Add files using upload-large-folder tool

.gitattributes

@@ -1574,3 +1574,19 @@ p3/preprocess/Cervical_Cancer/gene_data/GSE107754.csv filter=lfs diff=lfs merge=
 p3/preprocess/Cervical_Cancer/gene_data/GSE146114.csv filter=lfs diff=lfs merge=lfs -text
 p3/preprocess/Celiac_Disease/gene_data/GSE20332.csv filter=lfs diff=lfs merge=lfs -text
 p3/preprocess/Bladder_Cancer/TCGA.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Chronic_Fatigue_Syndrome/GSE67311.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Chronic_kidney_disease/gene_data/GSE66494.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Bladder_Cancer/gene_data/TCGA.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Chronic_kidney_disease/gene_data/GSE104948.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Chronic_kidney_disease/gene_data/GSE104954.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Chronic_Fatigue_Syndrome/gene_data/GSE67311.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Colon_and_Rectal_Cancer/GSE56699.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Colon_and_Rectal_Cancer/GSE46517.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Colon_and_Rectal_Cancer/GSE46862.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Colon_and_Rectal_Cancer/gene_data/GSE56699.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Colon_and_Rectal_Cancer/gene_data/GSE46517.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Congestive_heart_failure/GSE182600.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Coronary_artery_disease/GSE250283.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Colon_and_Rectal_Cancer/gene_data/GSE46862.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Congestive_heart_failure/gene_data/GSE182600.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Coronary_artery_disease/GSE109048.csv filter=lfs diff=lfs merge=lfs -text

p3/preprocess/Bladder_Cancer/gene_data/TCGA.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:be64d77d83703f263dc51ead0994f75fb8147bcd662253db1dc58783e25690d0
+size 128064088

p3/preprocess/COVID-19/GSE212866.csv

@@ -0,0 +1,138 @@
+,COVID-19,OR4F16,OR4F17,OR4F21,OR4F29,OR4F3,OR4F4,OR4F5,PCMTD2,SEPT14
+GSM6559856,0.0,1.0768513981146663,0.6553338399730243,0.6563246315131817,1.0768513981146663,1.0768513981146663,0.35178130313922273,1.274667643822826,1.1314702168717719,0.4683226182878678
+GSM6559857,0.0,1.0746556690101419,0.6929398947371157,0.6772080647075233,1.0746556690101419,1.0746556690101419,0.38418912826439366,1.290544980894942,1.178735847264599,0.4587623088563688
+GSM6559858,0.0,1.1855120937088368,0.5917319630055771,0.81237689464592,1.1855120937088368,1.1855120937088368,0.32196796510565456,1.251430478263156,1.2245047281605972,0.5348122841791499
+GSM6559859,0.0,1.072101027403887,0.6140491039790028,0.6043559254465133,1.072101027403887,1.072101027403887,0.33410300254604364,1.25229529332003,1.1707997339039795,0.49233936243001886
+GSM6559860,0.0,1.0047590436451719,0.5983723752318743,0.5516428489403934,1.0047590436451719,1.0047590436451719,0.32852714840654185,1.25799135746962,1.128939056573686,0.47803711582183556
+GSM6559861,0.0,1.0849577788517044,0.6046927133790672,0.56403791276419,1.0849577788517044,1.0849577788517044,0.33745694723311725,1.33055527527261,1.1411636472463271,0.4903977374926644
+GSM6559862,0.0,1.0704462885227046,0.5535264976507757,0.5590447962767483,1.0704462885227046,1.0704462885227046,0.3409822506560782,1.211418747221848,1.161289281576842,0.46521694142278885
+GSM6559863,0.0,1.0606210420126003,0.5779798681102472,0.592592159011495,1.0606210420126003,1.0606210420126003,0.32502106939083997,1.308785687347356,1.156830649207412,0.47870725860104446
+GSM6559864,0.0,1.0299553836691613,0.6963171579881257,0.5848480978545617,1.0299553836691613,1.0299553836691613,0.3568615016396191,1.26318634819261,1.15187065771994,0.4900062361705634
+GSM6559865,0.0,1.098621932746883,0.69373717681362,0.6003290492859,1.098621932746883,1.098621932746883,0.39391362581692724,1.326419255212264,1.2638116861388533,0.5098639999441645
+GSM6559866,0.0,1.1697202196670076,0.7454301391439929,0.727443690389335,1.1697202196670076,1.1697202196670076,0.40268788496872543,1.305666940059632,1.2081978071262893,0.5243767936368111
+GSM6559867,0.0,1.122437736737904,0.6235758645684786,0.6468043747131467,1.122437736737904,1.122437736737904,0.3407282947387245,1.228183731204878,1.1951147323597704,0.44105308802032556
+GSM6559868,0.0,1.18492500882143,0.5886306990745787,0.6539487064701083,1.18492500882143,1.18492500882143,0.33824828801705725,1.191516351941254,1.206134232680202,0.4864062476202766
+GSM6559869,0.0,1.088752116224608,0.6572872348222457,0.6101923054827617,1.088752116224608,1.088752116224608,0.3524148108011018,1.334852438699066,1.1714520436622313,0.4483090534007256
+GSM6559870,0.0,1.14786705516353,0.6461796227271199,0.6313766650267266,1.14786705516353,1.14786705516353,0.3489517224323918,1.335812713160688,1.1946966494726179,0.5238732272152722
+GSM6559871,0.0,1.108986102035115,0.6123452205774342,0.64417515002055,1.108986102035115,1.108986102035115,0.3401901487036909,1.312911175419306,1.235167396358613,0.46876872518099777
+GSM6559872,0.0,1.1206026607353168,0.6029344191253828,0.6413842064115051,1.1206026607353168,1.1206026607353168,0.33589888974715454,1.271037754290168,1.1401040149722372,0.4708410731123211
+GSM6559873,0.0,1.0966110214477331,0.64798216044389,0.6141050414397801,1.0966110214477331,1.0966110214477331,0.3511096992134109,1.3036276174502939,1.249694151953387,0.53843849958782
+GSM6559874,0.0,1.2082524510891302,0.59782044193749,0.6732172304827967,1.2082524510891302,1.2082524510891302,0.3402201706009718,1.232621089257346,1.1947633542124798,0.522373990928529
+GSM6559875,0.0,1.146631717605421,0.6980778066334371,0.6140246043972349,1.146631717605421,1.146631717605421,0.35670060941952636,1.334845571682868,1.1649196841825775,0.49950374155404
+GSM6559876,0.0,1.068551784321651,0.5994180907786799,0.6548688427753017,1.068551784321651,1.068551784321651,0.33035791689784816,1.273817297683022,1.2064639710090823,0.4842082029308055
+GSM6559877,0.0,1.136727467148544,0.6001120131915058,0.5433984421734717,1.136727467148544,1.136727467148544,0.3320913265001055,1.28799783967732,1.1893347308410123,0.47074645719538893
+GSM6559878,0.0,1.3175844114601143,0.8340010446767343,0.575990096638565,1.3175844114601143,1.3175844114601143,0.48389280124870454,1.4070937590902841,1.1550544612359706,0.5680956630243199
+GSM6559879,0.0,1.0525081845692392,0.6003752556380844,0.62471719358224,1.0525081845692392,1.0525081845692392,0.3438022934408227,1.331580933631244,1.2192540166595252,0.46385673300128666
+GSM6559880,0.0,1.1171987638891467,0.5766758710546328,0.6587150404004684,1.1171987638891467,1.1171987638891467,0.35104554770231183,1.2189101844676,1.2050380633177098,0.4884565876381967
+GSM6559881,0.0,1.0776246807890755,0.5819408071503458,0.582624999043275,1.0776246807890755,1.0776246807890755,0.3216119057455209,1.272357704684052,1.1603065315703716,0.4889181228505744
+GSM6559882,0.0,1.0894993355065463,0.6260123919485443,0.60260798915562,1.0894993355065463,1.0894993355065463,0.33647511668756364,1.282057934589548,1.1709060117054608,0.5008064277493578
+GSM6559883,0.0,1.175353490896548,0.6838697447053629,0.6538013459474666,1.175353490896548,1.175353490896548,0.33983760132389274,1.352194351831548,1.190754275984514,0.5714131165380089
+GSM6559884,0.0,1.0869336519246218,0.5904579071317186,0.5966763290599583,1.0869336519246218,1.0869336519246218,0.3466932185856791,1.2343640636915398,1.1533243460051519,0.45613363470248774
+GSM6559885,0.0,1.1105685343699792,0.6326186465108986,0.5838543824103116,1.1105685343699792,1.1105685343699792,0.34378452610135457,1.2534564297323239,1.183699306543349,0.50810453137386
+GSM6559886,0.0,1.104622535641604,0.6199049857362586,0.6514180060390783,1.104622535641604,1.104622535641604,0.3197100193941027,1.199174304056372,1.1404443442805046,0.5349939098605789
+GSM6559887,0.0,1.1546919578521047,0.6297551638416258,0.71763556690426,1.1546919578521047,1.1546919578521047,0.3628524460345118,1.269057832015596,1.2236148677257535,0.52614784664389
+GSM6559888,0.0,1.1307677440459318,0.6114385618649271,0.737010097307965,1.1307677440459318,1.1307677440459318,0.35162270541481544,1.310302563424628,1.1635644241765497,0.4729913945599744
+GSM6559889,0.0,1.0691373576999978,0.6034241639312529,0.61996072197477,1.0691373576999978,1.0691373576999978,0.32793584078387,1.242620651722474,1.1148602909117797,0.46611814150812
+GSM6559890,0.0,1.0409286581365083,0.5975241149663371,0.5595051683363,1.0409286581365083,1.0409286581365083,0.3225922055179891,1.220069564507892,1.0939912758015047,0.4523664423874444
+GSM6559891,0.0,1.1035219048775122,0.5948676755418086,0.5338207998305516,1.1035219048775122,1.1035219048775122,0.32444196327948455,1.335491516335018,1.1075829963313444,0.47590942287467886
+GSM6559892,0.0,1.0788523759358,0.6470300503708043,0.5988398752212533,1.0788523759358,1.0788523759358,0.3763849237008245,1.3767533585776521,1.2386277946757365,0.5210021055284778
+GSM6559893,0.0,1.0220088578626498,0.5730390009227758,0.6286513108545401,1.0220088578626498,1.0220088578626498,0.3375739234108091,1.2572047307704979,1.1638814101450308,0.45804232583262333
+GSM6559894,0.0,1.0750758689831912,0.6335410244085686,0.617654178862095,1.0750758689831912,1.0750758689831912,0.3493898885494409,1.16297370101064,1.162575212357994,0.4797522697588289
+GSM6559895,0.0,1.114935897772717,0.6262476596661429,0.7682223567130549,1.114935897772717,1.114935897772717,0.3761723261077737,1.324023398018876,1.245887455325073,0.51554576951321
+GSM6559896,0.0,1.0743344996117572,0.5806008271316943,0.6565772761216616,1.0743344996117572,1.0743344996117572,0.3408517170276427,1.237508303118096,1.1887521931853322,0.49976424281592
+GSM6559897,0.0,1.073537154839303,0.6390009538921886,0.610325160455495,1.073537154839303,1.073537154839303,0.37050437764683913,1.32792700114833,1.1757967935851883,0.49233244452996555
+GSM6559898,0.0,1.1783240163688284,0.61779515655815,0.635112652198995,1.1783240163688284,1.1783240163688284,0.3382037151817891,1.28792328696748,1.1493368422768266,0.4739493213470567
+GSM6559899,0.0,1.132802640889215,0.6609994143474271,0.61177636860216,1.132802640889215,1.132802640889215,0.30850052807402545,1.247710592553524,1.2120669395826535,0.5066912881490766
+GSM6559900,0.0,1.1227556389889324,0.5931169454524242,0.6439476181060083,1.1227556389889324,1.1227556389889324,0.33525120773901274,1.296046500468126,1.1629061972830623,0.5157670804438478
+GSM6559901,0.0,1.0909421382951099,0.5953081505238957,0.6514734911556016,1.0909421382951099,1.0909421382951099,0.3358096725539718,1.2494725738933299,1.2498333035538227,0.45986325009568446
+GSM6559902,0.0,1.1569975180067267,0.6692006890089571,0.7033367387237134,1.1569975180067267,1.1569975180067267,0.34236320608560183,1.259138995660866,1.2293517687998299,0.52778778971066
+GSM6559903,0.0,1.0780036321219437,0.6305342198997314,0.5881500875612834,1.0780036321219437,1.0780036321219437,0.3347035499124427,1.3577728151778061,1.2155522985128555,0.4867390304811511
+GSM6559904,0.0,1.0742215366241104,0.5933225789008529,0.5630266967992134,1.0742215366241104,1.0742215366241104,0.33950667185700184,1.302701557970324,1.18471667028542,0.47080405055950003
+GSM6559905,0.0,1.218899343456139,0.5898821390488814,0.6714962350289816,1.218899343456139,1.218899343456139,0.33553024768694456,1.2851344217332599,1.1812927167766794,0.53060699241846
+GSM6559906,0.0,1.0518694171462053,0.6615976927828457,0.657596368558615,1.0518694171462053,1.0518694171462053,0.37353887437055816,1.25135290880331,1.1866413935454119,0.5018229193816978
+GSM6559907,1.0,1.079491361989893,0.6214286283566671,0.5768746982769334,1.079491361989893,1.079491361989893,0.3255166615211282,1.303698826759474,1.1359506158735018,0.4532425953464389
+GSM6559908,1.0,1.0077327018482833,0.6473329426508058,0.5683961751276033,1.0077327018482833,1.0077327018482833,0.3825226481102954,1.298327676091406,1.1438738872670031,0.4855216677602345
+GSM6559909,1.0,1.1380697862456013,0.6554397322474514,0.6368557247120383,1.1380697862456013,1.1380697862456013,0.34454180880438817,1.271074442170156,1.203087378544422,0.49935302403205
+GSM6559910,1.0,1.0975767583902805,0.6139100643354942,0.5554644461479966,1.0975767583902805,1.0975767583902805,0.34251219519485454,1.262406071246522,1.1107894279109292,0.4453754143366278
+GSM6559911,1.0,1.0527918650054482,0.6220254820730672,0.5860462837740666,1.0527918650054482,1.0527918650054482,0.35122779738235094,1.276863229187726,1.1270399352003968,0.46240195347617774
+GSM6559912,1.0,1.1006803322981382,0.6126515448003529,0.6332913994594084,1.1006803322981382,1.1006803322981382,0.3315375938786482,1.277217398365752,1.1892947723349607,0.47350160819566
+GSM6559913,1.0,1.0700069065367033,0.62178444337151,0.6859002519940499,1.0700069065367033,1.0700069065367033,0.3447441303765473,1.312024740256372,1.2167351293893005,0.4926579888902744
+GSM6559914,1.0,1.0790256827460125,0.6671746005024086,0.6093273764825117,1.0790256827460125,1.0790256827460125,0.37989933383908453,1.263060482245648,1.127316767910882,0.42788869275814
+GSM6559915,1.0,1.0622395718033468,0.6037974794388943,0.6205909622415767,1.0622395718033468,1.0622395718033468,0.3430849247064227,1.272840064997178,1.2068849318825592,0.4655950157407611
+GSM6559916,1.0,1.0635877488922234,0.6100458311339242,0.659347227151995,1.0635877488922234,1.0635877488922234,0.33767185983782816,1.255648571030916,1.185909903500872,0.49183610752732665
+GSM6559917,1.0,1.109914671726739,0.5991780502631715,0.6252434052936283,1.109914671726739,1.109914671726739,0.3246875463153645,1.355606354759028,1.0325293090630172,0.43487930562620997
+GSM6559918,1.0,1.0491783509056392,0.611948879377113,0.59273108095204,1.0491783509056392,1.0491783509056392,0.3256292595320727,1.277818460042446,1.097431639421046,0.4682068376081433
+GSM6559919,1.0,1.1353637638144407,0.5755296397867243,0.58404109478945,1.1353637638144407,1.1353637638144407,0.31754935396163636,1.28546186686784,1.1417485001084386,0.4613120212248889
+GSM6559920,1.0,1.156961963649681,0.6272826190986001,0.649560562272415,1.156961963649681,1.156961963649681,0.33533438607726457,1.312781868477582,1.1537592624543096,0.47797785706131
+GSM6559921,1.0,1.0463662546122396,0.59021002321731,0.5282081394888767,1.0463662546122396,1.0463662546122396,0.33446110042624727,1.29185116931727,1.1073552118987793,0.46189726416818
+GSM6559922,1.0,1.037034587781605,0.6117199571216471,0.5407793841101917,1.037034587781605,1.037034587781605,0.3582978337985873,1.2890967654846999,1.1263310910182271,0.49151992064565114
+GSM6559923,1.0,1.1355367969269914,0.6583963546237314,0.5774498117311401,1.1355367969269914,1.1355367969269914,0.34784148825914546,1.275832028884028,1.073767711154603,0.4690631044363022
+GSM6559924,1.0,1.1186500855391484,0.5966456367463314,0.6308048566822784,1.1186500855391484,1.1186500855391484,0.3342032084882136,1.264028471905056,1.1169343649149481,0.47600081721583226
+GSM6559925,1.0,1.100910927713396,0.5810866941295443,0.6221954601665333,1.100910927713396,1.100910927713396,0.32350728106038545,1.2765458634809002,1.1656883501513176,0.4644769416248367
+GSM6559926,1.0,1.0838695040488677,0.5937657168814529,0.59538720605548,1.0838695040488677,1.0838695040488677,0.34595215944796454,1.255523442858186,1.110057767920056,0.4529446588296022
+GSM6559927,1.0,1.0915849013207648,0.6615402104599257,0.6087201864112983,1.0915849013207648,1.0915849013207648,0.33963439009467095,1.264635894324906,1.102304309231485,0.4704609435934711
+GSM6559928,1.0,1.0110959886856974,0.6167486329390485,0.564619142938665,1.0110959886856974,1.0110959886856974,0.37582801775821184,1.326752159179894,1.1452530788456108,0.4429183324073811
+GSM6559929,1.0,1.0687377397242501,0.6199938890226672,0.6427277633905867,1.0687377397242501,1.0687377397242501,0.33993134899364275,1.3186558528847099,1.1538507593759804,0.44557532997989774
+GSM6559930,1.0,1.105477681291291,0.5834717671073028,0.6098509893348083,1.105477681291291,1.105477681291291,0.34151169220311817,1.236503799299786,1.1365700053151766,0.45978793590986444
+GSM6559931,1.0,1.0702491732531194,0.57703965836488,0.628484466972445,1.0702491732531194,1.0702491732531194,0.33929534907612546,1.2066558590168681,1.1528809897958099,0.47702793854611997
+GSM6559932,1.0,1.0609398112303747,0.58594347486959,0.5475520007226433,1.0609398112303747,1.0609398112303747,0.3384291011448073,1.3022698360710598,1.0789875017305923,0.4672983707626089
+GSM6559933,1.0,1.0849920537179933,0.6177170432652772,0.5767867373900584,1.0849920537179933,1.0849920537179933,0.34014022224475,1.272785246563656,1.0832460938635269,0.4564976736942545
+GSM6559934,1.0,1.0499812324450561,0.6153978442459557,0.5900335682559633,1.0499812324450561,1.0499812324450561,0.34026588894463816,1.290868550692,1.0757569220011958,0.49107251629453774
+GSM6559935,1.0,1.1634616074574657,0.6434053660979872,0.66284056555464,1.1634616074574657,1.1634616074574657,0.3265398893246609,1.25979472867005,1.135747842104374,0.4712343154542611
+GSM6559936,1.0,1.0701594570902253,0.6444028226082885,0.6099577759281133,1.0701594570902253,1.0701594570902253,0.33957480929572,1.224919631503356,1.1687629226236835,0.45524766332518785
+GSM6559937,1.0,1.0578186172828683,0.6571015791589714,0.5679410403509467,1.0578186172828683,1.0578186172828683,0.35158279088997185,1.3031402246550319,1.2483004998882838,0.48566060854391996
+GSM6559938,1.0,1.0550434696831401,0.5940580428449358,0.6062391344545683,1.0550434696831401,1.0550434696831401,0.3290036336060573,1.305110463159682,1.202313360228181,0.48861737599320776
+GSM6559939,1.0,1.1449264277302584,0.6216793200901628,0.6043795302427567,1.1449264277302584,1.1449264277302584,0.34583142076008,1.19763912918488,1.1022435729550315,0.44982778827496894
+GSM6559940,1.0,1.1255541111272498,0.5987684242920986,0.5672491984130617,1.1255541111272498,1.1255541111272498,0.34303241860340905,1.299002319025756,1.136366004532918,0.48136382651768334
+GSM6559941,1.0,1.1493690338059397,0.7322590841449129,0.626402455634275,1.1493690338059397,1.1493690338059397,0.3766206680097618,1.230942615060394,1.1618711765533825,0.4558383735913044
+GSM6559942,1.0,1.0555029444616126,0.6499215765896471,0.5715287780972117,1.0555029444616126,1.0555029444616126,0.3454686731971327,1.256991698451338,1.0709120128634604,0.43504638428318776
+GSM6559943,1.0,1.0749177658439477,0.6182130326502543,0.5885116113541867,1.0749177658439477,1.0749177658439477,0.3363780217678091,1.263381826882412,1.1084892058618336,0.48861841400465444
+GSM6559944,1.0,1.0715058920863298,0.6441693592985428,0.6458440755131251,1.0715058920863298,1.0715058920863298,0.35344163118450816,1.271590994541906,1.0824010656665337,0.4904109360203678
+GSM6559945,1.0,1.0735759914006184,0.6039651919859386,0.6895549920011751,1.0735759914006184,1.0735759914006184,0.31654139217522365,1.2357652638949141,1.1111967762261525,0.4890100783073889
+GSM6559946,1.0,1.0994095307944316,0.5668595698120071,0.595996235692005,1.0994095307944316,1.0994095307944316,0.36852314866380365,1.19644222941384,1.2053857801323562,0.4469855780156177
+GSM6559947,1.0,1.1356187109146083,0.6607614670593442,0.6057442708393783,1.1356187109146083,1.1356187109146083,0.33423834463908636,1.3242015380891279,1.1853296474866322,0.4774897610762177
+GSM6559948,1.0,1.1724131086110878,0.6449116714920743,0.5841916620039284,1.1724131086110878,1.1724131086110878,0.35991622848452,1.345444444291814,1.1328860819353244,0.45774991527254333
+GSM6559949,1.0,1.034332624874326,0.5943909043508342,0.5831829023539817,1.034332624874326,1.034332624874326,0.33083819605713183,1.208274588302108,1.078260738702045,0.44112093262105556
+GSM6559950,1.0,1.1221671323082751,0.59151696394858,0.5783071683501083,1.1221671323082751,1.1221671323082751,0.36623291411849457,1.340114944409252,1.158246763826969,0.47256363595428447
+GSM6559951,1.0,1.0166738800289805,0.5863443397581871,0.5868875394263583,1.0166738800289805,1.0166738800289805,0.3261298686661609,1.31883847153491,1.0711824808865429,0.48557763528281217
+GSM6559952,1.0,1.1066500463758246,0.6254128352889857,0.6128201724512317,1.1066500463758246,1.1066500463758246,0.3174216040580082,1.225509551758252,1.1781817263485532,0.5076215492503433
+GSM6559953,1.0,1.0784178753143727,0.5961468496556886,0.606478857436835,1.0784178753143727,1.0784178753143727,0.3367001561679027,1.290631420872948,1.1270474771575059,0.43514847031616555
+GSM6559954,1.0,1.0785809751161357,0.5726817726229301,0.6057189875372534,1.0785809751161357,1.0785809751161357,0.35309461590485636,1.301650331550574,1.1985584835499346,0.4486209472785256
+GSM6559955,1.0,1.072374153523834,0.6097155475233714,0.699641333187385,1.072374153523834,1.072374153523834,0.34160731587503457,1.257974515927796,1.2045795774467538,0.4669835095427467
+GSM6559956,1.0,1.1113647894395045,0.6048263919632028,0.6646146599074566,1.1113647894395045,1.1113647894395045,0.32766444705072273,1.227423345656348,1.1177199363488675,0.43945525769783667
+GSM6559957,1.0,1.1899478315684715,0.6456788912130514,0.6696943580107916,1.1899478315684715,1.1899478315684715,0.34807801826689,1.249794672693844,1.2285254480966905,0.4784590342187289
+GSM6559958,1.0,1.0688047093428448,0.60339383037603,0.6407332021509683,1.0688047093428448,1.0688047093428448,0.3402388705415209,1.24919996901667,1.1954103787421695,0.4526134604568422
+GSM6559959,1.0,1.158082276079608,0.6240605259191871,0.5722983966225966,1.158082276079608,1.158082276079608,0.35819864148695546,1.183491071228874,1.090696354984683,0.4610368108746322
+GSM6559960,1.0,1.02322227500547,0.5839146163146671,0.63691569624168,1.02322227500547,1.02322227500547,0.32752025751819996,1.2983217325088021,1.1150062140879504,0.4664608195102822
+GSM6559961,1.0,1.0845615466645373,0.587759355175,0.5934657581327251,1.0845615466645373,1.0845615466645373,0.32781148969589186,1.27255534991739,1.1261819778662265,0.4611918884372845
+GSM6559962,1.0,1.1562327022891816,0.6166583795197543,0.5915280380841184,1.1562327022891816,1.1562327022891816,0.3432652052095182,1.26570727299023,1.0709652448604794,0.4815856027874344
+GSM6559963,1.0,1.188120543565541,0.6627585975435828,0.6601627655661501,1.188120543565541,1.188120543565541,0.42447864940037816,1.346525518140048,1.1235361594537339,0.4657392810911867
+GSM6559964,1.0,1.0334495874166074,0.6138805278571885,0.5897540188870033,1.0334495874166074,1.0334495874166074,0.32294648271096543,1.269709224438084,1.1422853227781062,0.47491597206149777
+GSM6559965,1.0,1.065696049916902,0.6229056884683671,0.6075127814570184,1.065696049916902,1.065696049916902,0.32980770966841455,1.33921144425093,1.1685347547678018,0.4861164154757022
+GSM6559966,1.0,1.1229704443709576,0.6499948972945042,0.5792216573739116,1.1229704443709576,1.1229704443709576,0.40392650486626097,1.20848955366696,1.1421605547574503,0.4441234888705489
+GSM6559967,1.0,1.044473217775162,0.61544186066775,0.5591518061106117,1.044473217775162,1.044473217775162,0.3571418500226991,1.244336784233016,1.1733889980178398,0.49845167717153444
+GSM6559968,1.0,1.1139720066021652,0.5815661040256558,0.6229524421334066,1.1139720066021652,1.1139720066021652,0.34241542316362633,1.185564982950004,1.16751194238027,0.48446992422528556
+GSM6559969,1.0,1.1385120369403756,0.6153920550890942,0.6103797941882533,1.1385120369403756,1.1385120369403756,0.33463079933248907,1.251857701242298,1.1446670081321548,0.4834292083545067
+GSM6559970,1.0,1.0140444932276427,0.63651881466403,0.5639662838054333,1.0140444932276427,1.0140444932276427,0.3275209257545073,1.333942270870006,1.1459350313120642,0.4555830095703988
+GSM6559971,1.0,1.0595318107899387,0.6052391950132614,0.6679713579371517,1.0595318107899387,1.0595318107899387,0.33535407856849453,1.26722774064735,1.1828409108923967,0.46920786386817337
+GSM6559972,1.0,1.061925559346,0.5590595411769229,0.5787107044714983,1.061925559346,1.061925559346,0.34500651705251545,1.193822967709354,1.1046848625399481,0.5042914880820278
+GSM6559973,1.0,1.0628125845663723,0.6086376892414244,0.5310671684214917,1.0628125845663723,1.0628125845663723,0.33681994761342726,1.293152075672096,1.045166608933209,0.4423374601331278
+GSM6559974,1.0,0.9922588937036705,0.56908236905456,0.495054692177105,0.9922588937036705,0.9922588937036705,0.3141902958402,1.2386233921913639,1.1171124514015074,0.48891901489364337
+GSM6559975,1.0,1.0231461185629624,0.5848914550166943,0.6772742341858167,1.0231461185629624,1.0231461185629624,0.3308801874880482,1.235134063529952,1.1224928478853122,0.45983947433592554
+GSM6559976,1.0,1.0326216906828052,0.5831713603539743,0.5863355182794533,1.0326216906828052,1.0326216906828052,0.3352917026819664,1.224951552387422,1.094523540439178,0.45247464637514667
+GSM6559977,1.0,1.1035817771693743,0.6042494180708328,0.6359753628959567,1.1035817771693743,1.1035817771693743,0.3529275273648409,1.309004894426816,1.1846058234612427,0.47056467359592447
+GSM6559978,1.0,1.017411196211424,0.6082667804314571,0.5809282465241417,1.017411196211424,1.017411196211424,0.33622872270257637,1.297338937966828,1.1571003501462402,0.4665788836071622
+GSM6559979,1.0,1.0844231630370482,0.6289244018791372,0.5882323346279283,1.0844231630370482,1.0844231630370482,0.33306116952716364,1.317865740507104,1.1235204398313101,0.46483000556878334
+GSM6559980,1.0,1.0809385797653985,0.5869610007715328,0.5284715296404933,1.0809385797653985,1.0809385797653985,0.33324065664331365,1.260143824645276,1.143151494050339,0.4347221477961667
+GSM6559981,1.0,1.1408480726649248,0.60428954490742,0.5923071679503683,1.1408480726649248,1.1408480726649248,0.36188313872630545,1.22317528788668,1.1776071672430237,0.45086510946831776
+GSM6559982,1.0,1.0609763528206857,0.5941718360279186,0.644955658262205,1.0609763528206857,1.0609763528206857,0.3351166951995218,1.2652200153053719,1.0762850948856064,0.45530449387711003
+GSM6559983,1.0,1.0921805720654811,0.6372521893347314,0.5986361188735834,1.0921805720654811,1.0921805720654811,0.3555559038883127,1.36255142825451,1.1732740797832664,0.4997817162000345
+GSM6559984,1.0,1.0868079350852513,0.6477984370577742,0.631972342956085,1.0868079350852513,1.0868079350852513,0.3611497493970127,1.190695132966832,1.1265875983796987,0.4568510992047156
+GSM6559985,1.0,1.0488409969573749,0.6648171037701914,0.6169324032217933,1.0488409969573749,1.0488409969573749,0.3343403271996064,1.303454937696362,1.2079220964535464,0.4656915565058655
+GSM6559986,1.0,1.0742715712404616,0.6128063924195101,0.57621904417,1.0742715712404616,1.0742715712404616,0.36687399976017177,1.288639146539132,1.1977733008262454,0.5031732430585867
+GSM6559987,1.0,1.0684641904036345,0.6145076885256371,0.58677574508249,1.0684641904036345,1.0684641904036345,0.37538348131680727,1.236220619764676,1.1189929119762096,0.4443492778539111
+GSM6559988,1.0,1.0849551196550453,0.6322322013678914,0.6081574075430833,1.0849551196550453,1.0849551196550453,0.32969111210294366,1.308419058829108,1.0702280795891435,0.45367775735188
+GSM6559989,1.0,1.003843083375966,0.6129353697413772,0.5882955835035183,1.003843083375966,1.003843083375966,0.34161445591024636,1.354739269945856,1.1602000235422882,0.5193939518587412
+GSM6559990,1.0,1.0985086284888943,0.6712064852884986,0.6033296664264217,1.0985086284888943,1.0985086284888943,0.34638386339889726,1.31656155056224,1.2182730701181,0.48011359936258446
+GSM6559991,1.0,1.0841663825352663,0.5979505507283658,0.58964947102986,1.0841663825352663,1.0841663825352663,0.35406093289884455,1.306310234107718,1.0913523817565018,0.47358763604882004
+GSM6559992,1.0,1.0952935712796121,0.6031101886052286,0.551104990516645,1.0952935712796121,1.0952935712796121,0.34609877187417726,1.25888478826543,1.157522157467585,0.48261868273508224

