DA scores from these two models.
\n\nfinal <- get.OSC.model(obj = mods1, OSC.comp = 1)\nplot.PLS(final, results = "scores", color = groups, label = FALSE)\n
final <- results <- get.OSC.model(obj = mods2, OSC.comp = 1)\nplot.PLS(final, results = "scores", color = groups, label = FALSE)\n
In this simple example of covariate adjustment using a linear model all that is really happening is the intercept is now the same for all relationships/species. We can confirm this looking at all the pairwise relationships or on a multivariate exploratory level through PCA, or using the supervised approch of O-PLS-DA.
\n\n© Dmitry Grapov (2014)
\r\n"
},
{
"path": "Demos/Statistical Analysis/style.R",
"content": "\noptions(rstudio.markdownToHTML = \n function(inputFile, outputFile) { \n require(markdown)\n markdownToHTML(inputFile, outputFile, stylesheet=system.file(\"misc\", \"docco-template.html\", \n package = \"knitr\")) \n }\n)\n"
},
{
"path": "Demos/Translating Between Chemical Identifiers/Translations.r",
"content": "#load necessary functions\r\n#install background packages\r\ninstall.packages(\"devtools\");install.packages(\"RJSONIO\")\r\nlibrary(devtools);library(RJSONIO)\r\n\r\n#install packages for translations\r\n# The Chemical Translation System\r\ninstall_github(repo = \"CTSgetR\", username = \"dgrapov\")\r\nlibrary(CTSgetR)\r\n\r\n#for the Chemical Identifier Resolver (CIR) \r\ninstall_github(repo = \"CIRgetR\", username = \"dgrapov\")\r\nlibrary(CIRgetR)\r\n\r\n#InchiKeys used for example\r\nid<-c(\"ZKHQWZAMYRWXGA-KQYNXXCUSA-N\", \"BAWFJGJZGIEFAR-NNYOXOHSSA-O\",\"QNAYBMKLOCPYGJ-REOHCLBHSA-N\") \r\n#create.csv to simulate loading fromm .csv (file written to current directory, getwd())\r\nwrite.csv(data.frame(InchiKey=id),file=\"InchIKeys.csv\",row.names=FALSE) #create.csv to simulate loading fromm .csv\r\n#upload .csv \r\nid<-read.csv(file=\"InchIKeys.csv\",header=TRUE)\r\n\r\n# Goal: translate from inchiKeys to ChemSpider Ids \r\n\r\n#use Chemical Identifier Resolver (CIR) by the CADD Group at the NCI/NIH\r\nresults<-CIRgetR(id,to= \"chemspider_id\",return.all=FALSE) \r\n\r\n#use the Chemical Translation System\r\nresults2<-CTSgetR(id,from=\"InChIKey\",to=\"ChemSpider\",parallel=FALSE)\r\n\r\n#are there any differences between two results?\r\nmiss.match<-!as.matrix(results2)%in%as.matrix(results)|!as.matrix(results)%in%as.matrix(results2)\r\npaste(sum(miss.match),\"difference(s) between results\",sep=\" \") \r\ndata.frame(CIR= results[,1], CTS = results2[,1])[miss.match,]#two different records for both are Alanine\r\n\r\n#CTS (but not CIR) can be used to generate InChI key/code from identifier\r\nCSid<-results[miss.match,] # convert CIR ChemSpider Id to inChiKey\r\nresults3<-CTSgetR(CSid,from=\"ChemSpider\",to=\"InChIKey\",parallel=FALSE)\r\n\r\n#compare keys\r\n<<<<<<< HEAD\r\nif(as.matrix(results3)==as.matrix(id[miss.match,,drop=FALSE]))cat(\"codes match!\",\"\\n\") else cat(\"codes DO NOT match!\",\"\\n\")\r\n=======\r\nif(results3==id[miss.match])cat(\"codes match!\",\"\\n\") else cat(\"codes DO NOT match!\",\"\\n\")\r\n>>>>>>> 527fe4c248ad2040b68cecee54e55a80e05bade5\r\n\r\n#here is a more advanced example for translating from one ID to many\r\n##translate InchI Key to allpossible options available in CIR\r\nCIR.options<-c(\"smiles\", \"names\", \"iupac_name\", \"cas\", \"inchi\", \"stdinchi\", \"inchikey\", \"stdinchikey\",\r\n\t\t\"ficts\", \"ficus\", \"uuuuu\", \"image\", \"file\", \"mw\", \"monoisotopic_mass\",\"chemspider_id\",\r\n\t\t\"pubchem_sid\", \"chemnavigator_sid\", \"formula\", \"chemnavigator_sid\")\t\t\r\n\t\r\nall.results.CIR<-sapply(1:length(CIR.options), function(i)\r\n\t{\r\n\t\tcat(CIR.options[i],\"\\n\")\r\n\t\tCIRgetR(id=id,to=CIR.options[i],return.all=FALSE)\r\n\t})\r\nnames(all.results.CIR)<-CIR.options\t\r\nall.results.CIR<-data.frame(all.results.CIR )# object\r\n\r\n\r\n#get all possible options from CTS\r\nCTS.options<-CTS.options()\r\nCTS.options # see options\r\nid<-results2\r\nall.results.CTS<-sapply(1:length(CTS.options), function(i)\r\n\t{\r\n\t\tcat(CTS.options[i],\"\\n\")\r\n\t\tCTSgetR(id=id,to=CTS.options[i],from=\"ChemSpider\")\r\n\t})\r\nnames(all.results.CTS)<-CTS.options\t\r\nall.results.CTS<-data.frame(all.results.CTS) # object\r\n\r\n#calculate % error for each querry as a percent of asked translations\r\nCIR.error<-round(((sum(unlist(all.results.CIR)==\"This is meant to be an introduction to dplyr which covers dplyr basics, gets into a little bit of programming with dplyr and ends with brief mention of some gotchas and a benchmarking comparison to base for the split-apply strategy. You might also find Rstudio’s Data Wrangling Cheat Sheet featuring dplyr useful (this is also where I borrowed some of the images used in this tutorial).
