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Repository: dgrapov/TeachingDemos
Branch: master
Commit: d949dfa2435e
Files: 79
Total size: 55.6 MB
Directory structure:
gitextract_swixnm2d/
├── .gitignore
├── Demos/
│ ├── Biochemical and Chemical Similarity Networks/
│ │ ├── Edge list.xlsx
│ │ ├── Node attributes.xlsx
│ │ ├── Tutorial- Building Biochemical and Chemical Similarity Based Networks .r
│ │ ├── Tutorial- Building Biochemical and Chemical Similarity Networks.pptx
│ │ └── partial network.cys
│ ├── Data Analysis Workflow/
│ │ ├── data/
│ │ │ └── data cube
│ │ ├── data analysis workflow.R
│ │ ├── images/
│ │ │ └── hsa00010.xml
│ │ ├── report/
│ │ │ ├── Data Analysis Workflows 2 2015.pptx
│ │ │ ├── Data Analysis Workflows 2015.pptx
│ │ │ ├── Data Analysis Workflows part 1 2015.pptx
│ │ │ ├── Data Analysis Workflows part 2 2015.pptx
│ │ │ ├── hsa00010.xml
│ │ │ ├── report.Rmd
│ │ │ ├── report.html
│ │ │ └── report.md
│ │ └── results/
│ │ ├── IMPaLA_results.csv
│ │ ├── statistical_results.csv
│ │ └── statistical_results_sig.csv
│ ├── Introduction to Network Mapping/
│ │ ├── Demonstration.pptx
│ │ ├── biochem network edge list.xlsx
│ │ ├── biochem network node attributes.xlsx
│ │ ├── biochemical network.cys
│ │ ├── name edge list.xlsx
│ │ ├── name network.cys
│ │ └── node attributes.xlsx
│ ├── Mapped Network From Data (Biochemical and Structural)/
│ │ ├── .RData
│ │ ├── data.csv
│ │ ├── edge.list.csv
│ │ ├── network code.r
│ │ ├── network visualization.cys
│ │ ├── node.attributes.csv
│ │ ├── node.attributes.xls
│ │ ├── sample info.csv
│ │ ├── statistics.csv
│ │ └── variable info.csv
│ ├── Pathway Analysis/
│ │ ├── .xml
│ │ ├── KEGG Pathway Enrichment.Rmd
│ │ ├── KEGG Pathway Enrichment.html
│ │ ├── KEGG Pathway Enrichment.md
│ │ ├── ath00020.xml
│ │ ├── ath00260.path
│ │ ├── ath00260.xml
│ │ ├── ath00650.xml
│ │ ├── athp
│ │ └── hsa00010.xml
│ ├── Predictive Modeling/
│ │ ├── Iris O-PLS-DA/
│ │ │ ├── O-PLS modeling of Iris data.Rmd
│ │ │ ├── O-PLS modeling of Iris data.html
│ │ │ ├── O-PLS modeling of Iris data.md
│ │ │ ├── O-PLS_modeling_of_Iris_data.html
│ │ │ ├── O-PLS_modeling_of_Iris_data.md
│ │ │ └── demo.r
│ │ ├── O-PLS/
│ │ │ ├── OPLS example.Rmd
│ │ │ ├── OPLS_example.html
│ │ │ └── OPLS_example.md
│ │ └── Single and multiple Y O-PLS modeling/
│ │ ├── OPLS example.Rmd
│ │ ├── OPLS example.html
│ │ ├── OPLS example.md
│ │ └── OPLS example.r
│ ├── R Programming/
│ │ ├── R tutorial 1.R
│ │ ├── R tutorial 1.html
│ │ ├── R tutorial 1.md
│ │ └── R tutorial 1.rmd
│ ├── Statistical Analysis/
│ │ ├── Covariate Adjustment.Rmd
│ │ ├── Covariate Adjustment.html
│ │ ├── Covariate Adjustment.md
│ │ └── style.R
│ ├── Translating Between Chemical Identifiers/
│ │ └── Translations.r
│ └── dplyr/
│ ├── Tutorials.Rproj
│ ├── benchmark results
│ ├── data/
│ │ └── benchmark results
│ ├── hands_on_with_dplyr.Rmd
│ ├── hands_on_with_dplyr.html
│ └── hands_on_with_dplyr.md
├── README.html
├── README.md
├── TeachingDemos.Rproj
└── style.R
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
# History files
.Rhistory
# Example code in package build process
*-Ex.R
.Rproj.user
================================================
FILE: Demos/Biochemical and Chemical Similarity Networks/Tutorial- Building Biochemical and Chemical Similarity Based Networks .r
================================================
#load needed functions: package devium which is stored on a github
source("http://pastebin.com/raw.php?i=UyDBTA57")
# get sample chemical identifiers here:https://docs.google.com/spreadsheet/ccc?key=0Ap1AEMfo-fh9dFZSSm5WSHlqMC1QdkNMWFZCeWdVbEE#gid=1
#Pubchem CIDs = cids
cids # overview
nrow(cids) # how many
str(cids) # structure, wan't numeric
cids<-as.numeric(as.character(unlist(cids))) # hack to break factor
#get KEGG RPAIRS
#making an edge list based on CIDs from KEGG reactant pairs
KEGG.edge.list<-CID.to.KEGG.pairs(cid=cids,database=get.KEGG.pairs(),lookup=get.CID.KEGG.pairs())
head(KEGG.edge.list)
dim(KEGG.edge.list) # a two column list with CID to CID connections based on KEGG RPAIS