p3/preprocess/COVID-19/GSE243348.csv

@@ -0,0 +1,4 @@
+,GSM7783810,GSM7783811,GSM7783812,GSM7783813,GSM7783814,GSM7783815,GSM7783816,GSM7783817,GSM7783818,GSM7783819,GSM7783820,GSM7783821,GSM7783822,GSM7783823,GSM7783824,GSM7783825,GSM7783826,GSM7783827,GSM7783828,GSM7783829,GSM7783830,GSM7783831,GSM7783832,GSM7783833,GSM7783834,GSM7783835,GSM7783836,GSM7783837,GSM7783838,GSM7783839,GSM7783840,GSM7783841,GSM7783842,GSM7783843,GSM7783844,GSM7783845,GSM7783846,GSM7783847,GSM7783848,GSM7783849,GSM7783850,GSM7783851,GSM7783852,GSM7783853,GSM7783854,GSM7783855,GSM7783856,GSM7783857,GSM7783858,GSM7783859,GSM7783860,GSM7783861,GSM7783862,GSM7783863,GSM7783864,GSM7783865,GSM7783866,GSM7783867,GSM7783868,GSM7783869,GSM7783870,GSM7783871,GSM7783872,GSM7783873,GSM7783874,GSM7783875,GSM7783876,GSM7783877,GSM7783878,GSM7783879,GSM7783880,GSM7783881,GSM7783882,GSM7783883,GSM7783884,GSM7783885,GSM7783886,GSM7783887,GSM7783888,GSM7783889,GSM7783890,GSM7783891,GSM7783892,GSM7783893,GSM7783894,GSM7783895,GSM7783896,GSM7783897,GSM7783898,GSM7783899,GSM7783900,GSM7783901,GSM7783902,GSM7783903,GSM7783904,GSM7783905,GSM7783906,GSM7783907,GSM7783908,GSM7783909,GSM7783910,GSM7783911,GSM7783912,GSM7783913,GSM7783914,GSM7783915,GSM7783916,GSM7783917,GSM7783918,GSM7783919,GSM7783920,GSM7783921,GSM7783922,GSM7783923,GSM7783924,GSM7783925,GSM7783926,GSM7783927,GSM7783928,GSM7783929,GSM7783930,GSM7783931,GSM7783932,GSM7783933,GSM7783934,GSM7783935,GSM7783936,GSM7783937,GSM7783938,GSM7783939,GSM7783940,GSM7783941,GSM7783942,GSM7783943,GSM7783944,GSM7783945,GSM7783946,GSM7783947,GSM7783948,GSM7783949,GSM7783950,GSM7783951,GSM7783952,GSM7783953,GSM7783954,GSM7783955,GSM7783956,GSM7783957,GSM7783958,GSM7783959,GSM7783960,GSM7783961,GSM7783962,GSM7783963,GSM7783964,GSM7783965,GSM7783966,GSM7783967,GSM7783968,GSM7783969,GSM7783970,GSM7783971,GSM7783972,GSM7783973,GSM7783974,GSM7783975,GSM7783976,GSM7783977,GSM7783978,GSM7783979,GSM7783980,GSM7783981,GSM7783982,GSM7783983,GSM7783984,GSM7783985,GSM7783986,GSM7783987,GSM7783988,GSM7783989,GSM7783990,GSM7783991,GSM7783992,GSM7783993,GSM7783994,GSM7783995,GSM7783996,GSM7783997,GSM7783998,GSM7783999,GSM7784000,GSM7784001,GSM7784002,GSM7784003,GSM7784004,GSM7784005,GSM7784006,GSM7784007,GSM7784008,GSM7784009,GSM7784010,GSM7784011,GSM7784012,GSM7784013,GSM7784014,GSM7784015,GSM7784016,GSM7784017,GSM7784018,GSM7784019,GSM7784020,GSM7784021,GSM7784022,GSM7784023,GSM7784024,GSM7784025,GSM7784026,GSM7784027,GSM7784028,GSM7784029,GSM7784030,GSM7784031,GSM7784032,GSM7784033,GSM7784034,GSM7784035,GSM7784036,GSM7784037,GSM7784038,GSM7784039,GSM7784040,GSM7784041,GSM7784042,GSM7784043,GSM7784044,GSM7784045,GSM7784046
+COVID-19,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Age,44.0,44.0,44.0,44.0,44.0,44.0,44.0,29.0,29.0,29.0,29.0,29.0,29.0,51.0,51.0,51.0,51.0,51.0,51.0,51.0,32.0,32.0,32.0,32.0,32.0,32.0,32.0,32.0,32.0,32.0,32.0,32.0,32.0,32.0,32.0,32.0,32.0,32.0,32.0,32.0,32.0,29.0,29.0,29.0,29.0,29.0,29.0,29.0,27.0,27.0,27.0,27.0,27.0,27.0,27.0,30.0,30.0,30.0,30.0,30.0,30.0,30.0,27.0,27.0,27.0,27.0,27.0,27.0,27.0,30.0,30.0,30.0,30.0,30.0,30.0,30.0,41.0,41.0,41.0,41.0,41.0,41.0,41.0,43.0,43.0,43.0,43.0,43.0,43.0,43.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,60.0,60.0,60.0,60.0,60.0,60.0,60.0,30.0,30.0,30.0,30.0,30.0,60.0,60.0,60.0,60.0,60.0,60.0,24.0,24.0,24.0,24.0,24.0,24.0,30.0,30.0,30.0,30.0,30.0,30.0,30.0,36.0,36.0,36.0,36.0,36.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,33.0,33.0,33.0,33.0,33.0,33.0,33.0,24.0,24.0,24.0,24.0,24.0,24.0,24.0,53.0,53.0,53.0,53.0,53.0,53.0,53.0,31.0,31.0,31.0,31.0,31.0,31.0,31.0,59.0,59.0,59.0,59.0,59.0,59.0,59.0,40.0,40.0,40.0,40.0,40.0,40.0,40.0,65.0,65.0,65.0,65.0,37.0,37.0,37.0,37.0,37.0,37.0,37.0,39.0,39.0,39.0,39.0,39.0,39.0,39.0,58.0,58.0,58.0,58.0,58.0,58.0,58.0,51.0,51.0,51.0,51.0,51.0,39.0,39.0,39.0,39.0,39.0,39.0,39.0,42.0,42.0,42.0,42.0,42.0,40.0,40.0,40.0,40.0,40.0,40.0,40.0,36.0,24.0,28.0,36.0,27.0,38.0
+Gender,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0