\nThe dplyr package from Hadley Wickham is plain awesome. It features consistent and succinct syntax, is computationally fast and getting better with every release. The dplyr package has replaced many common more verbose R idioms which I had to previously rely upon for most common data analysis tasks.
For example, many data analysis tasks involve the procedure of splitting the data set based on a grouping variable and then applying a function to each of the groups (split-apply). Lets say I want to calculate the median values for a few parameters for cars with different numbers of cylinders using the mtcars data set.
\nSet up the data for the example.
\n#some data prep\ndata(mtcars)\ndata<-mtcars\ndata$cyl<-factor(data$cyl)Split-lapply-apply in base:
#select some variable of interest\nvars<-c("mpg","wt","qsec")\ntmp.data<-data[,colnames(data)%in%vars]\n\n#split the data on the number of cylinders\nbig.l<-split(tmp.data,data$cyl)\n\n#apply some function of interest to all columns\nresults<-lapply(big.l, function(x) apply(x,2,median))\n\n#bind results and add splitting info\ndata.frame(cyl=names(results),do.call("rbind",results))## cyl mpg wt qsec\n## 4 4 26.0 2.200 18.900\n## 6 6 19.7 3.215 18.300\n## 8 8 15.2 3.755 17.175Now the same process using dplyr:
suppressPackageStartupMessages(library(dplyr))\n\n#variables of interest\nvars<-c("mpg","wt","qsec")\n\ndata %>% group_by(cyl) %>% select(one_of(vars)) %>% summarise_each(funs(median(.)))## Source: local data frame [3 x 4]\n## \n## cyl mpg wt qsec\n## 1 4 26.0 2.200 18.900\n## 2 6 19.7 3.215 18.300\n## 3 8 15.2 3.755 17.175Switching from base to dplyr for data manipulation feels a little like this:
base
dplyr
Each of the individual dplyr verbs are discussed in more detail below, but the use of %>% or the pipe operator is worth mentioning now. The %>% operator is imported from magrittr and for the purpose of this tutorial we can simply think of it as then. From the cheatsheet referenced above:
I highly recommend that you take a look at the dplyr vignetts for more detailed description of all of this packages capabilities. One immediate addition in dplyr you might notice is tbl_df which is a local data frame and mostly behaves like the classical data.frame but is more convenient for working with large data.
tbl_df(mtcars)## Source: local data frame [32 x 11]\n## \n## mpg cyl disp hp drat wt qsec vs am gear carb\n## 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4\n## 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4\n## 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1\n## 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1\n## 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2\n## 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1\n## 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4\n## 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2\n## 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2\n## 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4\n## .. ... ... ... ... ... ... ... .. .. ... ...# control the number of rows\nprint(tbl_df(mtcars),n=5)## Source: local data frame [32 x 11]\n## \n## mpg cyl disp hp drat wt qsec vs am gear carb\n## 1 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4\n## 2 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4\n## 3 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1\n## 4 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1\n## 5 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2\n## .. ... ... ... ... ... ... ... .. .. ... ...You can can make sure all columns are output to the screen using options(dplyr.width = Inf). glimpse is another useful function which is an analogue of str but tries to show you more of the data.
str(mtcars)## 'data.frame': 32 obs. of 11 variables:\n## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...\n## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...\n## $ disp: num 160 160 108 258 360 ...\n## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...\n## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...\n## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...\n## $ qsec: num 16.5 17 18.6 19.4 17 ...\n## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...\n## $ am : num 1 1 1 0 0 0 0 0 0 0 ...\n## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...\n## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...glimpse(mtcars)## Observations: 32\n## Variables:\n## $ mpg (dbl) 21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19....\n## $ cyl (dbl) 6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8, 8, 8, 8, 8, 8, 4, 4, ...\n## $ disp (dbl) 160.0, 160.0, 108.0, 258.0, 360.0, 225.0, 360.0, 146.7, 1...\n## $ hp (dbl) 110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123, 180, ...\n## $ drat (dbl) 3.90, 3.90, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.9...\n## $ wt (dbl) 2.620, 2.875, 2.320, 3.215, 3.440, 3.460, 3.570, 3.190, 3...\n## $ qsec (dbl) 16.46, 17.02, 18.61, 19.44, 17.02, 20.22, 15.84, 20.00, 2...\n## $ vs (dbl) 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, ...\n## $ am (dbl) 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, ...\n## $ gear (dbl) 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, ...\n## $ carb (dbl) 4, 4, 1, 1, 2, 1, 4, 2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, ...For the purpose of this tutorial we will be mostly working with data.frames, however it should be noted that dplyr syntax abstracts away the need to specify the kind of object being manipulated and most everything we will cover can also be applied to interact with a variety of database objects.