# how did I get this?
#1) convert from CID to KEGG using get.CID.KEGG.pairs(), which is a table stored:https://gist.github.com/dgrapov/4964546
#2) get KEGG RPAIRS using get.KEGG.pairs() which is a table stored:https://gist.github.com/dgrapov/4964564
#3) return CID pairs
#get EDGES based on chemical similarity (Tanimoto distances >0.07)
tanimoto.edges<-CID.to.tanimoto(cids=cids, cut.off = .7, parallel=FALSE)
head(tanimoto.edges)
# how did I get this?
#1) Use R package ChemmineR to querry Pubchem PUG to get molecular fingerprints
#2) calculate simialrity coefficient
#3) return edges with similarity above cut.off
#after a little bit of formatting make combined KEGG + tanimoto edge list
# https://docs.google.com/spreadsheet/ccc?key=0Ap1AEMfo-fh9dFZSSm5WSHlqMC1QdkNMWFZCeWdVbEE#gid=2
#now upload this and a sample node attribute table (https://docs.google.com/spreadsheet/ccc?key=0Ap1AEMfo-fh9dFZSSm5WSHlqMC1QdkNMWFZCeWdVbEE#gid=1)
#to Cytoscape
================================================
FILE: Demos/Data Analysis Workflow/data analysis workflow.R
================================================
#------------------------
# Dmitry Grapov, PhD
# CDS- Creative Data Solutions
# 10/2/15
#------------------------
#------------------------
# Example of an Omics
# data analysis strategies
# 1) exploratory analysis
# 2) statistical analysis
# 3) predictive modeling
# 4) functional analysis
#dependencies
{
pkg<-c("ggplot2","dplyr","R.utils","fdrtool","caret","randomForest","pROC")
lapply(pkg, function(x) {
if(!require(x,character.only = TRUE)) install.packages(x,character.only = TRUE)
}
)
#bioConductor
source("https://bioconductor.org/biocLite.R")
if(!require("pcaMethods")) biocLite("pcaMethods")
if(!require("pathview")) biocLite("pathview")
if(!require("KEGGREST")) biocLite("KEGGREST")
}
#set working directory
wd<-"C:/Users/Dmitry/Dropbox/Software/TeachingDemos/Demos/Data Analysis Workflow/"
setwd(wd)
#load devium functions
{
sourceDirectory( "R",recursive=TRUE,verbose=TRUE)
}
# load data
# anonymized real-world data set
# comparing sick and healthy patients
# metabolite and protein profiles
{
load(file="data/data cube") # data.obj
data.cube<-data.obj$raw
#take a look at the parts
str(data.cube)
}
# 1) exploratory analysis
{
#Principal Components Analysis (PCA)
{
#raw
{
args<-list( pca.data = data.cube$data,
pca.algorithm = "svd",
pca.components = 8,
pca.center = FALSE,
pca.scaling = "none",
pca.cv = "q2"
)
#calculate and view scree plot
res<-devium.pca.calculate(args,return="list",plot=TRUE)
#plot results
#scores highlighting healthy and sick
p.args<-list(
pca = res,
results = "scores",
color = data.cube$sample.meta[,"group",drop=FALSE],
font.size = 3
)
do.call("plot.PCA",p.args)
#loadings
p.args<-list(
pca = res,
results = "loadings",
color = data.cube$variable.meta[,"type",drop=FALSE],
font.size = 3
)
do.call("plot.PCA",p.args)
#now try with scaling
args<-list( pca.data = data.cube$data,
pca.algorithm = "svd",
pca.components = 8,
pca.center = TRUE,
pca.scaling = "uv",
pca.cv = "q2"
)
#calculate and view scree plot
res2<-devium.pca.calculate(args,return="list",plot=TRUE)
#plot results
#scores highlighting healthy and sick
p.args<-list(
pca = res2,
results = "scores",
color = data.cube$sample.meta[,"group",drop=FALSE],
font.size = 3
)
do.call("plot.PCA",p.args)
#loadings
p.args<-list(
pca = res2,
results = "loadings",
color = data.cube$variable.meta[,"type",drop=FALSE],
font.size = 3
)
do.call("plot.PCA",p.args)
#scores highlighting batches
p.args<-list(
pca = res2,
results = "scores",
color = data.cube$sample.meta[,"batch",drop=FALSE],
font.size =3
)
do.call("plot.PCA",p.args)
}
#normalized
{
data.cube<-data.obj$normalized
#now try with scaling
args<-list( pca.data = data.cube$data,
pca.algorithm = "svd",
pca.components = 8,
pca.center = TRUE,
pca.scaling = "uv",
pca.cv = "q2"
)
#calculate and view scree plot
res<-devium.pca.calculate(args,return="list",plot=TRUE)
#plot results
#scores highlighting healthy and sick
p.args<-list(
pca = res,
results = "scores",
color = data.cube$sample.meta[,"group",drop=FALSE],
font.size = 3
)
do.call("plot.PCA",p.args)
#scores highlighting batches
p.args<-list(
pca = res,
results = "scores",
color = data.cube$sample.meta[,"batch",drop=FALSE],
font.size =3
)
do.call("plot.PCA",p.args)
}
}
}
# 2) statistical analysis
{
#get summaries and t-test stats
