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co-expressed\"\n\"CRO-03G007470\",\"CRO-06G029240\",0.8861224711627721,6.8938442023324615,5.47675290212559e-6,4.82410231446652e-4,\"co-expressed\"\n\"CRO-03G009710\",\"CRO-06G029240\",0.9655081908197392,13.370025088601713,2.828102503396558e-9,8.667045809740458e-7,\"co-expressed\"\n\"CRO-03G010790\",\"CRO-06G029240\",0.9519738053292476,11.210386692554273,2.353461207586687e-8,5.120946716807929e-6,\"co-expressed\"\n\"CRO-03G013290\",\"CRO-06G029240\",0.9586559190231139,12.146431023687155,9.032895872096454e-9,2.3053717281644103e-6,\"co-expressed\"\n\"CRO-03G013540\",\"CRO-06G029240\",0.9826827526163414,19.12136822814904,3.345151257430452e-11,2.209715555293816e-8,\"co-expressed\"\n\"CRO-03G014580\",\"CRO-06G029240\",0.7863550058816718,4.589444112127578,2.5367232802119753e-4,0.009489723173929583,\"co-expressed\"\n\"CRO-03G015100\",\"CRO-06G029240\",0.9517833196944943,11.18652793310557,2.4136838153769072e-8,5.229725769903197e-6,\"co-expressed\"\n\"CRO-03G017440\",\"CRO-06G029240\",0.8428142282888889,5.646198478741078,3.9902391226805873e-5,0.0023369775278129674,\"co-expressed\"\n\"CRO-03G017740\",\"CRO-06G029240\",0.8567497278591015,5.9896471517531635,2.260821899112766e-5,0.0015012853261039763,\"co-expressed\"\n\"CRO-03G019000\",\"CRO-06G029240\",0.9800936190831355,17.79918924683855,8.22343506230394e-11,4.6258750316086376e-8,\"co-expressed\"\n\"CRO-03G019430\",\"CRO-06G029240\",0.8921264269866616,7.119761946234496,3.909519403953837e-6,3.65884557777401e-4,\"co-expressed\"\n\"CRO-03G019870\",\"CRO-06G029240\",0.8972453398155672,7.326901893924261,2.886783314734139e-6,2.8499121604080857e-4,\"co-expressed\"\n\"CRO-03G020840\",\"CRO-06G029240\",0.836146957916132,5.4963365446582735,5.1392514281613625e-5,0.002840685943304527,\"co-expressed\"\n\"CRO-03G021090\",\"CRO-06G029240\",0.929422745509637,9.081109152457357,2.719664969331002e-7,3.9727279076943474e-5,\"co-expressed\"\n\"CRO-03G021490\",\"CRO-06G029240\",0.9703585432444098,14.477097775104852,1.06849768495406e-9,3.845036961451373e-7,\"co-expressed\"\n\"CRO-03G022230\",\"CRO-06G029240\",0.884088437221767,6.821083468624939,6.113561775121617e-6,5.274415566220052e-4,\"co-expressed\"\n\"CRO-03G022780\",\"CRO-06G029240\",0.8931980561938443,7.161959189923572,3.673655079081334e-6,3.474974740468781e-4,\"co-expressed\"\n\"CRO-03G023380\",\"CRO-06G029240\",0.8028340996462731,4.855161732986845,1.5712033244607077e-4,0.006640290632538372,\"co-expressed\"\n\"CRO-03G023790\",\"CRO-06G029240\",0.9308920756050986,9.188150169893797,2.3806177163693322e-7,3.549504092369309e-5,\"co-expressed\"\n\"CRO-03G026550\",\"CRO-06G029240\",0.8397600796842212,5.576490233013966,4.486940311424402e-5,0.002556521093681389,\"co-expressed\"\n\"CRO-03G028180\",\"CRO-06G029240\",0.883219794068077,6.790556174010563,6.403655147570365e-6,5.472424521532159e-4,\"co-expressed\"\n\"CRO-03G029230\",\"CRO-06G029240\",0.8365414508170134,5.504969967379341,5.064443180109716e-5,0.002809012062226157,\"co-expressed\"\n\"CRO-03G030600\",\"CRO-06G029240\",0.9715686011958476,14.795855697061508,8.173012127910333e-10,3.071536763358783e-7,\"co-expressed\"\n\"CRO-03G030620\",\"CRO-06G029240\",0.9669734015949815,13.679021682057698,2.1403929810293567e-9,6.864792403508172e-7,\"co-expressed\"\n\"CRO-03G032340\",\"CRO-06G029240\",0.9446505284286159,10.381624715098145,5.8156417495351556e-8,1.0917069107946878e-5,\"co-expressed\"\n\"CRO-03G032840\",\"CRO-06G029240\",0.9496945892539108,10.933645706512342,3.163649306148754e-8,6.563567298156005e-6,\"co-expressed\"\n\"CRO-04G001480\",\"CRO-06G029240\",0.9019770409826083,7.531831923305046,2.149984300062455e-6,2.2362322251632264e-4,\"co-expressed\"\n\"CRO-04G001500\",\"CRO-06G029240\",0.858614861305703,6.0391176946000265,2.0860071623626982e-5,0.0014076840208647502,\"co-expressed\"\n\"CRO-04G003650\",\"CRO-06G029240\",0.9689656389087214,14.133169894523476,1.4353091887185374e-9,4.929560532486135e-7,\"co-expressed\"\n\"CRO-04G004180\",\"CRO-06G029240\",0.7864102897052889,4.5902896892493015,2.532823889041617e-4,0.009479281585483052,\"co-expressed\"\n\"CRO-04G004580\",\"CRO-06G029240\",0.8964906056791642,7.295451717240511,3.0217484479671883e-6,2.9598343842660794e-4,\"co-expressed\"\n\"CRO-04G004640\",\"CRO-06G029240\",0.93352787807782045,9.388667908058913,1.8608838507605227e-7,2.887810819182493e-5,\"co-expressed\"\n\"CRO-04G005040\",\"CRO-06G029240\",0.9092628514442995,7.876543924997572,1.3250794216349353e-6,1.4977812398172662e-4,\"co-expressed\"\n\"CRO-04G009330\",\"CRO-06G029240\",0.9495407064970116,10.915622983055622,3.2258772360642235e-8,6.668018539004232e-6,\"co-expressed\"\n\"CRO-04G011160\",\"CRO-06G029240\",0.9239378016136459,8.708321646662705,4.3646045639023014e-7,5.915867796774384e-5,\"co-expressed\"\n\"CRO-04G013510\",\"CRO-06G029240\",0.9474809190287533,10.681862523636264,4.163082564866217e-8,8.269728392674503e-6,\"co-expressed\"\n\"CRO-04G013530\",\"CRO-06G029240\",0.9294092568699288,9.080141450474864,2.722955250523483e-7,3.9768884758546445e-5,\"co-expressed\"\n\"CRO-04G013580\",\"CRO-06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  },
  {
    "path": "Data/Network_Example/Fig4c_2.csv",
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CC\",0.4343654405401227,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G005050\",\"Epidermis\",0.5458298758327501,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G005870\",\"Idioblast\",0.28851139148674554,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G006240\",\"Epidermis\",0.1719538475672767,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G006390\",\"Epidermis\",0.44050793605908517,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G006470\",\"Idioblast\",0.3816762503469131,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G006740\",\"IPAP\",3.187296432670024,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G006750\",\"IPAP\",3.063893104586333,\"Chr1\",5625533,5628506,\"mRNA\",\"Chr1\",5625624,5628226,\"+\",\"CRO_T133061\",\"CRO_01G006750\",\"G8H\",\"cytochrome P450, family 76, subfamily C, polypeptide\",11,\"Iridoid\",\"known genes\"\n\"CRO-01G007510\",\"Phloem CC\",0.00450239120716726,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G007620\",\"Epidermis\",0.12299257701633905,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G008860\",\"Epidermis\",0.4398345950678182,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G009680\",\"Epidermis\",0.6647521066657485,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G009920\",\"Epidermis\",0.007129550577836128,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G010020\",\"IPAP\",1.6961349865496098,\"Chr1\",10069834,10076490,\"mRNA\",\"Chr1\",10069890,10076442,\"+\",\"CRO_T130745\",\"CRO_01G010020\",\"HDS\",\"4-hydroxy-3-methylbut-2-enyl diphosphate synthase\",6,\"MEP\",\"known genes\"\n\"CRO-01G011220\",\"Idioblast\",1.43366900117133,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G011240\",\"Idioblast\",2.042526589896269,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G013240\",\"Epidermis\",0.08465212630303097,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G013280\",\"IPAP\",1.0246718948409188,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G013530\",\"Epidermis\",1.1870393656026677,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G014580\",\"Idioblast\",0.21114892185605977,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G015320\",\"Epidermis\",0.31809072758848445,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G020080\",\"IPAP\",0.6149098247646101,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G020260\",\"Phloem CC\",0.32219586418776003,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G020380\",\"Phloem CC\",0.12737069019638678,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G020710\",\"Idioblast\",3.238232127973818,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G021480\",\"Epidermis\",0.4777016655831821,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G023900\",\"Idioblast\",0.22773472619170348,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G024090\",\"Vasculature\",0.10376507722259772,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G026840\",\"Epidermis\",1.022687540020148,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G027000\",\"Idioblast\",0.4815976927490962,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G027010\",\"IPAP\",2.7691267963521384,\"Chr1\",68443982,68446273,\"mRNA\",\"Chr1\",68443983,68454728,\"+\",\"CRO_T107879\",\"CRO_01G027010/CRO_01G027020\",\"GOR\",\"aspartate aminotransferase\",12,\"Iridoid\",\"known genes\"\n\"CRO-01G027200\",\"Epidermis\",1.005947301454865,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G027620\",\"Epidermis\",0.34915113238209644,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G027990\",\"Idioblast\",0.45051617918606884,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G028400\",\"Phloem CC\",0.7295497304269247,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G028790\",\"Epidermis\",1.0131759696216482,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G029740\",\"Phloem CC\",0.3167309117114357,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G030140\",\"Phloem CC\",0.1091800628605482,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G030290\",\"Idioblast\",0.7314020897009624,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G030300\",\"Phloem CC\",0.03335764244398772,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G030330\",\"Epidermis\",0.2415770771695128,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G030490\",\"IPAP\",1.5398852685766222,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G030520\",\"Epidermis\",0.2000471057188353,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G030540\",\"IPAP\",3.010786254705942,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G030560\",\"Phloem CC\",0.17361225690513843,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G030570\",\"Idioblast\",1.2193201071835336,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G030620\",\"Epidermis\",0.03951703462897611,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G030660\",\"Epidermis\",0.33705998359327977,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G030870\",\"IPAP\",0.0788575708561546,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G031150\",\"Epidermis\",0.5336424026401783,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G031240\",\"Epidermis\",0.11115496187052036,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G031710\",\"Epidermis\",0.19371548948506387,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G032060\",\"Epidermis\",0.2026010321548522,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G032230\",\"Vasculature\",0.11442750515423114,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G032260\",\"Epidermis\",1.1104309896702247,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G032400\",\"Phloem CC\",0.14755500814957861,\"Chr1\",76817167,76820326,\"mRNA\",\"Chr1\",76817506,76820232,\"+\",\"CRO_T116107\",\"CRO_01G032400\",\"HYS\",\"\",24,\"Alkaloid\",\"known genes\"\n\"CRO-01G033160\",\"Epidermis\",0.7481151622526201,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G033220\",\"Phloem CC\",0.8949571781453922,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-01G033300\",\"Phloem CC\",0.2406373431652296,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G000180\",\"Idioblast\",0.3759554591181062,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G000210\",\"Epidermis\",0.3846859023479128,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G000230\",\"Epidermis\",0.875101735880576,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G000620\",\"Phloem CC\",0.21136475220844658,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G000810\",\"IPAP\",0.5792813777312644,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G000920\",\"Idioblast\",3.16822318138137,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G000960\",\"IPAP\",0.08355393451356083,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G001090\",\"Idioblast\",4.289581458452137,\"Chr2\",1605798,1607603,\"mRNA\",\"Chr2\",1605943,1607262,\"+\",\"CRO_T120021\",\"CRO_02G001090\",\"DAT\",\"HXXXD-type acyl-transferase family protein\",39,\"Late Alkaloid\",\"known genes\"\n\"CRO-02G002300\",\"Phloem CC\",0.6583852232674162,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G002310\",\"Phloem CC\",0.6382043944308594,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G002580\",\"Idioblast\",1.7494305824947838,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G002610\",\"Phloem CC\",0.3716824855540311,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G002960\",\"Epidermis\",0.4884591122871072,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G003890\",\"IPAP\",1.593042476464628,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G004380\",\"Epidermis\",0.7416188266657389,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G005050\",\"Epidermis\",0.4938565841334329,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G005470\",\"IPAP\",1.121072263488235,\"Chr2\",8870224,8876373,\"mRNA\",\"Chr2\",8870234,8876254,\"-\",\"CRO_T136385\",\"CRO_02G005470\",\"DXS\",\"Deoxyxylulose-5-phosphate synthase\",1,\"MEP\",\"known genes\"\n\"CRO-02G005520\",\"Idioblast\",3.4864771352544204,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G005560\",\"Mesophyll\",0.015642297420609935,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G006330\",\"Epidermis\",1.3996941925118336,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G007260\",\"Idioblast\",0.3504767763734299,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G008440\",\"Epidermis\",0.21843576889414607,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G010700\",\"Epidermis\",0.38961957512184137,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G011880\",\"Idioblast\",0.7757132137732121,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G011920\",\"Phloem CC\",0.21266956577999,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G012840\",\"Epidermis\",0.08569463580650684,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G013920\",\"Idioblast\",0.8990910937715669,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G014720\",\"Idioblast\",0.19026616478388825,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G015130\",\"Idioblast\",0.05649553313266371,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G016670\",\"Idioblast\",3.8449820727143034,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G016720\",\"Idioblast\",1.9077321958237883,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G016760\",\"Idioblast\",0.9723019244360718,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G016790\",\"Epidermis\",0.20039318464116387,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G018160\",\"Epidermis\",0.13193710464885852,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G018360\",\"Idioblast\",0.04059132601415257,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G018640\",\"Epidermis\",0.5481522079732843,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G018880\",\"Epidermis\",0.08231734688943942,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G019950\",\"Idioblast\",0.20872712762699744,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G019960\",\"IPAP\",1.195517833467224,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G020220\",\"Epidermis\",0.1465490306309994,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G020830\",\"Idioblast\",1.9414339757083532,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G021150\",\"Idioblast\",0.4690040836208546,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G021920\",\"Phloem CC\",0.10673473115737653,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G022910\",\"Idioblast\",1.028540666188628,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G023440\",\"Vasculature\",0.0054608268485657465,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G023750\",\"Guard cells\",0.055924752677492334,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G025030\",\"Epidermis\",0.2882225520395012,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G025060\",\"Phloem CC\",0.41051362123950325,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G025780\",\"Epidermis\",0.5262380952377579,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G025790\",\"Idioblast\",3.096413567291425,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G026670\",\"Epidermis\",0.5894167773177625,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G028240\",\"Phloem CC\",0.5217100290836565,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G028270\",\"Phloem CC\",0.07541539466895372,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G028550\",\"Idioblast\",0.5466750545173514,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G029040\",\"Idioblast\",0.22134209387756348,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G029050\",\"Epidermis\",1.2370684985392855,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G029250\",\"Phloem CC\",0.46827885478202047,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G029620\",\"Idioblast\",3.8159497183374214,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G029900\",\"Epidermis\",0.39758226346557624,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-02G029910\",\"Vasculature\",0.08828708209103833,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G000120\",\"Idioblast\",2.2686991468838573,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G000490\",\"Epidermis\",0.32394004238388224,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G000880\",\"Epidermis\",0.689200870732308,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G002560\",\"Phloem CC\",0.6870863512902458,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G003150\",\"Idioblast\",1.2480809542844455,\"Chr3\",3757109,3762586,\"mRNA\",\"Chr3\",3761117,3762520,\"-\",\"CRO_T111273\",\"CRO_03G003150\",\"NMT\",\"gamma-tocopherol methyltransferase\",37,\"Late Alkaloid\",\"known genes\"\n\"CRO-03G003340\",\"Epidermis\",0.2226957837182701,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G003370\",\"Epidermis\",0.5661947328207563,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G003540\",\"Idioblast\",2.368053218778153,\"Chr3\",4216807,4219441,\"mRNA\",\"Chr3\",4217156,4225015,\"-\",\"CRO_T111318\",\"CRO_03G003540/CRO_03G003550\",\"SS\",\"Serpentine Synthase\",NA,\"Alkaloid\",\"known genes\"\n\"CRO-03G003560\",\"Epidermis\",0.8195787913552542,\"Chr3\",4238124,4248095,\"mRNA\",\"Chr3\",4240485,4248047,\"+\",\"CRO_T128799\",\"CRO_03G003560\",\"SDG1\",\"Glycosyl hydrolase superfamily protein\",23,\"Alkaloid\",\"known genes\"\n\"CRO-03G003570\",\"Epidermis\",0.4069974069038161,\"Chr3\",4292518,4302271,\"mRNA\",\"Chr3\",4297272,4302781,\"+\",\"CRO_T111320\",\"CRO_03G003570\",\"SDG2\",\"beta glucosidase\",24,\"Alkaloid\",\"known genes\"\n\"CRO-03G003720\",\"Phloem CC\",0.5776139658566918,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G003800\",\"Epidermis\",1.5348960512530663,\"Chr3\",4645629,4647016,\"mRNA\",\"Chr3\",4645676,4646668,\"+\",\"CRO_T139139\",\"CRO_03G003800\",\"CS\",\"alpha/beta-Hydrolases superfamily protein\",31,\"Alkaloid\",\"known genes\"\n\"CRO-03G003880\",\"Epidermis\",0.5809443588375128,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G004950\",\"IPAP\",2.7892233728530647,\"Chr3\",6526121,6528131,\"mRNA\",\"Chr3\",6526176,6527851,\"+\",\"CRO_T138994\",\"CRO_03G004950\",\"IO\",\"cytochrome P450, family 76, subfamily G, polypeptide\",14,\"Iridoid\",\"known genes\"\n\"CRO-03G005010\",\"Phloem CC\",0.5569536875216035,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G005030\",\"Epidermis\",0.4338754491570987,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G005050\",\"IPAP\",0.7330424257036081,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G005280\",\"Epidermis\",0.020488067112425085,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G007300\",\"Phloem CC\",0.10919049939573286,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G007470\",\"Epidermis\",0.9729972201013745,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G009210\",\"Idioblast\",0.9806805421196971,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G009710\",\"Epidermis\",0.2853497096167665,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G010300\",\"Idioblast\",0.533161225590108,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G010740\",\"Phloem CC\",0.18407394652779518,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G010760\",\"Idioblast\",0.8965617275740585,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G010790\",\"Epidermis\",0.912481682005889,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G011320\",\"Idioblast\",2.029694560879515,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G011370\",\"Idioblast\",2.1674686592572052,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G011520\",\"Idioblast\",0.19479859572550617,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G011930\",\"Phloem CC\",0.10299309778468915,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G012410\",\"Idioblast\",0.2625606187212039,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G012910\",\"Epidermis\",1.428032204415206,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G012920\",\"Epidermis\",0.29602900420721623,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G013090\",\"Phloem CC\",0.39223581006738256,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G013290\",\"Epidermis\",0.6209042897943554,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G013540\",\"Epidermis\",0.2691529705491145,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G014580\",\"Epidermis\",0.22248124898212865,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G015100\",\"Epidermis\",0.11067611472480011,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G016950\",\"Idioblast\",2.112202951123073,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G017440\",\"Guard cells\",0.06753824930730494,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G017610\",\"IPAP\",0.15345590083045502,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G017740\",\"Idioblast\",0.027088286879604864,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G017900\",\"Epidermis\",0.14639395334195315,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G017950\",\"Epidermis\",0.49859321654315,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G018360\",\"IPAP\",1.7462037590896304,\"Chr3\",51553563,51556569,\"mRNA\",\"Chr3\",51553372,51556477,\"-\",\"CRO_T106494\",\"CRO_03G018360\",\"7DLH\",\"cytochrome P450, family 72, subfamily A, polypeptide\",16,\"Iridoid\",\"known genes\"\n\"CRO-03G018710\",\"Idioblast\",0.23166571342762665,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G019000\",\"Epidermis\",0.7363020912704638,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G019430\",\"Epidermis\",0.9208774020468293,\"Chr3\",54209120,54212389,\"mRNA\",\"Chr3\",54209161,54212360,\"+\",\"CRO_T103723\",\"CRO_03G019430\",\"LAMT\",\"S-adenosyl-L-methionine-dependent methyltransferases superfamily protein\",17,\"Alkaloid\",\"known genes\"\n\"CRO-03G019510\",\"Phloem CC\",0.1163017621476696,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G019710\",\"Idioblast\",2.3827185065126053,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G019870\",\"Epidermis\",1.458352431188217,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G019910\",\"Epidermis\",0.7649913568406596,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G020500\",\"Phloem CC\",0.5721425076693372,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G020840\",\"Idioblast\",0.06942382787416432,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G021090\",\"Epidermis\",0.18051581531434685,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G021490\",\"Epidermis\",0.01971735207183354,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G021610\",\"Idioblast\",3.069474185865642,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G022230\",\"Epidermis\",1.3779103631859788,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G022780\",\"Phloem