\nThe most common dplyr functions also referred to as verbs are as follows (see more in the introduction vignette):
The following commands will be demonstrated using the hflights data set.
\nsuppressPackageStartupMessages(library(hflights))## Warning: package 'hflights' was built under R version 3.1.3(flights <- tbl_df(hflights))## Source: local data frame [227,496 x 21]\n## \n## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier FlightNum\n## 1 2011 1 1 6 1400 1500 AA 428\n## 2 2011 1 2 7 1401 1501 AA 428\n## 3 2011 1 3 1 1352 1502 AA 428\n## 4 2011 1 4 2 1403 1513 AA 428\n## 5 2011 1 5 3 1405 1507 AA 428\n## 6 2011 1 6 4 1359 1503 AA 428\n## 7 2011 1 7 5 1359 1509 AA 428\n## 8 2011 1 8 6 1355 1454 AA 428\n## 9 2011 1 9 7 1443 1554 AA 428\n## 10 2011 1 10 1 1443 1553 AA 428\n## .. ... ... ... ... ... ... ... ...\n## Variables not shown: TailNum (chr), ActualElapsedTime (int), AirTime\n## (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest (chr),\n## Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),\n## CancellationCode (chr), Diverted (int)Which contains 227496 records for 21 variables for flights departing Houston airport for 2011.
\nUse filter to keep or select rows matching some criteria or condition(s).
base
flights[flights$Month == 1 & flights$DayofMonth == 1, ]## Source: local data frame [552 x 21]\n## \n## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier FlightNum\n## 1 2011 1 1 6 1400 1500 AA 428\n## 2 2011 1 1 6 728 840 AA 460\n## 3 2011 1 1 6 1631 1736 AA 1121\n## 4 2011 1 1 6 1756 2112 AA 1294\n## 5 2011 1 1 6 1012 1347 AA 1700\n## 6 2011 1 1 6 1211 1325 AA 1820\n## 7 2011 1 1 6 557 906 AA 1994\n## 8 2011 1 1 6 1824 2106 AS 731\n## 9 2011 1 1 6 654 1124 B6 620\n## 10 2011 1 1 6 1639 2110 B6 622\n## .. ... ... ... ... ... ... ... ...\n## Variables not shown: TailNum (chr), ActualElapsedTime (int), AirTime\n## (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest (chr),\n## Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),\n## CancellationCode (chr), Diverted (int)#could have also used subset\nsubset(flights, Month == 1 & DayofMonth == 1)## Source: local data frame [552 x 21]\n## \n## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier FlightNum\n## 1 2011 1 1 6 1400 1500 AA 428\n## 2 2011 1 1 6 728 840 AA 460\n## 3 2011 1 1 6 1631 1736 AA 1121\n## 4 2011 1 1 6 1756 2112 AA 1294\n## 5 2011 1 1 6 1012 1347 AA 1700\n## 6 2011 1 1 6 1211 1325 AA 1820\n## 7 2011 1 1 6 557 906 AA 1994\n## 8 2011 1 1 6 1824 2106 AS 731\n## 9 2011 1 1 6 654 1124 B6 620\n## 10 2011 1 1 6 1639 2110 B6 622\n## .. ... ... ... ... ... ... ... ...\n## Variables not shown: TailNum (chr), ActualElapsedTime (int), AirTime\n## (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest (chr),\n## Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),\n## CancellationCode (chr), Diverted (int)#be wary of using subset programmatically: http://stackoverflow.com/questions/9860090/in-r-why-is-better-than-subsetdplyr
#comma is the same as an ampersand (&)\nfilter(flights, Month == 1, DayofMonth == 1)## Source: local data frame [552 x 21]\n## \n## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier FlightNum\n## 1 2011 1 1 6 1400 1500 AA 428\n## 2 2011 1 1 6 728 840 AA 460\n## 3 2011 1 1 6 1631 1736 AA 1121\n## 4 2011 1 1 6 1756 2112 AA 1294\n## 5 2011 1 1 6 1012 1347 AA 1700\n## 6 2011 1 1 6 1211 1325 AA 1820\n## 7 2011 1 1 6 557 906 AA 1994\n## 8 2011 1 1 6 1824 2106 AS 731\n## 9 2011 1 1 6 654 1124 B6 620\n## 10 2011 1 1 6 1639 2110 B6 622\n## .. ... ... ... ... ... ... ... ...\n## Variables not shown: TailNum (chr), ActualElapsedTime (int), AirTime\n## (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest (chr),\n## Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),\n## CancellationCode (chr), Diverted (int)# use pipe(|) for or\nfilter(flights, Month == 1 | DayofMonth == 1)## Source: local data frame [25,769 x 21]\n## \n## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier FlightNum\n## 1 2011 1 1 6 1400 1500 AA 428\n## 2 2011 1 2 7 1401 1501 AA 428\n## 3 2011 1 3 1 1352 1502 AA 428\n## 4 2011 1 4 2 1403 1513 AA 428\n## 5 2011 1 5 3 1405 1507 AA 428\n## 6 2011 1 6 4 1359 1503 AA 428\n## 7 2011 1 7 5 1359 1509 AA 428\n## 8 2011 1 8 6 1355 1454 AA 428\n## 9 2011 1 9 7 1443 1554 AA 428\n## 10 2011 1 10 1 1443 1553 AA 428\n## .. ... ... ... ... ... ... ... ...\n## Variables not shown: TailNum (chr), ActualElapsedTime (int), AirTime\n## (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest (chr),\n## Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),\n## CancellationCode (chr), Diverted (int)We can also include any of the following operators in filter.