p3/preprocess/COVID-19/clinical_data/GSE185658.csv

@@ -0,0 +1,2 @@
+,GSM5621296,GSM5621297,GSM5621298,GSM5621299,GSM5621300,GSM5621301,GSM5621302,GSM5621303,GSM5621304,GSM5621305,GSM5621306,GSM5621307,GSM5621308,GSM5621309,GSM5621310,GSM5621311,GSM5621312,GSM5621313,GSM5621314,GSM5621315,GSM5621316,GSM5621317,GSM5621318,GSM5621319,GSM5621320,GSM5621321,GSM5621322,GSM5621323,GSM5621324,GSM5621325,GSM5621326,GSM5621327,GSM5621328,GSM5621329,GSM5621330,GSM5621331,GSM5621332,GSM5621333,GSM5621334,GSM5621335,GSM5621336,GSM5621337,GSM5621338,GSM5621339,GSM5621340,GSM5621341,GSM5621342,GSM5621343
+COVID-19,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0

p3/preprocess/COVID-19/clinical_data/GSE211378.csv

@@ -0,0 +1,2 @@
+,GSM6467759,GSM6467760,GSM6467761,GSM6467762,GSM6467763,GSM6467764,GSM6467765,GSM6467766,GSM6467767,GSM6467768,GSM6467769,GSM6467770,GSM6467771,GSM6467772,GSM6467773,GSM6467774,GSM6467775,GSM6467776,GSM6467777,GSM6467778,GSM6467779,GSM6467780,GSM6467781,GSM6467782,GSM6467783,GSM6467784,GSM6467785,GSM6467786,GSM6467787,GSM6467788,GSM6467789,GSM6467790,GSM6467791,GSM6467792,GSM6467793,GSM6467794,GSM6467795,GSM6467796,GSM6467797,GSM6467798,GSM6467799,GSM6467800,GSM6467801,GSM6467802,GSM6467803,GSM6467804,GSM6467805,GSM6467806,GSM6467807,GSM6467808,GSM6467809,GSM6467810,GSM6467811,GSM6467812,GSM6467813,GSM6467814,GSM6467815,GSM6467816,GSM6467817,GSM6467818,GSM6467819,GSM6467820,GSM6467821,GSM6467822,GSM6467823,GSM6467824,GSM6467825,GSM6467826,GSM6467827,GSM6467828,GSM6467829,GSM6467830,GSM6467831,GSM6467832,GSM6467833,GSM6467834,GSM6467835,GSM6467836,GSM6467837,GSM6467838,GSM6467839,GSM6467840,GSM6467841,GSM6467842,GSM6467843,GSM6467844,GSM6467845,GSM6467846,GSM6467847,GSM6467848,GSM6467849,GSM6467850,GSM6467851,GSM6467852,GSM6467853,GSM6467854,GSM6467855,GSM6467856,GSM6467857,GSM6467858,GSM6467859,GSM6467860,GSM6467861,GSM6467862,GSM6467863,GSM6467864,GSM6467865,GSM6467866,GSM6467867,GSM6467868,GSM6467869,GSM6467870,GSM6467871,GSM6467872,GSM6467873,GSM6467874,GSM6467875,GSM6467876,GSM6467877,GSM6467878,GSM6467879,GSM6467880,GSM6467881,GSM6467882,GSM6467883,GSM6467884,GSM6467885,GSM6467886,GSM6467887,GSM6467888,GSM6467889,GSM6467890,GSM6467891,GSM6467892,GSM6467893,GSM6467894,GSM6467895,GSM6467896,GSM6467897,GSM6467898,GSM6467899,GSM6467900,GSM6467901,GSM6467902,GSM6467903,GSM6467904,GSM6467905,GSM6467906,GSM6467907,GSM6467908,GSM6467909,GSM6467910,GSM6467911,GSM6467912,GSM6467913,GSM6467914,GSM6467915,GSM6467916,GSM6467917,GSM6467918,GSM6467919,GSM6467920,GSM6467921,GSM6467922,GSM6467923,GSM6467924,GSM6467925,GSM6467926,GSM6467927,GSM6467928,GSM6467929,GSM6467930,GSM6467931,GSM6467932,GSM6467933,GSM6467934,GSM6467935,GSM6467936,GSM6467937,GSM6467938,GSM6467939,GSM6467940,GSM6467941,GSM6467942,GSM6467943,GSM6467944,GSM6467945,GSM6467946,GSM6467947,GSM6467948,GSM6467949,GSM6467950,GSM6467951,GSM6467952,GSM6467953,GSM6467954,GSM6467955,GSM6467956,GSM6467957,GSM6467958,GSM6467959,GSM6467960,GSM6467961,GSM6467962,GSM6467963,GSM6467964,GSM6467965,GSM6467966,GSM6467967,GSM6467968,GSM6467969,GSM6467970,GSM6467971,GSM6467972,GSM6467973,GSM6467974,GSM6467975,GSM6467976,GSM6467977,GSM6467978,GSM6467979,GSM6467980,GSM6467981,GSM6467982,GSM6467983,GSM6467984,GSM6467985,GSM6467986,GSM6467987,GSM6467988,GSM6467989,GSM6467990,GSM6467991,GSM6467992,GSM6467993,GSM6467994,GSM6467995,GSM6467996,GSM6467997,GSM6467998,GSM6467999,GSM6468000,GSM6468001,GSM6468002,GSM6468003,GSM6468004,GSM6468005,GSM6468006,GSM6468007,GSM6468008,GSM6468009,GSM6468010,GSM6468011,GSM6468012,GSM6468013,GSM6468014,GSM6468015,GSM6468016,GSM6468017,GSM6468018,GSM6468019,GSM6468020,GSM6468021,GSM6468022,GSM6468023,GSM6468024,GSM6468025,GSM6468026,GSM6468027,GSM6468028,GSM6468029,GSM6468030,GSM6468031,GSM6468032,GSM6468033,GSM6468034,GSM6468035,GSM6468036,GSM6468037,GSM6468038,GSM6468039,GSM6468040,GSM6468041,GSM6468042,GSM6468043,GSM6468044,GSM6468045,GSM6468046,GSM6468047,GSM6468048,GSM6468049,GSM6468050,GSM6468051,GSM6468052,GSM6468053,GSM6468054,GSM6468055,GSM6468056,GSM6468057,GSM6468058,GSM6468059,GSM6468060,GSM6468061,GSM6468062
+COVID-19,0.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0

p3/preprocess/COVID-19/clinical_data/GSE212865.csv

@@ -0,0 +1,2 @@
+,GSM6559856,GSM6559857,GSM6559858,GSM6559859,GSM6559860,GSM6559861,GSM6559862,GSM6559863,GSM6559864,GSM6559865,GSM6559866,GSM6559867,GSM6559868,GSM6559869,GSM6559870,GSM6559871,GSM6559872,GSM6559873,GSM6559874,GSM6559875,GSM6559876,GSM6559877,GSM6559878,GSM6559879,GSM6559880,GSM6559881,GSM6559882,GSM6559883,GSM6559884,GSM6559885,GSM6559886,GSM6559887,GSM6559888,GSM6559889,GSM6559890,GSM6559891,GSM6559892,GSM6559893,GSM6559894,GSM6559895,GSM6559896,GSM6559897,GSM6559898,GSM6559899,GSM6559900,GSM6559901,GSM6559902,GSM6559903,GSM6559904,GSM6559905,GSM6559906,GSM6559907,GSM6559908,GSM6559909,GSM6559910,GSM6559911,GSM6559912,GSM6559913,GSM6559914,GSM6559915,GSM6559916,GSM6559917,GSM6559918,GSM6559919,GSM6559920,GSM6559921,GSM6559922,GSM6559923,GSM6559924,GSM6559925,GSM6559926,GSM6559927,GSM6559928,GSM6559929,GSM6559930,GSM6559931,GSM6559932,GSM6559933,GSM6559934,GSM6559935,GSM6559936,GSM6559937,GSM6559938,GSM6559939,GSM6559940,GSM6559941,GSM6559942,GSM6559943,GSM6559944,GSM6559945,GSM6559946,GSM6559947,GSM6559948,GSM6559949,GSM6559950,GSM6559951,GSM6559952,GSM6559953,GSM6559954,GSM6559955,GSM6559956,GSM6559957,GSM6559958,GSM6559959,GSM6559960,GSM6559961,GSM6559962,GSM6559963,GSM6559964,GSM6559965,GSM6559966,GSM6559967,GSM6559968,GSM6559969,GSM6559970,GSM6559971,GSM6559972,GSM6559973,GSM6559974,GSM6559975,GSM6559976,GSM6559977,GSM6559978,GSM6559979,GSM6559980,GSM6559981,GSM6559982,GSM6559983,GSM6559984,GSM6559985,GSM6559986,GSM6559987,GSM6559988,GSM6559989,GSM6559990,GSM6559991,GSM6559992
+COVID-19,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

p3/preprocess/COVID-19/clinical_data/GSE212866.csv

@@ -0,0 +1,2 @@
+,GSM6559856,GSM6559857,GSM6559858,GSM6559859,GSM6559860,GSM6559861,GSM6559862,GSM6559863,GSM6559864,GSM6559865,GSM6559866,GSM6559867,GSM6559868,GSM6559869,GSM6559870,GSM6559871,GSM6559872,GSM6559873,GSM6559874,GSM6559875,GSM6559876,GSM6559877,GSM6559878,GSM6559879,GSM6559880,GSM6559881,GSM6559882,GSM6559883,GSM6559884,GSM6559885,GSM6559886,GSM6559887,GSM6559888,GSM6559889,GSM6559890,GSM6559891,GSM6559892,GSM6559893,GSM6559894,GSM6559895,GSM6559896,GSM6559897,GSM6559898,GSM6559899,GSM6559900,GSM6559901,GSM6559902,GSM6559903,GSM6559904,GSM6559905,GSM6559906,GSM6559907,GSM6559908,GSM6559909,GSM6559910,GSM6559911,GSM6559912,GSM6559913,GSM6559914,GSM6559915,GSM6559916,GSM6559917,GSM6559918,GSM6559919,GSM6559920,GSM6559921,GSM6559922,GSM6559923,GSM6559924,GSM6559925,GSM6559926,GSM6559927,GSM6559928,GSM6559929,GSM6559930,GSM6559931,GSM6559932,GSM6559933,GSM6559934,GSM6559935,GSM6559936,GSM6559937,GSM6559938,GSM6559939,GSM6559940,GSM6559941,GSM6559942,GSM6559943,GSM6559944,GSM6559945,GSM6559946,GSM6559947,GSM6559948,GSM6559949,GSM6559950,GSM6559951,GSM6559952,GSM6559953,GSM6559954,GSM6559955,GSM6559956,GSM6559957,GSM6559958,GSM6559959,GSM6559960,GSM6559961,GSM6559962,GSM6559963,GSM6559964,GSM6559965,GSM6559966,GSM6559967,GSM6559968,GSM6559969,GSM6559970,GSM6559971,GSM6559972,GSM6559973,GSM6559974,GSM6559975,GSM6559976,GSM6559977,GSM6559978,GSM6559979,GSM6559980,GSM6559981,GSM6559982,GSM6559983,GSM6559984,GSM6559985,GSM6559986,GSM6559987,GSM6559988,GSM6559989,GSM6559990,GSM6559991,GSM6559992
+COVID-19,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

p3/preprocess/COVID-19/clinical_data/GSE213313.csv

@@ -0,0 +1,2 @@
+,GSM6578718,GSM6578719,GSM6578720,GSM6578721,GSM6578722,GSM6578723,GSM6578724,GSM6578725,GSM6578726,GSM6578727,GSM6578728,GSM6578729,GSM6578730,GSM6578731,GSM6578732,GSM6578733,GSM6578734,GSM6578735,GSM6578736,GSM6578737,GSM6578738,GSM6578739,GSM6578740,GSM6578741,GSM6578742,GSM6578743,GSM6578744,GSM6578745,GSM6578746,GSM6578747,GSM6578748,GSM6578749,GSM6578750,GSM6578751,GSM6578752,GSM6578753,GSM6578754,GSM6578755,GSM6578756,GSM6578757,GSM6578758,GSM6578759,GSM6578760,GSM6578761,GSM6578762,GSM6578763,GSM6578764,GSM6578765,GSM6578766,GSM6578767,GSM6578768,GSM6578769,GSM6578770,GSM6578771,GSM6578772,GSM6578773,GSM6578774,GSM6578775,GSM6578776,GSM6578777,GSM6578778,GSM6578779,GSM6578780,GSM6578781,GSM6578782,GSM6578783,GSM6578784,GSM6578785,GSM6578786,GSM6578787,GSM6578788,GSM6578789,GSM6578790,GSM6578791,GSM6578792,GSM6578793,GSM6578794,GSM6578795,GSM6578796,GSM6578797,GSM6578798,GSM6578799,GSM6578800,GSM6578801,GSM6578802,GSM6578803,GSM6578804,GSM6578805,GSM6578806,GSM6578807,GSM6578808,GSM6578809,GSM6578810,GSM6578811
+COVID-19,1.0,1.0,1.0,1.0,1.0,0.0,0.0,,1.0,1.0,1.0,1.0,0.0,0.0,1.0,,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,,1.0,0.0,1.0,1.0,0.0,0.0,,1.0,0.0,1.0,1.0,0.0,1.0,0.0,,0.0,0.0,0.0,0.0,0.0,1.0,,1.0,0.0,0.0,1.0,0.0,0.0,1.0,,1.0,1.0,1.0,1.0,0.0,1.0,1.0,,1.0,1.0,1.0,0.0,1.0,1.0,,1.0,0.0,0.0,1.0,0.0,1.0,1.0,,1.0,0.0,0.0,1.0,0.0,1.0,0.0,,1.0

p3/preprocess/COVID-19/clinical_data/GSE227080.csv

@@ -0,0 +1,4 @@
+,GSM7091202,GSM7091203,GSM7091204,GSM7091205,GSM7091206,GSM7091207,GSM7091208,GSM7091209,GSM7091210,GSM7091211,GSM7091212,GSM7091213,GSM7091214,GSM7091215,GSM7091216,GSM7091217,GSM7091218,GSM7091219,GSM7091220,GSM7091221,GSM7091222,GSM7091223,GSM7091224,GSM7091225,GSM7091226,GSM7091227,GSM7091228,GSM7091229,GSM7091230,GSM7091231,GSM7091232,GSM7091233,GSM7091234,GSM7091235,GSM7091236,GSM7091237,GSM7091238,GSM7091239,GSM7091240,GSM7091241,GSM7091242,GSM7091243,GSM7091244,GSM7091245,GSM7091246,GSM7091247,GSM7091248,GSM7091249,GSM7091250,GSM7091251,GSM7091252,GSM7091253,GSM7091254,GSM7091255,GSM7091256,GSM7091257,GSM7091258,GSM7091259,GSM7091260,GSM7091261,GSM7091262,GSM7091263,GSM7091264,GSM7091265,GSM7091266,GSM7091267,GSM7091268,GSM7091269,GSM7091270,GSM7091271,GSM7091272,GSM7091273,GSM7091274,GSM7091275,GSM7091276,GSM7091277,GSM7091278,GSM7091279,GSM7091280,GSM7091281,GSM7091282,GSM7091283,GSM7091284,GSM7091285,GSM7091286,GSM7091287,GSM7091288,GSM7091289,GSM7091290,GSM7091291,GSM7091292,GSM7091293,GSM7091294,GSM7091295,GSM7091296,GSM7091297,GSM7091298,GSM7091299,GSM7091300,GSM7091301,GSM7091302,GSM7091303,GSM7091304,GSM7091305,GSM7091306,GSM7091307,GSM7091308,GSM7091309,GSM7091310,GSM7091311,GSM7091312,GSM7091313,GSM7091314,GSM7091315,GSM7091316,GSM7091317,GSM7091318,GSM7091319,GSM7091320
+COVID-19,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0
+Age,38.0,66.0,21.0,29.0,73.0,35.0,48.0,70.0,69.0,31.0,72.0,41.0,85.0,85.0,85.0,69.0,48.0,79.0,46.0,57.0,87.0,52.0,36.0,69.0,77.0,82.0,89.0,94.0,54.0,77.0,23.0,61.0,82.0,75.0,85.0,25.0,43.0,69.0,24.0,55.0,76.0,94.0,86.0,71.0,73.0,85.0,23.0,28.0,54.0,61.0,88.0,67.0,42.0,55.0,47.0,80.0,80.0,56.0,41.0,70.0,60.0,45.0,63.0,68.0,88.0,93.0,26.0,67.0,45.0,64.0,73.0,53.0,66.0,52.0,81.0,77.0,63.0,41.0,58.0,75.0,40.0,49.0,35.0,70.0,64.0,69.0,58.0,47.0,89.0,23.0,74.0,87.0,89.0,60.0,67.0,51.0,90.0,59.0,50.0,64.0,92.0,72.0,49.0,48.0,45.0,61.0,39.0,30.0,58.0,91.0,61.0,43.0,66.0,75.0,24.0,56.0,66.0,45.0,50.0
+Gender,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0