CC\",0.21911241810566479,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G023260\",\"Idioblast\",0.6701602563182522,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G023380\",\"Epidermis\",0.6094988698471725,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G023790\",\"Guard cells\",0.041698090847274695,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G024460\",\"IPAP\",0.9117116799302054,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G025210\",\"Epidermis\",0.08291186151116813,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G025260\",\"Phloem CC\",0.31567948160548964,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G025290\",\"Epidermis\",0.3590400917276519,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G025980\",\"IPAP\",1.1622616661221303,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G026550\",\"Epidermis\",0.36444466549782684,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G026600\",\"IPAP\",0.1828643703052661,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G027430\",\"Idioblast\",0.2898740285361388,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G028000\",\"Idioblast\",0.23737453236544148,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G028180\",\"Epidermis\",1.0622706782343596,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G028320\",\"IPAP\",1.7510380536950974,\"Chr3\",68469248,68472525,\"mRNA\",\"Chr3\",68469314,68472256,\"+\",\"CRO_T110793\",\"CRO_03G028320\",\"HDR\",\"4-hydroxy-3-methylbut-2-enyl diphosphate reductase\",7,\"MEP\",\"known genes\"\n\"CRO-03G028570\",\"Phloem CC\",0.9090295949918381,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G028590\",\"Phloem CC\",1.878045425065193,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G028930\",\"Idioblast\",1.0271852066080442,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G029080\",\"Mesophyll\",0.07557535112834617,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G029230\",\"Epidermis\",0.35034183031367827,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G029990\",\"Idioblast\",0.8595093723523116,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G030550\",\"Idioblast\",1.560376786290133,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G030600\",\"Epidermis\",0.21616734581425492,\"Chr3\",69980350,69982908,\"mRNA\",\"Chr3\",69980470,69982789,\"-\",\"CRO_T110598\",\"CRO_03G030600\",\"T16H2\",\"cytochrome P450, family 71, subfamily B, polypeptide\",33,\"Alkaloid\",\"known genes\"\n\"CRO-03G030620\",\"Epidermis\",0.8935448827409883,\"Chr3\",69995710,69997731,\"mRNA\",\"Chr3\",69995704,69997603,\"-\",\"CRO_T110596\",\"CRO_03G030620\",\"16OMT\",\"O-methyltransferase family protein\",34,\"Alkaloid\",\"known genes\"\n\"CRO-03G030660\",\"Epidermis\",0.6720995171834736,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G030850\",\"Idioblast\",1.4736571784185546,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G031140\",\"Phloem CC\",0.38000219328885615,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G031560\",\"IPAP\",2.8906256833657564,\"Chr3\",70762775,70768219,\"mRNA\",\"Chr3\",70762911,70768155,\"-\",\"CRO_T119458\",\"CRO_03G031560\",\"GES\",\"terpene synthase\",10,\"Iridoid\",\"known genes\"\n\"CRO-03G031710\",\"Idioblast\",3.8912038103723243,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G032130\",\"Idioblast\",1.2510841316936618,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G032170\",\"IPAP\",0.6573283809275668,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G032330\",\"Epidermis\",0.11883871725219206,\"Chr3\",71668285,71672478,\"mRNA\",\"Chr3\",71668329,71672111,\"+\",\"CRO_T125329\",\"CRO_03G032330\",\"STR\",\"strictosidine synthase\",21,\"Alkaloid\",\"known genes\"\n\"CRO-03G032340\",\"Epidermis\",1.0953764629268927,\"Chr3\",71677828,71679666,\"mRNA\",\"Chr3\",71678061,71679563,\"-\",\"CRO_T125328\",\"CRO_03G032340\",\"TDC\",\"Pyridoxal phosphate (PLP)-dependent transferases superfamily protein\",20,\"Alkaloid\",\"known genes\"\n\"CRO-03G032840\",\"Phloem CC\",0.41039522892766533,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-03G032860\",\"Epidermis\",1.492555869378429,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G000460\",\"Idioblast\",1.363000253373979,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G000920\",\"Idioblast\",0.6183427180822758,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G001030\",\"Phloem CC\",0.39552334114357157,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G001260\",\"Idioblast\",0.05847368274123291,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G001480\",\"IPAP\",0.13039076991032417,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G001500\",\"Vasculature\",0.05164893952245062,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G003000\",\"Epidermis\",0.6967839298109977,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G003380\",\"Idioblast\",0.18737682253560958,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G003390\",\"Epidermis\",0.20862630383681624,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G003490\",\"Phloem CC\",0.2043563606625885,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G003650\",\"Epidermis\",0.6027671751934417,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G003990\",\"Idioblast\",0.3291526687767723,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G004180\",\"Vasculature\",0.1672907714732913,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G004580\",\"Epidermis\",1.3670272054411492,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G004640\",\"Epidermis\",0.8097208561257443,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G005040\",\"Epidermis\",0.9844027137889654,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G006190\",\"Phloem CC\",0.44921197099045773,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G006670\",\"Idioblast\",0.3453893661954661,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G007340\",\"Idioblast\",2.4783428366103877,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G007820\",\"Phloem CC\",0.3503025362608269,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G009330\",\"Phloem CC\",0.546800966220393,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G009870\",\"Phloem CC\",0.5693886417890295,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G010140\",\"Epidermis\",0.7906023522397448,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G011160\",\"Phloem CC\",0.12078875302582447,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G011370\",\"Idioblast\",1.7382080867812848,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G011630\",\"Idioblast\",0.9467107467880398,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G011640\",\"Epidermis\",0.12266032848014774,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G012380\",\"Epidermis\",0.11793909421456211,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G013510\",\"Epidermis\",1.0008171586301857,\"Chr4\",24039159,24042147,\"mRNA\",\"Chr4\",24039174,24042136,\"-\",\"CRO_T109448\",\"CRO_04G013510\",\"SLS1\",\"cytochrome P450, family 72, subfamily A, polypeptide\",18,\"Alkaloid\",\"known genes\"\n\"CRO-04G013530\",\"Epidermis\",1.1512680882998965,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G013580\",\"Epidermis\",1.263801035242315,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G013960\",\"Epidermis\",0.187107271917747,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G014480\",\"Epidermis\",0.11705391686570125,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G015710\",\"Epidermis\",0.37808915396354964,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G016070\",\"Phloem CC\",0.6910972817722856,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G017730\",\"Idioblast\",0.5277672801902867,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G017800\",\"Idioblast\",0.3752624908934163,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G019170\",\"Idioblast\",1.2531564961462196,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G019240\",\"Idioblast\",1.3636288132973813,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G020370\",\"IPAP\",0.6815769655542028,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G022580\",\"Phloem CC\",0.4411964762578345,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G023010\",\"Vasculature\",0.3042314979968897,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G023070\",\"Epidermis\",1.1545221521132825,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G023680\",\"Epidermis\",0.2821171134120817,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G024660\",\"IPAP\",2.035051054082893,\"Chr4\",59027167,59032457,\"mRNA\",\"Chr4\",59027180,59032222,\"-\",\"CRO_T102183\",\"CRO_04G024660\",\"DXR\",\"1-deoxy-D-xylulose 5-phosphate reductoisomerase\",2,\"MEP\",\"known genes\"\n\"CRO-04G025460\",\"IPAP\",0.12712481417204755,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G026600\",\"Idioblast\",1.0862152701282277,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G026660\",\"Vasculature\",0.1444145877368123,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G027070\",\"Idioblast\",0.2703911097580236,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G027110\",\"Idioblast\",2.6625597880477647,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G027150\",\"Epidermis\",0.2969739107720807,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G027250\",\"Epidermis\",0.42737572079246866,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G027410\",\"Epidermis\",1.0215680588567813,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G027620\",\"Idioblast\",0.6414296646624241,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G027770\",\"Vasculature\",0.01727351031918801,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G027970\",\"Idioblast\",0.6282359533106914,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G028630\",\"Epidermis\",0.7721668233658591,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G029380\",\"Idioblast\",0.5384484124389934,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G029840\",\"Idioblast\",1.3865186869440145,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G030450\",\"Epidermis\",0.645850368871544,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G030460\",\"Epidermis\",0.3852054499366042,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G030550\",\"Phloem CC\",0.09747107350384322,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G031660\",\"Epidermis\",0.3498796289616094,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G031680\",\"Phloem CC\",0.24493083442328056,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G032090\",\"IPAP\",0.808603250022958,\"Chr4\",67982251,67984041,\"mRNA\",NA,NA,NA,NA,NA,NA,\"GPPS(LSU)\",NA,9,\"MEP\",\"known genes\"\n\"CRO-04G032540\",\"Epidermis\",1.015008578435341,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G033370\",\"Idioblast\",2.127098739458961,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G033400\",\"Epidermis\",0.12471758894103964,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G033570\",\"Epidermis\",0.2130638526086357,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G034030\",\"Epidermis\",0.8991759346560516,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G034290\",\"Guard cells\",0.3820173680113399,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G034410\",\"Idioblast\",0.3184766818664876,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G034870\",\"Idioblast\",0.9557575087617105,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-04G034880\",\"Idioblast\",0.3845786587662966,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G000670\",\"Epidermis\",0.9008954975406113,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G001560\",\"Epidermis\",1.0317648504690669,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G002890\",\"Epidermis\",0.07939261369952479,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G003030\",\"Epidermis\",0.15150139336263058,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G003430\",\"Idioblast\",0.912513030494667,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G004700\",\"Epidermis\",0.5625854926824473,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G005230\",\"Phloem CC\",0.3216077119017853,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G005590\",\"Epidermis\",0.29976499760624653,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G005690\",\"Epidermis\",0.7007080248217096,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G006800\",\"Idioblast\",1.3018464760798834,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G007570\",\"Phloem CC\",0.3356922329378965,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G008000\",\"Epidermis\",0.41727891804737627,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G008140\",\"Phloem CC\",0.3651028746507632,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G008250\",\"Phloem CC\",0.7585294072443706,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G008380\",\"Vasculature\",0.12626581383735475,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G008760\",\"Epidermis\",0.8911100894179793,\"Chr5\",11150554,11154711,\"mRNA\",\"Chr5\",11150589,11154375,\"+\",\"CRO_T132421\",\"CRO_05G008760\",\"Redox2\",\"NAD(P)-linked oxidoreductase superfamily protein\",28,\"Alkaloid\",\"known genes\"\n\"CRO-05G008860\",\"Idioblast\",1.0087885369202503,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G010480\",\"Epidermis\",0.6789843336695511,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G010490\",\"Epidermis\",0.5413181545227572,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G010750\",\"Epidermis\",0.04059116441417376,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G010990\",\"Vasculature\",0.05358942287567517,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G011100\",\"Epidermis\",0.6225196483858986,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G011710\",\"IPAP\",1.4855268920747178,\"Chr5\",19946831,19953254,\"mRNA\",\"Chr5\",19946912,19958857,\"-\",\"CRO_T135862\",\"CRO_05G011710\",\"CMK\",\"4-(cytidine 5'-phospho)-2-C-methyl-D-erithritol kinase\",4,\"MEP\",\"known genes\"\n\"CRO-05G011810\",\"Idioblast\",1.3832479954320318,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G012390\",\"Epidermis\",0.6587168810587495,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G012510\",\"Phloem CC\",0.36366844814294985,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G014020\",\"Idioblast\",2.4255579353706103,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G014340\",\"Epidermis\",0.7227624610004967,\"Chr5\",25731024,25733040,\"mRNA\",\"Chr5\",25731028,25732967,\"-\",\"CRO_T127440\",\"CRO_05G014340\",\"GO\",\"cytochrome P450, family 71, subfamily B, polypeptide\",26,\"Alkaloid\",\"known genes\"\n\"CRO-05G015060\",\"Idioblast\",1.541098143442514,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G015740\",\"Idioblast\",3.9581653055332278,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G015990\",\"Epidermis\",0.9953668836591529,\"Chr5\",28288397,28292427,\"mRNA\",\"Chr5\",28288571,28292378,\"-\",\"CRO_T124298\",\"CRO_05G015990\",\"T3R\",\"elicitor-activated gene 3-1\",36,\"Alkaloid\",\"known genes\"\n\"CRO-05G016510\",\"Guard cells\",0.004575817181897353,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G016650\",\"Epidermis\",0.3896524421542896,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G017150\",\"Epidermis\",0.8231528255314938,\"Chr5\",33046579,33050289,\"mRNA\",\"Chr5\",33048389,33050270,\"+\",\"CRO_T129272\",\"CRO_05G017150\",\"Redox1\",\"cinnamyl alcohol dehydrogenase\",27,\"Alkaloid\",\"known genes\"\n\"CRO-05G017220\",\"Epidermis\",0.10008287165763922,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G017270\",\"Epidermis\",0.14561667466971848,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G017580\",\"Idioblast\",3.1825997039542413,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G019180\",\"IPAP\",0.03902341794832753,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G019570\",\"Idioblast\",0.6483928968606391,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G020220\",\"Epidermis\",0.20315917257776273,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G023030\",\"Epidermis\",0.7016568903055929,\"Chr5\",53079152,53081259,\"mRNA\",\"Chr5\",53079600,53080865,\"+\",\"CRO_T109653\",\"CRO_05G023030\",\"SAT\",\"HXXXD-type acyl-transferase family protein\",29,\"Alkaloid\",\"known genes\"\n\"CRO-05G025730\",\"Idioblast\",0.41101064088735006,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G025840\",\"Epidermis\",1.1182913471154765,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G026210\",\"Phloem CC\",0.4049585502697053,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G026340\",\"Epidermis\",0.08000594573951239,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G026400\",\"Epidermis\",0.6172102090188418,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G026830\",\"Epidermis\",0.1533529196736679,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G026860\",\"Mesophyll\",0.022010613020243274,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G026890\",\"Phloem CC\",0.008742481197268578,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G027210\",\"Idioblast\",1.3585668797132147,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G028660\",\"Idioblast\",2.1161783167926913,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G028770\",\"Epidermis\",0.6221052597609944,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G028810\",\"IPAP\",2.01149150368631,\"Chr5\",66310319,66312455,\"mRNA\",NA,NA,NA,NA,NA,NA,\"7DLGT\",NA,15,\"Iridoid\",\"known genes\"\n\"CRO-05G029210\",\"Epidermis\",0.3128499237349651,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G030010\",\"Phloem CC\",0.5990678635239918,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G030540\",\"Phloem CC\",0.8126011562221682,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G030570\",\"Epidermis\",0.6432014471365645,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G031480\",\"IPAP\",0.06570603265757893,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G031500\",\"Phloem CC\",0.5290413469598569,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G031820\",\"Idioblast\",0.9084720935149608,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G032410\",\"IPAP\",0.2535678292127344,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G033220\",\"Phloem CC\",0.8743493386111132,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G033630\",\"Idioblast\",3.6360012290351804,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-05G033920\",\"Epidermis\",0.19944038690463275,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G000190\",\"Idioblast\",0.6511934679382159,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G000250\",\"Epidermis\",0.978961501434566,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G000340\",\"Idioblast\",0.27566748789769147,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G000540\",\"IPAP\",1.214450111219955,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G000750\",\"Epidermis\",0.8085043542654748,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G000790\",\"Phloem CC\",0.25596603850823707,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G000970\",\"Epidermis\",0.2688564136951474,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G003000\",\"Epidermis\",0.48782799480757255,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G003270\",\"Idioblast\",0.7482510772079944,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G003880\",\"IPAP\",0.7269289879088033,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G004160\",\"Epidermis\",0.135819086218536,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G004180\",\"Epidermis\",0.149438520491261,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G004520\",\"Guard cells\",0.03372357182911062,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G006060\",\"Epidermis\",0.18826319621403306,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G006210\",\"Vasculature\",0.48644053966767764,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G006240\",\"IPAP\",0.6496460048911201,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G006500\",\"Epidermis\",0.07301841404315611,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G006930\",\"Idioblast\",0.6999259399156185,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G008460\",\"Epidermis\",0.9124401237430957,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G009840\",\"Phloem CC\",0.01647957063282352,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G009970\",\"Epidermis\",0.9396563734055975,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G010180\",\"Idioblast\",0.41516615302388893,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G010770\",\"Epidermis\",0.06189243166504098,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G012230\",\"Epidermis\",0.349867435383876,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G013360\",\"Idioblast\",3.7993367383182233,\"Chr6\",32319166,32322926,\"mRNA\",\"Chr6\",32319774,32322925,\"-\",\"CRO_T127167\",\"CRO_06G013360\",\"D4H\",\"2-oxoglutarate (2OG) and Fe(II)-dependent oxygenase superfamily protein\",38,\"Late Alkaloid\",\"known genes\"\n\"CRO-06G013580\",\"Idioblast\",0.28516381041670974,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G013610\",\"Idioblast\",2.057021401797553,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G014380\",\"Epidermis\",0.9721655131445588,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G014840\",\"IPAP\",0.07031276026903316,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G014960\",\"Vasculature\",0.5086390124348243,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G015840\",\"Idioblast\",0.24632811769489654,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G015960\",\"Epidermis\",0.3025457747261689,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G016280\",\"Epidermis\",0.9092788201459555,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G016480\",\"Idioblast\",0.10393598561076339,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G016650\",\"Epidermis\",0.23230334386993118,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G016970\",\"IPAP\",1.378393790788343,\"Chr6\",42922661,42927534,\"mRNA\",\"Chr6\",42922806,42927534,\"+\",\"CRO_T108158\",\"CRO_06G016970\",\"IPI\",\"isopentenyl diphosphate isomerase\",8,\"MEP\",\"known genes\"\n\"CRO-06G017960\",\"Epidermis\",1.2902715021295312,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G019130\",\"Epidermis\",0.06964245390392337,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G019160\",\"Epidermis\",0.4516129056250536,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G019330\",\"Idioblast\",0.26789454174492966,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G019550\",\"Epidermis\",0.5088501298186048,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G019770\",\"Idioblast\",0.3185097935360906,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G019990\",\"Idioblast\",0.9494221124155046,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G020060\",\"IPAP\",2.4423769128235038,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G021140\",\"Epidermis\",1.125090532512303,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G021260\",\"Epidermis\",0.2499282265224754,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G021310\",\"Idioblast\",0.5346543247071627,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G022090\",\"Phloem CC\",0.26125514866469163,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G022930\",\"Epidermis\",0.5177124566252563,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G024130\",\"Idioblast\",2.566471329964634,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G024600\",\"Epidermis\",0.6929097849366777,\"Chr6\",58926982,58929303,\"mRNA\",\"Chr6\",58926817,58929338,\"-\",\"CRO_T113154\",\"CRO_06G024600\",\"GS1\",\"elicitor-activated gene 3-2\",25,\"Alkaloid\",\"known genes\"\n\"CRO-06G024620\",\"Idioblast\",2.2132983061476375,\"Chr6\",58960021,58962404,\"mRNA\",\"Chr6\",58960034,58962383,\"-\",\"CRO_T113150\",\"CRO_06G024620\",\"THAS2\",\"\",NA,\"Late Alkaloid\",\"known