\nSlice is a variant of filter used to extract rows based on position.
\nbase
flights[1:10,]## Source: local data frame [10 x 21]\n## \n## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier FlightNum\n## 1 2011 1 1 6 1400 1500 AA 428\n## 2 2011 1 2 7 1401 1501 AA 428\n## 3 2011 1 3 1 1352 1502 AA 428\n## 4 2011 1 4 2 1403 1513 AA 428\n## 5 2011 1 5 3 1405 1507 AA 428\n## 6 2011 1 6 4 1359 1503 AA 428\n## 7 2011 1 7 5 1359 1509 AA 428\n## 8 2011 1 8 6 1355 1454 AA 428\n## 9 2011 1 9 7 1443 1554 AA 428\n## 10 2011 1 10 1 1443 1553 AA 428\n## Variables not shown: TailNum (chr), ActualElapsedTime (int), AirTime\n## (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest (chr),\n## Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),\n## CancellationCode (chr), Diverted (int)dplyr
slice(flights, 1:10)## Source: local data frame [10 x 21]\n## \n## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier FlightNum\n## 1 2011 1 1 6 1400 1500 AA 428\n## 2 2011 1 2 7 1401 1501 AA 428\n## 3 2011 1 3 1 1352 1502 AA 428\n## 4 2011 1 4 2 1403 1513 AA 428\n## 5 2011 1 5 3 1405 1507 AA 428\n## 6 2011 1 6 4 1359 1503 AA 428\n## 7 2011 1 7 5 1359 1509 AA 428\n## 8 2011 1 8 6 1355 1454 AA 428\n## 9 2011 1 9 7 1443 1554 AA 428\n## 10 2011 1 10 1 1443 1553 AA 428\n## Variables not shown: TailNum (chr), ActualElapsedTime (int), AirTime\n## (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest (chr),\n## Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),\n## CancellationCode (chr), Diverted (int)Order data based on specified columns.
\nbase
flights[order(flights$Month),]## Source: local data frame [227,496 x 21]\n## \n## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier FlightNum\n## 1 2011 1 1 6 1400 1500 AA 428\n## 2 2011 1 2 7 1401 1501 AA 428\n## 3 2011 1 3 1 1352 1502 AA 428\n## 4 2011 1 4 2 1403 1513 AA 428\n## 5 2011 1 5 3 1405 1507 AA 428\n## 6 2011 1 6 4 1359 1503 AA 428\n## 7 2011 1 7 5 1359 1509 AA 428\n## 8 2011 1 8 6 1355 1454 AA 428\n## 9 2011 1 9 7 1443 1554 AA 428\n## 10 2011 1 10 1 1443 1553 AA 428\n## .. ... ... ... ... ... ... ... ...\n## Variables not shown: TailNum (chr), ActualElapsedTime (int), AirTime\n## (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest (chr),\n## Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),\n## CancellationCode (chr), Diverted (int)dplyr
arrange(flights,Month)## Source: local data frame [227,496 x 21]\n## \n## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier FlightNum\n## 1 2011 1 1 6 1400 1500 AA 428\n## 2 2011 1 2 7 1401 1501 AA 428\n## 3 2011 1 3 1 1352 1502 AA 428\n## 4 2011 1 4 2 1403 1513 AA 428\n## 5 2011 1 5 3 1405 1507 AA 428\n## 6 2011 1 6 4 1359 1503 AA 428\n## 7 2011 1 7 5 1359 1509 AA 428\n## 8 2011 1 8 6 1355 1454 AA 428\n## 9 2011 1 9 7 1443 1554 AA 428\n## 10 2011 1 10 1 1443 1553 AA 428\n## .. ... ... ... ... ... ... ... ...\n## Variables not shown: TailNum (chr), ActualElapsedTime (int), AirTime\n## (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest (chr),\n## Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),\n## CancellationCode (chr), Diverted (int)#decreasing order\narrange(flights,desc(Month))## Source: local data frame [227,496 x 21]\n## \n## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier FlightNum\n## 1 2011 12 15 4 2113 2217 AA 426\n## 2 2011 12 16 5 2004 2128 AA 426\n## 3 2011 12 18 7 2007 2113 AA 426\n## 4 2011 12 19 1 2108 2223 AA 426\n## 5 2011 12 20 2 2008 2107 AA 426\n## 6 2011 12 21 3 2025 2124 AA 426\n## 7 2011 12 22 4 2021 2118 AA 426\n## 8 2011 12 23 5 2015 2118 AA 426\n## 9 2011 12 26 1 2013 2118 AA 426\n## 10 2011 12 27 2 2007 2123 AA 426\n## .. ... ... ... ... ... ... ... ...\n## Variables not shown: TailNum (chr), ActualElapsedTime (int), AirTime\n## (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest (chr),\n## Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),\n## CancellationCode (chr), Diverted (int)#break ties using more columns\narrange(flights,desc(Month),DayOfWeek)## Source: local data frame [227,496 x 21]\n## \n## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier FlightNum\n## 1 2011 12 19 1 2108 2223 AA 426\n## 2 2011 12 26 1 2013 2118 AA 426\n## 3 2011 12 5 1 558 926 AA 466\n## 4 2011 12 12 1 609 921 AA 466\n## 5 2011 12 19 1 603 913 AA 466\n## 6 2011 12 26 1 558 912 AA 466\n## 7 2011 12 5 1 1206 1311 AA 865\n## 8 2011 12 12 1 1339 1436 AA 865\n## 9 2011 12 19 1 1203 1314 AA 865\n## 10 2011 12 26 1 1200 1318 AA 865\n## .. ... ... ... ... ... ... ... ...\n## Variables not shown: TailNum (chr), ActualElapsedTime (int), AirTime\n## (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest (chr),\n## Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),\n## CancellationCode (chr), Diverted (int)Select columns from the data.