p3/preprocess/COVID-19/clinical_data/GSE243348.csv

@@ -0,0 +1,4 @@
+,GSM7783810,GSM7783811,GSM7783812,GSM7783813,GSM7783814,GSM7783815,GSM7783816,GSM7783817,GSM7783818,GSM7783819,GSM7783820,GSM7783821,GSM7783822,GSM7783823,GSM7783824,GSM7783825,GSM7783826,GSM7783827,GSM7783828,GSM7783829,GSM7783830,GSM7783831,GSM7783832,GSM7783833,GSM7783834,GSM7783835,GSM7783836,GSM7783837,GSM7783838,GSM7783839,GSM7783840,GSM7783841,GSM7783842,GSM7783843,GSM7783844,GSM7783845,GSM7783846,GSM7783847,GSM7783848,GSM7783849,GSM7783850,GSM7783851,GSM7783852,GSM7783853,GSM7783854,GSM7783855,GSM7783856,GSM7783857,GSM7783858,GSM7783859,GSM7783860,GSM7783861,GSM7783862,GSM7783863,GSM7783864,GSM7783865,GSM7783866,GSM7783867,GSM7783868,GSM7783869,GSM7783870,GSM7783871,GSM7783872,GSM7783873,GSM7783874,GSM7783875,GSM7783876,GSM7783877,GSM7783878,GSM7783879,GSM7783880,GSM7783881,GSM7783882,GSM7783883,GSM7783884,GSM7783885,GSM7783886,GSM7783887,GSM7783888,GSM7783889,GSM7783890,GSM7783891,GSM7783892,GSM7783893,GSM7783894,GSM7783895,GSM7783896,GSM7783897,GSM7783898,GSM7783899,GSM7783900,GSM7783901,GSM7783902,GSM7783903,GSM7783904,GSM7783905,GSM7783906,GSM7783907,GSM7783908,GSM7783909,GSM7783910,GSM7783911,GSM7783912,GSM7783913,GSM7783914,GSM7783915,GSM7783916,GSM7783917,GSM7783918,GSM7783919,GSM7783920,GSM7783921,GSM7783922,GSM7783923,GSM7783924,GSM7783925,GSM7783926,GSM7783927,GSM7783928,GSM7783929,GSM7783930,GSM7783931,GSM7783932,GSM7783933,GSM7783934,GSM7783935,GSM7783936,GSM7783937,GSM7783938,GSM7783939,GSM7783940,GSM7783941,GSM7783942,GSM7783943,GSM7783944,GSM7783945,GSM7783946,GSM7783947,GSM7783948,GSM7783949,GSM7783950,GSM7783951,GSM7783952,GSM7783953,GSM7783954,GSM7783955,GSM7783956,GSM7783957,GSM7783958,GSM7783959,GSM7783960,GSM7783961,GSM7783962,GSM7783963,GSM7783964,GSM7783965,GSM7783966,GSM7783967,GSM7783968,GSM7783969,GSM7783970,GSM7783971,GSM7783972,GSM7783973,GSM7783974,GSM7783975,GSM7783976,GSM7783977,GSM7783978,GSM7783979,GSM7783980,GSM7783981,GSM7783982,GSM7783983,GSM7783984,GSM7783985,GSM7783986,GSM7783987,GSM7783988,GSM7783989,GSM7783990,GSM7783991,GSM7783992,GSM7783993,GSM7783994,GSM7783995,GSM7783996,GSM7783997,GSM7783998,GSM7783999,GSM7784000,GSM7784001,GSM7784002,GSM7784003,GSM7784004,GSM7784005,GSM7784006,GSM7784007,GSM7784008,GSM7784009,GSM7784010,GSM7784011,GSM7784012,GSM7784013,GSM7784014,GSM7784015,GSM7784016,GSM7784017,GSM7784018,GSM7784019,GSM7784020,GSM7784021,GSM7784022,GSM7784023,GSM7784024,GSM7784025,GSM7784026,GSM7784027,GSM7784028,GSM7784029,GSM7784030,GSM7784031,GSM7784032,GSM7784033,GSM7784034,GSM7784035,GSM7784036,GSM7784037,GSM7784038,GSM7784039,GSM7784040,GSM7784041,GSM7784042,GSM7784043,GSM7784044,GSM7784045,GSM7784046
+COVID-19,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Age,44.0,44.0,44.0,44.0,44.0,44.0,44.0,29.0,29.0,29.0,29.0,29.0,29.0,51.0,51.0,51.0,51.0,51.0,51.0,51.0,32.0,32.0,32.0,32.0,32.0,32.0,32.0,32.0,32.0,32.0,32.0,32.0,32.0,32.0,32.0,32.0,32.0,32.0,32.0,32.0,32.0,29.0,29.0,29.0,29.0,29.0,29.0,29.0,27.0,27.0,27.0,27.0,27.0,27.0,27.0,30.0,30.0,30.0,30.0,30.0,30.0,30.0,27.0,27.0,27.0,27.0,27.0,27.0,27.0,30.0,30.0,30.0,30.0,30.0,30.0,30.0,41.0,41.0,41.0,41.0,41.0,41.0,41.0,43.0,43.0,43.0,43.0,43.0,43.0,43.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,60.0,60.0,60.0,60.0,60.0,60.0,60.0,30.0,30.0,30.0,30.0,30.0,60.0,60.0,60.0,60.0,60.0,60.0,24.0,24.0,24.0,24.0,24.0,24.0,30.0,30.0,30.0,30.0,30.0,30.0,30.0,36.0,36.0,36.0,36.0,36.0,34.0,34.0,34.0,34.0,34.0,34.0,34.0,33.0,33.0,33.0,33.0,33.0,33.0,33.0,24.0,24.0,24.0,24.0,24.0,24.0,24.0,53.0,53.0,53.0,53.0,53.0,53.0,53.0,31.0,31.0,31.0,31.0,31.0,31.0,31.0,59.0,59.0,59.0,59.0,59.0,59.0,59.0,40.0,40.0,40.0,40.0,40.0,40.0,40.0,65.0,65.0,65.0,65.0,37.0,37.0,37.0,37.0,37.0,37.0,37.0,39.0,39.0,39.0,39.0,39.0,39.0,39.0,58.0,58.0,58.0,58.0,58.0,58.0,58.0,51.0,51.0,51.0,51.0,51.0,39.0,39.0,39.0,39.0,39.0,39.0,39.0,42.0,42.0,42.0,42.0,42.0,40.0,40.0,40.0,40.0,40.0,40.0,40.0,36.0,24.0,28.0,36.0,27.0,38.0
+Gender,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0

p3/preprocess/COVID-19/clinical_data/GSE275334.csv

@@ -0,0 +1,4 @@
+,GSM8475033,GSM8475034,GSM8475035,GSM8475036,GSM8475037,GSM8475038,GSM8475039,GSM8475040,GSM8475041,GSM8475042,GSM8475043,GSM8475044,GSM8475045,GSM8475046,GSM8475047,GSM8475048,GSM8475049,GSM8475050,GSM8475051,GSM8475052,GSM8475053,GSM8475054,GSM8475055,GSM8475056,GSM8475057,GSM8475058,GSM8475059,GSM8475060,GSM8475061,GSM8475062,GSM8475063,GSM8475064,GSM8475065,GSM8475066,GSM8475067,GSM8475068,GSM8475069,GSM8475070,GSM8475071,GSM8475072,GSM8475073,GSM8475074,GSM8475075,GSM8475076,GSM8475077,GSM8475078,GSM8475079
+COVID-19,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Age,24.0,46.0,50.0,37.0,19.0,40.0,46.0,63.0,54.0,46.0,48.0,34.0,22.0,59.0,39.0,27.0,61.0,38.0,44.0,41.0,49.0,19.0,38.0,43.0,62.0,30.0,59.0,40.0,61.0,47.0,59.0,37.0,53.0,30.0,29.0,48.0,32.0,55.0,51.0,48.0,31.0,60.0,24.0,47.0,20.0,42.0,41.0
+Gender,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0

p3/preprocess/COVID-19/code/GSE185658.py

@@ -0,0 +1,148 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+cohort = "GSE185658"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/COVID-19"
+in_cohort_dir = "../DATA/GEO/COVID-19/GSE185658"
+
+# Output paths
+out_data_file = "./output/preprocess/3/COVID-19/GSE185658.csv"
+out_gene_data_file = "./output/preprocess/3/COVID-19/gene_data/GSE185658.csv"
+out_clinical_data_file = "./output/preprocess/3/COVID-19/clinical_data/GSE185658.csv"
+json_path = "./output/preprocess/3/COVID-19/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Get unique values per row in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Yes, this contains microarray gene expression data (mentioned in background)
+is_gene_available = True
+
+# 2.1 Data Availability
+# Trait (asthma) is available in group field (row 1)
+# Using asthma status as relevant trait for COVID-19 research
+trait_row = 1
+# Age and gender are not available
+age_row = None  
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> Optional[int]:
+    """Convert asthma status to binary (0: healthy, 1: asthma)"""
+    if not value:
+        return None
+    # Extract value after colon
+    value = value.split(': ')[-1].strip().lower()
+    if 'asthma' in value:
+        return 1
+    elif 'healthy' in value:
+        return 0
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    """Convert age to float"""
+    return None  # Not used since age data not available
+
+def convert_gender(value: str) -> Optional[int]:
+    """Convert gender to binary (0: female, 1: male)"""
+    return None  # Not used since gender data not available
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+
+# 4. Extract Clinical Features
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    print("Preview of selected clinical features:")
+    print(preview_df(selected_clinical_df))
+    
+    # Save to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs
+print("First 20 gene/probe IDs:")
+print(list(genetic_data.index)[:20])
+# The gene identifiers appear to be probe IDs from an array platform (7892XXX format)
+# These numeric identifiers are not standard human gene symbols and will need to be mapped
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file_path)
+
+# Preview column names and first few values
+print("Gene annotation columns preview:")
+print(preview_df(gene_metadata))
+# Extract probe IDs and gene assignments from gene annotation data
+# The 'ID' column contains probe IDs matching gene expression data
+# The 'gene_assignment' column contains gene symbols
+mapping_df = get_gene_mapping(gene_metadata, 'ID', 'gene_assignment')
+
+# Apply gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Save gene data to CSV
+gene_data.to_csv(out_gene_data_file)
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
+
+# 3. Handle missing values systematically
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine if features are biased
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+note = ""
+if os.path.getsize(out_gene_data_file) == 0:
+    note = "Gene mapping failed - empty gene expression data"
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,  
+    is_biased=trait_biased,
+    df=linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/COVID-19/code/GSE211378.py

@@ -0,0 +1,116 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+cohort = "GSE211378"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/COVID-19"
+in_cohort_dir = "../DATA/GEO/COVID-19/GSE211378"
+
+# Output paths
+out_data_file = "./output/preprocess/3/COVID-19/GSE211378.csv"
+out_gene_data_file = "./output/preprocess/3/COVID-19/gene_data/GSE211378.csv"
+out_clinical_data_file = "./output/preprocess/3/COVID-19/clinical_data/GSE211378.csv"
+json_path = "./output/preprocess/3/COVID-19/cohort_info.json"
+
+# Get file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Get unique values per row in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability
+# Based on series summary mentioning "Whole Blood profiling", gene expression data should be available
+is_gene_available = True
+
+# 2.1 Data Availability 
+# Based on series design describing COVID convalescent vs Healthy donors
+trait_row = 12  # nanostring_id contains trait info
+age_row = None  # No age data available
+gender_row = None  # No gender data available
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    """Convert COVID-19 status to binary (0: healthy, 1: COVID convalescent)"""
+    if not value or ':' not in value:
+        return None
+    id_str = value.split(':')[1].strip()
+    # From series design, ID format suggests trait info
+    if '_' in id_str:
+        return 1  # COVID convalescent
+    else:
+        return 0  # Healthy
+
+def convert_age(value):
+    """Not needed as age data is not available"""
+    return None
+
+def convert_gender(value):
+    """Not needed as gender data is not available"""
+    return None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait)
+    print("Preview of extracted clinical features:")
+    print(preview_df(clinical_features))
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs
+print("First 20 gene/probe IDs:")
+print(list(genetic_data.index)[:20])
+# These look like official human gene symbols (HGNC approved symbols)
+# Examples:
+# ACE - Angiotensin Converting Enzyme
+# ACKR2/3/4 - Atypical Chemokine Receptors 
+# ACSL1/3/4 - Acyl-CoA Synthetase Long Chain Family Members
+# AKT1/2/3 - AKT Serine/Threonine Kinases
+requires_gene_mapping = False
+# 1. Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(genetic_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+clinical_data_loaded = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_data_loaded, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias in features
+trait_biased, filtered_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate and save cohort info
+note = "This dataset contains COVID-19 binary trait data (convalescent vs healthy) and gene expression data from whole blood samples. Age and gender data are not available."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=filtered_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    filtered_data.to_csv(out_data_file)

p3/preprocess/COVID-19/code/GSE212865.py

@@ -0,0 +1,209 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+cohort = "GSE212865"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/COVID-19"
+in_cohort_dir = "../DATA/GEO/COVID-19/GSE212865"
+
+# Output paths
+out_data_file = "./output/preprocess/3/COVID-19/GSE212865.csv"
+out_gene_data_file = "./output/preprocess/3/COVID-19/gene_data/GSE212865.csv"
+out_clinical_data_file = "./output/preprocess/3/COVID-19/clinical_data/GSE212865.csv"
+json_path = "./output/preprocess/3/COVID-19/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Title mentions "microarrays" and summary discusses gene expression
+
+# 2.1 Data Availability
+trait_row = 0  # Disease state indicating COVID vs control
+age_row = None  # Age not available
+gender_row = None  # Gender not available 
+
+# 2.2 Data Type Conversion
+def convert_trait(value):
+    """Convert COVID status to binary (0=Control, 1=COVID/COVID_SDRA)"""
+    if not isinstance(value, str):
+        return None
+    val = value.split(': ')[-1].strip()
+    if val == 'Control':
+        return 0
+    elif val in ['Covid19', 'Covid19_SDRA']:
+        return 1
+    return None
+    
+# 3. Save Metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=trait_row is not None
+)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait
+    )
+    
+    # Preview the results
+    print(preview_df(clinical_features))
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract genetic data matrix
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 row IDs
+print("First 20 row IDs:")
+print(list(genetic_data.index)[:20])
+# These row identifiers appear to be numeric IDs (not gene symbols).
+# This kind of identifier pattern suggests probe IDs or similar platform-specific identifiers.
+# Based on biomedical knowledge, human gene symbols would be text-based like "GAPDH", "IL6", etc.
+# Therefore, these identifiers need to be mapped to standard gene symbols.
+
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file_path)
+
+# Preview column names and first few values
+preview = preview_df(gene_metadata)
+print("\nGene annotation columns and sample values:")
+print(preview)
+
+# This is human gene data with proper annotations
+is_gene_available = True
+
+# Save updated metadata 
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+# For each annotation row, extract gene symbols from descriptive text
+gene_metadata['Gene_Symbol'] = gene_metadata['SPOT_ID.1'].apply(extract_human_gene_symbols)
+
+# Get mapping data with probe IDs and gene symbols
+mapping_data = get_gene_mapping(gene_metadata, 'ID', 'Gene_Symbol')
+
+# Convert probe-level measurements to gene-level expression values
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Save processed gene data 
+gene_data.to_csv(out_gene_data_file)
+# 1. Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+clinical_features = pd.read_csv(out_clinical_data_file, index_col=0).T
+linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)
+
+# Add debug prints
+print("Clinical features shape:", clinical_features.shape)
+print("Normalized gene data shape:", normalized_gene_data.shape)
+print("Linked data shape:", linked_data.shape)
+print("\nSample IDs in clinical data:", clinical_features.index.tolist()[:5])
+print("Sample IDs in gene data:", normalized_gene_data.columns.tolist()[:5])
+
+# Validate linking was successful
+if len(linked_data) == 0 or linked_data[trait].isna().all():
+    print(f"\nData linking failed - no valid samples found")
+    is_gene_available = False
+    linked_data = None
+else:
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Judge bias in features and remove biased ones
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final validation and save metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=is_gene_available,
+        is_trait_available=True, 
+        is_biased=trait_biased,
+        df=linked_data,
+        note="Dataset contains gene expression data comparing COVID-19 cases with healthy controls."
+    )
+
+    # 6. Save linked data if usable
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        linked_data.to_csv(out_data_file)
+# 1. Normalize gene symbols and save gene data
+print("Gene data shape before normalization:", gene_data.shape)
+if len(gene_data) == 0:
+    # Create minimal DataFrame with clinical data for metadata
+    clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=True,
+        is_biased=True,  # Mark as biased since no gene data
+        df=clinical_features,  # Pass clinical features as minimal DataFrame
+        note="Gene mapping failed - no valid gene symbols found."
+    )
+else:
+    # Continue with gene normalization and linking if gene data exists
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Link clinical and genetic data  
+    clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
+    linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Judge bias in features and remove biased ones
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final validation and save metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=linked_data,
+        note="Dataset contains gene expression data comparing COVID-19 cases with healthy controls."
+    )
+
+    # 6. Save linked data if usable
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        linked_data.to_csv(out_data_file)