genes\"\n\"CRO-06G024630\",\"Epidermis\",0.4032478270409575,\"Chr6\",59015092,59017200,\"mRNA\",\"Chr6\",59015408,59016997,\"+\",\"CRO_T113148\",\"CRO_06G024630\",\"PAS\",\"FAD-binding Berberine family protein\",30,\"Alkaloid\",\"known genes\"\n\"CRO-06G025640\",\"Epidermis\",0.6701662818473362,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G025650\",\"Epidermis\",0.6159954402214182,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G025710\",\"Epidermis\",0.7278078921484169,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G025970\",\"Epidermis\",0.47223956711220005,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G026070\",\"Epidermis\",0.19698246846318584,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G026720\",\"Phloem CC\",1.1723983418253807,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G027030\",\"Phloem CC\",0.07511985227311141,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G027440\",\"Idioblast\",0.1607020638240427,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G027500\",\"Idioblast\",0.9541155762910061,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G028470\",\"Epidermis\",0.9988166125830824,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G028720\",\"Phloem CC\",0.19351242898653695,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G029240\",\"Epidermis\",0.15556491101167577,\"Chr6\",64918906,64920308,\"mRNA\",\"Chr6\",64919505,64920224,\"-\",\"CRO_T110361\",\"CRO_06G029240\",\"ORCA4\",\"\",NA,\"TF\",\"known genes\"\n\"CRO-06G029250\",\"Phloem CC\",0.09462998014715564,\"Chr6\",64937213,64938136,\"mRNA\",\"Chr6\",64937437,64938051,\"-\",\"CRO_T110360\",\"CRO_06G029250\",\"ORCA3\",\"\",NA,\"TF\",\"known genes\"\n\"CRO-06G029350\",\"Idioblast\",1.8138446873769591,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G030000\",\"Idioblast\",0.30355859797631446,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G030990\",\"Epidermis\",0.5232713143205624,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G031950\",\"Epidermis\",0.0389789152592131,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G032080\",\"Epidermis\",1.1085749401952087,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G032100\",\"Epidermis\",0.251194971073889,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G032200\",\"Vasculature\",0.0977255556281333,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G032870\",\"Phloem CC\",0.708484218009367,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G033010\",\"Idioblast\",1.7192586008985211,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G033690\",\"Idioblast\",0.33718192688255894,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-06G033800\",\"Epidermis\",0.6490854736919922,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G000120\",\"Epidermis\",0.8337353232010252,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G000280\",\"Phloem CC\",0.40035935339427886,\"Chr7\",384994,388125,\"mRNA\",\"Chr7\",385581,387662,\"+\",\"CRO_T124533\",\"CRO_07G000280\",\"MYC2\",\"\",NA,\"TF\",\"known genes\"\n\"CRO-07G001730\",\"Epidermis\",0.19118860600493928,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G001910\",\"Phloem CC\",0.3851681474159498,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G002170\",\"Idioblast\",0.8310997678439965,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G002680\",\"Idioblast\",0.3400093813714957,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G003880\",\"Epidermis\",0.8895884543542334,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G004010\",\"Epidermis\",0.16758631015431036,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G004110\",\"Mesophyll\",7.995576271034746e-4,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G004620\",\"Epidermis\",0.3367058173356035,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G004630\",\"Idioblast\",1.4627864557892265,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G004910\",\"Epidermis\",0.5736363419298506,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G005090\",\"Vasculature\",0.32756680659670645,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G005430\",\"IPAP\",1.1882410999194724,\"Chr7\",4969873,4972671,\"mRNA\",\"Chr7\",4970008,4972610,\"-\",\"CRO_T130236\",\"CRO_07G005430\",\"MCS\",\"isoprenoid F\",5,\"MEP\",\"known genes\"\n\"CRO-07G005460\",\"Epidermis\",0.23826874942185602,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G005690\",\"Idioblast\",0.3525860723319611,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G006000\",\"Epidermis\",0.45486870473522706,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G006070\",\"Idioblast\",0.18165763588544293,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G006160\",\"Epidermis\",0.4096246108721686,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G006840\",\"Idioblast\",0.5273047429124288,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G006850\",\"Idioblast\",1.8212844511176298,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G006910\",\"Epidermis\",0.48031300534748556,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G007510\",\"Epidermis\",0.29188105965427874,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G007580\",\"Idioblast\",0.8517193105216857,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G007670\",\"IPAP\",1.744462578134682,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G007680\",\"IPAP\",2.5571190765340597,\"Chr7\",6967404,6968570,\"mRNA\",\"Chr7\",6966534,6971921,\"+\",\"CRO_T130026\",\"CRO_07G007680/CRO_07G007690\",\"ISY\",\"NAD(P)-binding Rossmann-fold superfamily protein\",13,\"Iridoid\",\"known genes\"\n\"CRO-07G007700\",\"Epidermis\",1.1450684839968768,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G007980\",\"Epidermis\",0.31660712084873566,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G008320\",\"Vasculature\",0.3445325029815982,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G008340\",\"Epidermis\",1.296671599206864,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G008600\",\"Phloem CC\",0.2795415114739339,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G009080\",\"Epidermis\",0.08286414177210054,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G010060\",\"Idioblast\",3.415679320804025,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G010070\",\"Idioblast\",2.335440123912507,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G010080\",\"Epidermis\",1.0411906217433333,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G010160\",\"Idioblast\",0.41378853650329234,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G010240\",\"Idioblast\",1.6946771713373243,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G010530\",\"Idioblast\",0.3328932288893243,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G010620\",\"Phloem CC\",0.508398375005911,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G010880\",\"Idioblast\",0.3363688577643444,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G011030\",\"Epidermis\",0.224777031858805,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G011260\",\"Idioblast\",0.7868142991315301,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G011700\",\"Idioblast\",1.897051892798477,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G011860\",\"Epidermis\",0.9933165258592359,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G012950\",\"Epidermis\",0.4892518433392709,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G013280\",\"Phloem CC\",1.86659360657697,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G014640\",\"Epidermis\",0.08138709726026405,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G015460\",\"Idioblast\",0.6037413856230732,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G015670\",\"Epidermis\",0.7735908687384919,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G015860\",\"Idioblast\",0.6495407315188976,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G016140\",\"Epidermis\",0.34151115555462674,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G016640\",\"Epidermis\",1.130382856415752,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G017790\",\"Idioblast\",0.22647546631713839,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G018480\",\"IPAP\",0.3999212608254706,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G019670\",\"IPAP\",0.3522891326951685,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G020710\",\"Epidermis\",0.8710563441095814,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G021250\",\"Vasculature\",0.05689503032010134,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G021290\",\"IPAP\",0.5537579835594864,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G021850\",\"Epidermis\",0.5510761461393264,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-07G022150\",\"Epidermis\",0.8587990976423996,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-08G000290\",\"Phloem CC\",0.0665737713888274,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-08G000430\",\"Epidermis\",1.0018683701897533,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-08G000950\",\"Epidermis\",0.02425629142343527,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-08G001030\",\"Epidermis\",1.2634588793323562,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-08G001640\",\"Idioblast\",1.7436486000446083,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-08G001730\",\"Idioblast\",0.08900551214788911,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-08G001790\",\"Phloem CC\",0.09813825400532078,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-08G002610\",\"Epidermis\",0.5858039731076733,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,NA,\"\"\n\"CRO-08G003240\",\"Phloem 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  },
  {
    "path": "Data/Network_Example/Readme",
    "content": "This folder contains data used in `NetworkLayouts.Rmd`\n"
  },
  {
    "path": "Data/deidentified_cell_feature_data.csv",
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  {
    "path": "Heatmap_tutorial.md",
    "content": "\n# Introduction \nThis workflow is about how to programmatically reorder rows & columns of a heatmap. \nHeatmap is a versatile visualization method for many data types. \nHowever, for heatmaps to be effective, we should consider reordering rows & columns.\nSee [this figure](https://github.com/cxli233/FriendsDontLetFriends#5-friends-dont-let-friends-make-heatmap-without-considering-reordering-rows--columns) for an example.  \n\n# Required Packages \n```{r}\nlibrary(tidyverse) \nlibrary(patchwork) # not actually required, loaded for this tutorial only \n\nlibrary(RColorBrewer)\n```\n\n# Data \nWe will use this example data from my own research.\nThis is a collaboration between [Buell lab](https://buell-lab.github.io/) at UGA & [O'Connor lab](https://www.ice.mpg.de/) at the Max Planck Institute.  \n\nDon't worry about what this table is about, but this workflow should work with any kind of data. \nThis is a tidyverse workflow, so the input data do need to be in the tidy format. \nSee [this tutorial](https://r4ds.had.co.nz/tidy-data.html) for more info on tidy data. \n\n```{r}\nmy_data <- read_csv(\"../Data/heatmap_example.csv\", col_types = cols())\n\nhead(my_data)\n```\n\n`row` and `col` will be the rows and columns of the heatmap. \nThe `value` column will be used to color the heatmap. \n\n# Without reordering\nLet's see what will happen if we don't reorder the rows and columns. \n```{r}\nno_reorder <- my_data %>% \n  ggplot(aes(x = col, y = row)) +\n  geom_tile(aes(fill = value)) +\n  scale_fill_gradientn(colors = brewer.pal(9, \"YlGnBu\")) +\n  theme_classic() +\n  theme(axis.text.y.left = element_blank(),\n        axis.ticks.y.left = element_blank())\n\nno_reorder\n\nggsave(\"../Results/Heatmap_no_reorder.svg\", height = 3.5, width = 4.2, bg = \"white\")\nggsave(\"../Results/Heatmap_no_reorder.png\", height = 3.5, width = 4.2, bg = \"white\")\n```\n![heatmap_no_reorder](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Results/Heatmap_no_reorder.png)\n\nAs you can see, there is no pattern to be discerned here. \n\n# Reorder one dimension first\nTo make it easier to discern information hidden in the heatmap, we need to reorder rows & columns. \nMy approach is reordering one of dimensions (rows or columns) first. \nOften we can reorder one dimension based on some practical meaning. \nFor example, is one of the dimensions time series? Developmental stages? Different experimental conditions/treatments?\n\n## An example data where columns have practical meaning. \nLet's say we have an experiment with 10 genes and 6 samples. \n```{r}\nmy_data2 <- expand.grid(\n  genes = letters[1:10],\n  samples = 1:6)\n```\n\nAnd let's say samples 1, 3, 5 are controls, and samples 2, 4, 6 are treatments. \nAnd let's say we want to reorder it such that the controls appear first in (left) in the heatmap. \n```{r}\nmy_data2 <- my_data2 %>% \n  mutate(samples = factor(samples, \n                          levels = c(1, 3, 5, 2, 4, 6))) \n\nhead(my_data2)\n```\n\nTo (re)order a column of the data frame, you can use `factor()`.\nWithin the `factor()` function, you type in the desired order in `levels = c(...)`. \n\nNow, when you go plot it, what will we see?\n```{r}\nmy_data2 %>% \n  ggplot(aes(x = samples, y = genes)) +\n  theme_classic()\n\nggsave(\"../Results/Reorder_1_dim.svg\", height = 3, width = 3, bg = \"white\")\nggsave(\"../Results/Reorder_1_dim.png\", height = 3, width = 3, bg = \"white\")\n```\n![Reorder_one_dimension](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Results/Reorder_1_dim.png)\n\nNow you can see that the x axis is in this order `1, 3, 5, 2, 4, 6`, which is the order we specified. \n\n# Programmatic reordering of rows and columns\nWhat if we don't want to manually punch in the order? \nWe can reorder them using same basic statistics: __peak value__ and __number of peak values__.\n\n* peak value: at which column does a given row reaches its maximum value?\n* number of peak values: for any given column, how many rows reach their maximum values? \n\nA really simple but effective way to order rows and columns is to reorder columns by number of peak values. \nAfter that, reorder rows by peak values. \n\n1. Columns that have most rows reaching peak values appear first (to the left of heatmap).\n2. Columns that have the least rows reaching peak values appear last (to the right of the heatmap).\n3. Rows that reach peak value for the 1st column appear on top. \n4. Rows that reach peak value for the last column appear on the bottom. \n\nIt might be easier to understand how this plays out with an actual heatmap. \n\n## Finding peak values and number of peak values\nLet's come back to our `my_data` example.\nTo find the peak value for each row, use `group_by(row)` followed by `slice_max()`\n\n```{r}\nmy_data_peak_values <- my_data %>% \n  group_by(row) %>% \n  slice_max(order_by = value, n = 1, with_ties = F) %>% \n  rename(peaked_at = col) %>% \n  select(-value)\n\nhead(my_data_peak_values)\n```\n\nNow we have a data frame, each row is a row that will appear in the heatmap. \nFor each row, there is information on which column of the heatmap it peaks at. \n\nTo find how many rows peak at each column, use `group_by(column)` followed by `count()`.\n```{r}\nnumber_of_peaks <- my_data_peak_values %>% \n  group_by(peaked_at) %>% \n  count() %>% \n  arrange(-n)\n\nhead(number_of_peaks) \n```\nNow we have a data frame where each row is a column that will appear in the heatmap.\nAnd we have information on how many rows peaked at that column. \n\nNow we will reorder rows by columns. \n```{r}\nmy_data_peak_values_reordered <- my_data_peak_values %>% \n  inner_join(number_of_peaks, by = \"peaked_at\") %>% \n  arrange(-n)  \n\nmy_data_peak_values_reordered\n```\n\nAfter finding these basic statistics, we can join these statistics to our main data frame. \n```{r}\nmy_data_reordered <- my_data %>% \n  inner_join(number_of_peaks, by = c(\"col\" = \"peaked_at\")) %>% \n  mutate(col = reorder(col, -n)) %>%  # this reorders the columns\n  select(-n) %>% \n  inner_join(my_data_peak_values_reordered, by = \"row\") %>% \n  mutate(peaked_at = reorder(peaked_at, n)) %>% \n  mutate(order_rows = as.numeric(peaked_at)) %>% \n  mutate(row = reorder(row, order_rows)) # this reorders the rows by the \"peaked_at\" column. \n  \n\nhead(my_data_reordered)\n```\n\n# Now we can plot it\n```{r}\nreordered_heatmap <- my_data_reordered %>% \n  ggplot(aes(x = col, y = row)) +\n  geom_tile(aes(fill = value)) +\n  scale_fill_gradientn(colors = brewer.pal(9, \"YlGnBu\")) +\n  theme_classic() +\n  theme(axis.text.y.left = element_blank(),\n        axis.ticks.y.left = element_blank())\n\nreordered_heatmap\n```\n\n... and do a side-by-side comparison. \n```{r}\nwrap_plots(no_reorder+ \n             labs(title = \"Not reordered\"), \n           reordered_heatmap +\n             labs(title = \"Rows/columns reordered\"),\n           guides = \"collect\") \n\nggsave(\"../Results/Heatmap_reorder.svg\", height = 3.5, width = 8, bg = \"white\")\nggsave(\"../Results/Heatmap_reorder.png\", height = 3.5, width = 8, bg = \"white\")\n```\n![Side_by_side](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Results/Heatmap_reorder.png)\n\nNow we can see that a clear pattern emerged. \nEach column has a group of rows that reach peak level.\nOverall, the heatmap is also much easier to understand and is more informative. \n\n\n\n"
  },
  {
    "path": "LICENSE.md",
    "content": "MIT License\n\nCopyright (c) 2022 C. Li\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n"
  },
  {
    "path": "README.md",
    "content": "# Friends Don't Let Friends Make Bad Graphs \n\n\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7542491.svg)](https://doi.org/10.5281/zenodo.7542491)\n\n\nFriends don't let friends make certain types of data visualization - What are they and why are they bad. \n\n* Author: Chenxin Li, Ph.D., Assistant Professor at Department of Plant Biology, Michigan State University. \n* Contact: lichen27@msu.edu | [@chenxinli2.bsky.social](https://bsky.app/profile/chenxinli2.bsky.social)\n\nThis is an *opinionated* essay about good and bad practices in data visualization. \nExamples and explanations are below. \n\nThe `Scripts/` directory contains `.Rmd` files that generate the graphics shown below. \nIt requires R, RStudio, and the rmarkdown package. \n\n* R: [R Download](https://cran.r-project.org/bin/)\n* RStudio: [RStudio Download](https://www.rstudio.com/products/rstudio/download/)\n* rmarkdown can be installed using the install packages interface in RStudio\n\n# Table of contents\n\n1. [Friends Don't Let Friends Make Bar Plots For Mean Separation](https://github.com/cxli233/FriendsDontLetFriends#1-friends-dont-let-friends-make-bar-plots-for-means-separation)\n2. [Friends Don't Let Friends Make Violin Plots for Small Sample Sizes](https://github.com/cxli233/FriendsDontLetFriends#2-friends-dont-let-friends-make-violin-plots-for-small-sample-sizes)\n3. [Friends Don't Let Friends Use Bidirectional Color Scales for Unidirectional Data](https://github.com/cxli233/FriendsDontLetFriends#3-friends-dont-let-friends-use-bidirectional-color-scales-for-unidirectional-data)\n4. [Friends Don't Let Friends Make Bar Plot Meadow](https://github.com/cxli233/FriendsDontLetFriends#4-friends-dont-let-friends-make-bar-plot-meadow)\n5. [Friends Don't Let Friends Make Heatmap without Reordering Rows & Columns](https://github.com/cxli233/FriendsDontLetFriends#5-friends-dont-let-friends-make-heatmap-without-considering-reordering-rows--columns)\n6. [Friends Don't Let Friends Make Heatmap without Checking Outliers](https://github.com/cxli233/FriendsDontLetFriends#6-friends-dont-let-friends-make-heatmap-without-checking-outliers)\n7. [Friends Don't Let Friends Forget to Check Data Range at Each Factor Level](https://github.com/cxli233/FriendsDontLetFriends#7-friends-dont-let-friends-forget-to-check-data-range-at-each-factor-level)\n8. [Friends Don't Let Friends Make Network Graphs without Trying Different Layouts](https://github.com/cxli233/FriendsDontLetFriends#8-friends-dont-let-friends-make-network-graphs-without-trying-different-layouts) \n9. [Friends Don't Let Friends Confuse Position and Length Based Visualizations](https://github.com/cxli233/FriendsDontLetFriends#9-friends-dont-let-friends-confuse-position-based-visualizations-with-length-based-visualizations) \n10. [Friends Don't Let Friends Make Pie Charts](https://github.com/cxli233/FriendsDontLetFriends#10-friends-dont-let-friends-make-pie-chart) \n11. [Friends Don't Let Friends Make Concentric Donuts](https://github.com/cxli233/FriendsDontLetFriends#11-friends-dont-let-friends-make-concentric-donuts)\n12. [Friends Don't Let Friends Use Red/green and Rainbow for Color Scales](https://github.com/cxli233/FriendsDontLetFriends#12-friends-dont-let-friends-use-redgreen-and-rainbow-color-scales)\n13. [Friends Don't Let Friends Forget to Reorder Stacked Bar Plot](https://github.com/cxli233/FriendsDontLetFriends/tree/main#13-friends-dont-let-friends-forget-to-reorder-stacked-bar-plot)\n14. [Friends Don't Let Friends Mix Stacked Bars and Mean separation](https://github.com/cxli233/FriendsDontLetFriends/tree/main#14-friends-dont-let-friends-mix-stacked-bars-and-mean-separation)\n15. [Friends Don't Let Friends Use Histogram for Small Sample Sizes](https://github.com/cxli233/FriendsDontLetFriends/tree/main?tab=readme-ov-file#friends-dont-let-friends-use-histogram-for-small-sample-sizes)\n16. [Friends don't Let Friends Use Boxpot for Bimodal Data](https://github.com/cxli233/FriendsDontLetFriends?tab=readme-ov-file#friends-dont-let-friends-use-boxpot-for-bimodal-data)\n\n# 1. Friends Don't Let Friends Make Bar Plots for Means Separation\n\nThis has to be the first one. \nMeans separation plots are some of the most common in scientific publications. \nWe have two or more groups, which contains multiple observations; they may have different means, variances, and distributions. \nThe task of the visualization is to show the means and the spread (dispersion) of the data. \n\n![No Bar Plots for Means Separation](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Results/dont_bar_plot.png) \n\nIn this example, two groups have similar means and standard deviations, but quite different distributions. **Are they really \"the same\"?**\nJust don't use bar plot for means separation, or at least check a couple things before settling down on a bar plot. \n\nIt's worth mentioning that I was inspired by many researchers who have tweeted on the limitation of bar graphs. \nHere is a publication: [Weissgerber et al., 2015, PLOS Biology](https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002128). \n\n# 2. Friends Don't Let Friends Make Violin Plots for Small Sample Sizes \n\nThis is quite common in the literature as well, but unfortunately, violin plots (or any sort of smoothed distribution curves) make no sense for small n. \n\n![Beware of Violin Plots for Small Sample Sizes](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Results/Beware_of_small_n_box_violin_plot.png) \n\nDistributions and quartiles can vary widely with small n, even if the underlying observations are similar. \nDistribution and quartiles are only meaningful with large n. \nI did an experiment before, where I sampled the *same* normal distribution several times and computed the quartiles for each sample.\nThe quartiles only stablize when n gets larger than 50. \n\n# 3. Friends Don't Let Friends Use Bidirectional Color Scales for Unidirectional Data \n\nExcuse my language, but this is a truly data visualization sin, and again quite common. \nI can understand why this error is common, because it appears that many of us have not spent a lot of thoughts on this issue. \n\n![Are You Using the Right Color Scale for Your Data?](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Results/ColorScales.svg)\n\nColor scales are pretty, but we have to be extra careful.\nWhen color scales (or color gradients) are used to represent numerical data, the darkest and lightest colors should have special meanings.