\nbase
flights[,colnames(flights)%in%c("Month","DayOfWeek")]## Source: local data frame [227,496 x 2]\n## \n## Month DayOfWeek\n## 1 1 6\n## 2 1 7\n## 3 1 1\n## 4 1 2\n## 5 1 3\n## 6 1 4\n## 7 1 5\n## 8 1 6\n## 9 1 7\n## 10 1 1\n## .. ... ...dplyr
select(flights,Month,DayOfWeek)## Source: local data frame [227,496 x 2]\n## \n## Month DayOfWeek\n## 1 1 6\n## 2 1 7\n## 3 1 1\n## 4 1 2\n## 5 1 3\n## 6 1 4\n## 7 1 5\n## 8 1 6\n## 9 1 7\n## 10 1 1\n## .. ... ...#select using a dynamic variable\nvariables<-c("Month","DayOfWeek")\nselect(flights,one_of(variables))## Source: local data frame [227,496 x 2]\n## \n## Month DayOfWeek\n## 1 1 6\n## 2 1 7\n## 3 1 1\n## 4 1 2\n## 5 1 3\n## 6 1 4\n## 7 1 5\n## 8 1 6\n## 9 1 7\n## 10 1 1\n## .. ... ...#remove variables\nselect(flights,one_of(variables),-Month)## Source: local data frame [227,496 x 1]\n## \n## DayOfWeek\n## 1 6\n## 2 7\n## 3 1\n## 4 2\n## 5 3\n## 6 4\n## 7 5\n## 8 6\n## 9 7\n## 10 1\n## .. ...Select also provides many regular expression wrappers.
\nUse rename to change column names.
rename(flights,diverted=Diverted)## Source: local data frame [227,496 x 21]\n## \n## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier FlightNum\n## 1 2011 1 1 6 1400 1500 AA 428\n## 2 2011 1 2 7 1401 1501 AA 428\n## 3 2011 1 3 1 1352 1502 AA 428\n## 4 2011 1 4 2 1403 1513 AA 428\n## 5 2011 1 5 3 1405 1507 AA 428\n## 6 2011 1 6 4 1359 1503 AA 428\n## 7 2011 1 7 5 1359 1509 AA 428\n## 8 2011 1 8 6 1355 1454 AA 428\n## 9 2011 1 9 7 1443 1554 AA 428\n## 10 2011 1 10 1 1443 1553 AA 428\n## .. ... ... ... ... ... ... ... ...\n## Variables not shown: TailNum (chr), ActualElapsedTime (int), AirTime\n## (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest (chr),\n## Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),\n## CancellationCode (chr), diverted (int)Use theses verbs to create a new column variable which in the case of mutate will be added to the or created as a stand-alone variables transmute.
Lets calculate the wait time based on the difference between ArrTime and DepTime.