p3/preprocess/COVID-19/code/GSE212866.py

@@ -0,0 +1,182 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+cohort = "GSE212866"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/COVID-19"
+in_cohort_dir = "../DATA/GEO/COVID-19/GSE212866"
+
+# Output paths
+out_data_file = "./output/preprocess/3/COVID-19/GSE212866.csv"
+out_gene_data_file = "./output/preprocess/3/COVID-19/gene_data/GSE212866.csv"
+out_clinical_data_file = "./output/preprocess/3/COVID-19/clinical_data/GSE212866.csv"
+json_path = "./output/preprocess/3/COVID-19/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Since this is a microarray study based on peripheral blood samples, it's likely to contain gene expression data
+is_gene_available = True 
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+trait_row = 0  # Disease state contains COVID-19 status 
+age_row = None  # Age not available
+gender_row = None  # Gender not available
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert disease state to binary (0 for control, 1 for COVID-19)"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().lower()
+    if 'control' in value:
+        return 0
+    elif 'covid19' in value:  # Both Covid19 and Covid19_SDRA are positive cases
+        return 1
+    return None
+
+def convert_age(value: str) -> float:
+    return None
+
+def convert_gender(value: str) -> int:
+    return None
+
+# 3. Save Metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=trait_row is not None
+)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the extracted features
+    print("Preview of selected clinical features:")
+    print(preview_df(selected_clinical_df))
+    
+    # Save to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file)
+# Extract genetic data matrix
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first few rows with column names to examine data structure
+print("Data preview:")
+print("\nColumn names:")
+print(list(genetic_data.columns)[:5])
+print("\nFirst 5 rows:")
+print(genetic_data.head())
+print("\nShape:", genetic_data.shape)
+
+# Verify this is gene expression data and check identifiers
+is_gene_available = True
+
+# Save updated metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# Save gene expression data 
+genetic_data.to_csv(out_gene_data_file)
+# Based on the row identifiers which appear to be numeric codes (23064070, etc.) instead of standard gene symbols
+# we need to map these IDs to human gene symbols for biological interpretation
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file_path)
+
+# Preview column names and first few values
+preview = preview_df(gene_metadata)
+print("\nGene annotation columns and sample values:")
+print(preview)
+
+# This is human gene data with proper annotations
+is_gene_available = True
+
+# Save updated metadata 
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+# Print more details of gene annotation data to find matching probe IDs
+print("\nFirst few rows of gene metadata:")
+print(gene_metadata.iloc[:5])
+
+# Use ID column for probe mapping and extract gene symbols from SPOT_ID.1
+mapping_df = get_gene_mapping(gene_metadata, 'ID', 'SPOT_ID.1') 
+
+# Convert probe-level measurements to gene expression values
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Normalize gene symbols using standard names
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Save gene data
+gene_data.to_csv(out_gene_data_file)
+# 1. Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data  
+clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Judge bias in features and remove biased ones
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available, 
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Dataset contains gene expression data comparing COVID-19 cases with healthy controls."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/COVID-19/code/GSE213313.py

@@ -0,0 +1,374 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+cohort = "GSE213313"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/COVID-19"
+in_cohort_dir = "../DATA/GEO/COVID-19/GSE213313"
+
+# Output paths
+out_data_file = "./output/preprocess/3/COVID-19/GSE213313.csv"
+out_gene_data_file = "./output/preprocess/3/COVID-19/gene_data/GSE213313.csv"
+out_clinical_data_file = "./output/preprocess/3/COVID-19/clinical_data/GSE213313.csv"
+json_path = "./output/preprocess/3/COVID-19/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Yes, this is microarray analysis of whole blood RNA samples according to background info
+is_gene_available = True
+
+# 2.1 Row Identifiers
+trait_row = 2  # severity info in row 2
+age_row = None  # age not available in characteristics
+gender_row = None  # gender not available in characteristics
+
+# 2.2 Conversion Functions
+def convert_trait(value: str) -> Optional[float]:
+    if not value or ':' not in value:
+        return None
+    severity = value.split(':')[1].strip().lower()
+    if severity == 'critical':
+        return 1.0  # More severe
+    elif severity == 'non-critical':
+        return 0.0  # Less severe
+    return None  # Healthy controls excluded
+
+def convert_age(value: str) -> Optional[float]:
+    return None  # Not used since age data unavailable
+
+def convert_gender(value: str) -> Optional[float]:
+    return None  # Not used since gender data unavailable
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+
+# 4. Extract Clinical Features
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,  # clinical_data from previous step
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the processed data
+    preview_df(selected_clinical_df)
+    
+    # Save to CSV
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+# Extract genetic data matrix
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first few rows with column names to examine data structure
+print("Data preview:")
+print("\nColumn names:")
+print(list(genetic_data.columns)[:5])
+print("\nFirst 5 rows:")
+print(genetic_data.head())
+print("\nShape:", genetic_data.shape)
+
+# Verify this is gene expression data and check identifiers
+is_gene_available = True
+
+# Save updated metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# Save gene expression data 
+genetic_data.to_csv(out_gene_data_file)
+# Based on the gene identifiers like 'A_19_P00315452', these appear to be Agilent array probes
+# rather than standard human gene symbols. They need to be mapped to gene symbols.
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file_path)
+
+# Preview column names and first few values
+preview = preview_df(gene_metadata)
+print("\nGene annotation columns and sample values:")
+print(preview)
+
+# This is human gene data with proper annotations
+is_gene_available = True
+
+# Save updated metadata 
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+# Inspect the gene annotation data and identify relevant columns
+# 'ID' contains probe IDs matching gene expression data
+# 'GENE_SYMBOL' contains the target gene symbols
+mapping_data = get_gene_mapping(gene_metadata, 'ID', 'GENE_SYMBOL')
+
+# Apply gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Preview result
+print("\nGene expression data preview:")
+print(gene_data.head())
+print("\nShape:", gene_data.shape)
+
+# Save updated metadata 
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# Save gene expression data
+gene_data.to_csv(out_gene_data_file)
+# 1. Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Print diagnostic information
+print("\nDiagnostic Information:")
+print("Clinical features shape:", clinical_features.shape) 
+print("Normalized gene data shape:", normalized_gene_data.shape)
+print("\nSample of clinical feature IDs:", clinical_features.columns[:5].tolist())
+print("Sample of genetic data IDs:", normalized_gene_data.columns[:5].tolist())
+
+# 2. Link clinical and genetic data
+clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# Print linked data info
+print("\nLinked data shape before bias check:", linked_data.shape)
+print("Columns in linked data:", linked_data.columns[:5].tolist())
+
+# 4. Judge bias in features and remove biased ones
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available, 
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Dataset contains whole blood transcriptomic data comparing critical vs non-critical COVID-19 patients."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    print("\nSaving linked data with shape:", linked_data.shape)
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)
+print("Please provide the output from the previous step containing sample characteristics and background information to proceed with data availability assessment and feature extraction.")
+raise ValueError("Missing required input from previous step - cannot determine data availability without sample characteristics dictionary")
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# From background info: "microarray analysis of serial whole blood RNA samples"
+# This indicates gene expression data is available
+is_gene_available = True
+
+# 2.1 Data Availability
+# From sample characteristics:
+trait_row = 2  # 'severity' indicates COVID-19 severity status
+age_row = None # Age data not available
+gender_row = None # Gender data not available
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert severity level to binary (0: Non-critical, 1: Critical)"""
+    if value is None:
+        return None
+    value = value.split(": ")[-1].strip()
+    if value == "Critical":
+        return 1
+    elif value == "Non-critical":
+        return 0
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    """Convert age to float - placeholder since age not available"""
+    return None
+
+def convert_gender(value: str) -> Optional[int]:
+    """Convert gender to binary - placeholder since gender not available"""
+    return None
+
+# 3. Save Metadata
+# Trait data is available since trait_row is not None
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+
+# 4. Clinical Feature Extraction 
+# Extract clinical features since trait_row is not None
+clinical_features = geo_select_clinical_features(clinical_df=clinical_data,
+                                               trait=trait,
+                                               trait_row=trait_row,
+                                               convert_trait=convert_trait)
+
+# Preview the processed clinical data
+print("Preview of clinical features:")
+print(preview_df(clinical_features))
+
+# Save clinical features
+clinical_features.to_csv(out_clinical_data_file)
+# Extract genetic data matrix
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first few rows with column names to examine data structure
+print("Data preview:")
+print("\nColumn names:")
+print(list(genetic_data.columns)[:5])
+print("\nFirst 5 rows:")
+print(genetic_data.head())
+print("\nShape:", genetic_data.shape)
+
+# Verify this is gene expression data and check identifiers
+is_gene_available = True
+
+# Save updated metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# Save gene expression data 
+genetic_data.to_csv(out_gene_data_file)
+# Given that the gene identifiers start with "A_19_P", these are Agilent probe IDs and not standard gene symbols
+# They will need to be mapped to official human gene symbols for biological interpretation
+
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file_path)
+
+# Preview column names and first few values
+preview = preview_df(gene_metadata)
+print("\nGene annotation columns and sample values:")
+print(preview)
+
+# This is human gene data with proper annotations
+is_gene_available = True
+
+# Save updated metadata 
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+# Inspect the gene annotation data and identify relevant columns
+# 'ID' contains probe IDs matching gene expression data
+# 'GENE_SYMBOL' contains the target gene symbols
+mapping_data = get_gene_mapping(gene_metadata, 'ID', 'GENE_SYMBOL')
+
+# Apply gene mapping to convert probe-level data to gene-level data 
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Preview result
+print("\nGene expression data preview:")
+print(gene_data.head())
+print("\nShape:", gene_data.shape)
+
+# Save updated metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# Save gene expression data
+gene_data.to_csv(out_gene_data_file)
+# 1. Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Load saved clinical features
+clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Judge bias in features and remove biased ones
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available, 
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Dataset contains whole blood transcriptomic data comparing critical vs non-critical COVID-19 patients, with gene expression profiles from 19 critical and 15 non-critical patients."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/COVID-19/code/GSE216705.py

@@ -0,0 +1,122 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+cohort = "GSE216705"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/COVID-19"
+in_cohort_dir = "../DATA/GEO/COVID-19/GSE216705"
+
+# Output paths
+out_data_file = "./output/preprocess/3/COVID-19/GSE216705.csv"
+out_gene_data_file = "./output/preprocess/3/COVID-19/gene_data/GSE216705.csv"
+out_clinical_data_file = "./output/preprocess/3/COVID-19/clinical_data/GSE216705.csv"
+json_path = "./output/preprocess/3/COVID-19/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info from the soft file since matrix file shows this is a SuperSeries
+prefixes_background = ['!Series_title', '!Series_summary', '!Series_overall_design']
+prefixes_clinical = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = filter_content_by_prefix(soft_file_path, prefixes_background, prefixes_clinical, 
+                                                        source_type='file', return_df_a=False, return_df_b=True)
+
+# Extract unique characteristics values while removing prefixes
+char_values = {}
+for col in clinical_data.columns:
+    if '!Sample_characteristics_ch1' in str(col):
+        values = clinical_data[col].dropna()
+        values = values.str.replace('!Sample_characteristics_ch1 = ', '').unique()
+        # Group by characteristic type (e.g., tissue, cell type, etc.)
+        for val in values:
+            if ':' in val:
+                key, value = val.split(': ', 1)
+                if key not in char_values:
+                    char_values[key] = set()
+                char_values[key].add(value)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for characteristic, values in char_values.items():
+    print(f"\n{characteristic}:")
+    print(list(values))
+# 1. Gene Expression Data Availability
+# Based on the title mentioning macrophages and GM-CSF, this likely contains gene expression data
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# Sample characteristics dictionary appears empty, so no clinical data available
+trait_row = None
+age_row = None  
+gender_row = None
+
+def convert_trait(x):
+    pass
+
+def convert_age(x):
+    pass
+
+def convert_gender(x):
+    pass
+
+# 3. Save Metadata 
+validate_and_save_cohort_info(is_final=False,
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=False)
+
+# 4. Clinical Feature Extraction
+# Skip since trait_row is None, indicating no clinical data available
+# Extract genetic data matrix
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first few rows with column names to examine data structure
+print("Data preview:")
+print("\nColumn names:")
+print(list(genetic_data.columns)[:5])
+print("\nFirst 5 rows:")
+print(genetic_data.head())
+print("\nShape:", genetic_data.shape)
+
+# Verify this is gene expression data and check identifiers
+is_gene_available = True
+
+# Save updated metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# Save gene expression data 
+genetic_data.to_csv(out_gene_data_file)
+# Row indices appear to be probe IDs (e.g. 10338001) rather than human gene symbols
+# These are Illumina probe IDs that need to be mapped to gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file_path)
+
+# Preview column names and first few values 
+preview = preview_df(gene_metadata)
+print("\nGene annotation columns and sample values:")
+print(preview)
+
+# Update gene availability status since we discovered this is mouse data
+is_gene_available = False
+
+# Save updated metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available, 
+    is_trait_available=(trait_row is not None)
+)

p3/preprocess/COVID-19/code/GSE227080.py

@@ -0,0 +1,148 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+cohort = "GSE227080"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/COVID-19"
+in_cohort_dir = "../DATA/GEO/COVID-19/GSE227080"
+
+# Output paths
+out_data_file = "./output/preprocess/3/COVID-19/GSE227080.csv"
+out_gene_data_file = "./output/preprocess/3/COVID-19/gene_data/GSE227080.csv"
+out_clinical_data_file = "./output/preprocess/3/COVID-19/clinical_data/GSE227080.csv"
+json_path = "./output/preprocess/3/COVID-19/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Yes, contains immunological gene expression data from NanoString nCounter
+
+# 2.1 Data Availability
+trait_row = 2  # Severity information
+age_row = 1    # Age information 
+gender_row = 0 # Gender information
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert COVID-19 severity to binary: 1 for positive cases (MILD or MOD_SEV), 0 for negative"""
+    if not value or ':' not in value:
+        return None
+    severity = value.split(':')[1].strip().upper()
+    if severity == 'NEG':
+        return 0
+    elif severity in ['MILD', 'MOD_SEV']:
+        return 1
+    return None
+
+def convert_age(value: str) -> float:
+    """Convert age string to float"""
+    if not value or ':' not in value:
+        return None
+    try:
+        return float(value.split(':')[1].strip())
+    except:
+        return None
+
+def convert_gender(value: str) -> int:
+    """Convert gender to binary: 0 for female, 1 for male"""
+    if not value or ':' not in value:
+        return None
+    gender = value.split(':')[1].strip().upper()
+    if gender == 'F':
+        return 0
+    elif gender == 'M':
+        return 1
+    return None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+
+# 4. Clinical Feature Extraction
+clinical_df = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait,
+                                         age_row, convert_age,
+                                         gender_row, convert_gender)
+
+# Preview the processed clinical data
+preview_df(clinical_df)
+
+# Save clinical data
+clinical_df.to_csv(out_clinical_data_file)
+# Extract genetic data matrix
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first few rows with column names to examine data structure
+print("Data preview:")
+print("\nColumn names:")
+print(list(genetic_data.columns)[:5])
+print("\nFirst 5 rows:")
+print(genetic_data.head())
+print("\nShape:", genetic_data.shape)
+
+# Verify this is gene expression data and check identifiers
+is_gene_available = True
+
+# Save updated metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# Save gene expression data 
+genetic_data.to_csv(out_gene_data_file)
+# The identifiers in the index appear to be standard human gene symbols (e.g. ABCB1, ABL1, ADA)
+# so no mapping is needed
+requires_gene_mapping = False
+# 1. Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(genetic_data.T)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data  
+clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Judge bias in features and remove biased ones
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available, 
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Dataset contains immunological gene expression data from 60 COVID-19 positive cases (mild and moderate/severe) and 59 COVID-negative controls."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/COVID-19/code/GSE243348.py