data<-data.cube$data
meta<-data.cube$sample.meta[,"group",drop=FALSE]
#get summary
.summary<-stats.summary(data,comp.obj=meta,formula=colnames(meta),sigfigs=3,log=FALSE,rel=1,do.stats=TRUE)
stats.obj<-cbind(data.cube$variable.meta,.summary)
head(stats.obj)
write.csv(stats.obj,file="results/statistical_results.csv")
}
# 3) Predictive modeling
{
#create a classification model using random forests
#generate training/test set
set.seed(998)
data<-data.cube$data
inTraining <- createDataPartition(data.cube$sample.meta$group, p = 2/3, list = FALSE)
train.data <- data[ inTraining,]
test.data <- data[-inTraining,]
train.y <- data.cube$sample.meta$group[ inTraining] %>% droplevels()
test.y <- data.cube$sample.meta$group[ -inTraining] %>% droplevels()
#set model parameters
fitControl <- trainControl(## 10-fold CV
method = "repeatedcv",
number = 3,
## repeated ten times
repeats = 3,
classProbs = TRUE,
verboseIter =TRUE,
summaryFunction = twoClassSummary
)
#fit model to the training data
set.seed(825)
fit<- train(train.y ~ ., data = train.data,
method = "rf",
trControl = fitControl,
metric = "ROC",
tuneLength = 3
)
fit
# create ROC curve
#create ROC curve for test
plot(roc(as.numeric(predict(fit,newdata=train.data)),as.numeric(train.y)))
#predict the test set
pred<-predict(fit,newdata=test.data)
prob<-predict(fit,newdata=test.data,type="prob")
obs<-test.y
#get performance stats
twoClassSummary(data=data.frame(obs,pred,prob),lev=levels(pred))
#create ROC curve for test
plot(roc(as.numeric(pred),as.numeric(obs)))
}
# 4) pathway enrichment analysis
{
# carry out analysis to identify enriched pathways
# Here used IMPaLa: http://impala.molgen.mpg.de/
# load gene names as entrez, metbolites as KEGG
#format data to show fold changes in pathway
#get formatted data for pathview
library(KEGGREST)
library(pathview)
data<-stats.obj
#metabolite
met<-data %>% dplyr::filter(type =="metabolite") %>%
dplyr::select(ID,mean.sick_mean.healthy) %>%
mutate(FC=log(mean.sick_mean.healthy)) %>% dplyr::select(-mean.sick_mean.healthy)
#protein
prot<-data %>% dplyr::filter(type =="protein") %>%
dplyr::select(ID,mean.sick_mean.healthy) %>%
mutate(FC=log(mean.sick_mean.healthy)) %>% dplyr::select(-mean.sick_mean.healthy)
#set rownames
rownames(met)<-met[,1];met<-met[,-1,drop=FALSE]
rownames(prot)<-prot[,1];prot<-prot[,-1,drop=FALSE]
#select pathway to view
path<-"Glycolysis / Gluconeogenesis"
#get KEGG code and pathway IDs
data(korg)
organism <- "homo sapiens"
matches <- unlist(sapply(1:ncol(korg), function(i) {
agrep(organism, korg[, i])
}))
(kegg.code <- korg[matches, 1, drop = F])
#get kegg pathway names
library(KEGGREST)
pathways <- keggList("pathway", kegg.code)
#get code of our pathway of interest
map<-grepl(path,pathways) %>% pathways[.] %>% names(.) %>% gsub("path:","",.)
#create image
setwd("./images")
pv.out <- pathview(gene.data = prot, cpd.data = met, gene.idtype = "SYMBOL",
pathway.id = map, species = kegg.code, out.suffix = map, keys.align = "y",
kegg.native = T, match.data = T, key.pos = "topright")
}
================================================
FILE: Demos/Data Analysis Workflow/images/hsa00010.xml
================================================
<?xml version="1.0"?>
<!DOCTYPE pathway SYSTEM "http://www.kegg.jp/kegg/xml/KGML_v0.7.1_.dtd">
<!-- Creation date: Feb 23, 2015 15:46:33 +0900 (GMT+09:00) -->
<pathway name="path:hsa00010" org="hsa" number="00010"
title="Glycolysis / Gluconeogenesis"
image="http://www.kegg.jp/kegg/pathway/hsa/hsa00010.png"
link="http://www.kegg.jp/kegg-bin/show_pathway?hsa00010">
<entry id="13" name="hsa:226 hsa:229 hsa:230" type="gene" reaction="rn:R01070"
link="http://www.kegg.jp/dbget-bin/www_bget?hsa:226+hsa:229+hsa:230">
<graphics name="ALDOA, ALDA, GSD12, HEL-S-87p..." fgcolor="#000000" bgcolor="#BFFFBF"
type="rectangle" x="483" y="407" width="46" height="17"/>
</entry>
<entry id="37" name="hsa:217 hsa:219 hsa:223 hsa:224 hsa:501" type="gene" reaction="rn:R00710"
link="http://www.kegg.jp/dbget-bin/www_bget?hsa:217+hsa:219+hsa:223+hsa:224+hsa:501">
<graphics name="ALDH2, ALDH-E2, ALDHI, ALDM..." fgcolor="#000000" bgcolor="#BFFFBF"
type="rectangle" x="284" y="943" width="46" height="17"/>
</entry>
<entry id="38" name="ko:K01905" type="ortholog" reaction="rn:R00229"
link="http://www.kegg.jp/dbget-bin/www_bget?K01905">
<graphics name="K01905" fgcolor="#000000" bgcolor="#FFFFFF"