base
#transmute like\nhead(wait<-flights$ArrTime - flights$DepTime)## [1] 100 100 150 110 102 144#mutate like\nhead(flights2<-cbind(flights,wait))## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier FlightNum\n## 1 2011 1 1 6 1400 1500 AA 428\n## 2 2011 1 2 7 1401 1501 AA 428\n## 3 2011 1 3 1 1352 1502 AA 428\n## 4 2011 1 4 2 1403 1513 AA 428\n## 5 2011 1 5 3 1405 1507 AA 428\n## 6 2011 1 6 4 1359 1503 AA 428\n## TailNum ActualElapsedTime AirTime ArrDelay DepDelay Origin Dest Distance\n## 1 N576AA 60 40 -10 0 IAH DFW 224\n## 2 N557AA 60 45 -9 1 IAH DFW 224\n## 3 N541AA 70 48 -8 -8 IAH DFW 224\n## 4 N403AA 70 39 3 3 IAH DFW 224\n## 5 N492AA 62 44 -3 5 IAH DFW 224\n## 6 N262AA 64 45 -7 -1 IAH DFW 224\n## TaxiIn TaxiOut Cancelled CancellationCode Diverted wait\n## 1 7 13 0 0 100\n## 2 6 9 0 0 100\n## 3 5 17 0 0 150\n## 4 9 22 0 0 110\n## 5 9 9 0 0 102\n## 6 6 13 0 0 144dplyr
#stand alone\ntransmute(flights,diff = ArrTime - DepTime)## Source: local data frame [227,496 x 1]\n## \n## diff\n## 1 100\n## 2 100\n## 3 150\n## 4 110\n## 5 102\n## 6 144\n## 7 150\n## 8 99\n## 9 111\n## 10 110\n## .. ...#added to data\nmutate(flights,diff = ArrTime - DepTime)## Source: local data frame [227,496 x 22]\n## \n## Year Month DayofMonth DayOfWeek DepTime ArrTime UniqueCarrier FlightNum\n## 1 2011 1 1 6 1400 1500 AA 428\n## 2 2011 1 2 7 1401 1501 AA 428\n## 3 2011 1 3 1 1352 1502 AA 428\n## 4 2011 1 4 2 1403 1513 AA 428\n## 5 2011 1 5 3 1405 1507 AA 428\n## 6 2011 1 6 4 1359 1503 AA 428\n## 7 2011 1 7 5 1359 1509 AA 428\n## 8 2011 1 8 6 1355 1454 AA 428\n## 9 2011 1 9 7 1443 1554 AA 428\n## 10 2011 1 10 1 1443 1553 AA 428\n## .. ... ... ... ... ... ... ... ...\n## Variables not shown: TailNum (chr), ActualElapsedTime (int), AirTime\n## (int), ArrDelay (int), DepDelay (int), Origin (chr), Dest (chr),\n## Distance (int), TaxiIn (int), TaxiOut (int), Cancelled (int),\n## CancellationCode (chr), Diverted (int), diff (int)Many dplyr functions will let you use newly create variables in the same function which is creating the variable in the first place.
\ntransmute(flights,diff = ArrTime - DepTime, ratio = ArrTime/diff, ratio2 = diff/ratio)## Source: local data frame [227,496 x 3]\n## \n## diff ratio ratio2\n## 1 100 15.00000 6.666667\n## 2 100 15.01000 6.662225\n## 3 150 10.01333 14.980027\n## 4 110 13.75455 7.997356\n## 5 102 14.77451 6.903782\n## 6 144 10.43750 13.796407\n## 7 150 10.06000 14.910537\n## 8 99 14.68687 6.740715\n## 9 111 14.00000 7.928571\n## 10 110 14.11818 7.791372\n## .. ... ... ...The mutate_each function can be used to apply a function to every column in the dataframe. Lets bin each column into quartiles using the ntile function.
glimpse(mutate_each(flights,funs(ntile(.,n=4))))## Observations: 227496\n## Variables:\n## $ Year (int) 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...\n## $ Month (int) 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...\n## $ DayofMonth (int) 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2,...\n## $ DayOfWeek (int) 3, 4, 1, 1, 2, 2, 3, 3, 4, 1, 1, 2, 2, 3, 3,...\n## $ DepTime (int) 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 2, 2, 2,...\n## $ ArrTime (int) 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,...\n## $ UniqueCarrier (int) 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...\n## $ FlightNum (int) 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...\n## $ TailNum (int) 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3,...\n## $ ActualElapsedTime (int) 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...\n## $ AirTime (int) 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...\n## $ ArrDelay (int) 1, 1, 1, 3, 2, 2, 2, 1, 4, 4, 4, 3, 1, 2, 1,...\n## $ DepDelay (int) 2, 3, 1, 3, 3, 2, 2, 1, 4, 4, 4, 4, 2, 1, 2,...\n## $ Origin (int) 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...\n## $ Dest (int) 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,...\n## $ Distance (int) 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...\n## $ TaxiIn (int) 3, 3, 2, 4, 4, 3, 4, 3, 4, 3, 4, 1, 3, 2, 3,...\n## $ TaxiOut (int) 2, 1, 3, 4, 1, 2, 3, 2, 4, 4, 4, 2, 2, 3, 1,...\n## $ Cancelled (int) 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...\n## $ CancellationCode (int) 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...\n## $ Diverted (int) 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...Here is a list of some other dplyr convenience functions.
Carry out a function on the data frame returning a single value.
\nLets calculate the range for wait times and use this as an opportunity to involve %>%.
transmute(flights,diff = ArrTime - DepTime) %>% summarise(.,min=min(diff,na.rm=TRUE),max=max(diff,na.rm=TRUE))## Source: local data frame [1 x 2]\n## \n## min max\n## 1 -2322 1350Either we just discovered time travel or we should have been referencing the day and time in our calculation of the difference between arrival and departure. Lets count how many calculations we may have screwed up.
\n(bad<-transmute(flights,diff = ArrTime - DepTime) %>% filter(diff<0) %>% count(.))## Source: local data frame [1 x 1]\n## \n## n\n## 1 2718An error rate of 1.2 %may or may not be acceptable.