\nYou can decide what those special meanings are: e.g., max, min, mean, zero. But they should represent something meaningful. \nA data visualization sin for heat maps/color gradients is when the lightest or darkest colors are some arbitrary numbers. \n*This is as bad as the longest bar in a bar chart not being the largest value.* Can you imagine that?  \n\n# 4. Friends Don't Let Friends Make Bar Plot Meadow \n\nWe talked about no bar charts for mean separation, but this is a different issue. \nIt has to do with presenting results of a multi-factorial experiment. \nBar plot meadows are very common in scientific publications and unfortunately also *ineffective* in communicating the results. \n\n![Horrendous Giant Bar Plot vs. Better Designed Plot](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Results/AvoidBarPlotMeadow.png)\n\nData from: [Matand et al., 2020, BMC Plant Biology](https://link.springer.com/article/10.1186/s12870-020-2243-7)\n\nBar plot meadows are common because multi-factorial experiments are common. \nHowever, a bar plot meadow is poorly designed for its purpose. \nTo communicate results of a multi-factorial experiment, it requires thoughtful designs regarding grouping/faceting by factors of interest.\n\nIn this example, I focus on comparing the effect of `Treatment` & `Explant` on `Response` at the level of each `Variety`. \nHowever, if the focus is the effect of `Treatment` & `Variety` on `Response` at the level of each `Exaplant`, then it will require a different layout. \n\n# 5. Friends Don't Let Friends Make Heatmap without (Considering) Reordering Rows & Columns \n\nHeatmaps are very common in scientific publications, and *very very* common in omics papers. \nHowever, for heatmaps to be effective, we have to consider the ordering of rows & columns. \n\n![A Heatmap before and after reordering rows and columns](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Results/Reorder_rows_and_columns_for_heatmap.png) \n\nIn this example, I have cells as columns and features as rows. Grids are showing z scores. \nIt is impossible to get anything useful out of the heatmap without reordering rows and columns. \nWe can reorder rows and columns using clustering, but that is not the only way. \nOf course, if the rows and columns are mapping to physical entities (rows and columns of a 96-well plate), then you can't reorder them. \nBut it is a very good idea to at least consider reordering rows and columns. \n\nData from: [Li et al., 2022, BioRxiv](https://www.biorxiv.org/content/10.1101/2022.07.04.498697v1) \n\n## Bonus: heatmaps can be very pretty\n\n...if you are good are reordering rows/columns and choosing color gradients. \nHere is an example \"abstract aRt\" generated from simulated data. \n\n![aRt with Heatmap](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Results/Abstract_R_2022_11_24.svg)        \n\nR code for this aRt piece can be found [here](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Scripts/Abstract_aRt.R). \n\nFor a tutorial on how to reorder rows and columns of a heatmap, see this [markdown file](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Heatmap_tutorial.md). \n\n# 6. Friends Don't Let Friends Make Heatmap without Checking Outliers \n\nOutliers in heatmap can really change how we perceive and interpret the visualization. \nThis generalizes to all sort of visualizations that use colors to represent numeric data.\nLet me show you an example:\n\n![Did you check outliers](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Results/Check_outliers_for_heatmap.svg)\n\nIn this example, I have 2 observations. For each observations, I measured 20 features. \nWithout checking for outliers, it may appear that the 2 observations are overall similar, except at 2 features. \nHowever, after maxing out the color scale around 95th percentile of the data, it reveals that the two observations are distinct across all features. \n\n# 7. Friends Don't Let Friends Forget to Check Data Range at Each Factor Level \n\nThis is a common issue that many of us have encountered. \nIn a multifactor experiment, sometimes the range of the response variable changes widely between different factor levels. \n\n![Did you check data range at each factor level](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Results/Check_range_at_factor_level.svg)\n\nThis hypothetical experiment measured 3 compounds across 2 groups (control vs. treatment). \nWithout checking data range for each compound, you will likely have missed that the treatment had a strong effect on compound 1.\nThis is because the concentration of compound 1 has a much narrower range than the other compounds in this experiment. \n\n# 8. Friends Don't Let Friends Make Network Graphs without Trying Different Layouts\n\nNetwork graphs are common in scientific publications. They are super useful in presenting relationship data. \nHowever, the appearance (not the topology) of the network can make a huge difference in determining if a network graph is effective. \n\n![Try different network layouts](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Results/TryDifferentLayouts.svg) \n\nLayouts can drastically change the appearance of networks, making them easier or harder to interpret.\nHere are 3 network graphs from the same data. They look very different from each other.\nData from: [Li et al., 2022, BioRxiv](https://www.biorxiv.org/content/10.1101/2022.07.04.498697v1) \n\nHere is 9 different layouts for the _same_ network. They can look very different. \n\n![Different layouts](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Results/network_layouts.gif)\n\nThe R script to make this animation is available [here](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Scripts/Animated_networks.Rmd)\n\n![Different layout of the animation](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Results/Different_layouts.png)\n\n# 9. Friends Don't Let Friends Confuse Position-based Visualizations with Length-based Visualizations \n\nThis is always the elephant in the room and the essence of many misleading visualizations. \nIn this example, I measured a response variable across 3 time points. \nTwo of the following graphs are fine, but one of them is a data visualization crime. Can you see why? \n\n![Position vs. length based visualizations](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Results/Position_and_length_based_visualizations.svg)\n\nIn dot and line plots, values are represented by positions along the x and y axis.\nThe same idea applies to other position based visualizations, such as box plots. \nIn bar plots, values are represented by the distance from the x axis, and thus the length of the bar. \n\nThe 3rd graph is not 0-based, which makes the bar length at time point 2 about 3x longer than that at time point 1.\nIn fact, the true difference in means is closer to 1.6x. \nI hope you can see how confusing length and position based visualizations can lead to misleading graphs.   \n\n## Watch out for bar plots with broken axis \n\nBroken axis may be useful for depicting data across a wide range of numeric values. \n(Alternatively, log scaled axis can be used instead.) \nBroken axis are fine for position based graphics, because the data are represented by positions along the axis. \nHowever, we must be very careful with bar plots that have broken axis. Here is an example. \n\n![Broken axis](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Results/Broken_axis.svg) \n\nIn this example, two graphs (left vs. right) are showing the same data. \nHowever, by changing where the axis is broken, one can make certain bars looks longer or shorter. \nIn this example, the length of bar \"d\" can look *really* different.\nThe illusion of bar \"d\" being very short on the right graph boils down to bar plot being a length based graphics, not a position based graphics. \n\nExample R code for broken axis can be found [here](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Scripts/Broken_axis.R). \n\n# 10. Friends Don't Let Friends Make Pie Chart \n\nPie chart is a common type of visualization for fractional data, where fractions add up to 100%. \nThis is achieved by dividing a circle into sectors, and the sectors add up to a full circle. \nPie charts have been criticized, because human are much worse in reading angles and area than reading lengths. \nHere is a [blog post](https://www.data-to-viz.com/caveat/pie.html) that explores that. \n\n![Don't make pie charts](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Results/dont_pie_chart.svg)\n\nIn this example, we have two groups, each contains 4 sub-categories. \nIn classic pie charts, the angles (and thus arc lengths & sector area) represent the data. \nThe problem is that it is *very* difficult to compare between groups. \nWe can visually simplify the pie chart into donut charts, where the data are now represented by arc lengths. \nHowever, if we want to use lengths to represent the data, why don't we just unwrap the donut and make stacked bars?\nIn stacked bar graphs, bars are shown side-by-side and thus easier to compare across groups. \n\nFun fact: the scripts underlying stacked bars are much simpler than those underlying the pie charts and donut charts.\nIf you want to produce sub-optimal graph types with ggplot, you actually have to work extra hard.\n\n# 11. Friends Don't Let Friends Make Concentric Donuts\n\nIn this example, we have 3 groups, each of which contains two sub-categories (Type I or Type II). \n\n![Don't make concentric donuts](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Results/dont_concentric_donuts.svg)\n\nIn concentric donuts, you might be tempted to say the data are represented by the arc lengths, which is in fact **inaccurate**. \nThe arc lengths on the outer rings are much longer than those in the inner rings. \nGroup 2 and Group 3 have the same exact values, but the arc lengths of Group 3 are much longer. \nIn fact the data are represented by the *arc angles*, which we are bad at reading. \n\nSince outer rings are longer, the ordering of the groups (which group goes to which ring) has a big impact on the impression of the plot.\nIt can lead to the apparent paradox where larger values have shorter arcs. \nThe better (and simpler!) alternative is just unwrap the donuts and make a good old stacked bar plot. \nBTW, this is also my main issue with [circos plots](http://circos.ca/) and other circular plot layouts.\n\n# 12. Friends Don't Let Friends Use Red/Green and Rainbow color scales\n\n![are you making a \"safe\" heatmap?](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Results/Color_blind_grey_scale_safe_heatmap.svg)\n\nDeuteranomaly is the most common type of red/green colorblindness, occurring in 1/16 male and 1/256 female. \nAny color scales that use shades of red and shades of green in the same time would be a problem for a person with red/green colorblindness (third column of the figure). \nIn addition, red/green and rainbow do not preserve information well at all when printed on black/white (grey scale, second column in figure). \nMany scientific software still use red/green or rainbow as the default color scales, which drives me crazy. \nMore \"modern\" color scales, such as [viridis](https://cran.r-project.org/web/packages/viridis/vignettes/intro-to-viridis.html) are both colorblind-friendly and grey scale-safe (third row of figure). \nAnd they look nice too. \n\n# 13. Friends Don't Let Friends Forget to Reorder Stacked Bar Plot\nStacked bar plots are useful for visualizing proportion data. \nStacked bar plots are commonly used to visualize community structure or population structure or admixture analysis. \nThis kind of visualization boils down to a collection of samples, where each sample contains multiple classes of members. \nHowever, when we have many samples and many classes, stacked bar plots need to be optimized to be effective. \nAnd by \"optimize\" I mean the grouping and ordering of samples. \n\n![Reorder your stacked bars](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Results/Reorder_stacked_bars.png)\n\nHere we have an example data with 100 samples and 8 classes of member. \nDue to the number of samples and classes, it is very hard to discern anything from this graph without optimizing the order of bars. What the heck am I looking at? \nAfter reordering the bars, __wow__, that really made a difference, don't you think? \nFor a tutorial on how to optimize a stack bar plot, see [this script](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Scripts/stacked_bars_optimization.Rmd).\n\n# 14. Friends Don't Let Friends Mix Stacked Bars and Mean separation\nSometimes a visualization gets confusing and ineffective when it tries to too many things at once. \nOne such example is mixing stacked bar plots and mean separation plots. \nOne displays proportional data adding up to 100%, the other displays the difference in means and dispersion around means. \nThese are very distinct tasks in data visualization. \n\nIn this hypothetical experiment, we had blueberry plants assigned to two groups.\nOne group was the control; the other was treated with a chemical to make fruit development faster.  \nEach group had 5 plants.\nThe response of the treatment was divided into 3 categories: \nlight green fruits, light blue fruits, and dark blue fruits. \n100 fruits from each plant were examined and the number of fruits in each category was counted. \nThe percentage of fruits in each category was calculated and reported. \nThe question of the study is: did the chemical treatment work? \n\n![Don't mix stacked bar plots with mean separation plots](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Results/stacked_bar_vs_jitter.png) \n\nThe first stacked bar plot is fine as the standard way to visualize proportion data. \nIt is clear that all categories add up to 100%, \nand the chemical treatment strongly shifted the color profile towards the most developed stage (dark blue). \n\nThe middle stacked bar plot is problematic, \nmainly because it is trying to do two distinct data visualization tasks at once. \nWhen error bars and dots are overlaid onto the stacked bars, \nit become unclear which error bars and dots are being compared. \nDue to the nature of stacked bars, the error bars and dots of the upper stacks have to be shifted upwards,\nand thus interpretation of the y-axis for error bars and dots become not straightforward. \n\nFinally, if the main point of the visualization is mean separation and dispersion around the mean, \nthe third graph is the better choice. \nThere is no ambiguity on which comparisons are being made.\nAs shown in the first stacked bar plot, \nthe chemical treatment strongly increases the proportion of dark blue fruits, \nat the expense of lighter color fruits. \n\n# Friends don't let friends use histogram for small sample sizes \nI've seen histogram being proposed as the replacement for bar plots. \nHowever, a serious caveat for histogram is that histograms are not robust to bin numbers for small (and even moderate) sample sizes. \nIn a histogram, we first bin the data into a defined number of bins.\nThen we count how many observations are there for each bin and graph them. \n\n![Histogram with different sample sizes and bin numbers](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Results/Histogram_for_small_n.png)\n\nIn this example, I sampled _the same_ normal distribution 3 times with different sample sizes (n = 10, 100, and 1000).\nEven though they came from _the same_ normal distribution, the histograms look quite different based on the number of bins. \nTo showcase this, I plotted histograms for 10, 30, and 50 bins. \n\nFirst of all, histogram makes no sense for small sample sizes. With small sample sizes (n < 30), the much better practice is to graph all data points. \nSecond of all, you can see that the shape of the histogram is only robust to changing bin number when the sample size is fairly large (like 1000).\nEven if n = 100, the appearance of the histogram can change drastically as the number of bins changes. \n\n# Friends don't let friends use boxpot for bimodal data\nThis figure should speak for itself. Is your boxplot hiding something from you?  \n\n![Is your box plot hiding something from you](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Results/BoxPlots_for_binomial.png)\n\nBefore making a boxplot, one should check the distribution of their data, since box plots focus on median and quartiles, they cannot handle bimodal data (and by extension data with multiple modes).\nPloting all the data points using `geom_quasirandom()` from the [ggbeeswarm package](https://github.com/eclarke/ggbeeswarm) is the best practice for small sample to moderate (less than tens of thousands) sample sizes, as distribution-based graphics such as violin plots and histograms are not robust to small sample sizes. See [this section](https://github.com/cxli233/FriendsDontLetFriends#2-friends-dont-let-friends-make-violin-plots-for-small-sample-sizes) and [this section](https://github.com/cxli233/FriendsDontLetFriends/tree/main?tab=readme-ov-file#friends-dont-let-friends-use-histogram-for-small-sample-sizes) for details. \n\n# Conclusion (?)\n\nThat's it for now. I will update this when I have the time (and inspirations) to produce more examples. \nNot sure what the next one will be, but stay tuned! \n"
  },
  {
    "path": "Results/a",
    "content": "\r\n"
  },
  {
    "path": "Scripts/Abstract_aRt.R",
    "content": "# Dependencies \r\nlibrary(tidyverse)\r\nlibrary(patchwork)\r\n\r\nlibrary(RColorBrewer)\r\nlibrary(viridis)\r\nlibrary(rcartocolor)\r\n\r\nset.seed(666)\r\n\r\n# setwd(\"../Desktop/Li/OtherCoding/FriendsDontLetFriends/Scripts/\")\r\n\r\n# Data \r\nmy_data <- expand.grid(\r\n  x = 1:21,\r\n  y = 1:21) %>% \r\n  mutate(\r\n    group_x = case_when(\r\n      x <= 6 ~ 1,\r\n      x > 6 & x <= 11 ~ 2,\r\n      x > 11 & x <= 15 ~ 3,\r\n      x > 15 & x <= 18 ~ 4,\r\n      x > 18 & x <= 20 ~ 5,\r\n      x > 20 ~ 6\r\n    )\r\n  ) %>% \r\n  mutate(\r\n    group_y = case_when(\r\n      y <= 6 ~ 1,\r\n      y > 6 & y <= 11 ~ 2,\r\n      y > 11 & y <= 15 ~ 3,\r\n      y > 15 & y <= 18 ~ 4,\r\n      y > 18 & y <= 20 ~ 5,\r\n      y > 20 ~ 6\r\n    )\r\n  ) %>%\r\n  mutate(group_sq = (group_x - group_y)^2) %>% \r\n  mutate(\r\n    fill = case_when(\r\n      group_sq == 0 ~ 2,\r\n      group_sq == 1 ~ 0.5,\r\n      group_sq == 4 ~ 0,\r\n      group_sq == 9 ~ -0.5,\r\n      group_sq == 16 ~ -1,\r\n      group_sq == 25 ~ -2\r\n    )\r\n  ) %>%\r\n  cbind(\r\n    noise = rnorm(n = nrow(.), mean = 0, sd = 0.25)\r\n  ) %>% \r\n  mutate(fill2 = fill + noise)\r\n  \r\n# Graph\r\n\r\nheatmap <- my_data %>% \r\n  ggplot(aes(x = x , y = y)) +\r\n  facet_grid(group_y ~ group_x, space = \"free\", scales = \"free\") +\r\n  geom_tile(aes(fill = fill2)) +\r\n  scale_fill_gradientn(colors = rev(brewer.pal(11, \"RdBu\")),\r\n                      limits = c(-max(my_data$fill2), max(my_data$fill2))) +\r\n  labs(x = NULL,\r\n       y = NULL)+\r\n  theme_minimal()+\r\n  theme(\r\n    strip.text = element_blank(),\r\n    strip.background = element_blank(),\r\n    panel.grid = element_blank(),\r\n    axis.text = element_blank(),\r\n    legend.position = \"none\"\r\n  )\r\n\r\nstrip_left <- my_data %>% \r\n  filter(x == 1) %>% \r\n  ggplot(aes(x = 1, y = y)) +\r\n  facet_grid(group_y ~ group_x, space = \"free\", scales = \"free\") + \r\n  geom_tile(aes(fill = group_y)) +\r\n  scale_fill_gradientn(colors = viridis(n = 256)) +\r\n  labs(x = NULL,\r\n       y = NULL)+\r\n  theme_minimal()+\r\n  theme(\r\n    strip.text = element_blank(),\r\n    strip.background = element_blank(),\r\n    panel.grid = element_blank(),\r\n    axis.text = element_blank(),\r\n    legend.position = \"none\"\r\n    )\r\n\r\n\r\nstrip_bottom <- my_data %>% \r\n  filter(y == 1) %>% \r\n  ggplot(aes(x = x, y = 1)) +\r\n  facet_grid(group_y ~ group_x, space = \"free\", scales = \"free\") + \r\n  geom_tile(aes(fill = group_x)) +\r\n  scale_fill_gradientn(colors = rev(carto_pal(7, \"Magenta\"))) +\r\n  labs(x = NULL,\r\n       y = NULL)+\r\n  theme_minimal()+\r\n  theme(\r\n    strip.text = element_blank(),\r\n    strip.background = element_blank(),\r\n    panel.grid = element_blank(),\r\n    axis.text = element_blank(),\r\n    legend.position = \"none\"\r\n  )\r\n\r\nwrap_plots(\r\n  heatmap, strip_left,\r\n  strip_bottom, \r\n  nrow = 2, ncol = 2,\r\n  widths = c(1, 0.04),\r\n  heights = c(1, 0.04)\r\n)\r\n\r\nggsave(\"../Results/Abstract_R_2022_11_24.png\", height = 4, width = 4, bg = \"white\")\r\nggsave(\"../Results/Abstract_R_2022_11_24.svg\", height = 4, width = 4, bg = \"white\")"
  },
  {
    "path": "Scripts/Animated_networks.Rmd",