type="rectangle" x="146" y="911" width="46" height="17"/>
</entry>
<entry id="39" name="hsa:218 hsa:220 hsa:221 hsa:222" type="gene" reaction="rn:R00711"
link="http://www.kegg.jp/dbget-bin/www_bget?hsa:218+hsa:220+hsa:221+hsa:222">
<graphics name="ALDH3A1, ALDH3, ALDHIII..." fgcolor="#000000" bgcolor="#BFFFBF"
type="rectangle" x="259" y="964" width="46" height="17"/>
</entry>
<entry id="40" name="cpd:C00033" type="compound"
link="http://www.kegg.jp/dbget-bin/www_bget?C00033">
<graphics name="C00033" fgcolor="#000000" bgcolor="#FFFFFF"
type="circle" x="146" y="953" width="8" height="8"/>
</entry>
<entry id="41" name="path:hsa00030" type="map"
link="http://www.kegg.jp/dbget-bin/www_bget?hsa00030">
<graphics name="Pentose phosphate pathway" fgcolor="#000000" bgcolor="#FFFFFF"
type="roundrectangle" x="656" y="339" width="62" height="237"/>
</entry>
<entry id="42" name="path:hsa00500" type="map"
link="http://www.kegg.jp/dbget-bin/www_bget?hsa00500">
<graphics name="Starch and sucrose metabolism" fgcolor="#000000" bgcolor="#FFFFFF"
type="roundrectangle" x="484" y="76" width="110" height="34"/>
</entry>
<entry id="43" name="ko:K01568" type="ortholog" reaction="rn:R00755"
link="http://www.kegg.jp/dbget-bin/www_bget?K01568">
<graphics name="K01568" fgcolor="#000000" bgcolor="#FFFFFF"
type="rectangle" x="378" y="912" width="46" height="17"/>
</entry>
<entry id="44" name="hsa:10327" type="gene" reaction="rn:R00746"
link="http://www.kegg.jp/dbget-bin/www_bget?hsa:10327">
<graphics name="AKR1A1, ALDR1, ALR, ARM, DD3, HEL-S-6" fgcolor="#000000" bgcolor="#BFFFBF"
type="rectangle" x="487" y="943" width="46" height="17"/>
</entry>
<entry id="45" name="hsa:124 hsa:125 hsa:126 hsa:127 hsa:128 hsa:130 hsa:131" type="gene" reaction="rn:R00754"
link="http://www.kegg.jp/dbget-bin/www_bget?hsa:124+hsa:125+hsa:126+hsa:127+hsa:128+hsa:130+hsa:131">
<graphics name="ADH1A, ADH1..." fgcolor="#000000" bgcolor="#BFFFBF"
type="rectangle" x="439" y="936" width="46" height="17"/>
</entry>
<entry id="46" name="ko:K01568" type="ortholog" reaction="rn:R00014"
link="http://www.kegg.jp/dbget-bin/www_bget?K01568">
<graphics name="K01568" fgcolor="#000000" bgcolor="#FFFFFF"
type="rectangle" x="431" y="874" width="46" height="17"/>
</entry>
<entry id="47" name="hsa:5160 hsa:5161 hsa:5162" type="gene" reaction="rn:R00014"
link="http://www.kegg.jp/dbget-bin/www_bget?hsa:5160+hsa:5161+hsa:5162">
<graphics name="PDHA1, PDHA, PDHCE1A, PHE1A..." fgcolor="#000000" bgcolor="#BFFFBF"
type="rectangle" x="431" y="853" width="46" height="17"/>
</entry>
<entry id="48" name="hsa:5160 hsa:5161 hsa:5162" type="gene" reaction="rn:R03270"
link="http://www.kegg.jp/dbget-bin/www_bget?hsa:5160+hsa:5161+hsa:5162">
<graphics name="PDHA1, PDHA, PDHCE1A, PHE1A..." fgcolor="#000000" bgcolor="#BFFFBF"
type="rectangle" x="319" y="865" width="46" height="17"/>
</entry>
<entry id="49" name="hsa:1737" type="gene" reaction="rn:R02569"
link="http://www.kegg.jp/dbget-bin/www_bget?hsa:1737">
<graphics name="DLAT, DLTA, PDC-E2, PDCE2" fgcolor="#000000" bgcolor="#BFFFBF"
type="rectangle" x="210" y="865" width="46" height="17"/>
</entry>
<entry id="50" name="hsa:160287 hsa:3939 hsa:3945 hsa:3948 hsa:92483" type="gene" reaction="rn:R00703"
link="http://www.kegg.jp/dbget-bin/www_bget?hsa:160287+hsa:3939+hsa:3945+hsa:3948+hsa:92483">
<graphics name="LDHAL6A, LDH6A..." fgcolor="#000000" bgcolor="#BFFFBF"
type="rectangle" x="558" y="864" width="46" height="17"/>
</entry>
<entry id="51" name="path:hsa00620" type="map"
link="http://www.kegg.jp/dbget-bin/www_bget?hsa00620">
<graphics name="Pyruvate metabolism" fgcolor="#000000" bgcolor="#FFFFFF"
type="roundrectangle" x="384" y="763" width="78" height="34"/>
</entry>
<entry id="52" name="hsa:5313 hsa:5315" type="gene" reaction="rn:R00200"
link="http://www.kegg.jp/dbget-bin/www_bget?hsa:5313+hsa:5315">
<graphics name="PKLR, PK1, PKL, PKR, PKRL, RPK..." fgcolor="#000000" bgcolor="#BFFFBF"
type="rectangle" x="483" y="770" width="46" height="17"/>
</entry>
<entry id="53" name="path:hsa00020" type="map"
link="http://www.kegg.jp/dbget-bin/www_bget?hsa00020">
<graphics name="Citrate cycle (TCA cycle)" fgcolor="#000000" bgcolor="#FFFFFF"
type="roundrectangle" x="64" y="800" width="47" height="154"/>
</entry>
<entry id="54" name="hsa:2023 hsa:2026 hsa:2027" type="gene" reaction="rn:R00658"