\nWe can use summarise_each to apply a function to every column in the data set. Lets calculate the median and median absolute deviation for every numeric variable.
vars<-colnames(flights)[sapply(flights,is.numeric)]\nflights %>% select(one_of(vars)) %>% summarise_each(.,funs(median=median(.,na.rm=TRUE),mad=mad(.,na.rm=TRUE)))## Source: local data frame [1 x 32]\n## \n## Year_median Month_median DayofMonth_median DayOfWeek_median\n## 1 2011 7 16 4\n## Variables not shown: DepTime_median (int), ArrTime_median (dbl),\n## FlightNum_median (dbl), ActualElapsedTime_median (dbl), AirTime_median\n## (dbl), ArrDelay_median (dbl), DepDelay_median (int), Distance_median\n## (dbl), TaxiIn_median (dbl), TaxiOut_median (int), Cancelled_median\n## (dbl), Diverted_median (dbl), Year_mad (dbl), Month_mad (dbl),\n## DayofMonth_mad (dbl), DayOfWeek_mad (dbl), DepTime_mad (dbl),\n## ArrTime_mad (dbl), FlightNum_mad (dbl), ActualElapsedTime_mad (dbl),\n## AirTime_mad (dbl), ArrDelay_mad (dbl), DepDelay_mad (dbl), Distance_mad\n## (dbl), TaxiIn_mad (dbl), TaxiOut_mad (dbl), Cancelled_mad (dbl),\n## Diverted_mad (dbl)Here are some additional functions which can be used with summarise.
\nBreak the data sets into groups of rows.
\ngroup_by adds the final piece of the puzzle we need to execute the split-apply strategy to our hearts content. This function becomes very powerful when combined with the previously discussed dplyr verbs. For example lets calculate which day of the week has the most cancellations.
flights %>% group_by(DayOfWeek) %>% \n select(Cancelled) %>% summarise_each(funs(canceled=sum(.,na.rm=TRUE),\n total=n(),\n percent_cancelled=round(canceled/total*100,1)))## Source: local data frame [7 x 4]\n## \n## DayOfWeek canceled total percent_cancelled\n## 1 1 344 34360 1.0\n## 2 2 369 31649 1.2\n## 3 3 396 31926 1.2\n## 4 4 616 34902 1.8\n## 5 5 663 34972 1.9\n## 6 6 272 27629 1.0\n## 7 7 313 32058 1.0We can also use group_by to generate groups using more than one variable. For example lets calculate the median AirTime times by Distance and TailNum.
(slowest<-flights %>% group_by(Distance,TailNum) %>% \n select(AirTime) %>% summarise_each(funs(mean(.,na.rm=TRUE))))## Source: local data frame [44,698 x 3]\n## Groups: Distance\n## \n## Distance TailNum AirTime\n## 1 79 N14940 30.00000\n## 2 79 N14943 29.00000\n## 3 79 N17928 22.00000\n## 4 127 NaN\n## 5 127 N11106 29.00000\n## 6 127 N11107 24.00000\n## 7 127 N11155 26.00000\n## 8 127 N11187 25.00000\n## 9 127 N11189 26.00000\n## 10 127 N11535 27.33333\n## .. ... ... ...Lets identify the 3 slowest and fastest planes based on AirTime for some arbitrary Distance.
#choose arbitrary distance\ntmp<-slowest %>% na.omit(.) %>% \n filter(.,Distance==781) %>%\n arrange(AirTime)\n\n#not clear why, but the results can't be bound directly\ntmp %>% head(.,3)## Source: local data frame [3 x 3]\n## Groups: Distance\n## \n## Distance TailNum AirTime\n## 1 781 N275WN 85\n## 2 781 N453WN 87\n## 3 781 N725SW 88tmp %>% tail(.,3) %>% arrange(desc(.))## Source: local data frame [3 x 3]\n## Groups: Distance\n## \n## Distance TailNum AirTime\n## 1 781 N474WN 155\n## 2 781 N16646 127\n## 3 781 N14653 139A common data analysis task might be to carry out some group-wise normalization or adjustments of the data. For example we may want to calculate the day of the week with the slowest flights, but also adjust for differences between individual planes. To do this we will start by calculating the average speed for each plane.
\n#calculate speed\nflights<-flights %>% mutate(hrs=AirTime/60, speed=Distance/hrs)\n\n(averages<-flights %>%\n group_by(TailNum) %>% \n select(.,speed) %>%\n summarise_each(funs(mean(.,na.rm=TRUE))) %>% \n rename(.,mean_speed=speed))## Source: local data frame [3,320 x 2]\n## \n## TailNum mean_speed\n## 1 NaN\n## 2 N0EGMQ 462.9232\n## 3 N10156 441.9362\n## 4 N10575 413.5374\n## 5 N11106 436.8909\n## 6 N11107 437.8388\n## 7 N11109 434.9298\n## 8 N11113 438.9109\n## 9 N11119 441.4825\n## 10 N11121 436.0297\n## .. ... ...Next lets express the overall speed for each plane as ratio to the mean plane speed. To do this we will use one of the powerful join capabilities in dplyr.
We will join with the original data set based on TailNum and calculate the plane-adjusted measure of speed.
right_join(flights,averages,by="TailNum") %>%\n mutate(norm_speed = speed / mean_speed) %>% \n group_by(DayOfWeek) %>% \n select(contains("speed")) %>%\n summarise_each(funs(mean(.,na.rm=TRUE)))## Source: local data frame [7 x 4]\n## \n## DayOfWeek speed mean_speed norm_speed\n## 1 1 419.2984 420.5830 0.9965149\n## 2 2 418.2220 420.4509 0.9942130\n## 3 3 419.2926 420.4236 0.9968347\n## 4 4 420.3597 420.3783 0.9995784\n## 5 5 419.9916 420.4688 0.9985305\n## 6 6 427.3059 422.7704 1.0107539\n## 7 7 423.7673 421.4198 1.0053550So if things worked out like we expected it looks like Saturday flights are fastest and Tuesday the slowest.