    "content": "---\r\ntitle: \"Animate networks\"\r\nauthor: \"Chenxin Li\"\r\ndate: \"2023-01-15\"\r\noutput: html_document\r\n---\r\n\r\n```{r setup, include=FALSE}\r\nknitr::opts_chunk$set(echo = TRUE)\r\n```\r\n\r\n# Introduction\r\nLet's make animated network graphs! \r\n\r\n# Packages \r\n```{r, echo=FALSE, results='hide'} \r\nlibrary(tidyverse)\r\nlibrary(igraph)\r\nlibrary(ggraph)\r\n\r\nlibrary(gganimate)\r\nlibrary(readxl)\r\nlibrary(RColorBrewer)\r\n```\r\n\r\nTo do network stuff, we need `igraph` and `ggraph`. \r\n`igraph` is a network analysis package that can do a lot of heavy-lifting when it comes of dealing with networks. \r\n`ggraph` is a `ggplot` extension for network visualization.  \r\n\r\nTo animate ggplot objects, we need gganime. \r\n\r\n# Load an example network \r\n## Make a network object from edges and nodes \r\nWe will use an example. Here is a hypothetical network that I made up. \r\n```{r}\r\nmy_network_edges <- read_excel(\"../Data/Example_network_edges.xlsx\")\r\n\r\nmy_network_edges\r\n```\r\nWithout a given node table, \r\nyou can actually produce a node table from the edge table. \r\nThe node table is just a non-redundant list of all members of `from` and `to` in the edge table. \r\n\r\n```{r}\r\nmy_network_nodes <- data.frame(\r\n  nodes = unique(c(my_network_edges$From, my_network_edges$To))\r\n) %>% \r\n  #mutate(nodes = as.character(nodes)) %>% \r\n  mutate(module = case_when(\r\n   nodes %in% c(1:10) ~ \"Module 1\",\r\n   nodes %in% c(11:20) ~ \"Module 2\",\r\n   nodes %in% c(21:30) ~ \"Module 3\",\r\n  ))\r\n\r\nmy_network_nodes\r\n```\r\n\r\n`unique(c(example_network_edges$From, example_network_edges$To)` takes the members of `from` and `to` from `example_network_edges` and take the non-redundant set. \r\n\r\nTo make a network, use the `graph_from_data_frame()` function from the `igraph` package. \r\n```{r}\r\nmy_network <- graph_from_data_frame(\r\n  d = my_network_edges,\r\n  vertices = my_network_nodes,\r\n  directed = F\r\n)\r\n\r\nmy_network\r\n```\r\n\r\nThe `d` in `graph_from_data_frame()` specifies the edge table.\r\n`vertices` species the node table. \r\nIn this example I set `directed` to `FALSE`.\r\nThis means from node 1 to node 2 is the same from node 2 to node 1. \r\n\r\n## Using ggraph \r\nTo make a network diagram, we will use `ggraph`.\r\n```{r}\r\nkk_vis <- ggraph(my_network, \r\n       layout = \"kk\"\r\n       ) +\r\n  geom_edge_link(color = \"grey70\") +\r\n  geom_node_point(size = 3, shape = 21, color = \"white\", aes(fill = module)) +\r\n  geom_node_text(repel = T, aes(label = my_network_nodes$nodes)) +\r\n  scale_fill_manual(values = brewer.pal(8, \"Set2\")) +\r\n  theme_void()\r\n```\r\nThere are 3 fundamental elements to a network diagram. \r\n\r\n1. Layout, which controls how nodes are placed in the 2-D plane. In this diagram I used \"kk\" or \"Kamada-Kawai\" layout algorithm. More about layout algorithms is discussed below. \r\n2. Edges. In this example, I used `geom_edge_diagonal()`, which draws curves between nodes.\r\n3. Nodes, which is provided by `geom_node_point()`, draws each node as a point. \r\n\r\n# Try different network layouts\r\nThere are multiple layout algorithms for network diagrams. \r\nLayouts can drastically change the appearance of networks, making them easier or harder to interpret. \r\n\r\n\r\nMy go-to is \"kk\", which generally gives good results.\r\nBut I encourage you to read more about different layouts [here](https://www.data-imaginist.com/2017/ggraph-introduction-layouts/) and [here](https://r-graph-gallery.com/247-network-chart-layouts.html). \r\n\r\n## Pull out x & y coordinates\r\n```{r}\r\ncircle_xy <- ggraph(my_network, layout = \"circle\")$data %>% \r\n  mutate(layout = \"circle\")\r\n\r\nstar_xy <- ggraph(my_network, layout = \"star\")$data %>% \r\n  mutate(layout = \"star\")\r\n\r\nkk_xy <- ggraph(my_network, layout = \"kk\")$data %>% \r\n  mutate(layout = \"kk\")\r\n\r\nmds_xy <- ggraph(my_network, layout = \"mds\")$data %>% \r\n  mutate(layout = \"mds\")\r\n\r\nfr_xy <- ggraph(my_network, layout = \"fr\")$data %>% \r\n  mutate(layout = \"fr\")\r\n\r\ngem_xy <- ggraph(my_network, layout = \"gem\")$data %>% \r\n  mutate(layout = \"gem\")\r\n\r\nstress_xy <- ggraph(my_network, layout = \"stress\")$data %>% \r\n  mutate(layout = \"stress\")\r\n\r\ntree_xy <- ggraph(my_network, layout = \"tree\")$data %>% \r\n  mutate(layout = \"tree\")\r\n\r\nsugiyama_xy <- ggraph(my_network, layout = \"sugiyama\")$data %>% \r\n  mutate(layout = \"sugiyama\")\r\n\r\nnode_xy <- rbind(\r\n  circle_xy,\r\n  star_xy,\r\n  kk_xy,\r\n  mds_xy,\r\n  fr_xy,\r\n  stress_xy,\r\n  gem_xy,\r\n  tree_xy,\r\n  sugiyama_xy\r\n) %>% \r\n   mutate(module = case_when(\r\n   name %in% c(1:10) ~ \"Module 1\",\r\n   name %in% c(11:20) ~ \"Module 2\",\r\n   name %in% c(21:30) ~ \"Module 3\",\r\n  )) %>% \r\n  select(x, y, name, layout, module)\r\n\r\nhead(node_xy)\r\n```\r\n\r\n\r\n## Generate the edge data frame\r\n\r\n```{r}\r\ncircle_edges <- my_network_edges %>% \r\n  mutate(edge_id = paste0(From, \"-\", To)) %>% \r\n  mutate(From = as.character(From)) %>% \r\n  mutate(To = as.character(To)) %>% \r\n  inner_join(circle_xy %>% \r\n               select(x, y, name, layout), by = c(\"From\" = \"name\")) %>% \r\n  rename(\r\n    temx = x, \r\n    temy = y,\r\n  ) %>% \r\n  inner_join(circle_xy %>% \r\n               select(x, y, name, layout), by = c(\"To\" = \"name\", \"layout\")) %>% \r\n  rename(\r\n    x = temx,\r\n    y = temy,\r\n    xend = x,\r\n    yend = y\r\n  )\r\n\r\nstar_edges <- my_network_edges %>% \r\n  mutate(edge_id = paste0(From, \"-\", To)) %>% \r\n  mutate(From = as.character(From)) %>% \r\n  mutate(To = as.character(To)) %>% \r\n  inner_join(star_xy %>% \r\n               select(x, y, name, layout), by = c(\"From\" = \"name\")) %>% \r\n  rename(\r\n    temx = x, \r\n    temy = y,\r\n  ) %>% \r\n  inner_join(star_xy %>% \r\n               select(x, y, name, layout), by = c(\"To\" = \"name\", \"layout\")) %>% \r\n  rename(\r\n    x = temx,\r\n    y = temy,\r\n    xend = x,\r\n    yend = y\r\n  )\r\n\r\nkk_edges <- my_network_edges %>% \r\n  mutate(edge_id = paste0(From, \"-\", To)) %>% \r\n  mutate(From = as.character(From)) %>% \r\n  mutate(To = as.character(To)) %>%\r\n  inner_join(kk_xy %>% \r\n               select(x, y, name, layout), by = c(\"From\" = \"name\")) %>% \r\n  rename(\r\n    temx = x, \r\n    temy = y,\r\n  ) %>% \r\n  inner_join(kk_xy %>% \r\n               select(x, y, name, layout), by = c(\"To\" = \"name\", \"layout\")) %>% \r\n  rename(\r\n    x = temx,\r\n    y = temy,\r\n    xend = x,\r\n    yend = y\r\n  )\r\n\r\nmds_edges <- my_network_edges %>% \r\n  mutate(edge_id = paste0(From, \"-\", To)) %>% \r\n  mutate(From = as.character(From)) %>% \r\n  mutate(To = as.character(To)) %>%\r\n  inner_join(mds_xy %>% \r\n               select(x, y, name, layout), by = c(\"From\" = \"name\")) %>% \r\n  rename(\r\n    temx = x, \r\n    temy = y,\r\n  ) %>% \r\n  inner_join(mds_xy %>% \r\n               select(x, y, name, layout), by = c(\"To\" = \"name\", \"layout\")) %>% \r\n  rename(\r\n    x = temx,\r\n    y = temy,\r\n    xend = x,\r\n    yend = y\r\n  )\r\n\r\nfr_edges <- my_network_edges %>% \r\n  mutate(edge_id = paste0(From, \"-\", To)) %>% \r\n  mutate(From = as.character(From)) %>% \r\n  mutate(To = as.character(To)) %>%\r\n  inner_join(fr_xy %>% \r\n               select(x, y, name, layout), by = c(\"From\" = \"name\")) %>% \r\n  rename(\r\n    temx = x, \r\n    temy = y,\r\n  ) %>% \r\n  inner_join(fr_xy %>% \r\n               select(x, y, name, layout), by = c(\"To\" = \"name\", \"layout\")) %>% \r\n  rename(\r\n    x = temx,\r\n    y = temy,\r\n    xend = x,\r\n    yend = y\r\n  )\r\n\r\nstress_edges <- my_network_edges %>% \r\n  mutate(edge_id = paste0(From, \"-\", To)) %>% \r\n  mutate(From = as.character(From)) %>% \r\n  mutate(To = as.character(To)) %>% \r\n  inner_join(stress_xy %>% \r\n               select(x, y, name, layout), by = c(\"From\" = \"name\")) %>% \r\n  rename(\r\n    temx = x, \r\n    temy = y,\r\n  ) %>% \r\n  inner_join(stress_xy %>% \r\n               select(x, y, name, layout), by = c(\"To\" = \"name\", \"layout\")) %>% \r\n  rename(\r\n    x = temx,\r\n    y = temy,\r\n    xend = x,\r\n    yend = y\r\n  )\r\n\r\ngem_edges <- my_network_edges %>% \r\n  mutate(edge_id = paste0(From, \"-\", To)) %>% \r\n  mutate(From = as.character(From)) %>% \r\n  mutate(To = as.character(To)) %>% \r\n  inner_join(gem_xy %>% \r\n               select(x, y, name, layout), by = c(\"From\" = \"name\")) %>% \r\n  rename(\r\n    temx = x, \r\n    temy = y,\r\n  ) %>% \r\n  inner_join(gem_xy %>% \r\n               select(x, y, name, layout), by = c(\"To\" = \"name\", \"layout\")) %>% \r\n  rename(\r\n    x = temx,\r\n    y = temy,\r\n    xend = x,\r\n    yend = y\r\n  )\r\n\r\ntree_edges <- my_network_edges %>% \r\n  mutate(edge_id = paste0(From, \"-\", To)) %>% \r\n  mutate(From = as.character(From)) %>% \r\n  mutate(To = as.character(To)) %>%\r\n  inner_join(tree_xy %>% \r\n               select(x, y, name, layout), by = c(\"From\" = \"name\")) %>% \r\n  rename(\r\n    temx = x, \r\n    temy = y,\r\n  ) %>% \r\n  inner_join(tree_xy %>% \r\n               select(x, y, name, layout), by = c(\"To\" = \"name\", \"layout\")) %>% \r\n  rename(\r\n    x = temx,\r\n    y = temy,\r\n    xend = x,\r\n    yend = y\r\n  )\r\n\r\nsugiyama_edges <- my_network_edges %>% \r\n  mutate(edge_id = paste0(From, \"-\", To)) %>% \r\n  mutate(From = as.character(From)) %>% \r\n  mutate(To = as.character(To)) %>% \r\n  inner_join(sugiyama_xy %>% \r\n               select(x, y, name, layout), by = c(\"From\" = \"name\")) %>% \r\n  rename(\r\n    temx = x, \r\n    temy = y,\r\n  ) %>% \r\n  inner_join(sugiyama_xy %>% \r\n               select(x, y, name, layout), by = c(\"To\" = \"name\", \"layout\")) %>% \r\n  rename(\r\n    x = temx,\r\n    y = temy,\r\n    xend = x,\r\n    yend = y\r\n  )\r\n\r\nedge_data_frame <- rbind(\r\n  circle_edges,\r\n  star_edges,\r\n  kk_edges,\r\n  mds_edges,\r\n  fr_edges,\r\n  stress_edges,\r\n  gem_edges,\r\n  tree_edges,\r\n  sugiyama_edges\r\n) \r\n\r\nhead(edge_data_frame)\r\n```\r\n\r\n \r\n\r\n# Make the animation \r\nEach frame is a layout \r\n```{r}\r\nmy_animate <- ggplot() +\r\n  geom_segment(\r\n    data = edge_data_frame %>% \r\n      mutate(layout = factor(layout, levels = c(\r\n        \"circle\", \"star\", \"tree\", \r\n        \"sugiyama\", \"mds\", \"fr\",\r\n        \"kk\", \"gem\", \"stress\"\r\n      ))),\r\n    aes(x = x, xend = xend, y = y, yend = yend, group = edge_id),\r\n    color = \"grey70\", linewidth = 1, \r\n  ) +\r\n  geom_point(\r\n    data = node_xy %>% \r\n      mutate(layout = factor(layout, levels = c(\r\n        \"circle\", \"star\", \"tree\", \r\n        \"sugiyama\", \"mds\", \"fr\",\r\n        \"kk\", \"gem\", \"stress\"\r\n      ))), aes(x, y, fill = module),\r\n    shape = 21, size = 4, color = \"white\", alpha = 0.8\r\n  ) +\r\n  labs(title = \"layout: {closest_state}\",\r\n       fill = NULL) +\r\n  scale_fill_manual(values = brewer.pal(8, \"Set2\")) +\r\n  ease_aes(\"linear\") +\r\n  transition_states(layout, state_length = 2, transition_length = 1) + \r\n  theme_void() +\r\n  theme(\r\n    plot.title = element_text(size = 20),\r\n    legend.position = \"none\"\r\n  ) +\r\n  view_follow()\r\n\r\nanimate(my_animate, height = 4, width = 4, units = \"in\", res = 300)\r\nanim_save(\"../Results/network_layouts.gif\")\r\n```\r\n\r\n```{r}\r\nggplot() +\r\n  facet_wrap(~layout, scales = \"free\") +\r\n  geom_segment(\r\n    data = edge_data_frame %>% \r\n      mutate(layout = factor(layout, levels = c(\r\n        \"circle\", \"star\", \"tree\", \r\n        \"sugiyama\", \"mds\", \"fr\",\r\n        \"kk\", \"gem\", \"stress\"\r\n      ))),\r\n    aes(x = x, xend = xend, y = y, yend = yend, group = edge_id),\r\n    color = \"grey70\", \r\n  ) +\r\n  geom_point(\r\n    data = node_xy %>% \r\n      mutate(layout = factor(layout, levels = c(\r\n        \"circle\", \"star\", \"tree\", \r\n        \"sugiyama\", \"mds\", \"fr\",\r\n        \"kk\", \"gem\", \"stress\"\r\n      ))), aes(x, y, fill = module),\r\n    shape = 21, size = 3, color = \"white\", alpha = 0.8\r\n  ) +\r\n  labs(title = \"Different layouts of the SAME network\\n\",\r\n       fill = NULL,\r\n       x = NULL,\r\n       y = NULL) +\r\n  scale_fill_manual(values = brewer.pal(8, \"Set2\")) +\r\n  theme_minimal() +\r\n  theme(\r\n    plot.title = element_text(size = 12),\r\n    legend.position = \"none\",\r\n    panel.spacing = unit(1, \"lines\"),\r\n    panel.background = element_rect(fill = NA, color = NA),\r\n    strip.background = element_rect(fill = NA, color = NA),\r\n    panel.grid = element_blank(),\r\n    axis.text = element_blank(),\r\n    strip.text = element_text(size = 12)\r\n  )\r\n\r\nggsave(\"../Results/Different_layouts.svg\", height = 6, width = 6, bg = \"white\")\r\nggsave(\"../Results/Different_layouts.png\", height = 6, width = 6, bg = \"white\")\r\n```\r\n\r\n"
  },
  {
    "path": "Scripts/Bar_meadow_for_factorial.Rmd",
    "content": "---\r\ntitle: \"BarMeadow\"\r\nauthor: \"Chenxin Li\"\r\ndate: '2022-05-24'\r\noutput: html_document\r\n---\r\n\r\n```{r setup, include=FALSE}\r\nknitr::opts_chunk$set(echo = TRUE)\r\n```\r\n\r\nThis is an Rmarkdown document.If you don't have the rmarkdown package, you will have to install it to run this. \r\nThe grey areas are code chunks. To run a code chunk click the green triangle at the upper right of each chunk. \r\n\r\n# Packages \r\n```{r}\r\nlibrary(tidyverse) \r\nlibrary(ggbeeswarm) # to make jitter plots  \r\nlibrary(readxl) # to load excel files \r\n\r\nlibrary(viridis) # for the colors \r\nlibrary(patchwork)\r\n\r\nlibrary(emmeans) # for estimated marginal means and statistics \r\n```\r\n\r\nYou will also need `multcomp` and `multcompView` packages for compact letter display (CLD). \r\nInstall them if you don't have them. \r\n\r\n# Data \r\n\r\nData from:  \r\n    Matand, K., Shoemake, M. and Li, C., 2020. \r\n    High frequency in vitro regeneration of adventitious shoots in daylilies (Hemerocallis sp) stem tissue using thidiazuron. \r\n    BMC Plant Biology, 20(1), pp.1-10.\r\n  \r\nFor simiplicity, we will be only three of the 19 cultivars: Alias, Bright, and Rococo\r\n\r\n```{r}\r\nstem_data <- read_excel(\"../Data/Matand_2020_Stem_Data.xlsx\") \r\n\r\nstem_data2 <- stem_data %>% \r\n  mutate(Treatment = factor(Treatment, levels = c(\"T1\", \"T5\", \"T10\"))) %>% \r\n  filter(str_detect(Variety, \"Alias|Bright|Rococo\"))\r\n\r\nhead(stem_data)\r\n```\r\n\r\n\r\n# Stat\r\n```{r}\r\nmodel_stem <- lm(Primordia ~ Explant * Treatment * Variety, data = stem_data2)\r\nanova(model_stem)\r\n```\r\nRun a quick ANOVA to check which factors/interactions account for the most variances. \r\n\r\n# Bad example\r\n## CLD  \r\nFor the classic bar plot meadow, let's generate all pairwise comparisons.\r\nThis is indicated by the ~ Variety:Explant:Treatment syntax. \r\n\r\n```{r}\r\nest_all <- emmeans(model_stem, pairwise ~ Variety:Explant:Treatment)    \r\nresults_all <- multcomp::cld(est_all$emmeans, Letters = \"abcdefghijklmnopqrst\") %>% \r\n  as.data.frame() %>% \r\n  mutate(grouping = str_remove_all(.group, \" \"))\r\n\r\nhead(results_all)\r\n```\r\n\r\n\r\n## Plot \r\nThe classic bar plot meadow ususally only plots mean and se. \r\nWe will generate that first. \r\n\r\n```{r}\r\nbarplotMeadow <- stem_data2 %>% \r\n  group_by(Variety, Treatment, Explant) %>% \r\n  summarise(\r\n    mean = mean(Primordia),\r\n    sd = sd(Primordia),\r\n    NN = n()\r\n  ) %>% \r\n  ungroup() %>% \r\n  mutate(se = sd/sqrt(NN)) %>% \r\n  ggplot(aes(x = interaction(Variety, Treatment, Explant), y = mean)) +\r\n  geom_bar(stat = \"identity\", position = position_dodge2(), alpha = 0.8, width = 0.7) +\r\n  geom_errorbar(aes(ymin = mean - se, ymax = mean + se), width = 0.2) +\r\n  geom_text(data = results_all, aes(label = grouping, y = upper.CL), vjust = -0.2) +\r\n  labs(x = NULL,\r\n       y = \"Mean\",\r\n       caption = \"This is horrendous.\\nWhat am I looking at?\") +\r\n  theme_classic() +\r\n  theme(\r\n    axis.text.x = element_text(angle = 45, hjust = 1),\r\n    plot.caption = element_text(hjust = 0, size = 12)\r\n  ) +\r\n  ggtitle(\"Classic bar plot meadow\\ny ~ Variety:Treatment:Explant\")\r\n\r\nbarplotMeadow\r\n```\r\n\r\n# Good example \r\n## CLD\r\n\r\nIn this example, we will focus on comparing the effect of explant and treatment combination at the level of each cultivar. \r\nThis is indicated by the ~ Explant:Treatment | Variety syntax. \r\n\r\nYou can also compare the effect of treatment and cultivar at the level of each explant, if that's what you are interested in. \r\nIn that case, you will need ~ Treatment:Variety | Explant. \r\n\r\n```{r}\r\nest_by_factor <- emmeans(model_stem, pairwise ~ Explant:Treatment | Variety)\r\nresults_by_factor <- multcomp::cld(est_by_factor$emmeans, Letters = \"abcdefghijklmnopqrst\") %>% \r\n  as.data.frame() %>% \r\n  mutate(grouping = str_remove_all(.group, \" \"))\r\n\r\nhead(results_by_factor)\r\n```\r\n\r\n \r\n## plot\r\n```{r}\r\nDotPlot_by_factor <- stem_data2 %>% \r\n  ggplot(aes(x = Treatment, y = Primordia)) +\r\n  facet_wrap( ~ Variety + Explant, scales = \"free_x\", strip.position = \"bottom\") +\r\n  ggbeeswarm::geom_quasirandom(aes(fill = Treatment), \r\n                               shape = 21, color = \"white\", size = 2.5, alpha = 0.8) +\r\n  geom_text(data = results_by_factor, \r\n            aes(label = grouping, y = upper.CL + 35), size = 5) +\r\n  scale_fill_manual(values = viridis(10)[c(1, 5, 8)]) +\r\n  labs(y = \"Response\",\r\n       x = NULL,\r\n       caption = \"That's better.\\nReader's attention is more focused.\") +\r\n  theme_classic() +\r\n  theme(\r\n    legend.position = \"none\",\r\n    strip.background = element_blank(),\r\n    panel.spacing.y = unit(1.2, \"lines\"),\r\n    strip.text = element_text(size = 10),\r\n    strip.placement = \"outside\",\r\n    plot.caption = element_text(hjust = 0, size = 12)\r\n  ) +\r\n  ggtitle(\"Better designed for multifactorial experiments\\ny ~ Treatment:Explant | Variety\")\r\n\r\nDotPlot_by_factor\r\n```\r\nThis has variety as rows and explant as columns, which allows us to compare across the same variety. \r\nAgain, this is indicated by ~ Explant:Treatment | Variety syntax. \r\n\r\n\r\n# Put together\r\n```{r}\r\nwrap_plots(barplotMeadow, DotPlot_by_factor, nrow = 1)\r\n\r\nggsave(\"../Results/AvoidBarPlotMeadow.svg\", height = 7, width = 10, bg = \"white\")\r\nggsave(\"../Results/AvoidBarPlotMeadow.png\", height = 7, width = 10, bg = \"white\")\r\n```\r\n\r\n\r\n\r\n\r\n\r\n\r\n"
  },
  {
    "path": "Scripts/Bar_plots_for_means_separation.Rmd",
    "content": "---\r\ntitle: \"Bar_charts\"\r\nauthor: \"Chenxin Li\"\r\ndate: \"4/5/2022\"\r\noutput: html_document\r\n---\r\n\r\n```{r setup, include=FALSE}\r\nknitr::opts_chunk$set(echo = TRUE)\r\n```\r\n\r\nThis is an Rmarkdown document.If you don't have the rmarkdown package, you will have to install it to run this. \r\nThe grey areas are code chunks. To run a code chunk click the green triangle at the upper right of each chunk.  \r\n\r\n# Package\r\n```{r}\r\nlibrary(tidyverse)\r\nlibrary(ggbeeswarm) # to make jitter plots  \r\nlibrary(RColorBrewer) # for the colors \r\nlibrary(patchwork) # for putting ggplot objects into one graph \r\n```\r\n\r\n# Data\r\n\r\nGroup is sampled from a normal distribution with mean = 1 and sd = 1. \r\nGroup is sampled from a lognormal distribution with mean = 1 and sd = 1. \r\n\r\n```{r}\r\nset.seed(666)\r\ngroup1 <- rnorm(n = 100, mean = 1, sd = 1)\r\ngroup2 <- rlnorm(n = 100, \r\n                 meanlog = log(1^2/sqrt(1^2 + 1^2)), \r\n                 sdlog = sqrt(log(1+(1^2/1^2))))\r\n\r\nsd(group1)\r\nsd(group2)\r\n\r\nmean(group1)\r\nmean(group2)\r\n\r\nmedian(group1)\r\nmedian(group2)\r\n\r\nIQR(group1)\r\nIQR(group2)\r\n```\r\n\r\n```{r}\r\ngroups_long <- cbind(\r\n  group1,\r\n  group2\r\n) %>% \r\n  as.data.frame() %>% \r\n  gather(\"group\", \"response\", 1:2)\r\n\r\nhead(groups_long)\r\n```\r\n\r\n# Tests\r\n```{r}\r\nt.test(group1, group2)\r\n\r\nwilcox.test(group1, group2)\r\n\r\nks.test(group1, group2)\r\n```\r\n\r\nYou will notice different tests give different results. \r\nMight seem odd at first, but these tests are not testing for the same thing, so it is not odd after all.\r\n\r\n# Plot\r\n## bar\r\n```{r}\r\nbar <- groups_long %>% \r\n  ggplot(aes(x = group, y = response)) +\r\n  geom_bar(stat = \"summary\", fun = mean, \r\n           width = 0.7, alpha = 0.8,\r\n           aes(fill = group)) + \r\n  stat_summary(geom = \"errorbar\", fun.data = \"mean_se\",\r\n               width = 0.1, size = 1) +\r\n  scale_fill_manual(values = brewer.pal(8, \"Accent\")[1:2]) +\r\n  labs(x = \"Group\",\r\n       y = \"Response\",\r\n       caption = paste0(\"They are the same!\\nP = \", \r\n                        signif(t.test(group1, group2)$p.value, 2),\r\n                        \" (t test)\")) +\r\n  theme_classic() +\r\n  theme(\r\n    text = element_text(size = 12, face = \"bold\", color = \"black\"),\r\n    axis.text = element_text(color = \"black\"),\r\n    legend.position = \"none\",\r\n    plot.title = element_text(size = 10),\r\n    plot.caption = element_text(hjust = 0)\r\n  ) +\r\n  ggtitle(\r\n    paste0(\r\n      \"group1: mean = \", signif(mean(group1), 2), \r\n      \"; sd = \", signif(sd(group1), 2), \"\\n\",\r\n      \"group2: mean = \", signif(mean(group2), 2), \r\n      \"; sd = \", signif(sd(group2), 2)\r\n    )) \r\n  \r\n  \r\n\r\nbar\r\n```\r\n## Boxplot\r\n```{r}\r\nbox <- groups_long %>% \r\n  ggplot(aes(x = group, y = response)) +\r\n  geom_boxplot(width = 0.7, alpha = 0.8, \r\n          aes(fill = group)) +\r\n  scale_fill_manual(values = brewer.pal(8, \"Accent\")[1:2]) +\r\n  labs(x = \"Group\",\r\n       y = \"Response\",\r\n       caption = paste0(\"Hmmmmm...\\nP = \", \r\n                        signif(wilcox.test(group1, group2)$p.value, 2),\r\n                        \" (Wilcoxon rank sum test)\")) +\r\n  theme_classic() +\r\n  theme(\r\n    text = element_text(size = 12, face = \"bold\", color = \"black\"),\r\n    axis.text = element_text(color = \"black\"),\r\n    legend.position = \"none\",\r\n    plot.title = element_text(size = 10),\r\n    plot.caption = element_text(hjust = 0)\r\n  ) +\r\n  ggtitle(\r\n    paste0(\r\n      \"group1: median = \", signif(median(group1), 2), \r\n      \"; IQR = \", signif(IQR(group1), 2), \"\\n\",\r\n      \"group2: median = \", signif(median(group2), 2), \r\n      \"; IQR = \", signif(IQR(group2), 2)\r\n    )\r\n  )\r\n\r\nbox\r\n```\r\n## Dot plot\r\n```{r}\r\ndotplot <- groups_long %>% \r\n  ggplot(aes(x = group, y = response)) +\r\n  ggbeeswarm::geom_quasirandom(\r\n    shape = 21, color = \"white\", \r\n    alpha = 0.8, size = 3,\r\n    aes(fill = group)\r\n  ) +\r\n  scale_fill_manual(values = brewer.pal(8, \"Accent\")[1:2]) +\r\n  labs(x = \"Group\",\r\n       y = \"Response\",\r\n        caption = paste0(\"OH!!!\\nP = \", \r\n                        signif(ks.test(group1, group2)$p.value, 2),\r\n                        \" (Kolmogorov–Smirnov test)\")) +\r\n  theme_classic() +\r\n  theme(\r\n    text = element_text(size = 12, face = \"bold\", color = \"black\"),\r\n    axis.text = element_text(color = \"black\"),\r\n    legend.position = \"none\",\r\n    plot.title = element_text(size = 10),\r\n    plot.caption = element_text(hjust = 0)\r\n  ) +\r\n  ggtitle(\r\n    paste0(\r\n      \"group1: median = \", signif(median(group1), 2), \r\n      \"; IQR = \", signif(IQR(group1), 2), \"\\n\",\r\n      \"group2: median = \", signif(median(group2), 2), \r\n      \"; IQR = \", signif(IQR(group2), 2)\r\n    )\r\n  )\r\n\r\ndotplot \r\n```\r\n# combine plots\r\n```{r}\r\nwrap_plots(\r\n  bar, box, dotplot, nrow = 1\r\n)\r\n\r\nggsave(\"../Results/dont_bar_plot.svg\", height = 5, width = 10)\r\nggsave(\"../Results/dont_bar_plot.png\", height = 5, width = 10)\r\n```\r\n\r\n"
  },
  {
    "path": "Scripts/BoxPlot_for_Binomial.Rmd",
    "content": "---\r\ntitle: \"BoxPlot_for_Binomial\"\r\nauthor: \"Chenxin Li\"\r\ndate: \"2024-12-10\"\r\noutput: html_document\r\n---\r\n\r\n```{r setup, include=FALSE}\r\nknitr::opts_chunk$set(echo = TRUE)\r\n```\r\n\r\n# Friends don't let friends use boxpot for binomial data\r\n\r\n# Packages\r\n```{r}\r\nlibrary(tidyverse)\r\nlibrary(RColorBrewer)\r\nlibrary(ggbeeswarm)\r\n\r\nlibrary(patchwork)\r\n```\r\n\r\n## Data \r\n```{r}\r\nset.seed(666)\r\n```\r\n\r\n```{r}\r\ndata1 <- data.frame(\r\n  response = rnorm(n = 100, mean = 5, sd = 2)\r\n) %>% \r\n  mutate(group = \"group1\")\r\n\r\ndata2 <- data.frame(\r\n  response = c(\r\n    rnorm(n = 50, mean = 2.5, sd = 1),\r\n    rnorm(n = 50, mean = 7.5, sd = 1)\r\n  )) %>% \r\n    mutate(group = \"group2\")\r\n\r\ndata3 <- data.frame(\r\n  response = c(\r\n    rnorm(n = 33, mean = 2, sd = 0.5),\r\n    rnorm(n = 33, mean = 5, sd = 0.5),\r\n    rnorm(n = 33, mean = 8, sd = 0.5)\r\n  )) %>% \r\n    mutate(group = \"group3\")\r\n```\r\n\r\n## Bad example\r\n```{r}\r\nBox <- rbind(\r\n  data1, \r\n  data2,\r\n  data3\r\n) %>% \r\n  ggplot(aes(x = group, y = response)) +\r\n  geom_boxplot(aes(fill = group), alpha = 0.8, width = 0.7) +\r\n  scale_fill_manual(values = brewer.pal(8, \"Set2\")) +\r\n  labs(title = \"Very similar!\") + \r\n  theme_classic()\r\n\r\nBox\r\n```\r\n## Good example\r\n```{r}\r\nDots <- rbind(\r\n  data1, \r\n  data2,\r\n  data3\r\n) %>% \r\n  ggplot(aes(x = group, y = response)) +\r\n  geom_quasirandom(aes(color = group), alpha = 0.8) +\r\n  scale_color_manual(values = brewer.pal(8, \"Set2\")) +\r\n  labs(title = \"I guess not!\") + \r\n  theme_classic()\r\n\r\nDots\r\n```\r\n# Wrap them together\r\n```{r}\r\nwrap_plots(\r\n  Box, Dots,\r\n  nrow = 1\r\n) &\r\n  theme(legend.position = \"none\")\r\n\r\nggsave(\"../Results/BoxPlots_for_binomial.svg\", width = 5, height = 2.5)\r\nggsave(\"../Results/BoxPlots_for_binomial.png\", width = 5, height = 2.5)\r\n```\r\nBefore making a box plot, one should check the distribution of their data,\r\nsince box plots focus on median and quartiles, \r\nthey cannot handle binomial data (and by extension data with multiple modes).\r\nPloting all the data points using `geom_quasirandom()` from the [ggbeeswarm package](https://github.com/eclarke/ggbeeswarm) is the best practice for small sample to moderate (less than tens of thousands) sample sizes, \r\nas dots are robust to small sample sizes, \r\nwhereas distribution-based graphics such as violin plots and histograms are not.\r\nSee [this section](https://github.com/cxli233/FriendsDontLetFriends#2-friends-dont-let-friends-make-violin-plots-for-small-sample-sizes) and [this section](https://github.com/cxli233/FriendsDontLetFriends/tree/main?tab=readme-ov-file#friends-dont-let-friends-use-histogram-for-small-sample-sizes) for details. "
  },
  {
    "path": "Scripts/Broken_axis.R",