link="http://www.kegg.jp/dbget-bin/www_bget?hsa:2023+hsa:2026+hsa:2027">
<graphics name="ENO1, ENO1L1, MPB1, NNE, PPH..." fgcolor="#000000" bgcolor="#BFFFBF"
type="rectangle" x="483" y="701" width="46" height="17"/>
</entry>
<entry id="55" name="hsa:441531 hsa:5223 hsa:5224 hsa:669" type="gene" reaction="rn:R01518"
link="http://www.kegg.jp/dbget-bin/www_bget?hsa:441531+hsa:5223+hsa:5224+hsa:669">
<graphics name="PGAM4, PGAM-B, PGAM1, PGAM3, dJ1000K24.1..." fgcolor="#000000" bgcolor="#BFFFBF"
type="rectangle" x="458" y="630" width="46" height="17"/>
</entry>
<entry id="56" name="hsa:2597 hsa:26330" type="gene" reaction="rn:R01061"
link="http://www.kegg.jp/dbget-bin/www_bget?hsa:2597+hsa:26330">
<graphics name="GAPDH, G3PD, GAPD, HEL-S-162eP..." fgcolor="#000000" bgcolor="#BFFFBF"
type="rectangle" x="458" y="484" width="46" height="17"/>
</entry>
<entry id="57" name="hsa:7167" type="gene" reaction="rn:R01015"
link="http://www.kegg.jp/dbget-bin/www_bget?hsa:7167">
<graphics name="TPI1, HEL-S-49, TIM, TPI, TPID" fgcolor="#000000" bgcolor="#BFFFBF"
type="rectangle" x="386" y="449" width="46" height="17"/>
</entry>
<entry id="58" name="hsa:5211 hsa:5213 hsa:5214" type="gene" reaction="rn:R04779"
link="http://www.kegg.jp/dbget-bin/www_bget?hsa:5211+hsa:5213+hsa:5214">
<graphics name="PFKL, ATP-PFK, PFK-B, PFK-L..." fgcolor="#000000" bgcolor="#BFFFBF"
type="rectangle" x="458" y="336" width="46" height="17"/>
</entry>
<entry id="59" name="hsa:2203 hsa:8789" type="gene" reaction="rn:R04780"
link="http://www.kegg.jp/dbget-bin/www_bget?hsa:2203+hsa:8789">
<graphics name="FBP1, FBP..." fgcolor="#000000" bgcolor="#BFFFBF"
type="rectangle" x="408" y="336" width="46" height="17"/>
</entry>
<entry id="60" name="ko:K02777 ko:K02778 ko:K02779 ko:K02790 ko:K02791" type="ortholog" reaction="rn:R02738"
link="http://www.kegg.jp/dbget-bin/www_bget?K02777+K02778+K02779+K02790+K02791">
<graphics name="K02777..." fgcolor="#000000" bgcolor="#FFFFFF"
type="rectangle" x="552" y="196" width="46" height="17"/>
</entry>
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================================================
FILE: Demos/Data Analysis Workflow/report/Data Analysis Workflows 2 2015.pptx
================================================
[File too large to display: 21.1 MB]
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FILE: Demos/Data Analysis Workflow/report/Data Analysis Workflows 2015.pptx
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[File too large to display: 21.1 MB]
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FILE: Demos/Data Analysis Workflow/report/hsa00010.xml
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<!-- Creation date: Feb 23, 2015 15:46:33 +0900 (GMT+09:00) -->
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================================================
FILE: Demos/Data Analysis Workflow/report/report.Rmd
================================================
---
output:
html_document:
keep_md: yes
---
#### [Part 1](http://www.slideshare.net/dgrapov/data-analysis-workflows-part-1-2015)
<iframe src="http://www.slideshare.net/dgrapov/data-analysis-workflows-part-1-2015" frameborder="0" allowfullscreen></iframe>
##### 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`.
1. exploratory analysis
2. statistical analysis
3. predictive modeling
4. functional analysis
```{r,warning=FALSE,echo=TRUE,message=FALSE}
#set working directory
wd<-"C:/Users/Dmitry/Dropbox/Software/TeachingDemos/Demos/Data Analysis Workflow/"
setwd(wd)
#load dependancies
pkg<-c("ggplot2","dplyr","R.utils","fdrtool","caret","randomForest","pROC")
out<-lapply(pkg, function(x) {
if(!require(x,character.only = TRUE)) install.packages(x,character.only = TRUE)
}
)
#bioConductor
source("https://bioconductor.org/biocLite.R")
if(!require("pcaMethods")) biocLite("pcaMethods")
if(!require("pathview")) biocLite("pathview")
if(!require("KEGGREST")) biocLite("KEGGREST")
#load devium functions
#sourceDirectory( "R",recursive=TRUE)
source("http://pastebin.com/raw.php?i=UyDBTA57")
```
```{r,warning=FALSE,echo=TRUE,message=FALSE}
#load data
setwd(wd)
load(file="data/data cube") # data.obj
```
##### This data set contains `r ncol(data.obj$raw$data)` measurements for `r nrow(data.obj$raw$data)` samples. The samples are comprised of sick and healthy patients measured across two analytical batches.