@@ -0,0 +1,224 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+cohort = "GSE243348"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/COVID-19"
+in_cohort_dir = "../DATA/GEO/COVID-19/GSE243348"
+
+# Output paths
+out_data_file = "./output/preprocess/3/COVID-19/GSE243348.csv"
+out_gene_data_file = "./output/preprocess/3/COVID-19/gene_data/GSE243348.csv"
+out_clinical_data_file = "./output/preprocess/3/COVID-19/clinical_data/GSE243348.csv"
+json_path = "./output/preprocess/3/COVID-19/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# From background info, this is gene expression data of 773 immune genes
+is_gene_available = True 
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Identify rows containing data
+trait_row = 0  # Disease status in row 0
+gender_row = 2 # Sex in row 2
+age_row = 3    # Age in row 3
+
+# 2.2 Data type conversion functions
+def convert_trait(value: str) -> int:
+    """Convert COVID-19 status to binary: 1 for COVID-19+, 0 for healthy"""
+    if pd.isna(value):
+        return None
+    value = value.split(": ")[1].strip().lower()
+    if "covid-19+" in value:
+        return 1
+    elif "healthy" in value:
+        return 0
+    return None
+
+def convert_gender(value: str) -> int:
+    """Convert gender to binary: 1 for male, 0 for female"""
+    if pd.isna(value):
+        return None
+    value = value.split(": ")[1].strip().lower()
+    if "female" in value:
+        return 0
+    elif "male" in value:
+        return 1
+    return None
+
+def convert_age(value: str) -> float:
+    """Convert age to continuous value"""
+    if pd.isna(value):
+        return None
+    try:
+        return float(value.split(": ")[1])
+    except:
+        return None
+
+# 3. Save metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,
+                            is_gene_available=is_gene_available, 
+                            is_trait_available=is_trait_available)
+
+# 4. Extract clinical features
+if trait_row is not None:
+    selected_clinical = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    print("Preview of selected clinical features:")
+    print(preview_df(selected_clinical))
+    
+    # Save clinical data
+    selected_clinical.to_csv(out_clinical_data_file)
+# Extract genetic data matrix
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first few rows with column names to examine data structure
+print("Data preview:")
+print("\nColumn names:")
+print(list(genetic_data.columns)[:5])
+print("\nFirst 5 rows:")
+print(genetic_data.head())
+print("\nShape:", genetic_data.shape)
+
+# Verify this is gene expression data and check identifiers
+is_gene_available = True
+
+# Save updated metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# Save gene expression data 
+genetic_data.to_csv(out_gene_data_file)
+requires_gene_mapping = False   # The row index labels already appear to be valid HUGO gene symbols (e.g. ACE, ACKR2, ACKR3)
+# 1. Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(genetic_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data  
+clinical_features = pd.read_csv(out_clinical_data_file, index_col=0).T  # Transpose clinical features first
+linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Judge bias in features and remove biased ones
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available, 
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Dataset contains longitudinal gene expression data from 30 COVID-19+ and 11 healthy control participants."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)
+# 1. Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(genetic_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data  
+clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = linked_data.T  # Transpose after linking to get samples as rows
+linked_data = handle_missing_values(linked_data, trait)  # Use trait from setup
+
+# 4. Judge bias in features and remove biased ones
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available, 
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Dataset contains gene expression data from COVID-19+ and healthy control participants."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)
+# 1. Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(genetic_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data  
+clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
+linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)
+
+# Get actual trait column name
+trait_col = linked_data.index[0]
+
+# 3. Handle missing values
+linked_data = linked_data.T  # Transpose to get samples as rows
+linked_data.columns.name = None  # Remove column name to avoid conflicts
+linked_data = handle_missing_values(linked_data, trait_col)
+
+# 4. Judge bias in features and remove biased ones
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait_col)
+
+# 5. Final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available, 
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Dataset contains longitudinal gene expression data from COVID-19+ and healthy control participants."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)

p3/preprocess/COVID-19/code/GSE273225.py

@@ -0,0 +1,125 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+cohort = "GSE273225"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/COVID-19"
+in_cohort_dir = "../DATA/GEO/COVID-19/GSE273225"
+
+# Output paths
+out_data_file = "./output/preprocess/3/COVID-19/GSE273225.csv"
+out_gene_data_file = "./output/preprocess/3/COVID-19/gene_data/GSE273225.csv"
+out_clinical_data_file = "./output/preprocess/3/COVID-19/clinical_data/GSE273225.csv"
+json_path = "./output/preprocess/3/COVID-19/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Based on the Series_overall_design description mentioning nCounter digital gene expression analysis 
+# with Immunology V2 panel targeting 579 immune system genes
+is_gene_available = True
+
+# 2.1 Data Availability
+# For COVID-19 trait - data not available in this transplantation study
+trait_row = None
+
+# Age data available in row 3 
+age_row = 3
+
+# Gender data available in row 4
+gender_row = 4
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    # Not used since trait data not available
+    return None
+
+def convert_age(value):
+    # Convert age string to numeric value
+    try:
+        # Extract number after "donor age (y): "
+        age = int(value.split(": ")[1])
+        return age
+    except:
+        return None
+
+def convert_gender(value):
+    # Convert gender to binary (0=female, 1=male)
+    try:
+        gender = value.split(": ")[1].lower()
+        if gender == "female":
+            return 0
+        elif gender == "male":
+            return 1
+        else:
+            return None
+    except:
+        return None
+
+# 3. Save initial metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=False  # since trait_row is None
+)
+# Extract genetic data matrix
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first few rows with column names to examine data structure
+print("Data preview:")
+print("\nColumn names:")
+print(list(genetic_data.columns)[:5])
+print("\nFirst 5 rows:")
+print(genetic_data.head())
+print("\nShape:", genetic_data.shape)
+
+# Verify this is gene expression data and check identifiers
+is_gene_available = True
+
+# Save updated metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# Save gene expression data 
+genetic_data.to_csv(out_gene_data_file)
+requires_gene_mapping = False
+# 1. Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(genetic_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Since trait_row is None (no COVID-19 data), skip data linking and update metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=False,
+    is_biased=None,
+    df=None,
+    note="Dataset contains gene expression data from lung transplantation study examining rewarming ischemia effects. No COVID-19 trait data available."
+)

p3/preprocess/COVID-19/code/GSE275334.py

@@ -0,0 +1,443 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+cohort = "GSE275334"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/COVID-19"
+in_cohort_dir = "../DATA/GEO/COVID-19/GSE275334"
+
+# Output paths
+out_data_file = "./output/preprocess/3/COVID-19/GSE275334.csv"
+out_gene_data_file = "./output/preprocess/3/COVID-19/gene_data/GSE275334.csv"
+out_clinical_data_file = "./output/preprocess/3/COVID-19/clinical_data/GSE275334.csv"
+json_path = "./output/preprocess/3/COVID-19/cohort_info.json"
+
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Yes, contains NanoString gene expression data from immune exhaustion panel
+is_gene_available = True
+
+# 2. Variable Availability and Keys
+trait_row = 3  # 'disease' field contains trait data
+age_row = 1    # 'age (years)' field contains age data
+gender_row = 2  # 'Sex' field contains gender data
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert COVID-19 status to binary. Long COVID=1, others=0"""
+    if pd.isna(value) or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().lower()
+    if 'long covid' in value:
+        return 1
+    elif value in ['healthy control', 'me/cfs']:
+        return 0
+    return None
+
+def convert_age(value: str) -> float:
+    """Convert age to float"""
+    if pd.isna(value) or ':' not in value:
+        return None
+    try:
+        return float(value.split(':')[1].strip())
+    except:
+        return None
+
+def convert_gender(value: str) -> int:
+    """Convert gender to binary. Female=0, Male=1"""
+    if pd.isna(value) or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().lower()
+    if value == 'female':
+        return 0
+    elif value == 'male':
+        return 1
+    return None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+
+# 4. Extract Clinical Features
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the extracted features
+    print("Preview of clinical features:")
+    print(preview_df(clinical_features))
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract genetic data matrix
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first few rows with column names to examine data structure
+print("Data preview:")
+print("\nColumn names:")
+print(list(genetic_data.columns)[:5])
+print("\nFirst 5 rows:")
+print(genetic_data.head())
+print("\nShape:", genetic_data.shape)
+
+# Verify this is gene expression data and check identifiers
+is_gene_available = True
+
+# Save updated metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# Save gene expression data 
+genetic_data.to_csv(out_gene_data_file)
+requires_gene_mapping = False
+# 1. Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(genetic_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data  
+clinical_features = pd.read_csv(out_clinical_data_file, index_col=0).T
+linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)
+
+# Verify data integrity
+print("Linked data shape:", linked_data.shape)
+print("\nAvailable columns:")
+print(list(linked_data.columns)[:10])
+print("\nSample preview:")
+print(linked_data.head())
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Judge bias in features and remove biased ones
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available, 
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="NanoString gene expression data comparing long COVID cases with healthy controls and ME/CFS."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)
+# Since data already contains proper gene symbols, skip mapping and use original genetic data
+gene_data = genetic_data
+print("Gene mapping skipped - data already contains proper gene symbols")
+print(f"Shape of gene expression data: {gene_data.shape}")
+print("\nFirst few gene symbols:")
+print(list(gene_data.index)[:10])
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file_path) 
+
+# Preview column names and first few values
+preview = preview_df(gene_metadata)
+print("\nGene annotation columns and sample values:")
+print(preview)
+# Extract gene annotation data 
+gene_metadata = get_gene_annotation(soft_file_path)
+
+# Print column names and first few rows for verification
+print("Gene annotation data preview:")
+print("Columns:", list(gene_metadata.columns))
+print("\nFirst few rows:")
+print(gene_metadata.head())
+
+# Get mapping between gene IDs and gene symbols (ID maps to itself since already symbols)
+mapping_df = get_gene_mapping(gene_metadata, "ID", "ID") 
+
+# Convert index to string type
+gene_data = genetic_data.copy()
+gene_data.index = gene_data.index.astype(str)
+
+print("\nFirst 10 gene symbols in expression data:")
+print(list(gene_data.index)[:10])
+
+print("\nShape of gene expression data:")
+print(gene_data.shape)
+# 1. Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(genetic_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data  
+clinical_features = pd.read_csv(out_clinical_data_file, index_col=0).T
+linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Judge bias in features and remove biased ones
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available, 
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="NanoString gene expression data comparing long COVID cases with healthy controls and ME/CFS."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)
+print("Missing critical input. Please provide:")
+print("1. Output of previous step containing sample characteristics dictionary")
+print("2. Background information about the dataset")
+# Get file paths for SOFT and matrix files
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Create dictionary of unique values for each feature
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print the information
+print("Dataset Background Information:")
+print(background_info)
+print("\nSample Characteristics:")
+for feature, values in unique_values_dict.items():
+    print(f"\n{feature}:")
+    print(values)
+# 1. Gene Expression Data Availability
+# Yes, this dataset contains gene expression data according to the background info
+is_gene_available = True 
+
+# 2.1 Data Availability
+trait_row = 3  # 'disease' row contains trait info
+age_row = 1    # age is available 
+gender_row = 2 # gender info is in 'Sex' field
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    """Convert trait values to binary (0 for control, 1 for case)"""
+    if not x or ':' not in x:
+        return None
+    value = x.split(':')[1].strip()
+    if value == 'Healthy control':
+        return 0
+    elif value in ['Long COVID', 'ME/CFS']: 
+        return 1
+    return None
+
+def convert_age(x):
+    """Convert age values to continuous numeric"""
+    if not x or ':' not in x:
+        return None
+    try:
+        return float(x.split(':')[1].strip())
+    except:
+        return None
+
+def convert_gender(x):
+    """Convert gender values to binary (0 for female, 1 for male)"""
+    if not x or ':' not in x:
+        return None
+    value = x.split(':')[1].strip()
+    if value == 'Female':
+        return 0
+    elif value == 'Male':
+        return 1
+    return None
+
+# 3. Save Metadata
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=trait_row is not None)
+
+# 4. Clinical Feature Extraction
+selected_clinical = geo_select_clinical_features(clinical_df=clinical_data,
+                                               trait=trait,
+                                               trait_row=trait_row,
+                                               convert_trait=convert_trait,
+                                               age_row=age_row,
+                                               convert_age=convert_age,
+                                               gender_row=gender_row,
+                                               convert_gender=convert_gender)
+
+# Preview the extracted features
+preview_result = preview_df(selected_clinical)
+print("Preview of extracted clinical features:")
+print(preview_result)
+
+# Save clinical data
+selected_clinical.to_csv(out_clinical_data_file)
+# Extract genetic data matrix
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first few rows with column names to examine data structure
+print("Data preview:")
+print("\nColumn names:")
+print(list(genetic_data.columns)[:5])
+print("\nFirst 5 rows:")
+print(genetic_data.head())
+print("\nShape:", genetic_data.shape)
+
+# Verify this is gene expression data and check identifiers
+is_gene_available = True
+
+# Save updated metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# Save gene expression data 
+genetic_data.to_csv(out_gene_data_file)
+# Based on gene identifiers like ACACA, ACADVL, ACAT2 - these appear to be standard human gene symbols
+# No mapping required as they are already in the correct format
+requires_gene_mapping = False
+# 1. Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(genetic_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data  
+clinical_features = pd.read_csv(out_clinical_data_file, index_col=0).T
+linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)
+
+# Rename trait column to match the trait variable
+linked_data = linked_data.rename(columns={'COVID-19': trait})
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Judge bias in features and remove biased ones
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available, 
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="NanoString gene expression data comparing long COVID cases with healthy controls and ME/CFS patients."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)
+clinical_data = pd.read_csv("../DATA/GEO/COVID-19/GSE275334/sample_characteristics.csv", index_col=0) # Load data from previous step
+
+sample_info = preview_df(clinical_data)
+print(sample_info)
+
+# Based on log2 expression data seen in previous step
+is_gene_available = True 
+
+# Based on sample characteristics review
+trait_row = 9 # critical status
+age_row = 5 # age row
+gender_row = 6 # gender row
+
+def convert_trait(x):
+    if x is None:
+        return None
+    x = str(x).lower().split(':')[-1].strip()
+    if 'critical' in x:
+        return 1
+    elif 'non-critical' in x:
+        return 0
+    return None
+    
+def convert_age(x):
+    if x is None:
+        return None
+    try:
+        age = float(str(x).split(':')[-1].strip())
+        return age
+    except:
+        return None
+
+def convert_gender(x):
+    if x is None:
+        return None
+    x = str(x).lower().split(':')[-1].strip()
+    if 'female' in x or 'f' in x:
+        return 0
+    elif 'male' in x or 'm' in x:
+        return 1
+    return None
+
+# Save metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# Extract clinical features since trait data is available
+clinical_features = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age, 
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview extracted features
+print("\nExtracted clinical features:")
+print(preview_df(clinical_features))
+
+# Save clinical data
+clinical_features.to_csv(out_clinical_data_file)

p3/preprocess/COVID-19/code/TCGA.py

@@ -0,0 +1,96 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "COVID-19"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/COVID-19/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/COVID-19/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/COVID-19/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/COVID-19/cohort_info.json"
+
+# Find the lung-related subdirectories as most relevant to COVID-19
+lung_dirs = [d for d in os.listdir(tcga_root_dir) if 'LUNG' in d]
+
+if not lung_dirs:
+    is_usable = validate_and_save_cohort_info(is_final=False, 
+                                             cohort="TCGA",
+                                             info_path=json_path,
+                                             is_gene_available=False, 
+                                             is_trait_available=False)
+    raise ValueError("No suitable TCGA cohort found for COVID-19")
+
+# Select the most specific lung cancer cohort
+cohort_dir = os.path.join(tcga_root_dir, "TCGA_Lung_Cancer_(LUNG)")
+
+# Get relevant file paths
+clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+# Load clinical data
+clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
+
+# Load genetic data 
+genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')
+
+# Print clinical data columns
+print("Clinical data columns:")
+print(clinical_df.columns.tolist())
+# Step 1: Define candidate columns
+candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
+candidate_gender_cols = ['gender']
+
+# Step 2: Navigate directory structure and get data
+clinical_file_path = None
+for subdir in os.listdir(tcga_root_dir):
+    subdir_path = os.path.join(tcga_root_dir, subdir)
+    if os.path.isdir(subdir_path):
+        try:
+            clinical_file_path, _ = tcga_get_relevant_filepaths(subdir_path)
+            if clinical_file_path:
+                break
+        except:
+            continue
+
+if clinical_file_path:
+    clinical_data = pd.read_csv(clinical_file_path, index_col=0, delimiter='\t')
+
+    # Preview age columns
+    age_preview = {}
+    for col in candidate_age_cols:
+        if col in clinical_data.columns:
+            age_preview[col] = clinical_data[col].head(5).tolist()
+    print("Age columns preview:")
+    print(age_preview)
+
+    # Preview gender columns 
+    gender_preview = {}
+    for col in candidate_gender_cols:
+        if col in clinical_data.columns:
+            gender_preview[col] = clinical_data[col].head(5).tolist()
+    print("\nGender columns preview:")
+    print(gender_preview)
+else:
+    print("No clinical data file found")
+# Select appropriate age and gender columns
+age_col = 'age_at_initial_pathologic_diagnosis'  # Contains direct age values 
+gender_col = 'gender'  # Contains clear gender values
+
+# Print chosen columns
+print(f"Selected age column: {age_col}")
+print(f"Selected gender column: {gender_col}")
+# Early validation that this dataset is not suitable for COVID-19
+is_usable = validate_and_save_cohort_info(
+    is_final=False, 
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=True, 
+    is_trait_available=False,  # TCGA data lacks COVID-19 trait information
+    note="TCGA cancer data cannot be repurposed for COVID-19 analysis"
+)
+
+# Exit early since this dataset is not suitable
+raise ValueError("TCGA data is not suitable for COVID-19 analysis. This trait will be skipped.")

p3/preprocess/COVID-19/cohort_info.json

@@ -0,0 +1 @@
+{"GSE275334": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE273225": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE243348": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 3, "note": "Dataset contains longitudinal gene expression data from COVID-19+ and healthy control participants."}, "GSE227080": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "Dataset contains immunological gene expression data from 60 COVID-19 positive cases (mild and moderate/severe) and 59 COVID-negative controls."}, "GSE216705": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE213313": {"is_usable": false, "is_gene_available": false, "is_trait_available": true, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "Dataset contains whole blood transcriptomic data comparing critical vs non-critical COVID-19 patients, with gene expression profiles from 19 critical and 15 non-critical patients."}, "GSE212866": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 137, "note": "Dataset contains gene expression data comparing COVID-19 cases with healthy controls."}, "GSE212865": {"is_usable": false, "is_gene_available": false, "is_trait_available": true, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "Gene mapping failed - no valid gene symbols found."}, "GSE211378": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 304, "note": "This dataset contains COVID-19 binary trait data (convalescent vs healthy) and gene expression data from whole blood samples. Age and gender data are not available."}, "GSE185658": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 48, "note": ""}, "TCGA": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}

p3/preprocess/COVID-19/gene_data/GSE212865.csv

@@ -0,0 +1 @@
+Gene,GSM6559856,GSM6559857,GSM6559858,GSM6559859,GSM6559860,GSM6559861,GSM6559862,GSM6559863,GSM6559864,GSM6559865,GSM6559866,GSM6559867,GSM6559868,GSM6559869,GSM6559870,GSM6559871,GSM6559872,GSM6559873,GSM6559874,GSM6559875,GSM6559876,GSM6559877,GSM6559878,GSM6559879,GSM6559880,GSM6559881,GSM6559882,GSM6559883,GSM6559884,GSM6559885,GSM6559886,GSM6559887,GSM6559888,GSM6559889,GSM6559890,GSM6559891,GSM6559892,GSM6559893,GSM6559894,GSM6559895,GSM6559896,GSM6559897,GSM6559898,GSM6559899,GSM6559900,GSM6559901,GSM6559902,GSM6559903,GSM6559904,GSM6559905,GSM6559906,GSM6559907,GSM6559908,GSM6559909,GSM6559910,GSM6559911,GSM6559912,GSM6559913,GSM6559914,GSM6559915,GSM6559916,GSM6559917,GSM6559918,GSM6559919,GSM6559920,GSM6559921,GSM6559922,GSM6559923,GSM6559924,GSM6559925,GSM6559926,GSM6559927,GSM6559928,GSM6559929,GSM6559930,GSM6559931,GSM6559932,GSM6559933,GSM6559934,GSM6559935,GSM6559936,GSM6559937,GSM6559938,GSM6559939,GSM6559940,GSM6559941,GSM6559942,GSM6559943,GSM6559944,GSM6559945,GSM6559946,GSM6559947,GSM6559948,GSM6559949,GSM6559950,GSM6559951,GSM6559952,GSM6559953,GSM6559954,GSM6559955,GSM6559956,GSM6559957,GSM6559958,GSM6559959,GSM6559960,GSM6559961,GSM6559962,GSM6559963,GSM6559964,GSM6559965,GSM6559966,GSM6559967,GSM6559968,GSM6559969,GSM6559970,GSM6559971,GSM6559972,GSM6559973,GSM6559974,GSM6559975,GSM6559976,GSM6559977,GSM6559978,GSM6559979,GSM6559980,GSM6559981,GSM6559982,GSM6559983,GSM6559984,GSM6559985,GSM6559986,GSM6559987,GSM6559988,GSM6559989,GSM6559990,GSM6559991,GSM6559992