    "content": "\r\nlibrary(tidyverse)\r\nlibrary(patchwork)\r\nlibrary(RColorBrewer)\r\n\r\n\r\na <- data.frame(\r\n  x = c(\"a\", \"b\", \"c\", \"d\", \"e\"),\r\n  y = c(10, 20, 30, 450, 500)\r\n)\r\n\r\nlower <- a %>% \r\n  ggplot(aes(x = x, y = y)) +\r\n  geom_bar(stat = \"identity\", aes(fill = x),\r\n           width = 0.7, color = \"black\") +\r\n  geom_text(aes(label = y), vjust = -0.2) +\r\n  scale_fill_manual(values = brewer.pal(8, \"Set2\")) +\r\n  theme_classic() +\r\n  theme(legend.position = \"none\",\r\n        axis.title.y = element_text(hjust =1 )) +\r\n  coord_cartesian(ylim = c(0, 50))\r\n\r\nupper1 <- a %>% \r\n  ggplot(aes(x = x, y = y)) +\r\n  geom_bar(stat = \"identity\", aes(fill = x),\r\n           width = 0.7, color = \"black\") +\r\n  geom_text(aes(label = y), vjust = -0.2) +\r\n  scale_fill_manual(values = brewer.pal(8, \"Set2\")) +\r\n  labs(x = NULL,\r\n       y = NULL) +\r\n  theme_classic() +\r\n  theme(legend.position = \"none\",\r\n        axis.line.x = element_blank(),\r\n        axis.ticks.x = element_blank(),\r\n        axis.text.x = element_blank()) +\r\n  coord_cartesian(ylim = c(400, 550))\r\n\r\nupper2 <- a %>% \r\n  ggplot(aes(x = x, y = y)) +\r\n  geom_bar(stat = \"identity\", aes(fill = x),\r\n           width = 0.7, color = \"black\") +\r\n  geom_text(aes(label = y), vjust = -0.2) +\r\n  scale_fill_manual(values = brewer.pal(8, \"Set2\")) +\r\n  labs(x = NULL,\r\n       y = NULL) +\r\n  theme_classic() +\r\n  theme(legend.position = \"none\",\r\n        axis.line.x = element_blank(),\r\n        axis.ticks.x = element_blank(),\r\n        axis.text.x = element_blank()) +\r\n  coord_cartesian(ylim = c(450, 520))\r\n\r\n\r\nbroken1 <- wrap_plots(upper1, lower, nrow = 2) &\r\n  theme(axis.text = element_text(color = \"black\"),\r\n        text = element_text(size = 14))\r\n\r\n broken2 <- wrap_plots(upper2, lower, nrow = 2) &\r\n  theme(axis.text = element_text(color = \"black\"),\r\n        text = element_text(size = 14))\r\n\r\nwrap_plots(broken1, broken2, nrow = 1)\r\n\r\nggsave(\"../Results/Broken_axis.svg\", height = 4, width = 6, bg = \"white\")\r\nggsave(\"../Results/Broken_axis.png\", height = 4, width = 6, bg = \"white\")\r\n\r\n\r\n\r\n\r\nleft <- a %>% \r\n  ggplot(aes(x = y, y = x)) +\r\n  geom_bar(stat = \"identity\", aes(fill = x),\r\n           width = 0.7, color = \"black\") +\r\n  geom_text(aes(label = y), hjust = -0.2) +\r\n  scale_fill_manual(values = brewer.pal(8, \"Set2\")) +\r\n  theme_classic() +\r\n  theme(legend.position = \"none\",\r\n        axis.title.x = element_text(hjust =1 )) +\r\n  coord_cartesian(xlim = c(0, 50))\r\n\r\n\r\nright <- a %>% \r\n  ggplot(aes(x = y, y = x)) +\r\n  geom_bar(stat = \"identity\", aes(fill = x),\r\n           width = 0.7, color = \"black\") +\r\n  geom_text(aes(label = y),hjust = -0.2) +\r\n  scale_fill_manual(values = brewer.pal(8, \"Set2\")) +\r\n  labs(x = NULL,\r\n       y = NULL) +\r\n  theme_classic() +\r\n  theme(legend.position = \"none\",\r\n        axis.line.y = element_blank(),\r\n        axis.ticks.y = element_blank(),\r\n        axis.text.y = element_blank()) +\r\n  coord_cartesian(xlim = c(400, 600))\r\n\r\nwrap_plots(left, right, nrow = 1) &\r\n  theme(axis.text = element_text(color = \"black\"),\r\n        text = element_text(size = 14))\r\n"
  },
  {
    "path": "Scripts/Check_outlier_for_heatmaps.Rmd",
    "content": "---\r\ntitle: \"CheckOutlierForHeatmap\"\r\nauthor: \"Chenxin Li\"\r\ndate: '2022-06-07'\r\noutput: html_document\r\n---\r\n\r\n```{r setup, include=FALSE}\r\nknitr::opts_chunk$set(echo = TRUE)\r\n```\r\n\r\n# Packages\r\n```{r}\r\nlibrary(tidyverse)\r\nlibrary(patchwork)\r\nlibrary(RColorBrewer)\r\nlibrary(viridis)\r\n```\r\n\r\n# Data\r\nI will simulate some data. You can ignore this chunk. \r\n```{r}\r\nset.seed(666)\r\n\r\ngroup1 <- rnorm(mean = 1, sd = 0.2, n = 8)\r\ngroup2 <- rnorm(mean = 0, sd = 0.2, n = 12)\r\n\r\ngroup3 <- rnorm(mean = 0, sd = 0.2, n = 8)\r\ngroup4 <- rnorm(mean = 1, sd = 0.2, n = 10)\r\n\r\ngroup5 <- rnorm(mean = 10, sd = 1, n = 2)\r\n```\r\n\r\n\r\n```{r}\r\ntoydata <- data.frame(\r\n  observation1 = c(group1, group2),\r\n  observation2 = c(group3, group4, group5)\r\n  ) %>% \r\n  mutate(feature = 1:20) %>% \r\n  pivot_longer(cols = !feature, names_to = \"observation\", values_to = \"value\") %>% \r\n  mutate(observation2 = str_remove(observation, \"observation\"))\r\n\r\ntoydata\r\n```\r\n# Plot\r\n## Without checking outliers \r\n```{r}\r\np1 <- toydata %>% \r\n  mutate(observation2 = fct_rev(observation2)) %>% \r\n  ggplot(aes(x = observation2, y = feature)) +\r\n  geom_tile(aes(fill = value)) +\r\n  scale_fill_gradientn(colors = viridis(n = 10)) +\r\n  labs(x = \"Observations\",\r\n       y = \"Features\",\r\n       caption = \"Only two values are different!\\n\",\r\n       title = \"Without checking for outliers\") +\r\n  theme_classic() +\r\n  theme(\r\n    text = element_text(size = 14),\r\n   # legend.position = \"bottom\",\r\n    plot.caption = element_text(hjust = 0),\r\n    plot.title = element_text(size = 12, face = \"bold\")\r\n  ) +\r\n  coord_flip()\r\n\r\np1\r\n```\r\n\r\n## Check for outliers \r\n```{r}\r\np2 <- toydata %>% \r\n  mutate(observation2 = fct_rev(observation2)) %>%\r\n  mutate(rank = rank(value, ties.method = \"first\")) %>% \r\n  ggplot(aes(x = value, y = rank)) +\r\n  geom_point(shape = 21, color = \"grey20\", \r\n             aes(fill = value), size = 3) +\r\n  scale_fill_gradientn(colors = viridis(n = 10)) +\r\n  labs(caption = \"Wait...\\n\",\r\n       title = \"Did you check outliers?\") +\r\n  theme_classic() +\r\n  theme(\r\n    text = element_text(size = 14),\r\n    legend.position = \"none\",\r\n    plot.caption = element_text(hjust = 0),\r\n    plot.title = element_text(size = 12, face = \"bold\")\r\n  )\r\n\r\np2 \r\n```\r\n\r\n## Max out colors to address outliers \r\n```{r}\r\nquantile(toydata$value, 0.95) \r\n```\r\nWe can max out the color scale around 95th percentile. Let's see what that does. \r\n\r\n```{r}\r\np3 <- toydata %>% \r\n  mutate(observation2 = fct_rev(observation2)) %>%\r\n  mutate(rank = rank(value, ties.method = \"first\")) %>% \r\n  mutate(value2 = case_when(\r\n    value >= 2 ~ 2,\r\n    T ~ value\r\n  )) %>% \r\n  ggplot(aes(x = value, y = rank)) +\r\n  geom_point(shape = 21, color = \"grey20\", \r\n             aes(fill = value2), size = 3) +\r\n  scale_fill_gradientn(colors = viridis(n = 10),\r\n                       breaks = c(0, 1, 2),\r\n                       labels = c(\"0\", \"1\", \">2\")\r\n                       ) +\r\n  labs(fill = \"value\",\r\n       caption = \"That's better...\",\r\n       title = \"Lower values better visualized\") +\r\n  theme_classic() +\r\n  theme(\r\n    text = element_text(size = 14),\r\n    legend.position = \"none\",\r\n    plot.caption = element_text(hjust = 0),\r\n    plot.title = element_text(size = 12, face = \"bold\")\r\n  )\r\n\r\np3\r\n```\r\n\r\n\r\n```{r}\r\np4 <- toydata %>% \r\n  mutate(observation2 = fct_rev(observation2)) %>% \r\n  mutate(value2 = case_when(\r\n    value >= 1.5 ~ 2,\r\n    T ~ value\r\n  )) %>% \r\n  ggplot(aes(x = observation2, y = feature)) +\r\n  geom_tile(aes(fill = value2)) +\r\n  scale_fill_gradientn(colors = viridis(n = 10),\r\n                       breaks = c(0, 1, 2),\r\n                       labels = c(\"0\", \"1\", \">2\")\r\n                       ) +\r\n  labs(x = \"Observations\",\r\n       y = \"Features\",\r\n       fill = \"value\",\r\n       caption = \"Two observations are very different!\",\r\n       title = \"Maxed out color around 95th percentile\") +\r\n  theme_classic() +\r\n  theme(\r\n    text = element_text(size = 14),\r\n    #legend.position = \"bottom\",\r\n    plot.caption = element_text(hjust = 0),\r\n    plot.title = element_text(size = 12, face = \"bold\")\r\n  ) +\r\n  coord_flip()\r\n\r\np4\r\n```\r\n# Put graphs together \r\n```{r}\r\nwrap_plots(p1, p2, p4, p3, \r\n           nrow = 2, ncol = 2, \r\n           widths = c(1, 0.7))\r\n\r\nggsave(\"../Results/Check_outliers_for_heatmap.svg\", height = 5, width = 8, bg = \"white\")\r\nggsave(\"../Results/Check_outliers_for_heatmap.png\", height = 5, width = 8, bg = \"white\")\r\n```\r\n\r\n"
  },
  {
    "path": "Scripts/Check_range_at_factor_levels.Rmd",
    "content": "---\r\ntitle: \"CheckRangeAtFactorLevel\"\r\nauthor: \"Chenxin Li\"\r\ndate: '2022-07-01'\r\noutput: html_document\r\n---\r\n\r\n```{r setup, include=FALSE}\r\nknitr::opts_chunk$set(echo = TRUE)\r\n```\r\n\r\n# Packages\r\n```{r}\r\nlibrary(tidyverse) \r\nlibrary(ggbeeswarm) # to make jitter plots \r\n\r\nlibrary(emmeans) # for estimated marginal means and stats \r\n\r\nlibrary(RColorBrewer) # for colors \r\n\r\nlibrary(patchwork) # for putting ggplot objects together \r\n```\r\n\r\n# Data \r\n```{r}\r\n# Generate a toy example here \r\nset.seed(666)\r\n\r\nM1 <- data.frame(\r\n  conc = rnorm(n = 8, mean = 0.03, sd = 0.01)\r\n) %>% \r\n  mutate(group = \"ctrl\") %>% \r\n  rbind(\r\n    data.frame(\r\n      conc = rnorm(n = 6, mean = 0.25, sd = 0.02)\r\n    ) %>% \r\n      mutate(group = \"trt\")\r\n  ) %>% \r\n  mutate(metabolite = \"Compound 1\")\r\n\r\nM2 <- data.frame(\r\n  conc = rnorm(n = 8, mean = 6, sd = 1)\r\n) %>% \r\n  mutate(group = \"ctrl\") %>% \r\n  rbind(\r\n    data.frame(\r\n      conc = rnorm(n = 6, mean = 5.5, sd = 1.1)\r\n    ) %>% \r\n      mutate(group = \"trt\")\r\n  ) %>% \r\n  mutate(metabolite = \"Compound 2\")\r\n\r\nM3 <- data.frame(\r\n  conc = rnorm(n = 8, mean = 20, sd = 0.5)\r\n) %>% \r\n  mutate(group = \"ctrl\") %>% \r\n  rbind(\r\n    data.frame(\r\n      conc = rnorm(n = 6, mean = 19.5, sd = 1.2)\r\n    ) %>% \r\n      mutate(group = \"trt\")\r\n  ) %>% \r\n  mutate(metabolite = \"Compound 3\")\r\n\r\ntoydata <- rbind(\r\n  M1, M2, M3\r\n)\r\n\r\nhead(toydata)\r\n```\r\n\r\nIn this hypothetical example, we have 3 metabolites that we measured across two groups: 1 control group and 1 treatment group. \r\n\r\n# Graph\r\n## Bad example\r\n```{r}\r\nsame_scale <- toydata %>% \r\n  ggplot(aes(x = group, y = conc)) +\r\n  facet_grid(. ~ metabolite) +\r\n  geom_bar(stat = \"summary\", fill = NA, aes(color = group), \r\n           alpha = 0.8, fun = mean, width = 0.5, size = 0.8) +\r\n  ggbeeswarm::geom_quasirandom(\r\n    shape = 21, size = 2.5, alpha = 0.8, color = \"white\",\r\n    aes(fill = group), width = 0.2\r\n  ) +\r\n  scale_fill_manual(values = c(\"grey20\", \"tomato1\")) +\r\n  scale_color_manual(values = c(\"grey20\", \"tomato1\")) +\r\n  labs(y = \"Conc.\",\r\n       x = \"Group\",\r\n       title = \"Basic layout\",\r\n       caption = \"My treatment wasn't doing anything!\\n\") +\r\n  theme_classic() +\r\n  theme(\r\n    legend.position = \"none\",\r\n    text = element_text(size = 14, color = \"black\"),\r\n    axis.text = element_text(color = \"black\"),\r\n    panel.spacing = unit(1, \"lines\"),\r\n    title = element_text(size = 12, face = \"bold\"),\r\n    plot.caption= element_text(size = 12, hjust = 0)\r\n  )\r\n\r\nsame_scale\r\n```\r\n\r\n## Good example \r\n### stat \r\n```{r}\r\nmodel_conc <- lm(log(conc) ~ metabolite * group, data = toydata)\r\nanova(model_conc)\r\n```\r\n```{r}\r\nest_conc <- emmeans(model_conc, pairwise ~ group | metabolite)\r\n\r\nconc_contrast <- est_conc$contrasts %>% \r\n  as.data.frame()\r\n\r\nconc_contrast\r\n```\r\n\r\n```{r}\r\nfree_scale <- toydata %>% \r\n  ggplot(aes(x = group, y = conc)) +\r\n  facet_wrap(. ~ metabolite, scales = \"free_y\") +\r\n  geom_bar(stat = \"summary\", fill = NA, aes(color = group), \r\n           alpha = 0.8, fun = mean, width = 0.5, size = 0.8) +\r\n  ggbeeswarm::geom_quasirandom(\r\n    shape = 21, size = 2.5, alpha = 0.8, color = \"white\",\r\n    aes(fill = group), width = 0.2\r\n  ) +\r\n  geom_text(data = conc_contrast, aes(label = paste0(\r\n    \"P = \", signif(p.value, 2)\r\n  )), size = 3.5, x = 1.5, y = Inf, vjust = 1.75) +\r\n  scale_fill_manual(values = c(\"grey20\", \"tomato1\")) +\r\n  scale_color_manual(values = c(\"grey20\", \"tomato1\")) +\r\n  labs(y = \"Conc.\",\r\n       x = \"Group\",\r\n       title = \"Did you check range at each factor level?\",\r\n       caption = \"OH!!!\") +\r\n  theme_classic() +\r\n  theme(\r\n    legend.position = \"none\",\r\n    text = element_text(size = 14, color = \"black\"),\r\n    axis.text = element_text(color = \"black\"),\r\n    panel.spacing = unit(1, \"lines\"),\r\n    title = element_text(size = 12, face = \"bold\"),\r\n    plot.caption= element_text(size = 12, hjust = 0)\r\n  )\r\n\r\nfree_scale\r\n```\r\n\r\n# Put them together\r\n```{r}\r\nwrap_plots(same_scale, free_scale, nrow = 2)\r\n\r\nggsave(\"../Results/Check_range_at_factor_level.svg\", height = 6, width = 5, bg = \"white\")\r\nggsave(\"../Results/Check_range_at_factor_level.png\", height = 6, width = 5, bg = \"white\")\r\n```\r\n\r\n\r\n"
  },
  {
    "path": "Scripts/Divergent_gradient_for_unidirectional_data.Rmd",
    "content": "---\r\ntitle: \"Colorscales\"\r\nauthor: \"Chenxin Li\"\r\ndate: '2022-05-06'\r\noutput: html_document\r\n---\r\n\r\n```{r setup, include=FALSE}\r\nknitr::opts_chunk$set(echo = TRUE)\r\n```\r\n\r\nThis is an Rmarkdown document.If you don't have the rmarkdown package, you will have to install it to run this. \r\nThe grey areas are code chunks. To run a code chunk click the green triangle at the upper right of each chunk. \r\n\r\n# Packages \r\n```{r}\r\nlibrary(tidyverse) \r\nlibrary(ggbeeswarm) # to make jitter plots \r\nlibrary(RColorBrewer) # for colors \r\nlibrary(viridis) # for colors \r\nlibrary(patchwork) # for putting graphs together \r\n```\r\n\r\n# Simulate data \r\nJust some arbitrary sequential/directional data \r\n```{r}\r\nDir1 <- data.frame(\r\n  \"FPKM\" = c(5, 10, 20, 40, 60, 80, 100, 120),\r\n  \"Gene\" = 1:8\r\n)  \r\n\r\nDir2 <- data.frame(\r\n  \"log2FC\" = c(-10, -4, -2, 0, 2, 4, 10),\r\n  \"Gene\" = 1:7\r\n)\r\n\r\nDir3 <- data.frame(\r\n  \"z.score\" = c(-2.5, -2, -1, 0, 1, 2, 2.5),\r\n  \"Gene\" = 1:7\r\n)\r\n  \r\nhead(Dir3)\r\n```\r\n\r\n# Graph\r\n## Good example 1 \r\n```{r}\r\nA <- Dir1 %>% \r\n ggplot(aes(x = Gene, y = FPKM)) +\r\n  geom_hline(yintercept = 0, size = 1) +\r\n  geom_segment(aes(x = Gene, xend = Gene), \r\n               yend = 0, size = 1.2, alpha = 0.8, color = \"grey60\") +\r\n  geom_point(shape = 21, size = 4, aes(fill = FPKM)) +\r\n  scale_fill_gradientn(colors = viridis(n = 10)) +\r\n  labs(caption = \"This is good.\") +\r\n  theme_minimal() +\r\n  theme(\r\n    text = element_text(size = 14, color = \"black\"), \r\n    axis.text = element_text(color = \"black\"), \r\n    axis.text.y = element_blank(), \r\n    legend.position = \"none\",\r\n    plot.caption = element_text(hjust = 0, size = 12)\r\n  ) +\r\n  coord_flip()+\r\n  ggtitle(\"Darkest color = Min\\nLightest color = Max\")\r\n\r\nB <- Dir1 %>% \r\n  ggplot(aes(x = \" \", y = Gene)) +\r\n  geom_tile(aes(fill = FPKM)) +\r\n  scale_fill_gradientn(colors = viridis(n = 10)) +\r\n  theme_void() +\r\n  theme(\r\n    legend.position = \"none\"\r\n  )\r\n\r\nGood1 <- wrap_plots(A, B, nrow = 1, widths = c(1, 0.1))\r\n\r\nGood1\r\n```\r\nThis is a good example, where lightest color = max, and darkest color = min. \r\n\r\n\r\n## Good example 2 \r\n```{r}\r\nC <- Dir2 %>% \r\n ggplot(aes(x = Gene, y = log2FC)) +\r\n  geom_hline(yintercept = 0, size = 1) +\r\n  geom_segment(aes(x = Gene, xend = Gene), \r\n               yend = 0, size = 1.2, alpha = 0.8, color = \"grey60\") +\r\n  geom_point(shape = 21, size = 4, aes(fill = log2FC)) +\r\n  scale_fill_gradientn(colors = rev(brewer.pal(11, \"RdBu\"))) +\r\n  labs(caption = \"This is good.\") +\r\n  theme_minimal() +\r\n  theme(\r\n    text = element_text(size = 14, color = \"black\"), \r\n    axis.text = element_text(color = \"black\"), \r\n    axis.text.y = element_blank(), \r\n    legend.position = \"none\",\r\n    plot.caption = element_text(hjust = 0, size = 12)\r\n  ) +\r\n  coord_flip()+\r\n  ggtitle(\"Lightest color = 0\\nDarkest colors = Max absolutes\")\r\n\r\nD <- Dir2 %>% \r\n  ggplot(aes(x = \" \", y = Gene)) +\r\n  geom_tile(aes(fill = log2FC)) +\r\n  scale_fill_gradientn(colors = rev(brewer.pal(11, \"RdBu\"))) +\r\n  theme_void() +\r\n  theme(\r\n    legend.position = \"none\"\r\n  )\r\n\r\nGood2 <- wrap_plots(C, D, nrow = 1, widths = c(1, 0.1))\r\n\r\nGood2\r\n```\r\nThis is a good example, where lightest color = 0 and have a special meaning, and darkest colors = max absolute values.  \r\n\r\n## Good example 3\r\n```{r}\r\nE <- Dir3 %>% \r\n ggplot(aes(x = Gene, y = z.score)) +\r\n  geom_hline(yintercept = 0, size = 1) +\r\n  geom_segment(aes(x = Gene, xend = Gene), \r\n               yend = 0, size = 1.2, alpha = 0.8, color = \"grey60\") +\r\n  geom_point(shape = 21, size = 4, aes(fill = z.score)) +\r\n  scale_fill_gradientn(colors = brewer.pal(9, \"YlGnBu\")) +\r\n  labs(caption = \"This is good.\",\r\n       y = \"z score\") +\r\n  theme_minimal() +\r\n  theme(\r\n    text = element_text(size = 14, color = \"black\"), \r\n    axis.text = element_text(color = \"black\"), \r\n    axis.text.y = element_blank(), \r\n    legend.position = \"none\",\r\n    plot.caption = element_text(hjust = 0, size = 12)\r\n  ) +\r\n  coord_flip()+\r\n  ggtitle(\"Darkest color = Max\\nLightest color = Min\")\r\n\r\neF <- Dir3 %>% \r\n  ggplot(aes(x = \" \", y = Gene)) +\r\n  geom_tile(aes(fill = z.score)) +\r\n  scale_fill_gradientn(colors = brewer.pal(9, \"YlGnBu\")) +\r\n  theme_void() +\r\n  theme(\r\n    legend.position = \"none\"\r\n  )\r\n\r\nGood3 <- wrap_plots(E, eF, nrow = 1, widths = c(1, 0.1))\r\n\r\nGood3\r\n```\r\n\r\nThis is a good example, where lightest color = min, and darkest colors = max. \r\n\r\n## Bad example \r\n```{r}\r\nG <- Dir1 %>% \r\n ggplot(aes(x = Gene, y = FPKM)) +\r\n  geom_hline(yintercept = 0, size = 1) +\r\n  geom_segment(aes(x = Gene, xend = Gene), \r\n               yend = 0, size = 1.2, alpha = 0.8, color = \"grey60\") +\r\n  geom_point(shape = 21, size = 4, aes(fill = FPKM)) +\r\n  scale_fill_gradientn(colors = rev(brewer.pal(11, \"RdBu\"))) +\r\n  labs(caption = \"A data visualization sin\") +\r\n  theme_minimal() +\r\n  theme(\r\n    text = element_text(size = 14, color = \"black\"), \r\n    axis.text = element_text(color = \"black\"), \r\n    axis.text.y = element_blank(), \r\n    legend.position = \"none\",\r\n    plot.caption = element_text(hjust = 0, size = 12)\r\n  ) +\r\n  coord_flip()+\r\n  ggtitle(\"Lighest color means nothing\\n(neither mean nor median).\")\r\n\r\nH <- Dir1 %>% \r\n  ggplot(aes(x = \" \", y = Gene)) +\r\n  geom_tile(aes(fill = FPKM)) +\r\n  scale_fill_gradientn(colors = rev(brewer.pal(11, \"RdBu\"))) +\r\n  theme_void() +\r\n  theme(\r\n    legend.position = \"none\"\r\n  )\r\n\r\nBad <- wrap_plots(G, H, nrow = 1, widths = c(1, 0.1))\r\n\r\nBad\r\n```\r\n\r\nThis is a bad example and a data visualization sin. \r\nSomehow the lightest color does not mean anything special. \r\n\r\n# Put them all together\r\n```{r}\r\nwrap_plots(\r\n  Good1, Good2, Good3, Bad,\r\n  nrow = 2, ncol = 2\r\n)\r\n\r\nggsave(\"../Results/ColorScales.svg\", height = 7.5, width = 7.5, bg = \"white\")\r\nggsave(\"../Results/ColorScales.png\", height = 7.5, width = 7.5, bg = \"white\")\r\n```\r\n\r\n\r\n"
  },
  {
    "path": "Scripts/Histogram_for_small_n.Rmd",
    "content": "---\r\ntitle: \"Histogram_for_small_n\"\r\nauthor: \"Chenxin Li\"\r\ndate: \"2024-12-10\"\r\noutput: html_document\r\n---\r\n\r\n```{r setup, include=FALSE}\r\nknitr::opts_chunk$set(echo = TRUE)\r\n```\r\n\r\n# Friends don't let friends use histogram for small sample sizes \r\n\r\n# Packages\r\n```{r}\r\nlibrary(tidyverse)\r\nlibrary(RColorBrewer)\r\nlibrary(ggbeeswarm)\r\nlibrary(viridis)\r\nlibrary(patchwork)\r\n```\r\n\r\n# Data\r\n```{r}\r\nset.seed(666)\r\n```\r\n\r\n```{r}\r\nn10 <- data.frame(\r\n  response = rnorm(n = 10)\r\n) %>% \r\n  mutate(group = \"n = 10\")\r\n\r\nn100 <- data.frame(\r\n  response = rnorm(n = 100)\r\n) %>% \r\n  mutate(group = \"n = 100\")\r\n\r\nn1000 <- data.frame(\r\n  response = rnorm(n = 1000)\r\n) %>% \r\n  mutate(group = \"n = 1000\")\r\n```\r\n\r\n# Graphs\r\n```{r}\r\nbins10 <- rbind(\r\n  n10, n100, n1000\r\n) %>% \r\n  ggplot(aes(x = response)) +\r\n  facet_wrap(~group, scales = \"free\", ncol = 1) +\r\n  geom_histogram(bins = 10, width = 0.7, color = \"white\", alpha = 0.8, \r\n                 fill = viridis(n = 8, begin = 0.1, end = 0.8)[1]) +\r\n  labs(title = \"10 bins\") +\r\n  theme_classic() + \r\n  theme(panel.spacing = unit(1, \"lines\"),\r\n        strip.placement = \"outside\",\r\n        strip.background = element_blank(),\r\n        strip.text = element_text(hjust = 0)) \r\n\r\nbins10\r\n```\r\n\r\n```{r}\r\nbins30 <- rbind(\r\n  n10, n100, n1000\r\n) %>% \r\n  ggplot(aes(x = response)) +\r\n  facet_wrap(~group, scales = \"free\", ncol = 1) +\r\n  geom_histogram(bins = 30, width = 0.7, color = \"white\",\r\n                 fill = viridis(n = 8, begin = 0.1, end = 0.8)[4]) +\r\n  labs(title = \"30 bins\") +\r\n  theme_classic() + \r\n  theme(panel.spacing = unit(1, \"lines\"),\r\n        strip.placement = \"outside\",\r\n        strip.background = element_blank(),\r\n        strip.text = element_text(hjust = 0)) \r\n\r\nbins30\r\n```\r\n\r\n```{r}\r\nbins50 <- rbind(\r\n  n10, n100, n1000\r\n) %>% \r\n  ggplot(aes(x = response)) +\r\n  facet_wrap(~group, scales = \"free\", ncol = 1) +\r\n  geom_histogram(bins = 50, width = 0.7, color = \"white\",\r\n                 fill = viridis(n = 8, begin = 0.1, end = 0.8)[7]) +\r\n  labs(title = \"50 bins\") +\r\n  theme_classic() + \r\n  theme(panel.spacing = unit(1, \"lines\"),\r\n        strip.placement = \"outside\",\r\n        strip.background = element_blank(),\r\n        strip.text = element_text(hjust = 0))\r\n\r\nbins50\r\n```\r\n\r\n# wrap them\r\n```{r}\r\nwrap_plots(\r\n  bins10, bins30, bins50 +\r\n    labs(caption = \"\\nWow, the appearance does change with different bin numbers.\"),\r\n  ncol = 3\r\n) &\r\n  theme(plot.caption = element_text(size = 10))\r\n\r\nggsave(\"../Results/Histogram_for_small_n.svg\", height = 6, width = 8)\r\nggsave(\"../Results/Histogram_for_small_n.png\", height = 6, width = 8)\r\n```\r\nI've seen histogram being proposed as the replacement for bar plots. \r\nHowever, a serious caveat for histogram is that histograms are not robust to bin numbers for small (and even moderate) sample sizes. \r\nWhat is a histogram anyway? In a histogram, we first bin the data into a defined number of bins.\r\nThen we count how many observations are there for each bin and graph them. \r\n\r\nIn this example, I sampled _the same_ normal distribution 3 times with different sample sizes (n = 10, 100, and 1000).\r\nEven though they came from _the same_ normal distribution, the histograms look quite different based on the number of bins. \r\nTo showcase this, I plotted histogram for 10, 30, and 50 bins. \r\n\r\nFirst of all, histogram makes no sense for small sample sizes. With small sample sizes (n < 30), the better practice is to graph all data points. \r\nSecond of all, you can see that the shape of the histogram is only robust to changing bin number when the sample size is fairly large (like 1000).\r\nEven if n = 100, the appearance of the histogram can change drastically as the number of bins changes. \r\n\r\n"
  },
  {
    "path": "Scripts/Mixing_stacked_bar_and_mean_separation.Rmd",