```{r, echo=TRUE}
table(data.obj$raw$sample.meta$group)
table(data.obj$raw$sample.meta$batch)
```
****
### Exploratory Analysis
****
##### 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.
##### PCA has three main components we can use to evaluate our data.
##### 1. Variance explained by each component
```{r, echo=TRUE,message=FALSE,warning=FALSE}
data.cube<-data.obj$raw
args<-list( pca.data = data.cube$data,
pca.algorithm = "svd",
pca.components = 8,
pca.center = FALSE,
pca.scaling = "none",
pca.cv = "q2"
)
#calculate and view scree plot
res<-devium.pca.calculate(args,return="list",plot=TRUE)
```
##### 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.
##### 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.
```{r, echo=TRUE,message=FALSE,warning=FALSE}
#plot results
#scores highlighting healthy and sick
p.args<-list(
pca = res,
results = "scores",
color = data.cube$sample.meta[,"group",drop=FALSE],
font.size = 3
)
do.call("plot.PCA",p.args)
```
#### Based on the scores above the sick and healthy samples look fairly similiar. Lets next look at the variable loadings.
#### 3. Variable loadings show the contribution of each varible to the calculated scores.
```{r, echo=TRUE,message=FALSE,warning=FALSE}
#loadings
p.args<-list(
pca = res,
results = "loadings",
color = data.cube$variable.meta[,"type",drop=FALSE],
font.size = 3
)
do.call("plot.PCA",p.args)
```
#### 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.
#### Next lets recalculate the PCA and mean center and scale all the variables by their standard deviation (autoscale).
#### Variance explained
```{r, echo=TRUE,message=FALSE,warning=FALSE}
args<-list( pca.data = data.cube$data,
pca.algorithm = "svd",
pca.components = 8,
pca.center = TRUE,
pca.scaling = "uv",
pca.cv = "q2"
)
#calculate and view scree plot
res2<-devium.pca.calculate(args,return="list",plot=TRUE)
```
#### Variable loadings
```{r, echo=TRUE,message=FALSE,warning=FALSE}
#loadings
p.args<-list(
pca = res2,
results = "loadings",
color = data.cube$variable.meta[,"type",drop=FALSE],
font.size = 3
)
do.call("plot.PCA",p.args)
```
#### Sample scores
```{r, echo=TRUE,message=FALSE,warning=FALSE}
#plot results
#scores highlighting healthy and sick
p.args<-list(
pca = res2,
results = "scores",
color = data.cube$sample.meta[,"group",drop=FALSE],
font.size = 3
)
do.call("plot.PCA",p.args)
```
#### There are some noticible differences in PCA after we scaled our data.
1. Variable magnitude no longer drives the majority of the variance.
2. We can see more resolution in variable loadings for the first 2 PCs.
3. There is an unexplained group structure in the score.
#### Next we can try mapping other meta data to score to see if we can explain the cluster pattern. Lets show the analytical batches in the samples scores.
```{r, echo=TRUE,message=FALSE,warning=FALSE}
p.args<-list(
pca = res2,
results = "scores",
color = data.cube$sample.meta[,"batch",drop=FALSE],
font.size =3
)
do.call("plot.PCA",p.args)
```
#### 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.
###### [Metabolomics and Beyond: Challenges and Strategies for Next-generation Omic Analyses](https://imdevsoftware.files.wordpress.com/2015/09/clipboard01.png?w=300&h=225)
[](https://www.youtube.com/watch?v=4AhBN5Q1oMs)
##### [Evaluation of data normalization methods](http://www.slideshare.net/dgrapov/case-study-metabolomic-data-normalization-example)
****
#### [Part 3](hhttp://www.slideshare.net/dgrapov/data-analysis-workflows-part-2-2015?related=1)
<iframe width="420" height="315" src="http://www.slideshare.net/dgrapov/data-analysis-workflows-part-2-2015?related=1" frameborder="0" allowfullscreen></iframe>
### Statistical Analysis
****
##### 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.
```{r, echo=TRUE,message=FALSE,warning=FALSE}
#get summaries and t-test stats
data.cube<-data.obj$normalized
data<-data.cube$data
meta<-data.cube$sample.meta[,"group",drop=FALSE]
#get summary
.summary<-stats.summary(data,comp.obj=meta,formula=colnames(meta),sigfigs=3,log=FALSE,rel=1,do.stats=TRUE)
stats.obj<-cbind(data.cube$variable.meta,.summary)
stats.obj %>% arrange(group_p.values) %>% head(.)
#write.csv(stats.obj,file="results/statistical_results.csv")
```
#### We can visualize the differences in means for the top most altered metabolite and protein as a box plot.
```{r, echo=TRUE,message=FALSE,warning=FALSE}
top.met<-stats.obj %>% filter(type =="metabolite") %>%
arrange(group_p.values) %>% dplyr::select(ID) %>%
dplyr:: slice(.,1) %>% unlist(.) %>% as.character(.)
id<-as.character(stats.obj$ID) %in% top.met
tmp.data<-data.frame(data[,id,drop=FALSE],meta)
#make plot
ggplot(tmp.data,aes_string(x="group",y=colnames(tmp.data)[1],fill="group")) +
geom_boxplot() + ggtitle(as.character(stats.obj$description[id])) +ylab("")
top.met<-stats.obj %>% filter(type =="protein") %>%
arrange(group_p.values) %>% dplyr::select(ID) %>%
dplyr:: slice(.,1) %>% unlist(.) %>% as.character(.)