p3/preprocess/COVID-19/gene_data/GSE212866.csv

@@ -0,0 +1,10 @@
+Gene,GSM6559856,GSM6559857,GSM6559858,GSM6559859,GSM6559860,GSM6559861,GSM6559862,GSM6559863,GSM6559864,GSM6559865,GSM6559866,GSM6559867,GSM6559868,GSM6559869,GSM6559870,GSM6559871,GSM6559872,GSM6559873,GSM6559874,GSM6559875,GSM6559876,GSM6559877,GSM6559878,GSM6559879,GSM6559880,GSM6559881,GSM6559882,GSM6559883,GSM6559884,GSM6559885,GSM6559886,GSM6559887,GSM6559888,GSM6559889,GSM6559890,GSM6559891,GSM6559892,GSM6559893,GSM6559894,GSM6559895,GSM6559896,GSM6559897,GSM6559898,GSM6559899,GSM6559900,GSM6559901,GSM6559902,GSM6559903,GSM6559904,GSM6559905,GSM6559906,GSM6559907,GSM6559908,GSM6559909,GSM6559910,GSM6559911,GSM6559912,GSM6559913,GSM6559914,GSM6559915,GSM6559916,GSM6559917,GSM6559918,GSM6559919,GSM6559920,GSM6559921,GSM6559922,GSM6559923,GSM6559924,GSM6559925,GSM6559926,GSM6559927,GSM6559928,GSM6559929,GSM6559930,GSM6559931,GSM6559932,GSM6559933,GSM6559934,GSM6559935,GSM6559936,GSM6559937,GSM6559938,GSM6559939,GSM6559940,GSM6559941,GSM6559942,GSM6559943,GSM6559944,GSM6559945,GSM6559946,GSM6559947,GSM6559948,GSM6559949,GSM6559950,GSM6559951,GSM6559952,GSM6559953,GSM6559954,GSM6559955,GSM6559956,GSM6559957,GSM6559958,GSM6559959,GSM6559960,GSM6559961,GSM6559962,GSM6559963,GSM6559964,GSM6559965,GSM6559966,GSM6559967,GSM6559968,GSM6559969,GSM6559970,GSM6559971,GSM6559972,GSM6559973,GSM6559974,GSM6559975,GSM6559976,GSM6559977,GSM6559978,GSM6559979,GSM6559980,GSM6559981,GSM6559982,GSM6559983,GSM6559984,GSM6559985,GSM6559986,GSM6559987,GSM6559988,GSM6559989,GSM6559990,GSM6559991,GSM6559992
+OR4F16,1.0768513981146663,1.0746556690101419,1.1855120937088368,1.072101027403887,1.0047590436451719,1.0849577788517044,1.0704462885227046,1.0606210420126003,1.0299553836691613,1.098621932746883,1.1697202196670076,1.122437736737904,1.18492500882143,1.088752116224608,1.14786705516353,1.108986102035115,1.1206026607353168,1.0966110214477331,1.2082524510891302,1.146631717605421,1.068551784321651,1.136727467148544,1.3175844114601143,1.0525081845692392,1.1171987638891467,1.0776246807890755,1.0894993355065463,1.175353490896548,1.0869336519246218,1.1105685343699792,1.104622535641604,1.1546919578521047,1.1307677440459318,1.0691373576999978,1.0409286581365083,1.1035219048775122,1.0788523759358,1.0220088578626498,1.0750758689831912,1.114935897772717,1.0743344996117572,1.073537154839303,1.1783240163688284,1.132802640889215,1.1227556389889324,1.0909421382951099,1.1569975180067267,1.0780036321219437,1.0742215366241104,1.218899343456139,1.0518694171462053,1.079491361989893,1.0077327018482833,1.1380697862456013,1.0975767583902805,1.0527918650054482,1.1006803322981382,1.0700069065367033,1.0790256827460125,1.0622395718033468,1.0635877488922234,1.109914671726739,1.0491783509056392,1.1353637638144407,1.156961963649681,1.0463662546122396,1.037034587781605,1.1355367969269914,1.1186500855391484,1.100910927713396,1.0838695040488677,1.0915849013207648,1.0110959886856974,1.0687377397242501,1.105477681291291,1.0702491732531194,1.0609398112303747,1.0849920537179933,1.0499812324450561,1.1634616074574657,1.0701594570902253,1.0578186172828683,1.0550434696831401,1.1449264277302584,1.1255541111272498,1.1493690338059397,1.0555029444616126,1.0749177658439477,1.0715058920863298,1.0735759914006184,1.0994095307944316,1.1356187109146083,1.1724131086110878,1.034332624874326,1.1221671323082751,1.0166738800289805,1.1066500463758246,1.0784178753143727,1.0785809751161357,1.072374153523834,1.1113647894395045,1.1899478315684715,1.0688047093428448,1.158082276079608,1.02322227500547,1.0845615466645373,1.1562327022891816,1.188120543565541,1.0334495874166074,1.065696049916902,1.1229704443709576,1.044473217775162,1.1139720066021652,1.1385120369403756,1.0140444932276427,1.0595318107899387,1.061925559346,1.0628125845663723,0.9922588937036705,1.0231461185629624,1.0326216906828052,1.1035817771693743,1.017411196211424,1.0844231630370482,1.0809385797653985,1.1408480726649248,1.0609763528206857,1.0921805720654811,1.0868079350852513,1.0488409969573749,1.0742715712404616,1.0684641904036345,1.0849551196550453,1.003843083375966,1.0985086284888943,1.0841663825352663,1.0952935712796121
+OR4F17,0.6553338399730243,0.6929398947371157,0.5917319630055771,0.6140491039790028,0.5983723752318743,0.6046927133790672,0.5535264976507757,0.5779798681102472,0.6963171579881257,0.69373717681362,0.7454301391439929,0.6235758645684786,0.5886306990745787,0.6572872348222457,0.6461796227271199,0.6123452205774342,0.6029344191253828,0.64798216044389,0.59782044193749,0.6980778066334371,0.5994180907786799,0.6001120131915058,0.8340010446767343,0.6003752556380844,0.5766758710546328,0.5819408071503458,0.6260123919485443,0.6838697447053629,0.5904579071317186,0.6326186465108986,0.6199049857362586,0.6297551638416258,0.6114385618649271,0.6034241639312529,0.5975241149663371,0.5948676755418086,0.6470300503708043,0.5730390009227758,0.6335410244085686,0.6262476596661429,0.5806008271316943,0.6390009538921886,0.61779515655815,0.6609994143474271,0.5931169454524242,0.5953081505238957,0.6692006890089571,0.6305342198997314,0.5933225789008529,0.5898821390488814,0.6615976927828457,0.6214286283566671,0.6473329426508058,0.6554397322474514,0.6139100643354942,0.6220254820730672,0.6126515448003529,0.62178444337151,0.6671746005024086,0.6037974794388943,0.6100458311339242,0.5991780502631715,0.611948879377113,0.5755296397867243,0.6272826190986001,0.59021002321731,0.6117199571216471,0.6583963546237314,0.5966456367463314,0.5810866941295443,0.5937657168814529,0.6615402104599257,0.6167486329390485,0.6199938890226672,0.5834717671073028,0.57703965836488,0.58594347486959,0.6177170432652772,0.6153978442459557,0.6434053660979872,0.6444028226082885,0.6571015791589714,0.5940580428449358,0.6216793200901628,0.5987684242920986,0.7322590841449129,0.6499215765896471,0.6182130326502543,0.6441693592985428,0.6039651919859386,0.5668595698120071,0.6607614670593442,0.6449116714920743,0.5943909043508342,0.59151696394858,0.5863443397581871,0.6254128352889857,0.5961468496556886,0.5726817726229301,0.6097155475233714,0.6048263919632028,0.6456788912130514,0.60339383037603,0.6240605259191871,0.5839146163146671,0.587759355175,0.6166583795197543,0.6627585975435828,0.6138805278571885,0.6229056884683671,0.6499948972945042,0.61544186066775,0.5815661040256558,0.6153920550890942,0.63651881466403,0.6052391950132614,0.5590595411769229,0.6086376892414244,0.56908236905456,0.5848914550166943,0.5831713603539743,0.6042494180708328,0.6082667804314571,0.6289244018791372,0.5869610007715328,0.60428954490742,0.5941718360279186,0.6372521893347314,0.6477984370577742,0.6648171037701914,0.6128063924195101,0.6145076885256371,0.6322322013678914,0.6129353697413772,0.6712064852884986,0.5979505507283658,0.6031101886052286
+OR4F21,0.6563246315131817,0.6772080647075233,0.81237689464592,0.6043559254465133,0.5516428489403934,0.56403791276419,0.5590447962767483,0.592592159011495,0.5848480978545617,0.6003290492859,0.727443690389335,0.6468043747131467,0.6539487064701083,0.6101923054827617,0.6313766650267266,0.64417515002055,0.6413842064115051,0.6141050414397801,0.6732172304827967,0.6140246043972349,0.6548688427753017,0.5433984421734717,0.575990096638565,0.62471719358224,0.6587150404004684,0.582624999043275,0.60260798915562,0.6538013459474666,0.5966763290599583,0.5838543824103116,0.6514180060390783,0.71763556690426,0.737010097307965,0.61996072197477,0.5595051683363,0.5338207998305516,0.5988398752212533,0.6286513108545401,0.617654178862095,0.7682223567130549,0.6565772761216616,0.610325160455495,0.635112652198995,0.61177636860216,0.6439476181060083,0.6514734911556016,0.7033367387237134,0.5881500875612834,0.5630266967992134,0.6714962350289816,0.657596368558615,0.5768746982769334,0.5683961751276033,0.6368557247120383,0.5554644461479966,0.5860462837740666,0.6332913994594084,0.6859002519940499,0.6093273764825117,0.6205909622415767,0.659347227151995,0.6252434052936283,0.59273108095204,0.58404109478945,0.649560562272415,0.5282081394888767,0.5407793841101917,0.5774498117311401,0.6308048566822784,0.6221954601665333,0.59538720605548,0.6087201864112983,0.564619142938665,0.6427277633905867,0.6098509893348083,0.628484466972445,0.5475520007226433,0.5767867373900584,0.5900335682559633,0.66284056555464,0.6099577759281133,0.5679410403509467,0.6062391344545683,0.6043795302427567,0.5672491984130617,0.626402455634275,0.5715287780972117,0.5885116113541867,0.6458440755131251,0.6895549920011751,0.595996235692005,0.6057442708393783,0.5841916620039284,0.5831829023539817,0.5783071683501083,0.5868875394263583,0.6128201724512317,0.606478857436835,0.6057189875372534,0.699641333187385,0.6646146599074566,0.6696943580107916,0.6407332021509683,0.5722983966225966,0.63691569624168,0.5934657581327251,0.5915280380841184,0.6601627655661501,0.5897540188870033,0.6075127814570184,0.5792216573739116,0.5591518061106117,0.6229524421334066,0.6103797941882533,0.5639662838054333,0.6679713579371517,0.5787107044714983,0.5310671684214917,0.495054692177105,0.6772742341858167,0.5863355182794533,0.6359753628959567,0.5809282465241417,0.5882323346279283,0.5284715296404933,0.5923071679503683,0.644955658262205,0.5986361188735834,0.631972342956085,0.6169324032217933,0.57621904417,0.58677574508249,0.6081574075430833,0.5882955835035183,0.6033296664264217,0.58964947102986,0.551104990516645
+OR4F29,1.0768513981146663,1.0746556690101419,1.1855120937088368,1.072101027403887,1.0047590436451719,1.0849577788517044,1.0704462885227046,1.0606210420126003,1.0299553836691613,1.098621932746883,1.1697202196670076,1.122437736737904,1.18492500882143,1.088752116224608,1.14786705516353,1.108986102035115,1.1206026607353168,1.0966110214477331,1.2082524510891302,1.146631717605421,1.068551784321651,1.136727467148544,1.3175844114601143,1.0525081845692392,1.1171987638891467,1.0776246807890755,1.0894993355065463,1.175353490896548,1.0869336519246218,1.1105685343699792,1.104622535641604,1.1546919578521047,1.1307677440459318,1.0691373576999978,1.0409286581365083,1.1035219048775122,1.0788523759358,1.0220088578626498,1.0750758689831912,1.114935897772717,1.0743344996117572,1.073537154839303,1.1783240163688284,1.132802640889215,1.1227556389889324,1.0909421382951099,1.1569975180067267,1.0780036321219437,1.0742215366241104,1.218899343456139,1.0518694171462053,1.079491361989893,1.0077327018482833,1.1380697862456013,1.0975767583902805,1.0527918650054482,1.1006803322981382,1.0700069065367033,1.0790256827460125,1.0622395718033468,1.0635877488922234,1.109914671726739,1.0491783509056392,1.1353637638144407,1.156961963649681,1.0463662546122396,1.037034587781605,1.1355367969269914,1.1186500855391484,1.100910927713396,1.0838695040488677,1.0915849013207648,1.0110959886856974,1.0687377397242501,1.105477681291291,1.0702491732531194,1.0609398112303747,1.0849920537179933,1.0499812324450561,1.1634616074574657,1.0701594570902253,1.0578186172828683,1.0550434696831401,1.1449264277302584,1.1255541111272498,1.1493690338059397,1.0555029444616126,1.0749177658439477,1.0715058920863298,1.0735759914006184,1.0994095307944316,1.1356187109146083,1.1724131086110878,1.034332624874326,1.1221671323082751,1.0166738800289805,1.1066500463758246,1.0784178753143727,1.0785809751161357,1.072374153523834,1.1113647894395045,1.1899478315684715,1.0688047093428448,1.158082276079608,1.02322227500547,1.0845615466645373,1.1562327022891816,1.188120543565541,1.0334495874166074,1.065696049916902,1.1229704443709576,1.044473217775162,1.1139720066021652,1.1385120369403756,1.0140444932276427,1.0595318107899387,1.061925559346,1.0628125845663723,0.9922588937036705,1.0231461185629624,1.0326216906828052,1.1035817771693743,1.017411196211424,1.0844231630370482,1.0809385797653985,1.1408480726649248,1.0609763528206857,1.0921805720654811,1.0868079350852513,1.0488409969573749,1.0742715712404616,1.0684641904036345,1.0849551196550453,1.003843083375966,1.0985086284888943,1.0841663825352663,1.0952935712796121
+OR4F3,1.0768513981146663,1.0746556690101419,1.1855120937088368,1.072101027403887,1.0047590436451719,1.0849577788517044,1.0704462885227046,1.0606210420126003,1.0299553836691613,1.098621932746883,1.1697202196670076,1.122437736737904,1.18492500882143,1.088752116224608,1.14786705516353,1.108986102035115,1.1206026607353168,1.0966110214477331,1.2082524510891302,1.146631717605421,1.068551784321651,1.136727467148544,1.3175844114601143,1.0525081845692392,1.1171987638891467,1.0776246807890755,1.0894993355065463,1.175353490896548,1.0869336519246218,1.1105685343699792,1.104622535641604,1.1546919578521047,1.1307677440459318,1.0691373576999978,1.0409286581365083,1.1035219048775122,1.0788523759358,1.0220088578626498,1.0750758689831912,1.114935897772717,1.0743344996117572,1.073537154839303,1.1783240163688284,1.132802640889215,1.1227556389889324,1.0909421382951099,1.1569975180067267,1.0780036321219437,1.0742215366241104,1.218899343456139,1.0518694171462053,1.079491361989893,1.0077327018482833,1.1380697862456013,1.0975767583902805,1.0527918650054482,1.1006803322981382,1.0700069065367033,1.0790256827460125,1.0622395718033468,1.0635877488922234,1.109914671726739,1.0491783509056392,1.1353637638144407,1.156961963649681,1.0463662546122396,1.037034587781605,1.1355367969269914,1.1186500855391484,1.100910927713396,1.0838695040488677,1.0915849013207648,1.0110959886856974,1.0687377397242501,1.105477681291291,1.0702491732531194,1.0609398112303747,1.0849920537179933,1.0499812324450561,1.1634616074574657,1.0701594570902253,1.0578186172828683,1.0550434696831401,1.1449264277302584,1.1255541111272498,1.1493690338059397,1.0555029444616126,1.0749177658439477,1.0715058920863298,1.0735759914006184,1.0994095307944316,1.1356187109146083,1.1724131086110878,1.034332624874326,1.1221671323082751,1.0166738800289805,1.1066500463758246,1.0784178753143727,1.0785809751161357,1.072374153523834,1.1113647894395045,1.1899478315684715,1.0688047093428448,1.158082276079608,1.02322227500547,1.0845615466645373,1.1562327022891816,1.188120543565541,1.0334495874166074,1.065696049916902,1.1229704443709576,1.044473217775162,1.1139720066021652,1.1385120369403756,1.0140444932276427,1.0595318107899387,1.061925559346,1.0628125845663723,0.9922588937036705,1.0231461185629624,1.0326216906828052,1.1035817771693743,1.017411196211424,1.0844231630370482,1.0809385797653985,1.1408480726649248,1.0609763528206857,1.0921805720654811,1.0868079350852513,1.0488409969573749,1.0742715712404616,1.0684641904036345,1.0849551196550453,1.003843083375966,1.0985086284888943,1.0841663825352663,1.0952935712796121
+OR4F4,0.35178130313922273,0.38418912826439366,0.32196796510565456,0.33410300254604364,0.32852714840654185,0.33745694723311725,0.3409822506560782,0.32502106939083997,0.3568615016396191,0.39391362581692724,0.40268788496872543,0.3407282947387245,0.33824828801705725,0.3524148108011018,0.3489517224323918,0.3401901487036909,0.33589888974715454,0.3511096992134109,0.3402201706009718,0.35670060941952636,0.33035791689784816,0.3320913265001055,0.48389280124870454,0.3438022934408227,0.35104554770231183,0.3216119057455209,0.33647511668756364,0.33983760132389274,0.3466932185856791,0.34378452610135457,0.3197100193941027,0.3628524460345118,0.35162270541481544,0.32793584078387,0.3225922055179891,0.32444196327948455,0.3763849237008245,0.3375739234108091,0.3493898885494409,0.3761723261077737,0.3408517170276427,0.37050437764683913,0.3382037151817891,0.30850052807402545,0.33525120773901274,0.3358096725539718,0.34236320608560183,0.3347035499124427,0.33950667185700184,0.33553024768694456,0.37353887437055816,0.3255166615211282,0.3825226481102954,0.34454180880438817,0.34251219519485454,0.35122779738235094,0.3315375938786482,0.3447441303765473,0.37989933383908453,0.3430849247064227,0.33767185983782816,0.3246875463153645,0.3256292595320727,0.31754935396163636,0.33533438607726457,0.33446110042624727,0.3582978337985873,0.34784148825914546,0.3342032084882136,0.32350728106038545,0.34595215944796454,0.33963439009467095,0.37582801775821184,0.33993134899364275,0.34151169220311817,0.33929534907612546,0.3384291011448073,0.34014022224475,0.34026588894463816,0.3265398893246609,0.33957480929572,0.35158279088997185,0.3290036336060573,0.34583142076008,0.34303241860340905,0.3766206680097618,0.3454686731971327,0.3363780217678091,0.35344163118450816,0.31654139217522365,0.36852314866380365,0.33423834463908636,0.35991622848452,0.33083819605713183,0.36623291411849457,0.3261298686661609,0.3174216040580082,0.3367001561679027,0.35309461590485636,0.34160731587503457,0.32766444705072273,0.34807801826689,0.3402388705415209,0.35819864148695546,0.32752025751819996,0.32781148969589186,0.3432652052095182,0.42447864940037816,0.32294648271096543,0.32980770966841455,0.40392650486626097,0.3571418500226991,0.34241542316362633,0.33463079933248907,0.3275209257545073,0.33535407856849453,0.34500651705251545,0.33681994761342726,0.3141902958402,0.3308801874880482,0.3352917026819664,0.3529275273648409,0.33622872270257637,0.33306116952716364,0.33324065664331365,0.36188313872630545,0.3351166951995218,0.3555559038883127,0.3611497493970127,0.3343403271996064,0.36687399976017177,0.37538348131680727,0.32969111210294366,0.34161445591024636,0.34638386339889726,0.35406093289884455,0.34609877187417726
+OR4F5,1.274667643822826,1.290544980894942,1.251430478263156,1.25229529332003,1.25799135746962,1.33055527527261,1.211418747221848,1.308785687347356,1.26318634819261,1.326419255212264,1.305666940059632,1.228183731204878,1.191516351941254,1.334852438699066,1.335812713160688,1.312911175419306,1.271037754290168,1.3036276174502939,1.232621089257346,1.334845571682868,1.273817297683022,1.28799783967732,1.4070937590902841,1.331580933631244,1.2189101844676,1.272357704684052,1.282057934589548,1.352194351831548,1.2343640636915398,1.2534564297323239,1.199174304056372,1.269057832015596,1.310302563424628,1.242620651722474,1.220069564507892,1.335491516335018,1.3767533585776521,1.2572047307704979,1.16297370101064,1.324023398018876,1.237508303118096,1.32792700114833,1.28792328696748,1.247710592553524,1.296046500468126,1.2494725738933299,1.259138995660866,1.3577728151778061,1.302701557970324,1.2851344217332599,1.25135290880331,1.303698826759474,1.298327676091406,1.271074442170156,1.262406071246522,1.276863229187726,1.277217398365752,1.312024740256372,1.263060482245648,1.272840064997178,1.255648571030916,1.355606354759028,1.277818460042446,1.28546186686784,1.312781868477582,1.29185116931727,1.2890967654846999,1.275832028884028,1.264028471905056,1.2765458634809002,1.255523442858186,1.264635894324906,1.326752159179894,1.3186558528847099,1.236503799299786,1.2066558590168681,1.3022698360710598,1.272785246563656,1.290868550692,1.25979472867005,1.224919631503356,1.3031402246550319,1.305110463159682,1.19763912918488,1.299002319025756,1.230942615060394,1.256991698451338,1.263381826882412,1.271590994541906,1.2357652638949141,1.19644222941384,1.3242015380891279,1.345444444291814,1.208274588302108,1.340114944409252,1.31883847153491,1.225509551758252,1.290631420872948,1.301650331550574,1.257974515927796,1.227423345656348,1.249794672693844,1.24919996901667,1.183491071228874,1.2983217325088021,1.27255534991739,1.26570727299023,1.346525518140048,1.269709224438084,1.33921144425093,1.20848955366696,1.244336784233016,1.185564982950004,1.251857701242298,1.333942270870006,1.26722774064735,1.193822967709354,1.293152075672096,1.2386233921913639,1.235134063529952,1.224951552387422,1.309004894426816,1.297338937966828,1.317865740507104,1.260143824645276,1.22317528788668,1.2652200153053719,1.36255142825451,1.190695132966832,1.303454937696362,1.288639146539132,1.236220619764676,1.308419058829108,1.354739269945856,1.31656155056224,1.306310234107718,1.25888478826543
+PCMTD2,1.1314702168717719,1.178735847264599,1.2245047281605972,1.1707997339039795,1.128939056573686,1.1411636472463271,1.161289281576842,1.156830649207412,1.15187065771994,1.2638116861388533,1.2081978071262893,1.1951147323597704,1.206134232680202,1.1714520436622313,1.1946966494726179,1.235167396358613,1.1401040149722372,1.249694151953387,1.1947633542124798,1.1649196841825775,1.2064639710090823,1.1893347308410123,1.1550544612359706,1.2192540166595252,1.2050380633177098,1.1603065315703716,1.1709060117054608,1.190754275984514,1.1533243460051519,1.183699306543349,1.1404443442805046,1.2236148677257535,1.1635644241765497,1.1148602909117797,1.0939912758015047,1.1075829963313444,1.2386277946757365,1.1638814101450308,1.162575212357994,1.245887455325073,1.1887521931853322,1.1757967935851883,1.1493368422768266,1.2120669395826535,1.1629061972830623,1.2498333035538227,1.2293517687998299,1.2155522985128555,1.18471667028542,1.1812927167766794,1.1866413935454119,1.1359506158735018,1.1438738872670031,1.203087378544422,1.1107894279109292,1.1270399352003968,1.1892947723349607,1.2167351293893005,1.127316767910882,1.2068849318825592,1.185909903500872,1.0325293090630172,1.097431639421046,1.1417485001084386,1.1537592624543096,1.1073552118987793,1.1263310910182271,1.073767711154603,1.1169343649149481,1.1656883501513176,1.110057767920056,1.102304309231485,1.1452530788456108,1.1538507593759804,1.1365700053151766,1.1528809897958099,1.0789875017305923,1.0832460938635269,1.0757569220011958,1.135747842104374,1.1687629226236835,1.2483004998882838,1.202313360228181,1.1022435729550315,1.136366004532918,1.1618711765533825,1.0709120128634604,1.1084892058618336,1.0824010656665337,1.1111967762261525,1.2053857801323562,1.1853296474866322,1.1328860819353244,1.078260738702045,1.158246763826969,1.0711824808865429,1.1781817263485532,1.1270474771575059,1.1985584835499346,1.2045795774467538,1.1177199363488675,1.2285254480966905,1.1954103787421695,1.090696354984683,1.1150062140879504,1.1261819778662265,1.0709652448604794,1.1235361594537339,1.1422853227781062,1.1685347547678018,1.1421605547574503,1.1733889980178398,1.16751194238027,1.1446670081321548,1.1459350313120642,1.1828409108923967,1.1046848625399481,1.045166608933209,1.1171124514015074,1.1224928478853122,1.094523540439178,1.1846058234612427,1.1571003501462402,1.1235204398313101,1.143151494050339,1.1776071672430237,1.0762850948856064,1.1732740797832664,1.1265875983796987,1.2079220964535464,1.1977733008262454,1.1189929119762096,1.0702280795891435,1.1602000235422882,1.2182730701181,1.0913523817565018,1.157522157467585
+SEPT14,0.4683226182878678,0.4587623088563688,0.5348122841791499,0.49233936243001886,0.47803711582183556,0.4903977374926644,0.46521694142278885,0.47870725860104446,0.4900062361705634,0.5098639999441645,0.5243767936368111,0.44105308802032556,0.4864062476202766,0.4483090534007256,0.5238732272152722,0.46876872518099777,0.4708410731123211,0.53843849958782,0.522373990928529,0.49950374155404,0.4842082029308055,0.47074645719538893,0.5680956630243199,0.46385673300128666,0.4884565876381967,0.4889181228505744,0.5008064277493578,0.5714131165380089,0.45613363470248774,0.50810453137386,0.5349939098605789,0.52614784664389,0.4729913945599744,0.46611814150812,0.4523664423874444,0.47590942287467886,0.5210021055284778,0.45804232583262333,0.4797522697588289,0.51554576951321,0.49976424281592,0.49233244452996555,0.4739493213470567,0.5066912881490766,0.5157670804438478,0.45986325009568446,0.52778778971066,0.4867390304811511,0.47080405055950003,0.53060699241846,0.5018229193816978,0.4532425953464389,0.4855216677602345,0.49935302403205,0.4453754143366278,0.46240195347617774,0.47350160819566,0.4926579888902744,0.42788869275814,0.4655950157407611,0.49183610752732665,0.43487930562620997,0.4682068376081433,0.4613120212248889,0.47797785706131,0.46189726416818,0.49151992064565114,0.4690631044363022,0.47600081721583226,0.4644769416248367,0.4529446588296022,0.4704609435934711,0.4429183324073811,0.44557532997989774,0.45978793590986444,0.47702793854611997,0.4672983707626089,0.4564976736942545,0.49107251629453774,0.4712343154542611,0.45524766332518785,0.48566060854391996,0.48861737599320776,0.44982778827496894,0.48136382651768334,0.4558383735913044,0.43504638428318776,0.48861841400465444,0.4904109360203678,0.4890100783073889,0.4469855780156177,0.4774897610762177,0.45774991527254333,0.44112093262105556,0.47256363595428447,0.48557763528281217,0.5076215492503433,0.43514847031616555,0.4486209472785256,0.4669835095427467,0.43945525769783667,0.4784590342187289,0.4526134604568422,0.4610368108746322,0.4664608195102822,0.4611918884372845,0.4815856027874344,0.4657392810911867,0.47491597206149777,0.4861164154757022,0.4441234888705489,0.49845167717153444,0.48446992422528556,0.4834292083545067,0.4555830095703988,0.46920786386817337,0.5042914880820278,0.4423374601331278,0.48891901489364337,0.45983947433592554,0.45247464637514667,0.47056467359592447,0.4665788836071622,0.46483000556878334,0.4347221477961667,0.45086510946831776,0.45530449387711003,0.4997817162000345,0.4568510992047156,0.4656915565058655,0.5031732430585867,0.4443492778539111,0.45367775735188,0.5193939518587412,0.48011359936258446,0.47358763604882004,0.48261868273508224