\nMost of the examples up to this point featured using dplyr in interactive mode. However there are variants of nearly every verb which are best suited for use inside other functions. To see what theses are take a look at verb_ versions of each function (e.g. summarise_).
Finally I will wrap with a relatively non-sophisticated benchmarking head-to-head comparison of base and dplyr speed for the almighty split-apply strategy.
Lets set up the data.
\nrows<-10000\ncols<-100\ngroups<-100\nsamples<-rows/groups\ntmp.data<-data.frame(matrix(rnorm(rows),rows,cols))\ntmp.data$group<-rep(1:groups,each=samples)This data set has 10^{4} rows, 100 columns and 100 groups with 100 samples each.
\nbase
ptm <- Sys.time()\n#split the data on the number of cylinders\nbig.l<-split(tmp.data,tmp.data$group)\n\n#apply some function of interest to all columns\nresults<-lapply(big.l, function(x) apply(x,2,median))\n\n#bind results and add splitting info\nresults<-data.frame(group=names(results),do.call("rbind",results))\n\n#elapsed time\n(bd<-Sys.time()-ptm )## Time difference of 0.5802019 secsdplyr
ptm <- Sys.time()\n\nresults<-tmp.data %>% group_by(group) %>% summarise_each(funs(median(.)))\n\n#elapsed time\n(ad<-Sys.time()-ptm )## Time difference of 0.3492 secsWow I just saved 0.2310019 seconds of my life!
\nCreate a benchmark visualization comparing base to dplyr for differing number of groups, rows and columns. Uncomment the code in the appendix below and modify as needed to re-run the benchmark.
\nload(file="benchmark results")\n\n#create a plot\nlibrary(reshape2)\nlibrary(ggplot2)\ntmp.data<-melt(res,id.vars=c("rows","columns","groups","samples")) %>% mutate(seconds=value*60)\n\nggplot(tmp.data, aes(y=seconds,x=groups,group=variable,color=variable)) + geom_line() +geom_point()+ facet_grid(rows ~ columns) +scale_y_log10()The plot above shows the calculation time for 10 replications in seconds (y-axis) for calculating the median of varying number of groups (x-axis), rows (y-facet) and columns (y-facet).
\ndplyr rownames are a second class citizen and are not stored.dplyr tutorial features pipes (%>%) it may be easier to learn both dplyr and %>% separately.dplyr functions only work on objects coercible to ~ data.frames. Lots of my debugging sessions start with trying to understand the data structure of objects I am passing to dplyr.# \n# #set up functions to time\n# base_fun<-function(data){\n# #split the data on the number of cylinders\n# big.l<-split(data,data$group)\n# \n# #apply some function of interest to all columns\n# results<-lapply(big.l, function(x) apply(x,2,median))\n# \n# #bind results and add splitting info\n# data.frame(group=names(results),do.call("rbind",results))\n# }\n# \n# dplyr_fun<-function(data){\n# \n# data %>% group_by(group) %>% summarise_each(funs(median(.)))\n# }\n# \n# #benchmark function\n# benchmark_fun<-function(rows,cols,groups){\n# \n# #set up data\n# samples<-floor(rows/groups)\n# tmp.data<-data.frame(matrix(rnorm(rows),rows,cols))\n# tmp.data$group<-rep(1:groups,length.out=rows)\n# \n# #base\n# base.time<-system.time(replicate(10,base_fun(tmp.data)))\n# \n# #dplyr\n# dplyr.time<-system.time(replicate(10,dplyr_fun(tmp.data)))\n# \n# data.frame(rows=rows,columns=cols,groups=groups,samples=samples,base=signif(base.time["elapsed"]/10,3),dplyr=signif(dplyr.time["elapsed"]/10,3))\n# \n# }\n# \n# #run benchmarks\n# len<-5\n# groups<-seq(5, 100,length.out=len) %>% signif(.,0)\n# rows<-seq(100, 10000,length.out=len) %>% signif(.,0)\n# cols<-seq(10, 100,length.out=len) %>% signif(.,0)\n# \n# #benchmarks\n# results<-list()\n# counter<-1\n# for(i in 1:length(groups)){ \n# .group<-groups[i]\n# for(j in 1:length(rows)){\n# .row<-rows[j]\n# for(k in 1:length(cols)){\n# .col<-cols[k]\n# results[[counter]]<- benchmark_fun(.row,.col,.group)\n# counter<-counter+1\n# }\n# } \n# } \n# \n# \n# res<-do.call("rbind",results)\n# save(res,file="benchmark results")\n# #create a plot\n# library(reshape2)\n# library(ggplot2)\n# tmp.data<-melt(res,id.vars=c("rows","columns","groups","samples")) %>% mutate(seconds=value*60)\n# \n# ggplot(tmp.data, aes(y=seconds,x=groups,group=variable,color=variable)) + geom_line() +geom_point()+ facet_grid(columns ~ rows) +scale_y_log10()Bioinformatics related demos and tutorials using the R programming\nlanguage.
\n