id<-as.character(stats.obj$ID) %in% top.met
tmp.data<-data.frame(data[,id,drop=FALSE],meta)
#make plot
ggplot(tmp.data,aes_string(x="group",y=colnames(tmp.data)[1],fill="group")) +
geom_boxplot() + ggtitle(as.character(stats.obj$description[id])) +ylab("")
```
****
### Predictive Modeling
****
#### 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).
```{r, echo=TRUE,message=FALSE,warning=FALSE}
#create a classification model using random forests
#generate training/test set
set.seed(998)
data<-data.cube$data
inTraining <- createDataPartition(data.cube$sample.meta$group, p = 2/3, list = FALSE)
train.data <- data[ inTraining,]
test.data <- data[-inTraining,]
train.y <- data.cube$sample.meta$group[ inTraining] %>% droplevels()
test.y <- data.cube$sample.meta$group[ -inTraining] %>% droplevels()
#set model parameters
fitControl <- trainControl(## 10-fold CV
method = "repeatedcv",
number = 3,
## repeated ten times
repeats = 3,
classProbs = TRUE,
summaryFunction = twoClassSummary
)
#fit model to the training data
set.seed(825)
fit<- train(train.y ~ ., data = train.data,
method = "rf",
trControl = fitControl,
metric = "ROC",
tuneLength = 3
)
```
#### Below the optimal model is chosen while varying the ```mtry``` or the number of variables randomly sampled as candidates at each split.
```{r, echo=TRUE,message=FALSE,warning=FALSE}
fit
````
#### Next we can evaluate the model performance based on predictions for the test set. We can also look at the ROC curve.
```{r, echo=TRUE,message=FALSE,warning=FALSE}
#predict the test set
pred<-predict(fit,newdata=test.data)
prob<-predict(fit,newdata=test.data,type="prob")
obs<-test.y
table(pred,obs)
#get performance stats
twoClassSummary(data=data.frame(obs,pred,prob),lev=levels(pred))
```
#### We can also look at the ROC curve.
```{r, echo=TRUE,message=FALSE,warning=FALSE}
x<-roc(obs,prob[,levels(pred)[1]],silent = TRUE)
plot(x)
```
#### Having validated our model next we can look at the most important variables driving the classification. We can look at the differences in performance when each variable is randomly permuted or the VIP.
```{r, echo=TRUE,message=FALSE,warning=FALSE}
#need to get variable names
vip<-varImp(fit)$importance # need to keep rownames
vip<-vip[order(vip[,1],decreasing=TRUE),,drop=FALSE][1:10,,drop=FALSE]
id<-colnames(train.data) %in% gsub('`','',rownames(vip))
tmp.data<-data.frame(importance=vip[,1],variable=factor(stats.obj$description[id],levels=stats.obj$description[id],ordered=FALSE))
#plot
ggplot(tmp.data, aes(x=variable,y=importance)) + geom_bar(stat="identity") + coord_flip()
id<-as.character(stats.obj$description) %in% as.character(tmp.data[1,2])
tmp.data<-data.frame(data[,id,drop=FALSE],meta)
#make plot
ggplot(tmp.data,aes_string(x="group",y=colnames(tmp.data)[1],fill="group")) +
geom_boxplot() + ggtitle(as.character(stats.obj$description[id])) +ylab("")
```
****
### Functional Analysis
****
#### 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.
```{r, echo=TRUE,message=FALSE,warning=FALSE}
#format data to show fold changes in pathway
#get formatted data for pathview
library(KEGGREST)
library(pathview)
data<-stats.obj
#metabolite
met<-data %>% dplyr::filter(type =="metabolite") %>%
dplyr::select(ID,mean.sick_mean.healthy) %>%
mutate(FC=log(mean.sick_mean.healthy)) %>% dplyr::select(-mean.sick_mean.healthy)
#protein
prot<-data %>% dplyr::filter(type =="protein") %>%
dplyr::select(ID,mean.sick_mean.healthy) %>%
mutate(FC=log(mean.sick_mean.healthy)) %>% dplyr::select(-mean.sick_mean.healthy)
#set rownames
rownames(met)<-met[,1];met<-met[,-1,drop=FALSE]
rownames(prot)<-prot[,1];prot<-prot[,-1,drop=FALSE]
#select pathway to view
path<-"Glycolysis / Gluconeogenesis"
```
#### Lets take a looka at the `r path` 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).
#### Metabolite data showing KEGG ids and log fold change
```{r, echo=TRUE,message=FALSE,warning=FALSE}
head(met)
```
#### Protein data showing the Entrez gene name and log fold changes
```{r, echo=TRUE,message=FALSE,warning=FALSE}
head(prot)
```
#### Next we need to get the pathway code for or pathway of interest.
```{r, echo=TRUE,message=FALSE,warning=FALSE}
data(korg)
organism <- "homo sapiens"
matches <- unlist(sapply(1:ncol(korg), function(i) {
agrep(organism, korg[, i])
}))
(kegg.code <- korg[matches, 1, drop = F])
```
#### Now we can visualize the changes between sick and healthy in the `r path` pathway.
```{r, echo=TRUE,message=FALSE,warning=FALSE}
setwd(wd)
pathways <- keggList("pathway", kegg.code)
#get code of our pathway of interest
map<-grepl(path,pathways) %>% pathways[.] %>% names(.) %>% gsub("path:","",.)