    "content": "---\r\ntitle: \"Stacked_bar_and_mean_sep\"\r\nauthor: \"Chenxin Li\"\r\ndate: \"2024-01-20\"\r\noutput: html_document\r\n---\r\n\r\n```{r setup, include=FALSE}\r\nknitr::opts_chunk$set(echo = TRUE)\r\n```\r\n\r\n# Packages\r\n```{r}\r\nlibrary(tidyverse) \r\n\r\nlibrary(emmeans) # for linear model \r\nlibrary(RColorBrewer) # for the colors \r\nlibrary(rcartocolor) # for the colors \r\nlibrary(patchwork) # for putting graphs together\r\n```\r\n\r\n# Data \r\n```{r}\r\n# Simulate some data \r\nset.seed(666)\r\n\r\ncontrol_1 <- rnorm(n = 5, mean = 30, sd = 5) \r\ncontrol_2 <- rnorm(n = 5, mean = 50, sd = 5)\r\ncontrol_3 <- rnorm(n = 5, mean = 20, sd = 5) \r\n\r\ntotal_mean <- 100\r\nadjustment_ctrl <- total_mean / sum(c(mean(control_1), mean(control_2), mean(control_3)))\r\ncontrol_1 <- control_1 * adjustment_ctrl\r\ncontrol_2 <- control_2 * adjustment_ctrl\r\ncontrol_3 <- control_3 * adjustment_ctrl\r\n\r\ntrt_1 <- rnorm(n = 5, mean = 10, sd = 5)\r\ntrt_2 <- rnorm(n = 5, mean = 30, sd = 5)\r\ntrt_3 <- rnorm(n = 5, mean = 60, sd = 5)\r\n\r\nadjustment_trt <- total_mean / sum(c(mean(trt_1), mean(trt_2), mean(trt_3)))\r\ntrt_1 <- trt_1 * adjustment_trt\r\ntrt_2 <- trt_2 * adjustment_trt\r\ntrt_3 <- trt_3 * adjustment_trt\r\n```\r\n\r\nLet's say in this hypothetical experiment, we had blueberry plants in two groups.\r\nOne group was the control; the other was treated with a chemical to make fruit development faster.  \r\nEach group had 5 plants. \r\nThe response of the treatment was divided into 3 categories: \r\n1) light green fruits, 2) light blue fruits, and 3) dark blue fruits. \r\n100 fruits from each plant were examined and the number of fruits in each category was counted. \r\nThe percentage of fruits in each category was calculated and reported. \r\nThe question of the study is: did the chemical treatment work? \r\n\r\n## Data wrangling \r\n```{r}\r\nmy_data <- rbind(\r\n  control_1 %>% \r\n  as.data.frame() %>% \r\n  mutate(group = \"ctrl\") %>% \r\n  mutate(category = \"light green\"), \r\n  \r\n  control_2 %>% \r\n  as.data.frame() %>% \r\n  mutate(group = \"ctrl\") %>% \r\n  mutate(category = \"light blue\"),\r\n  \r\n  control_3 %>% \r\n  as.data.frame() %>% \r\n  mutate(group = \"ctrl\") %>% \r\n  mutate(category = \"dark blue\"),\r\n  \r\n  trt_1 %>% \r\n  as.data.frame() %>% \r\n  mutate(group = \"trt\") %>% \r\n  mutate(category = \"light green\"), \r\n  \r\n  trt_2 %>% \r\n  as.data.frame() %>% \r\n  mutate(group = \"trt\") %>% \r\n  mutate(category = \"light blue\"),\r\n  \r\n  trt_3 %>% \r\n  as.data.frame() %>% \r\n  mutate(group = \"trt\") %>% \r\n  mutate(category = \"dark blue\")\r\n  ) %>% \r\n  rename(percent_of_fruit = \".\")\r\n\r\nhead(my_data)\r\n```\r\n\r\n# Stats\r\n## Summary stats \r\n```{r}\r\nmy_data_s <- my_data %>% \r\n  group_by(group, category) %>% \r\n  summarise(mean_percentage = mean(percent_of_fruit),\r\n            sd_percentage = sd(percent_of_fruit),\r\n            N = n()) %>% \r\n  ungroup()\r\n\r\nhead(my_data_s)\r\n```\r\n## Linear models \r\n```{r}\r\nmy_model1 <- lm(percent_of_fruit ~ group * category, data = my_data)\r\nanova(my_model1)\r\n```\r\n```{r}\r\nmy_estimates <- emmeans(my_model1, pairwise ~ group | category)\r\nmy_contrasts <- my_estimates$contrasts %>% \r\n  as.data.frame() %>% \r\n  mutate(txt_y = case_when(\r\n                category == \"light green\" ~ 45,\r\n                category == \"light blue\" ~ 55,\r\n                category == \"dark blue\" ~ 35\r\n              ))\r\n\r\nmy_contrasts\r\n```\r\n\r\n\r\n\r\n\r\n\r\n# Graphs \r\n## Stacked bar for \r\n```{r}\r\nstacked_bar_plot <-my_data_s %>% \r\n  ggplot(aes(x = group, y = mean_percentage)) +\r\n  geom_bar(stat = \"identity\", aes(fill = category), \r\n           width = 0.7, color = \"white\", alpha = 0.8) +\r\n  scale_y_continuous(breaks = c(0, 50, 100)) +\r\n  scale_fill_manual(values = c(\r\n    carto_pal(7, \"BluYl\")[2], \r\n    carto_pal(7, \"Purp\")[2],\r\n    carto_pal(7, \"Sunset\")[c(7)]\r\n  ), limits = c(\"light green\", \"light blue\", \"dark blue\")) +\r\n  labs(x = \"group\",\r\n       y = \"% of fruits\",\r\n       fill = \"fruit color\", \r\n       caption = \"This is fine.\") +\r\n  theme_classic() +\r\n  theme(\r\n    plot.caption = element_text(hjust = 0)\r\n  )\r\n\r\nstacked_bar_plot\r\n```\r\n\r\n\r\n## Graph, the wrong way \r\n```{r}\r\nstacked_bar_plot_bad <- my_data %>% \r\n  mutate(off_set = case_when(\r\n    group == \"ctrl\" & \r\n      category == \"light green\" ~ 0,\r\n    group == \"ctrl\" & \r\n      category == \"light blue\" ~ as.numeric(my_data_s[3, 3]),\r\n    group == \"ctrl\" & \r\n      category == \"dark blue\" ~ as.numeric(my_data_s[3, 3] + my_data_s[2, 3]),\r\n    group == \"trt\" & \r\n      category == \"light green\" ~ 0,\r\n    group == \"trt\" & \r\n      category == \"light blue\" ~ as.numeric(my_data_s[6, 3]),\r\n    group == \"trt\" & \r\n      category == \"dark blue\" ~ as.numeric(my_data_s[6, 3] + my_data_s[5, 3])\r\n  )) %>% \r\n  mutate(percent_of_fruit_offset = percent_of_fruit + off_set) %>% \r\n  ggplot(aes(x = group, y = percent_of_fruit_offset)) +\r\n  geom_bar(data = my_data %>% \r\n  group_by(group, category) %>% \r\n  summarise(mean_percentage = mean(percent_of_fruit)) %>% \r\n  ungroup(),\r\n  stat = \"identity\", aes(fill = category, y = mean_percentage),\r\n           width = 0.7, color = \"white\", alpha = 0.8) +\r\n  geom_point(fill = \"grey90\", size = 2, shape = 21, color = \"grey20\",\r\n             position = position_jitter(width = 0.1, seed = 666)) +\r\n  stat_summary(fun.data = mean_se, geom = \"linerange\", \r\n               aes(group = category), size = 1) +\r\n  scale_y_continuous(breaks = c(0, 50, 100)) +\r\n  scale_fill_manual(values = c(\r\n    carto_pal(7, \"BluYl\")[2],\r\n    carto_pal(7, \"Purp\")[2],\r\n    carto_pal(7, \"Sunset\")[c(7)]\r\n  ), limits = c(\"light green\", \"light blue\", \"dark blue\")) +\r\n  labs(x = \"group\",\r\n       y = \"% of fruits\",\r\n       fill = \"fruit color\",\r\n       caption = \"What am I comparing?\\nSome dots are above 100%?\") +\r\n  theme_classic() +\r\n  theme(\r\n    plot.caption = element_text(hjust = 0)\r\n  )\r\n\r\nstacked_bar_plot_bad\r\n```\r\n\r\n## Mean separation plot, the better way \r\n```{r}\r\njitter_plot <- my_data %>% \r\n  mutate(category = factor(category, levels = c(\r\n    \"light green\", \"light blue\", \"dark blue\"\r\n  ))) %>% \r\n  ggplot(aes(x = group, y = percent_of_fruit)) +\r\n  facet_grid(.~ category, scales = \"free_y\") +\r\n  geom_point(aes(fill = category), color = \"grey30\", size = 2, alpha = 0.8,\r\n             shape = 21, position = position_jitter(width = 0.1, seed = 666)) +\r\n  stat_summary(fun.data = mean_se, geom = \"linerange\", \r\n               aes(group = category), size = 1) +\r\n  geom_segment(data = my_contrasts, aes(y = txt_y, yend = txt_y),\r\n               x = 1.2, xend = 1.8) + \r\n  geom_text(data = my_contrasts, vjust = -0.25, x = 1.5,\r\n            size = 3, \r\n            aes(label = paste0(\r\n              \"p = \", signif(p.value, 2)\r\n            ),\r\n            y = txt_y)) +\r\n  scale_fill_manual(values = c(\r\n    carto_pal(7, \"BluYl\")[2],\r\n    carto_pal(7, \"Purp\")[2],\r\n    carto_pal(7, \"Sunset\")[c(7)]\r\n  ), limits = c(\"light green\", \"light blue\", \"dark blue\")) +\r\n  labs(x = \"group\",\r\n       y = \"% of fruits\",\r\n       fill = \"fruit color\",\r\n       caption = \"The better way for mean separation.\") +\r\n  guides(fill = \"none\") +\r\n  theme_classic() +\r\n  theme(\r\n    plot.caption = element_text(hjust = 0),\r\n    panel.spacing = unit(0.6, \"lines\"),\r\n    strip.text = element_blank(),\r\n    strip.background = element_blank()\r\n  )\r\n\r\njitter_plot\r\n```\r\n## wrap plots together \r\n```{r}\r\nwrap_plots(\r\n  stacked_bar_plot, stacked_bar_plot_bad, jitter_plot, \r\n  ncol = 3, guides = \"collect\", \r\n  widths = c(0.6, 0.6, 1)\r\n) & \r\n  theme(legend.position = \"top\")\r\n\r\nggsave(\"../Results/stacked_bar_vs_jitter.svg\", height = 4, width = 8, bg = \"white\")\r\nggsave(\"../Results/stacked_bar_vs_jitter.png\", height = 4, width = 8, bg = \"white\")\r\n```\r\nThe first stacked bar plot is fine as the standard way to visualize proportion data. \r\nIt is clear that all categories add up to 100%, \r\nand the chemical treatment strongly shifted the color profile towards the most developed stage (dark blue). \r\n\r\nThe middle stacked bar plot is problematic, \r\nmainly because it is trying to do two distinct data visualization tasks at once. \r\nWhen error bars and dots are overlaid onto the stacked bars, \r\nit become unclear which error bars and dots are being compared. \r\nDue to the nature of stacked bars, the error bars and dots of the upper stacks have to be shifted upwards,\r\nand thus interpretation of the y-axis for error bars and dots become not straightforward. \r\n\r\nFinally, if the main point of the visualization is mean separation and dispersion around the mean, \r\nthe third graph is the better choice. \r\nThere is no ambiguity on which comparisons are being made.\r\nAs shown in the first stacked bar plot, \r\nthe chemical treatment strongly increases the proportion of dark blue fruits, \r\nat the expense of lighter color fruits. \r\n\r\n\r\n"
  },
  {
    "path": "Scripts/NetworkLayouts.Rmd",
    "content": "---\r\ntitle: \"Fiends don't let friends make network graphs without trying different layouts\"\r\nauthor: \"Chenxin Li\"\r\ndate: '2022-07-06'\r\noutput: html_document\r\n---\r\n\r\n```{r setup, include=FALSE}\r\nknitr::opts_chunk$set(echo = TRUE)\r\n```\r\n\r\n# Pacakges \r\n```{r}\r\nlibrary(tidyverse)\r\nlibrary(igraph) # make graph (network) object\r\nlibrary(ggraph) # graphical grammar network graphs \r\nlibrary(viridis)\r\nlibrary(cowplot)\r\n```\r\n\r\n# Data \r\n```{r}\r\nedge_table <- read_csv(\"../Data/Network_Example/Fig4c_1.csv\", col_types = cols())\r\nnode_table <- read_csv(\"../Data/Network_Example/Fig4c_2.csv\", col_types = cols())\r\n\r\nhead(edge_table)\r\nhead(node_table)\r\n```\r\nData from preprint: [Li et al., 2022, BioRxiv](https://www.biorxiv.org/content/10.1101/2022.07.04.498697v1).\r\n\r\n# Produce graph/network object \r\nTo make a network, you need two things. \r\n\r\n1. The edge table. Each row is an edge and at least two columns: from and to. \r\n2. The node table, which is technically optional. It stores additional attributes of nodes. \r\n\r\n```{r}\r\nnetwork_object <- graph_from_data_frame(edge_table,\r\n                                     vertices = node_table %>% \r\n                                       mutate(tag2 = case_when(\r\n                                         is.na(tag) ~ \"\",\r\n                                         T ~ tag2)) %>% \r\n                                       mutate(tag3 = case_when(\r\n                          str_detect(tag, \"ORCA|MYC|DAT|D4H|SS|NMT|GS|STR|LAMT|TDC|ISY|IO|G8H\") ~ tag,\r\n                                       )),\r\n                                     directed = F)\r\n```\r\n\r\n# Circular\r\n```{r}\r\nCircular <- ggraph(network_object, layout = \"circle\") +\r\n  geom_edge_diagonal(color = \"grey70\", width = 0.5, alpha = 0.5) +\r\n  geom_node_point(alpha = 0.8, color = \"white\", shape = 21,\r\n                  aes(fill = segment, size = tag2)) + \r\n  geom_node_text(aes(label = tag3), size = 3, repel = T, max.overlaps = 10) +\r\n  scale_edge_color_manual(values = c(\"steelblue3\", \"tomato1\")) +\r\n  scale_fill_manual(values = c(viridis(5, option = \"D\"),  \"white\"),\r\n                    limits = c(\"TF\", \"MEP\", \"Iridoid\", \"Alkaloid\", \"Late Alkaloid\", \"\")) +\r\n  scale_size_manual(values = c(1, 2.5),\r\n                    limits = c(\"\", \"known genes\")) +\r\n  labs(fill = \"Stage of pathway\",\r\n       title = \"Circular layout\",\r\n       caption = \"WTH is going on?\\n\") +\r\n  guides(size = \"none\",\r\n         fill = guide_legend(override.aes = list(size = 4), \r\n                             title.position = \"top\", nrow = 3)) +\r\n  theme_void()+\r\n  theme(\r\n    text = element_text(size = 14), \r\n    legend.position = \"bottom\",\r\n    legend.justification = 1,\r\n    title = element_text(size = 12),\r\n    plot.caption = element_text(hjust = 0.2, size = 12)\r\n  )\r\n\r\nCircular\r\n```\r\n# MDS\r\n```{r}\r\nMDS <- ggraph(network_object, layout = \"mds\") +\r\n  geom_edge_diagonal(color = \"grey70\", width = 0.5, alpha = 0.5) +\r\n  geom_node_point(alpha = 0.8, color = \"white\", shape = 21,\r\n                  aes(fill = segment, size = tag2)) + \r\n  geom_node_text(aes(label = tag3), size = 3, repel = T, max.overlaps = 10) +\r\n  scale_edge_color_manual(values = c(\"steelblue3\", \"tomato1\")) +\r\n  scale_fill_manual(values = c(viridis(5, option = \"D\"),  \"white\"),\r\n                    limits = c(\"TF\", \"MEP\", \"Iridoid\", \"Alkaloid\", \"Late Alkaloid\", \"\")) +\r\n  scale_size_manual(values = c(1, 2.5),\r\n                    limits = c(\"\", \"known genes\")) +\r\n  labs(fill = \"Stage of pathway\",\r\n       title = \"MDS layout\",\r\n       caption = \"WOW that looked\\nreally different!\") +\r\n  guides(size = \"none\",\r\n         fill = guide_legend(override.aes = list(size = 4), \r\n                             title.position = \"top\", nrow = 3)) +\r\n  theme_void()+\r\n  theme(\r\n    text = element_text(size = 14), \r\n    legend.position = \"bottom\",\r\n    legend.justification = 1,\r\n    title = element_text(size = 12),\r\n    plot.caption = element_text(hjust = 0.2, size = 12)\r\n  )\r\n\r\nMDS\r\n```\r\n\r\n\r\n# kk \r\n```{r}\r\nkk <- ggraph(network_object, layout = \"kk\") +\r\n  geom_edge_diagonal(color = \"grey70\", width = 0.5, alpha = 0.5) +\r\n  geom_node_point(alpha = 0.8, color = \"white\", shape = 21,\r\n                  aes(fill = segment, size = tag2)) + \r\n  geom_node_text(aes(label = tag3), size = 3, repel = T, max.overlaps = 10) +\r\n  scale_edge_color_manual(values = c(\"steelblue3\", \"tomato1\")) +\r\n  scale_fill_manual(values = c(viridis(5, option = \"D\"),  \"white\"),\r\n                    limits = c(\"TF\", \"MEP\", \"Iridoid\", \"Alkaloid\", \"Late Alkaloid\", \"\")) +\r\n  scale_size_manual(values = c(1, 2.5),\r\n                    limits = c(\"\", \"known genes\")) +\r\n  labs(fill = \"Stage of pathway\",\r\n       title = \"Kamada-Kawai layout\",\r\n       caption = \"Better!\\n\") +\r\n  guides(size = \"none\",\r\n         fill = guide_legend(override.aes = list(size = 4), \r\n                             title.position = \"top\", nrow = 3)) +\r\n  theme_void()+\r\n  theme(\r\n    text = element_text(size = 14), \r\n    legend.position = \"bottom\",\r\n    legend.justification = 1,\r\n    title = element_text(size = 12),\r\n    plot.caption = element_text(hjust = 0.2, size = 12)\r\n  )\r\n\r\nkk\r\n```\r\n# Put examples together \r\n```{r}\r\nplot_grid(Circular, MDS, kk, \r\n          nrow = 1, align = \"h\", axis = \"lrtb\")\r\n\r\nggsave(\"../Results/TryDifferentLayouts.svg\", width = 8, height = 4, bg = \"white\")\r\nggsave(\"../Results/TryDifferentLayouts.png\", width = 8, height = 4, bg = \"white\")\r\n```\r\n\r\n"
  },
  {
    "path": "Scripts/NoConcentricDonuts.Rmd",
    "content": "---\r\ntitle: \"NoConcentricDonunts\"\r\nauthor: \"Chenxin Li\"\r\ndate: '2022-07-28'\r\noutput: html_document\r\n---\r\n\r\n```{r setup, include=FALSE}\r\nknitr::opts_chunk$set(echo = TRUE)\r\n```\r\n\r\n# Packages\r\n```{r}\r\nlibrary(tidyverse)\r\nlibrary(RColorBrewer)\r\nlibrary(patchwork) \r\n```\r\n\r\n# Data \r\nThis section generate the data. \r\n```{r}\r\ngroup1 <- data.frame(\r\n  \"Type\" = c(\"Type I\", \"Type II\"),\r\n  \"Percentage\" = c(20, 80)\r\n)\r\n\r\ngroup2 <- data.frame(\r\n  \"Type\" = c(\"Type I\", \"Type II\"),\r\n  \"Percentage\" = c(30, 70)\r\n)\r\n\r\ngroup3 <- data.frame(\r\n  \"Type\" = c(\"Type I\", \"Type II\"),\r\n  \"Percentage\" = c(30, 70)\r\n)\r\n\r\nblank <- data.frame(\r\n  \"Type\" = c(\"Type I\", \"Type II\"),\r\n  \"Percentage\" = c(0, 0)\r\n)\r\n\r\nhead(group2)\r\n```\r\n\r\nIn order for the concentric donuts to work, a \"blank\" data table is required as the first layer. You will see why. \r\n \r\n \r\nThe following section produces `ymax` and `ymin` values for the `geon_rect()` function.\r\nThese are required to the concentric donuts. \r\n\r\n```{r}\r\ngroup1$ymax <- cumsum(group1$Percentage)\r\ngroup1$ymin <- c(0, head(group1$ymax, n = (nrow(group1)-1)))\r\n\r\ngroup2$ymax <- cumsum(group2$Percentage)\r\ngroup2$ymin <- c(0, head(group2$ymax, n = (nrow(group2)-1)))\r\n\r\ngroup3$ymax <- cumsum(group3$Percentage)\r\ngroup3$ymin <- c(0, head(group3$ymax, n = (nrow(group3)-1)))\r\n\r\nblank$ymax <- cumsum(blank$Percentage)\r\nblank$ymin <- c(0, head(blank$ymax, n = (nrow(blank)-1)))\r\n\r\nhead(blank)\r\n```\r\n\r\n\r\n```{r}\r\nexample_data <- rbind(\r\n  blank %>% \r\n    mutate(group = 0),\r\n  group1 %>% \r\n    mutate(group = 1),\r\n  group2 %>% \r\n    mutate(group = 2),\r\n  group3 %>% \r\n    mutate(group = 3)\r\n)\r\n```\r\n\r\n\r\n# Graphics\r\n## Bad example 1\r\n```{r}\r\nbad_example_1 <- example_data %>% \r\n  ggplot(aes(x = group, y = Percentage)) +\r\n  geom_rect(aes(ymax = ymax, ymin = ymin,\r\n                xmax = group-0.25, xmin = group+0.25, \r\n                fill = Type), alpha = 0.8) +\r\n  annotate(geom = \"text\", label = c(\"Group1\", \"Group2\", \"Group3\"), \r\n           x = c(1, 2, 3), y = c(0, 0, 0), size = 4) +\r\n  scale_fill_manual(values = brewer.pal(8, \"Accent\")[c(1, 5)])+\r\n  labs(x = NULL,\r\n       y = NULL,\r\n       fill = NULL,\r\n       title = \"Arc lenghts are misleading\",\r\n       caption = \"Outer rings = much longer arcs\") +\r\n  theme_minimal() +\r\n  theme(\r\n    text = element_text(size = 14, color = \"black\"),\r\n    axis.text = element_text(color = \"black\"),\r\n    panel.grid = element_blank(),\r\n    axis.text.y = element_blank(),\r\n    legend.position = \"top\",\r\n    plot.title = element_text(size = 12),\r\n    plot.caption = element_text(hjust = 0)\r\n  ) +\r\n  coord_polar(theta = \"y\")\r\n\r\nbad_example_1\r\n```\r\nYou might be tempted to say the data is represented by the arc length, which is in fact inaccurate. \r\nThe arc length on the outer ring is much longer than that in the inner rings. \r\nGroup 2 and Group 3 have the same exact values, but the arc lengths of Group 3 are much longer. \r\nIn fact the data is represented by the arc angle, which we are bad are reading. \r\n \r\n## Bad example 2\r\n```{r}\r\nbad_example_2 <- example_data %>% \r\n  mutate(x = case_when(\r\n    group == 0 ~ 0,\r\n    group == 1 ~ 3,\r\n    group == 2 ~ 2,\r\n    group == 3 ~ 1\r\n  )) %>% \r\n  mutate(xmax = x - 0.25) %>% \r\n  mutate(xmin = x + 0.25) %>% \r\n  ggplot(aes(x = x, y = Percentage)) +\r\n  geom_rect(aes(ymax = ymax, ymin = ymin,\r\n                xmax = xmax, xmin = xmin, \r\n                fill = Type), alpha = 0.8) +\r\n  annotate(geom = \"text\", label = c(\"Group1\", \"Group2\", \"Group3\"), \r\n           x = c(3, 2, 1), y = c(0, 0, 0), size = 4) +\r\n  scale_fill_manual(values = brewer.pal(8, \"Accent\")[c(1,5)])+\r\n  labs(x = NULL,\r\n       y = NULL,\r\n       fill = NULL,\r\n        title = \"Ring order matters\",\r\n       caption = \"Shorter arcs can have larger values\") +\r\n  theme_minimal() +\r\n  theme(\r\n    text = element_text(size = 14, color = \"black\"),\r\n    axis.text = element_text(color = \"black\"),\r\n    panel.grid = element_blank(),\r\n    axis.text.y = element_blank(),\r\n    legend.position = \"top\",\r\n    plot.title = element_text(size = 12),\r\n    plot.caption = element_text(hjust = 0)\r\n  ) +\r\n  coord_polar(theta = \"y\")\r\n\r\nbad_example_2\r\n```\r\nAs you can see, the ordering of the categories (which group goes to which ring) has a big impact on the look of the graph. \r\n\r\n## Good example \r\n```{r}\r\ngood_example <- example_data %>%\r\n  filter(group != 0) %>% \r\n  mutate(Type = factor(Type, levels = c(\"Type II\", \"Type I\"))) %>% \r\n  ggplot(aes(x = as.factor(group), y = Percentage)) +\r\n  geom_bar(stat = \"identity\", aes(fill = Type), \r\n           width = 0.5, alpha = 0.8, color = \"white\") +\r\n  scale_fill_manual(values = brewer.pal(8, \"Accent\")[c(1,5)],\r\n                    limits = c(\"Type I\", \"Type II\"))+\r\n  labs(x = \"Group\",\r\n       y = \"%\",\r\n       fill = NULL,\r\n       title = \"Just unwrap the donuts!\",\r\n       caption = \"Simple but better\") +\r\n  theme_classic() +\r\n  theme(\r\n    text = element_text(size = 14, color = \"black\"),\r\n    axis.text = element_text(color = \"black\"),\r\n    legend.position = \"top\",\r\n    plot.title = element_text(size = 12),\r\n    plot.caption = element_text(hjust = 0)\r\n  )  \r\n\r\ngood_example\r\n```\r\n\r\n# Put them together \r\n```{r}\r\nwrap_plots(bad_example_1, bad_example_2, good_example, \r\n           nrow = 1, widths = c(1, 1, 0.75))\r\n\r\nggsave(\"../Results/dont_concentric_donuts.svg\", height = 4, width = 8, bg = \"white\")\r\nggsave(\"../Results/dont_concentric_donuts.png\", height = 4, width = 8, bg = \"white\")\r\n```\r\n\r\n \r\n\r\n"
  },
  {
    "path": "Scripts/NoPieCharts.Rmd",
    "content": "---\r\ntitle: \"NoPieCharts\"\r\nauthor: \"Chenxin Li\"\r\ndate: '2022-07-21'\r\noutput: html_document\r\n---\r\n\r\n```{r setup, include=FALSE}\r\nknitr::opts_chunk$set(echo = TRUE)\r\n```\r\n\r\n# Packages\r\n```{r}\r\nlibrary(tidyverse)\r\nlibrary(RColorBrewer)\r\nlibrary(patchwork)\r\n```\r\n\r\n# Data \r\nThis section simulate some data. You can ignore it. \r\n```{r}\r\ngroup1 <- data.frame(\r\n  \"Type\" = c(\"Type I\", \"Type II\", \"Type III\", \"Type IV\"),\r\n  \"Percentage\" = c(15, 35, 30, 20)\r\n)\r\n\r\ngroup2 <- data.frame(\r\n  \"Type\" = c(\"Type I\", \"Type II\", \"Type III\", \"Type IV\"),\r\n  \"Percentage\" = c(10, 25, 35, 30)\r\n)\r\n\r\nhead(group2)\r\n```\r\n# Graphs\r\n## Pie chart \r\n```{r}\r\ng1_pie <- group1 %>% \r\n  ggplot(aes(x = \"\", y = Percentage)) +\r\n  geom_bar(stat = \"identity\", width = 0.7,\r\n           color = \"white\",\r\n           aes(fill = Type)) +\r\n  annotate(geom = \"text\", x = -Inf, y = Inf, \r\n           label = \"Group 1\", size = 4, hjust = 0.5, vjust = 0.5) +\r\n  labs(fill  = NULL,\r\n       title = \"Pie charts:\\nArc lengths & area\\nrepresent data\") +\r\n  scale_fill_manual(values = brewer.pal(8, \"Set2\")) +\r\n  coord_polar(\"y\", start = 0, direction = -1) +\r\n  theme_void() +\r\n  theme(\r\n    text = element_text(size = 14),\r\n    plot.title = element_text(size = 12),\r\n    legend.position = \"none\"\r\n  )\r\n\r\ng2_pie <- group2 %>% \r\n  ggplot(aes(x = \"\", y = Percentage)) +\r\n  geom_bar(stat = \"identity\", width = 0.7, \r\n           color = \"white\",\r\n           aes(fill = Type)) +\r\n  annotate(geom = \"text\", x = -Inf, y = Inf, \r\n           label = \"Group 2\", size = 4, hjust = 0.5, vjust = 0.5) +\r\n  scale_fill_manual(values = brewer.pal(8, \"Set2\")) +\r\n  labs(fill  = NULL,\r\n       caption = \"Hard to compare groups.\\nArc length redundant with area.\") +\r\n  coord_polar(\"y\", start = 0, direction = -1) +\r\n  theme_void() +\r\n  theme(\r\n    text = element_text(size = 14),\r\n    plot.caption = element_text(hjust = 0),\r\n    legend.position = \"none\"\r\n  )\r\n\r\npies <- wrap_plots(g1_pie, g2_pie, nrow = 2)\r\npies\r\n```\r\n\r\n## Donut\r\n```{r}\r\ngroup1$ymax <- cumsum(group1$Percentage)\r\ngroup1$ymin <- c(0, head(group1$ymax, n = (nrow(group1)-1)))\r\n\r\nhead(group1)\r\n\r\ngroup2$ymax <- cumsum(group2$Percentage)\r\ngroup2$ymin <- c(0, head(group2$ymax, n = (nrow(group1)-1)))\r\n\r\nhead(group2)\r\n```\r\n\r\n```{r}\r\ng1_donut <- group1 %>% \r\n  ggplot(aes(x = 1:4, y = Percentage)) +\r\n  geom_rect(aes(ymin = ymin, ymax = ymax, fill = Type), \r\n            xmax = 4, xmin = 3, color = \"white\") +\r\n  annotate(geom = \"text\", x = 0, y = Inf, \r\n           label = \"Group 1\", size = 4, hjust = 0.5, vjust = 0.5) +\r\n  labs(fill  = NULL,\r\n       title = \"Donut charts:\\nArc lengths\\nrepresent data\") +\r\n  scale_fill_manual(values = brewer.pal(8, \"Set2\")) +\r\n  coord_polar(theta = \"y\", start = 0, direction = 1) +\r\n  theme_void() +\r\n  theme(\r\n    text = element_text(size = 14),\r\n    plot.title = element_text(size = 12),\r\n    legend.position = \"none\"\r\n  )\r\n\r\ng2_donut <- group2 %>% \r\n  ggplot(aes(x = 1:4, y = Percentage)) +\r\n  geom_rect(aes(ymin = ymin, ymax = ymax, fill = Type), \r\n            xmax = 4, xmin = 3, color = \"white\") +\r\n  annotate(geom = \"text\", x = 0, y = Inf, \r\n           label = \"Group 2\", size = 4, hjust = 0.5, vjust = 0.5) +\r\n  labs(fill  = NULL,\r\n       caption = \"Still hard to compare\\ngroups.\")  +\r\n  scale_fill_manual(values = brewer.pal(8, \"Set2\")) +\r\n  coord_polar(theta = \"y\", start = 0, direction = 1) +\r\n  theme_void() +\r\n  theme(\r\n    text = element_text(size = 14),\r\n    plot.caption = element_text(hjust = 0),\r\n    legend.position = \"none\"\r\n  )\r\n\r\ndonuts <- wrap_plots(g1_donut, g2_donut, nrow = 2)\r\ndonuts\r\n```\r\n## Stacked bars\r\n```{r}\r\nTwoGroups <- rbind(\r\n  group1 %>% \r\n    mutate(group = \"1\"),\r\n  group2 %>% \r\n    mutate(group = \"2\")\r\n)\r\n\r\nhead(TwoGroups)\r\n```\r\n\r\n```{r}\r\nStackedBar <- TwoGroups %>% \r\n  ggplot(aes(x = group, y = Percentage)) +\r\n  geom_bar(stat = \"identity\", aes(fill = Type),\r\n           width = 0.5, color = \"white\") +\r\n  labs(fill  = NULL,\r\n       x = \"Groups\",\r\n       title = \"Stacked Bars:\\nJust unwrap\\nthe polar coordinate!\",\r\n       caption = \"Easier to compare groups.\\nData represented by bar heights.\") +\r\n  scale_fill_manual(values = brewer.pal(8, \"Set2\")) +\r\n  theme_classic() +\r\n  theme(\r\n    text = element_text(size = 14),\r\n    axis.text = element_text(color = \"black\"),\r\n    plot.title = element_text(size = 12),\r\n    plot.caption = element_text(hjust = 0)\r\n  )\r\n\r\nStackedBar\r\n```\r\n# Put them together\r\n```{r}\r\nwrap_plots(pies, donuts, StackedBar, nrow = 1, \r\n           widths = c(1, 1, 0.4))\r\n\r\nggsave(\"../Results/dont_pie_chart.svg\", height = 4, width = 8, bg = \"white\")\r\nggsave(\"../Results/dont_pie_chart.png\", height = 4, width = 8, bg = \"white\")\r\n```\r\n\r\n"