map
#create image
setwd("report")
pv.out <- pathview(gene.data = prot, cpd.data = met, gene.idtype = "SYMBOL",
pathway.id = map, species = kegg.code, out.suffix = map, keys.align = "y",
kegg.native = T, match.data = T, key.pos = "topright")
```
****
#### This concludes this short tutorial. You may also find the following links useful.
* [Software tools](https://github.com/dgrapov)
* [More examples and demos](https://imdevsoftware.wordpress.com/)
© Dmitry Grapov (2015) <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/" target="_blank"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-sa/4.0/80x15.png" /></a>
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gitextract_swixnm2d/ ├── .gitignore ├── Demos/ │ ├── Biochemical and Chemical Similarity Networks/ │ │ ├── Edge list.xlsx │ │ ├── Node attributes.xlsx │ │ ├── Tutorial- Building Biochemical and Chemical Similarity Based Networks .r │ │ ├── Tutorial- Building Biochemical and Chemical Similarity Networks.pptx │ │ └── partial network.cys │ ├── Data Analysis Workflow/ │ │ ├── data/ │ │ │ └── data cube │ │ ├── data analysis workflow.R │ │ ├── images/ │ │ │ └── hsa00010.xml │ │ ├── report/ │ │ │ ├── Data Analysis Workflows 2 2015.pptx │ │ │ ├── Data Analysis Workflows 2015.pptx │ │ │ ├── Data Analysis Workflows part 1 2015.pptx │ │ │ ├── Data Analysis Workflows part 2 2015.pptx │ │ │ ├── hsa00010.xml │ │ │ ├── report.Rmd │ │ │ ├── report.html │ │ │ └── report.md │ │ └── results/ │ │ ├── IMPaLA_results.csv │ │ ├── statistical_results.csv │ │ └── statistical_results_sig.csv │ ├── Introduction to Network Mapping/ │ │ ├── Demonstration.pptx │ │ ├── biochem network edge list.xlsx │ │ ├── biochem network node attributes.xlsx │ │ ├── biochemical network.cys │ │ ├── name edge list.xlsx │ │ ├── name network.cys │ │ └── node attributes.xlsx │ ├── Mapped Network From Data (Biochemical and Structural)/ │ │ ├── .RData │ │ ├── data.csv │ │ ├── edge.list.csv │ │ ├── network code.r │ │ ├── network visualization.cys │ │ ├── node.attributes.csv │ │ ├── node.attributes.xls │ │ ├── sample info.csv │ │ ├── statistics.csv │ │ └── variable info.csv │ ├── Pathway Analysis/ │ │ ├── .xml │ │ ├── KEGG Pathway Enrichment.Rmd │ │ ├── KEGG Pathway Enrichment.html │ │ ├── KEGG Pathway Enrichment.md │ │ ├── ath00020.xml │ │ ├── ath00260.path │ │ ├── ath00260.xml │ │ ├── ath00650.xml │ │ ├── athp │ │ └── hsa00010.xml │ ├── Predictive Modeling/ │ │ ├── Iris O-PLS-DA/ │ │ │ ├── O-PLS modeling of Iris data.Rmd │ │ │ ├── O-PLS modeling of Iris data.html │ │ │ ├── O-PLS modeling of Iris data.md │ │ │ ├── O-PLS_modeling_of_Iris_data.html │ │ │ ├── O-PLS_modeling_of_Iris_data.md │ │ │ └── demo.r │ │ ├── O-PLS/ │ │ │ ├── OPLS example.Rmd │ │ │ ├── OPLS_example.html │ │ │ └── OPLS_example.md │ │ └── Single and multiple Y O-PLS modeling/ │ │ ├── OPLS example.Rmd │ │ ├── OPLS example.html │ │ ├── OPLS example.md │ │ └── OPLS example.r │ ├── R Programming/ │ │ ├── R tutorial 1.R │ │ ├── R tutorial 1.html │ │ ├── R tutorial 1.md │ │ └── R tutorial 1.rmd │ ├── Statistical Analysis/ │ │ ├── Covariate Adjustment.Rmd │ │ ├── Covariate Adjustment.html │ │ ├── Covariate Adjustment.md │ │ └── style.R │ ├── Translating Between Chemical Identifiers/ │ │ └── Translations.r │ └── dplyr/ │ ├── Tutorials.Rproj │ ├── benchmark results │ ├── data/ │ │ └── benchmark results │ ├── hands_on_with_dplyr.Rmd │ ├── hands_on_with_dplyr.html │ └── hands_on_with_dplyr.md ├── README.html ├── README.md ├── TeachingDemos.Rproj └── style.R
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"preview": "# Orthogonal Signal Correction Partial Least Squares Discriminant Analysis\r\n# O-PLS-DA demo using Iris data\r\nsource(\"htt"
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"preview": "# O-PLS Modeling\n\nLoad data set, and set model x and y.\n\n```r\n#load library now to prevent loading messages\nSys.setenv(A"
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"path": "Demos/Translating Between Chemical Identifiers/Translations.r",
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"preview": "#load necessary functions\r\n#install background packages\r\ninstall.packages(\"devtools\");install.packages(\"RJSONIO\")\r\nlibra"
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"preview": "# Hands-on with dplyr\nDmitry Grapov \n\n## Introduction\n\nThis is meant to be an introduction to [dplyr](http://cran.r-pro"
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"preview": "\r\n\r\n# Teaching Demos\r\n***\r\n\r\nBioinformatics related demos and tutorials using the R programming language.\r\n\r\n*** \r\n\r\n# C"
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// ... and 21 more files (download for full content)
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