  },
  {
    "path": "Scripts/Position_vs_length_based_visualizations.Rmd",
    "content": "---\r\ntitle: \"Position_vs_length_based_visualizations\"\r\nauthor: \"Chenxin Li\"\r\ndate: '2022-07-19'\r\noutput: html_document\r\n---\r\n\r\n```{r setup, include=FALSE}\r\nknitr::opts_chunk$set(echo = TRUE)\r\n```\r\n\r\n# Packages\r\n```{r}\r\nlibrary(tidyverse)\r\nlibrary(RColorBrewer)\r\nlibrary(cowplot)\r\n```\r\n\r\n# Data \r\nThis section simulate some data, you can ignore\r\n```{r}\r\nset.seed(666)\r\nExample_data1 <- rbind(\r\n  rnorm(3, mean = 5, sd = 0.5) %>%\r\n    as.data.frame() %>% \r\n    mutate(time = 1),\r\n  rnorm(3, mean = 8, sd = 0.5) %>% \r\n    as.data.frame() %>% \r\n    mutate(time = 2),\r\n  rnorm(3, mean = 10, sd = 0.5) %>%\r\n    as.data.frame() %>% \r\n    mutate(time = 3)\r\n) %>% \r\n  rename(response = \".\")\r\n\r\nhead(Example_data1)\r\n```\r\n\r\n# Graph \r\nDot plots/line graphs are position based visualizations. \r\nValues are determined by the position relative to x and y axis. \r\n\r\n## Dot/line graphs \r\n```{r}\r\nDotline <- Example_data1 %>% \r\n  ggplot(aes(x = time, y = response)) +\r\n  stat_summary(geom = \"line\", fun.data = mean_se, \r\n               size = 1.1, alpha = 0.8, color = \"grey60\") +\r\n  geom_point(aes(fill = as.factor(time)), shape = 21, color = \"grey20\",\r\n             alpha = 0.8, size = 3, position = position_jitter(0.1, seed = 666)) +\r\n  stat_summary(geom = \"errorbar\", fun.data = mean_se,\r\n               width = 0.05, alpha = 0.8) +\r\n  scale_fill_manual(values = brewer.pal(8, \"Accent\")) +\r\n  scale_x_continuous(breaks = c(1, 2, 3)) +\r\n  labs(x = \"Time point\",\r\n       y = \"Response\",\r\n       title = \"Dot/line graphs are\\nposition based\",\r\n       caption = \"\\nValues represented by positions\\nalong x & y axis\") +\r\n  theme_classic() +\r\n  theme(\r\n    legend.position = \"none\",\r\n    text = element_text(size = 14, color = \"black\"),\r\n    axis.text = element_text(color = \"black\"),\r\n    plot.title = element_text(size = 12),\r\n    plot.caption = element_text(hjust = 0)\r\n  )\r\n\r\nDotline\r\n```\r\nSame idea applies to box plot and other position based visualizations. \r\n\r\n## Bar graphs \r\n```{r}\r\nGoodBar <- Example_data1 %>% \r\n  ggplot(aes(x = time, y = response)) +\r\n  stat_summary(geom = \"bar\", fun.data = mean_se, aes(color = as.factor(time)),\r\n               size = 1.1, alpha = 0.8, fill = NA, width = 0.5) +\r\n  geom_point(aes(fill = as.factor(time)), shape = 21, color = \"grey20\",\r\n             alpha = 0.8, size = 3, position = position_jitter(0.1, seed = 666)) +\r\n  stat_summary(geom = \"errorbar\", fun.data = mean_se,\r\n               width = 0.05, alpha = 0.8) +\r\n  scale_fill_manual(values = brewer.pal(8, \"Accent\")) +\r\n  scale_color_manual(values = brewer.pal(8, \"Accent\")) +\r\n  scale_x_continuous(breaks = c(1, 2, 3)) +\r\n  labs(x = \"Time point\",\r\n       y = \"\\nResponse\",\r\n       title = \"Bar graphs are\\nlength based\",\r\n       caption = \"\\nValues represented by\\ndistance from x axis\") +\r\n  theme_classic() +\r\n  theme(\r\n    legend.position = \"none\",\r\n    text = element_text(size = 14, color = \"black\"),\r\n    axis.text = element_text(color = \"black\"),\r\n    plot.title = element_text(size = 12),\r\n    plot.caption = element_text(hjust = 0)\r\n  )\r\n\r\nGoodBar\r\n```\r\n\r\n## Bad example \r\n```{r}\r\nExample_data1_s <- Example_data1 %>% \r\n  group_by(time) %>% \r\n  summarise(response = mean(response))\r\n\r\nhead(Example_data1_s)\r\n```\r\n\r\n```{r}\r\nBadBar <- Example_data1 %>% \r\n  ggplot(aes(x = time, y = response)) +\r\n  geom_rect(data = Example_data1_s,\r\n            aes(ymax = response, xmin = time-0.25, xmax = time+0.25,\r\n                color = as.factor(time)), \r\n            ymin = 4.5, fill = NA, alpha = 0.8, size = 1.1) +\r\n  geom_point(aes(fill = as.factor(time)), shape = 21, color = \"grey20\",\r\n             alpha = 0.8, size = 3, position = position_jitter(0.1, seed = 666)) +\r\n  stat_summary(geom = \"errorbar\", fun.data = mean_se,\r\n               width = 0.05, alpha = 0.8) +\r\n  scale_fill_manual(values = brewer.pal(8, \"Accent\")) +\r\n  scale_color_manual(values = brewer.pal(8, \"Accent\")) +\r\n  scale_x_continuous(breaks = c(1, 2, 3)) +\r\n  ylim(c(4.25, 11)) + \r\n  labs(x = \"Time point\",\r\n       y = \"\\nResponse\",\r\n       title = \"NEVER DO THIS!\",\r\n       caption = \"\\nBar lengths are\\nmisleading\") +\r\n  theme_classic() +\r\n  theme(\r\n    legend.position = \"none\",\r\n    text = element_text(size = 14, color = \"black\"),\r\n    axis.text = element_text(color = \"black\"),\r\n    plot.title = element_text(size = 12),\r\n    plot.caption = element_text(hjust = 0)\r\n  )\r\n\r\nBadBar\r\n```\r\n\r\n# Put together \r\n```{r}\r\nplot_grid(Dotline, GoodBar, BadBar, \r\n          align = \"h\", axis = \"lrtb\", nrow = 1)\r\n\r\nggsave(\"../Results/Position_and_length_based_visualizations.svg\", height = 4, width = 8, bg = \"white\")\r\nggsave(\"../Results/Position_and_length_based_visualizations.png\", height = 4, width = 8, bg = \"white\")\r\n```\r\n\r\n\r\n"
  },
  {
    "path": "Scripts/Red_green.Rmd",
    "content": "---\r\ntitle: \"RedGreen\"\r\nauthor: \"Chenxin Li\"\r\ndate: \"2023-01-16\"\r\noutput: html_notebook\r\n---\r\n\r\n```{r setup, include=FALSE}\r\nknitr::opts_chunk$set(echo = TRUE)\r\n```\r\n\r\nFriends don't let friends use red/green or rainbow for heatmaps! \r\nDeuteranomaly is the most common type of red/green colorblindness, occurring in 1/16 male and 1/256 female. \r\nAny color scales that uses shades of red and shades of green in the same time would be a problem for a person with red/green colorblindness (third column of figure). \r\nIn addition, red/green and rainbow do not preserve information well at all when printed on black/white (grey scale, second column in figure). \r\nMany scientific software still use red/green or rainbow as the default color scales, which drives me crazy. \r\nMore \"modern\" color scales, such as [viridis](https://cran.r-project.org/web/packages/viridis/vignettes/intro-to-viridis.html) are both colorblind-friendly and grey scale-safe (third row of figure). \r\nAnd they look nice too. \r\n\r\n\r\n\r\n# Package\r\n```{r}\r\nlibrary(tidyverse)\r\nlibrary(viridis)\r\nlibrary(prismatic)\r\nlibrary(patchwork)\r\n```\r\n\r\n# Data\r\n```{r}\r\nabc_1 <- expand.grid(\r\n  a = c(1:10),\r\n  b = c(1:10)\r\n) %>% \r\n  mutate(c = a + b)\r\n\r\nhead(abc_1)\r\n```\r\n## Write a function to make grey scale \r\n```{r}\r\nto_grey_scale <- function(hex){\r\n  temp = col2rgb(hex) %>% \r\n    t() %>% \r\n    as.data.frame() %>% \r\n    mutate(gs = 0.299*red + 0.587*green + 0.114*blue)\r\n  \r\n  rgb(temp$gs, temp$gs, temp$gs, maxColorValue = 256)\r\n}\r\n```\r\n\r\n\r\n# Red/green\r\n```{r}\r\nRed_green <- abc_1 %>% \r\n  ggplot(aes(x = a, y = b)) +\r\n  geom_tile(aes(fill = c)) +\r\n  scale_fill_gradient2(low = \"red\", mid = \"white\", high = \"green\", midpoint = 11) +\r\n  labs(title = \"c = a + b\\nred/green\\nnormal\") +\r\n  theme_classic() +\r\n  theme(legend.key.width = unit(0.7, \"lines\"),\r\n        axis.text = element_text(color = \"black\"),\r\n        ) +\r\n  coord_fixed()\r\n\r\nRed_green\r\n```\r\n## Grey scale\r\n```{r}\r\nRed_green_gs <- abc_1 %>% \r\n  ggplot(aes(x = a, y = b)) +\r\n  geom_tile(aes(fill = c)) +\r\n  scale_fill_gradient2(low = to_grey_scale(\"red\"), \r\n                       mid = \"white\", \r\n                       high = to_grey_scale(\"green\"), midpoint = 11) +\r\n  labs(title = \"c = a + b\\nred/green\\ngrey scale\") +\r\n  theme_classic() +\r\n  theme(legend.key.width = unit(0.7, \"lines\"),\r\n        axis.text = element_text(color = \"black\"),\r\n        ) +\r\n  coord_fixed()\r\n\r\nRed_green_gs\r\n```\r\n\r\n## Red/green colorblind\r\n```{r}\r\nRed_green_deu <- abc_1 %>% \r\n  ggplot(aes(x = a, y = b)) +\r\n  geom_tile(aes(fill = c)) +\r\n  scale_fill_gradient2(low = clr_deutan(\"red\"), \r\n                       mid = clr_deutan(\"white\"), \r\n                       high = clr_deutan(\"green\"), midpoint = 11) +\r\n  labs(title = \"c = a + b\\nred/green\\ndeuteranopia\") +\r\n  theme_classic() +\r\n  theme(legend.key.width = unit(0.7, \"lines\"),\r\n        axis.text = element_text(color = \"black\"),\r\n        ) +\r\n  coord_fixed()\r\n\r\nRed_green_deu\r\n```\r\n\r\n\r\n# Rainbow\r\n```{r}\r\nRainbow1 <- abc_1 %>% \r\n  ggplot(aes(x = a, y = b)) +\r\n  geom_tile(aes(fill = c)) +\r\n  scale_fill_gradientn(colors = rainbow(20)) +\r\n  labs(title = \"c = a + b\\nrainbow\\nnormal\") +\r\n  theme_classic() +\r\n  theme(legend.key.width = unit(0.7, \"lines\"),\r\n        axis.text = element_text(color = \"black\"),\r\n        ) +\r\n  coord_fixed()\r\n\r\nRainbow1\r\n```\r\n## grey scale\r\n```{r}\r\nRainbow_gs <- abc_1 %>% \r\n  ggplot(aes(x = a, y = b)) +\r\n  geom_tile(aes(fill = c)) +\r\n  scale_fill_gradientn(colors = rainbow(20) %>% to_grey_scale()) +\r\n  labs(title = \"c = a + b\\nrainbow\\ngrey scale\") +\r\n  theme_classic() +\r\n  theme(legend.key.width = unit(0.7, \"lines\"),\r\n        axis.text = element_text(color = \"black\"),\r\n        ) +\r\n  coord_fixed()\r\n\r\nRainbow_gs\r\n```\r\n## Red/green color blind\r\n```{r}\r\nRainbow_deu <- abc_1 %>% \r\n  ggplot(aes(x = a, y = b)) +\r\n  geom_tile(aes(fill = c)) +\r\n  scale_fill_gradientn(colors = rainbow(20) %>% clr_deutan()) +\r\n  labs(title = \"c = a + b\\nrainbow\\ndeuteranopia\") +\r\n  theme_classic() +\r\n  theme(legend.key.width = unit(0.7, \"lines\"),\r\n        axis.text = element_text(color = \"black\"),\r\n        ) +\r\n  coord_fixed()\r\n\r\nRainbow_deu\r\n```\r\n\r\n\r\n# Viridis \r\n## Normal \r\n```{r}\r\nviridis_optionD <- abc_1 %>% \r\n  ggplot(aes(x = a, y = b)) +\r\n  geom_tile(aes(fill = c)) +\r\n  scale_fill_gradientn(colors = viridis(100)) +\r\n  labs(title = \"c = a + b\\nviridis\\nnormal\") +\r\n  theme_classic() +\r\n  theme(legend.key.width = unit(0.7, \"lines\"),\r\n        axis.text = element_text(color = \"black\")) +\r\n  coord_fixed()\r\n\r\nviridis_optionD\r\n```\r\n## Grey scale\r\n```{r}\r\nviridis_gs <- abc_1 %>% \r\n  ggplot(aes(x = a, y = b)) +\r\n  geom_tile(aes(fill = c)) +\r\n  scale_fill_gradientn(colors = to_grey_scale(viridis(100))) +\r\n  labs(title = \"c = a + b\\nviridis\\ngrey scale\") +\r\n  theme_classic() +\r\n  theme(legend.key.width = unit(0.7, \"lines\"),\r\n        axis.text = element_text(color = \"black\")) +\r\n  coord_fixed()\r\n\r\nviridis_gs\r\n```\r\n## Red/green color blind\r\n```{r}\r\nviridis_deu <- abc_1 %>% \r\n  ggplot(aes(x = a, y = b)) +\r\n  geom_tile(aes(fill = c)) +\r\n  scale_fill_gradientn(colors = clr_deutan(viridis(100))) +\r\n  labs(title = \"c = a + b\\nviridis\\ndeuteranopia\") +\r\n  theme_classic() +\r\n  theme(legend.key.width = unit(0.7, \"lines\"),\r\n        axis.text = element_text(color = \"black\"),\r\n        ) +\r\n  coord_fixed()\r\n\r\nviridis_deu\r\n```\r\n# Put in together: \r\n```{r}\r\nwrap_plots(\r\n  Red_green, Red_green_gs, Red_green_deu,\r\n  Rainbow1, Rainbow_gs, Rainbow_deu,\r\n  viridis_optionD, viridis_gs, viridis_deu,\r\n  nrow = 3, \r\n  ncol = 3\r\n)\r\n\r\nggsave(\"../Results/Color_blind_grey_scale_safe_heatmap.svg\", width = 10, height = 7, bg = \"white\")\r\nggsave(\"../Results/Color_blind_grey_scale_safe_heatmap.png\", width = 10, height = 7, bg = \"white\")\r\n```\r\n\r\n"
  },
  {
    "path": "Scripts/Reorder_rows_col_heatmap.Rmd",
    "content": "---\r\ntitle: \"Reorder_rows_cols_for_heatmap\"\r\nauthor: \"Chenxin Li\"\r\ndate: '2022-06-06'\r\noutput: html_document\r\n---\r\n\r\n```{r setup, include=FALSE}\r\nknitr::opts_chunk$set(echo = TRUE)\r\n```\r\n\r\nFriends don't let friends make heatmap without (considering) reordering rows and columns \r\n\r\n# packages \r\n```{r}\r\nlibrary(tidyverse) \r\n\r\nlibrary(RColorBrewer) # for the colors \r\nlibrary(viridis) # for the colors \r\nlibrary(patchwork) # for putting graphs together \r\n```\r\n\r\n# Load data\r\n```{r}\r\nheatmap_data <- read_csv(\"../Data/deidentified_cell_feature_data.csv\")\r\n\r\nhead(heatmap_data)\r\n```\r\n\r\nThis is from unpublished data, so I had to de-identify them. \r\nThis is already a tidy data frame, where each row is a cell.\r\n\r\n# Check summary \r\n```{r}\r\nheatmap_data %>% \r\n  group_by(cell_type) %>% \r\n  count()\r\n```\r\nThe cell types have already been identified - 3 cell types\r\n\r\n```{r}\r\nheatmap_data %>% \r\n  group_by(feature) %>% \r\n  count() %>% \r\n  nrow()\r\n```\r\n\r\nThere are 14 features that were recorded for each cell. \r\n\r\n\r\n# Bad example\r\n```{r}\r\nwithout_reordering <- heatmap_data %>% \r\n  mutate(z2 = case_when(\r\n    z >= 3 ~ 3,\r\n    T ~ z\r\n  )) %>% \r\n  ggplot(aes(x = cell, y = feature)) +\r\n  geom_tile(aes(fill = z2)) +\r\n  scale_fill_gradientn(colors = brewer.pal(9, \"PuBuGn\"), \r\n                       breaks = c(0, 3),\r\n                       labels = c(\"0\", \">3\")) +\r\n  scale_x_discrete(labels = NULL) +\r\n  labs(x = \"Cell\",\r\n       y = \"Feature\",\r\n       fill = \"z score\",\r\n       caption = \"Am I looking at a glitching TV?\\n\",\r\n       title = \"Before reordering rows and columns\") +\r\n  theme_classic() +\r\n  theme(\r\n    text = element_text(size = 14, color = \"black\"),\r\n    axis.text = element_text(color = \"black\"),\r\n    legend.position = \"bottom\",\r\n    axis.ticks.x = element_blank(),\r\n    plot.caption = element_text(hjust = 0),\r\n    plot.title = element_text(size = 12, face = \"bold\")\r\n  )\r\n\r\nwithout_reordering\r\n```\r\n\r\n# Good example\r\n## Reorder rows and columns \r\n```{r}\r\nafter_reordering <- heatmap_data %>% \r\n  mutate(z2 = case_when(\r\n    z >= 3 ~ 3,\r\n    T ~ z\r\n  )) %>% \r\n  mutate(tag = case_when(\r\n    str_detect(feature, \" 7\") ~ 1,\r\n    str_detect(feature, \" 8| 6| 1$\") ~ 2,\r\n    str_detect(feature, \" 2| 3| 4| 10\") ~ 4,\r\n    T ~ 3\r\n  )) %>% \r\n  mutate(feature = reorder(feature, -tag)) %>% \r\n  ggplot(aes(x = cell, y = feature)) +\r\n  facet_grid(. ~ cell_type, scales = \"free_x\", space = \"free_x\") +\r\n  geom_tile(aes(fill = z2)) +\r\n  scale_fill_gradientn(colors = brewer.pal(9, \"PuBuGn\"), \r\n                       breaks = c(0, 3),\r\n                       labels = c(\"0\", \">3\")) +\r\n  scale_x_discrete(labels = NULL) +\r\n  labs(x = \"Cell\",\r\n       y = \"Feature\",\r\n       fill = \"z score\",\r\n       caption = \"Much better.\",\r\n       title = \"After reordering rows and columns\") +\r\n  theme_classic() +\r\n  theme(\r\n    text = element_text(size = 14, color = \"black\"),\r\n    axis.text = element_text(color = \"black\"),\r\n    legend.position = \"bottom\",\r\n    axis.ticks.x = element_blank(),\r\n    plot.caption = element_text(hjust = 0),\r\n    plot.title = element_text(size = 12, face = \"bold\")\r\n  )\r\n\r\nafter_reordering\r\n```\r\n\r\n```{r}\r\nwrap_plots(without_reordering, \r\n           after_reordering, \r\n           nrow = 2)\r\n\r\nggsave(\"../Results/Reorder_rows_and_columns_for_heatmap.svg\", height = 8, width = 9.5, bg = \"white\")\r\nggsave(\"../Results/Reorder_rows_and_columns_for_heatmap.png\", height = 8, width = 9.5, bg = \"white\")\r\n```\r\n\r\n\r\n\r\n\r\n"
  },
  {
    "path": "Scripts/Violin_plot_for_small_n.Rmd",
    "content": "---\r\ntitle: \"small_n\"\r\nauthor: \"Chenxin Li\"\r\ndate: \"4/12/2022\"\r\noutput: html_document\r\n---\r\n\r\n```{r setup, include=FALSE}\r\nknitr::opts_chunk$set(echo = TRUE)\r\n```\r\n\r\nThis is an Rmarkdown document.If you don't have the rmarkdown package, you will have to install it to run this. \r\nThe grey areas are code chunks. To run a code chunk click the green triangle at the upper right of each chunk. \r\n\r\n# Packages \r\n```{r}\r\nlibrary(tidyverse) \r\nlibrary(ggbeeswarm) # to make jitter plots \r\n\r\nlibrary(emmeans) # for estimated marginal means and stats \r\n\r\nlibrary(RColorBrewer) # for colors \r\n\r\nlibrary(patchwork) # for putting ggplot objects together \r\n```\r\n\r\n# Data\r\nGenerate three sets of data from a normal distribution with mean = 1 and sd = 1.\r\n\r\n```{r}\r\nset.seed(666) # \r\n\r\ndata_set <- replicate(rnorm(5, mean = 1, sd  = 1), n = 3, simplify = T) %>% \r\n  as.data.frame() %>% \r\n  gather(\"sample\", \"Response\", 1:3) %>% \r\n  mutate(Group = str_replace(sample, \"V\", \"group\"))\r\n\r\n\r\nhead(data_set) \r\n```\r\n# Stats\r\n```{r}\r\nmodel_1 <- lm(Response ~ Group, data = data_set)\r\nanova(model_1)\r\n\r\nemmeans(model_1, ~Group)\r\n```\r\nGroup means are clearly not different, as they are sampled from the same distribution. \r\n\r\n# Plot\r\n## Violin \r\n```{r}\r\nviolin_eg <- data_set %>% \r\n  ggplot(aes(x = Group, y = Response)) +\r\n  geom_violin(aes(fill = Group), \r\n              alpha = 0.8) +\r\n  stat_summary(geom = \"point\", fun = median) +\r\n  scale_fill_brewer(palette = \"Accent\") +\r\n  labs(\r\n    caption = \"The distributions are different!\\nI wonder what's going on.\"\r\n  ) +\r\n  theme_classic()+\r\n  theme(\r\n    text = element_text(size=12, color = \"black\", face = \"bold\"),\r\n    axis.text = element_text(color = \"black\"),\r\n    legend.position = \"none\",\r\n    plot.caption = element_text(hjust = 0)\r\n  ) +\r\n  ggtitle(\"Points are median.\")\r\n\r\nviolin_eg\r\n```\r\n\r\n## Box plot\r\n```{r}\r\nbox_eg <- data_set %>% \r\n  ggplot(aes(x = Group, y = Response)) +\r\n  geom_boxplot(aes(fill = Group), \r\n              alpha = 0.8) +\r\n  scale_fill_brewer(palette = \"Accent\") +\r\n  labs(\r\n    caption = \"The quartiles are different!\\nI wonder what's going on.\"\r\n  ) +\r\n  theme_classic()+\r\n  theme(\r\n    text = element_text(size=12, color = \"black\", face = \"bold\"),\r\n    axis.text = element_text(color = \"black\"),\r\n    legend.position = \"none\",\r\n    plot.caption = element_text(hjust = 0)\r\n  ) +\r\n  ggtitle(\"Boxes span IQR.\")\r\n\r\nbox_eg\r\n```\r\n\r\n## jitter plot \r\n```{r}\r\njitter_eg <- data_set %>% \r\n  ggplot(aes(x = Group, y = Response)) +\r\n  geom_point(aes(fill = Group), \r\n             alpha = 0.8, shape = 21, size = 3, color = \"grey40\",\r\n             position = position_jitter(seed = 666, width = 0.2)) +\r\n  scale_fill_brewer(palette = \"Accent\") +\r\n  labs(\r\n    caption = \"Never mind...\\nToo little data to say anything.\"\r\n  ) +\r\n  theme_classic()+\r\n  theme(\r\n    text = element_text(size=12, color = \"black\", face = \"bold\"),\r\n    axis.text = element_text(color = \"black\"),\r\n    legend.position = \"none\",\r\n    plot.caption = element_text(hjust = 0)\r\n  ) +\r\n  ggtitle(\r\n    \"n = 5\"\r\n  )\r\n\r\njitter_eg\r\n```\r\n### together \r\n```{r}\r\nwrap_plots(violin_eg, box_eg, jitter_eg,\r\n          nrow = 1)\r\n\r\nggsave(\"../Results/Beware_of_small_n_box_violin_plot.svg\", height = 5, width = 10)\r\nggsave(\"../Results/Beware_of_small_n_box_violin_plot.png\", height = 5, width = 10)\r\n```\r\n\r\n"
  },
  {
    "path": "Scripts/stacked_bars_optimization.Rmd",
    "content": "---\r\ntitle: \"Optimize_stacked_bars\"\r\nauthor: \"Chenxin Li\"\r\ndate: \"2023-11-17\"\r\noutput: html_document\r\n---\r\n\r\n```{r setup, include=FALSE}\r\nknitr::opts_chunk$set(echo = TRUE)\r\n```\r\n\r\n# Introduction \r\nStacked bar plots are useful for visualizing proportion data. \r\nStacked bar plots are commonly used to visualize community structure or population structure or admixture analysis. \r\n\r\nThis kind of visualization boils down to a collection of samples, where each sample contains multiple classes of members. \r\nHowever, when we have many samples and many classes, stacked bar plots need to be optimized to be effective. \r\nAnd by \"optimize\" I mean the grouping and ordering of samples. \r\n\r\n# Required packages \r\n```{r}\r\nlibrary(tidyverse)\r\n\r\nlibrary(patchwork) # Not actually required, for this discussion only \r\nlibrary(RColorBrewer) # Only required for coloring the graphs \r\n```\r\n\r\n# Data \r\n```{r}\r\nset.seed(666)\r\n```\r\n\r\n```{r}\r\ngenerate_percentages <- function(N){\r\n  random_N <- rlnorm(n = N, sdlog = 3)\r\n  (random_N / sum(random_N)) * 100\r\n} \r\n\r\nmy_data <- replicate(100, generate_percentages(8)) %>% \r\n  as.data.frame() %>%\r\n  cbind(class = letters[1:8]) %>% \r\n  pivot_longer(cols = !class, names_to = \"sample\", values_to = \"percentage\")\r\n\r\nmy_data\r\n```\r\nDon't worry about what happened here. I just simulated some data. \r\nHere we have an example data with 100 samples and 8 classes of member. \r\n\r\n# Without optimization \r\nLet's see what will happen if we just make a plot as such without optimization.\r\n```{r}\r\nno_reorder <- my_data %>% \r\n  ggplot(aes(x = sample, y = percentage)) +\r\n  geom_bar(stat = \"identity\", aes(fill = class)) +\r\n  scale_fill_manual(values = brewer.pal(8, \"Set2\")) +\r\n  theme_classic() +\r\n  theme(axis.text.x = element_blank())\r\n\r\nno_reorder\r\n```\r\nDue to the number of samples and classes, it is very hard to discern anything from this graph without optimizing the order of bars. \r\n\r\n# Group samples by peak class \r\nA prerequisite for reordering samples is grouping them. \r\nUsually there is a natural way to group them.\r\nFor example grouping by control vs. treatment according to the design of the experiment. \r\nIn this example, we don't have any experimental condition, I will just group them by peak class. \r\nThis just means I am grouping samples by which class is the most abundant member in said sample. \r\n\r\n```{r}\r\nsample_grouping <- my_data %>% \r\n  group_by(sample) %>% \r\n  slice_max(order_by = percentage) %>% \r\n  select(class, sample) %>% \r\n  rename(peak_class = class)\r\n\r\nsample_grouping\r\n```\r\n\r\n# Reordering bars \r\nThis is a 3 step process. \r\n\r\n1. Grouping by sample grouping using `group_by()`.\r\n2. Rank samples at _each level of sample grouping_ with `rank()`. R knows it should rank at each level of sample grouping because we called `group_by()`.\r\n3. Reorder samples by rank using `reorder()`. \r\n\r\n```{r}\r\nmy_data_reordered <- my_data %>% \r\n  inner_join(sample_grouping, by = \"sample\") %>% \r\n  group_by(peak_class) %>% \r\n  mutate(rank = rank(percentage)) %>%  # rank samples at the level of each peak subtype\r\n  mutate(sample = reorder(sample, -rank)) %>%   # this reorders samples\r\n  ungroup()\r\n  \r\nhead(my_data_reordered)\r\n```\r\n\r\n# Plot reordered \r\nNow let's plot the optimized graph \r\n```{r}\r\nbars_reordered <- my_data_reordered %>% \r\n  ggplot(aes(x = sample, y = percentage)) +\r\n  geom_bar(stat = \"identity\", aes(fill = class)) +\r\n  scale_fill_manual(values = brewer.pal(8, \"Set2\")) +\r\n  theme_classic() +\r\n  theme(axis.text.x = element_blank(),\r\n        strip.background = element_blank(),\r\n        strip.text = element_blank(),\r\n        panel.spacing.x = unit(0.1, \"line\")) \r\n```\r\n\r\n...and make a comparison. \r\n\r\n```{r}\r\nwrap_plots(\r\n  no_reorder +\r\n    labs(title = \"Without reordering bars\"),\r\n  bars_reordered +\r\n    labs(title = \"Bars reordered\"), \r\n  guides = \"collect\",\r\n  nrow = 2\r\n) &\r\n  labs(y = \"relative abundance (%)\") &\r\n  theme(title = element_text(size = 10))\r\n\r\nggsave(\"../Results/Reorder_stacked_bars.svg\", height = 4, width = 6, bg = \"white\")\r\nggsave(\"../Results/Reorder_stacked_bars.png\", height = 4, width = 6, bg = \"white\")\r\n```\r\n\r\nAfter reordering the the bars, wow, that really made a difference, don't you think? \r\n"
  }
]