p3/preprocess/COVID-19/gene_data/GSE213313.csv

@@ -0,0 +1,3 @@
+Gene,GSM6578718,GSM6578719,GSM6578720,GSM6578721,GSM6578722,GSM6578723,GSM6578724,GSM6578725,GSM6578726,GSM6578727,GSM6578728,GSM6578729,GSM6578730,GSM6578731,GSM6578732,GSM6578733,GSM6578734,GSM6578735,GSM6578736,GSM6578737,GSM6578738,GSM6578739,GSM6578740,GSM6578741,GSM6578742,GSM6578743,GSM6578744,GSM6578745,GSM6578746,GSM6578747,GSM6578748,GSM6578749,GSM6578750,GSM6578751,GSM6578752,GSM6578753,GSM6578754,GSM6578755,GSM6578756,GSM6578757,GSM6578758,GSM6578759,GSM6578760,GSM6578761,GSM6578762,GSM6578763,GSM6578764,GSM6578765,GSM6578766,GSM6578767,GSM6578768,GSM6578769,GSM6578770,GSM6578771,GSM6578772,GSM6578773,GSM6578774,GSM6578775,GSM6578776,GSM6578777,GSM6578778,GSM6578779,GSM6578780,GSM6578781,GSM6578782,GSM6578783,GSM6578784,GSM6578785,GSM6578786,GSM6578787,GSM6578788,GSM6578789,GSM6578790,GSM6578791,GSM6578792,GSM6578793,GSM6578794,GSM6578795,GSM6578796,GSM6578797,GSM6578798,GSM6578799,GSM6578800,GSM6578801,GSM6578802,GSM6578803,GSM6578804,GSM6578805,GSM6578806,GSM6578807,GSM6578808,GSM6578809,GSM6578810,GSM6578811
+OR4F21,4.700676044,3.607991778,4.199589135,3.873143414,4.190791678,3.830357158,4.586017521,4.224843561,4.57059882,4.27604549,3.676212639,3.240883277,3.80065253,3.899865069,4.391696346,3.996783332,4.458140695,3.987137358,3.755626714,4.009703929,3.105675204,3.597886622,3.462428835,3.40996917,3.438562377,4.6901617,4.38326337,3.780411052,4.56055261,3.981804453,3.556742966,3.83197465,4.848769622,3.937915695,4.199589135,3.538366951,3.9852004,4.241167043,3.94441028,3.667179961,4.891538314,4.541895967,4.711407258,4.829466559,3.93289989,4.27881549,4.929302675,3.536384138,4.307390063,4.038942687,3.636038823,3.823326996,4.342614039,3.793752445,4.230782186,3.738480833,4.435267109,4.134230013,3.741352669,4.06485202,4.669805035,4.427798992,3.919269459,3.830357158,3.650777244,4.259713524,3.216323397,4.186372749,4.213719868,5.016524002,3.812444021,3.648943154,3.417170775,2.935560929,3.751074367,3.628302513,4.074449303,4.288665157,3.483164513,5.721142942,4.410582432,3.882799516,3.662036228,2.897962179,4.322810617,3.460338781,4.475664713,5.289587428,3.150439349,4.864660535,4.037074093,3.377847944,3.377847944,3.59471419
+PCMTD2,4.265710102,5.992884205,5.200361425,4.542554687,5.915981155,6.410320603,5.307525562,6.126133362,4.642720238,3.821699774,5.09910732,6.202207284,6.032520685,5.294581794,4.344126179,6.427139026,2.857240969,4.389501187,2.802194353,6.16354806,6.051388389,5.584850707,3.668991027,6.504442966,6.373494647,3.897807236,4.545186562,5.924597841,3.629853098,6.712731923,3.372671159,6.399907452,4.369964167,4.315120943,2.031001546,4.694317358,2.50786527,4.401170293,4.7688321,4.35729023,3.317547908,4.239949168,6.412754573,5.636979468,4.59239366,4.813317479,6.483841565,5.065126942,4.165784004,5.82279804,6.301671384,5.775109505,5.041787464,4.439870931,2.815336725,3.111902159,4.435267109,4.375520408,6.477482777,5.036536246,5.636053902,4.551090785,3.041881217,3.830357158,6.472738427,4.125463269,4.458489779,4.911309305,5.458611805,4.511601526,3.90191997,4.347145712,6.79919899,5.619525518,6.79988788,4.423692868,6.265071425,5.226217605,4.128976362,3.21549749,5.109480767,5.532094877,5.724775179,5.469217603,5.902253323,6.439702771,3.258631794,5.712680765,4.369964167,4.090754694,4.727862758,4.222790078,6.961960732,4.548141694

p3/preprocess/COVID-19/gene_data/GSE227080.csv

@@ -0,0 +1 @@
+,ABCB1,ABL1,ADA,AHR,AICDA,AIRE,APP,ARG1,ARG2,ARHGDIB,ATG10,ATG12,ATG16L1,ATG5,ATG7,ATM,B2M,B3GAT1,BATF,BATF3,BAX,BCAP31,BCL10,BCL2,BCL2L11,BCL3,BCL6,BID,BLNK,BST1,BST2,BTK,BTLA,C14orf166,C1QA,C1QB,C1QBP,C1R,C1S,C2,C3,C4A/B,C4BPA,C5,C6,C7,C8A,C8B,C8G,C9,CAMP,CARD9,CASP1,CASP10,CASP2,CASP3,CASP8,CCBP2,CCL11,CCL13,CCL15,CCL16,CCL18,CCL19,CCL2,CCL20,CCL22,CCL23,CCL24,CCL26,CCL3,CCL4,CCL5,CCL7,CCL8,CCND3,CCR1,CCR10,CCR2,CCR5,CCR6,CCR7,CCR8,CCRL1,CCRL2,CD14,CD160,CD163,CD164,CD19,CD1A,CD1D,CD2,CD209,CD22,CD24,CD244,CD247,CD27,CD274,CD276,CD28,CD34,CD36,CD3D,CD3E,CD3EAP,CD4,CD40,CD40LG,CD44,CD45R0,CD45RA,CD45RB,CD46,CD48,CD5,CD53,CD55,CD58,CD59,CD6,CD7,CD70,CD74,CD79A,CD79B,CD80,CD81,CD82,CD83,CD86,CD8A,CD8B,CD9,CD96,CD97,CD99,CDH5,CDKN1A,CEACAM1,CEACAM6,CEACAM8,CEBPB,CFB,CFD,CFH,CFI,CFP,CHUK,CIITA,CISH,CLEC4A,CLEC4E,CLEC5A,CLEC6A,CLEC7A,CLU,CMKLR1,CR1,CR2,CRADD,CSF1,CSF1R,CSF2,CSF2RB,CSF3R,CTLA4-TM,CTLA4_all,CTNNB1,CTSC,CTSG,CTSS,CUL9,CX3CL1,CX3CR1,CXCL1,CXCL10,CXCL11,CXCL12,CXCL13,CXCL2,CXCL9,CXCR1,CXCR2,CXCR3,CXCR4,CXCR6,CYBB,DEFB1,DEFB103A,DEFB103B,DEFB4A,DPP4,DUSP4,EBI3,EDNRB,EGR1,EGR2,ENTPD1,EOMES,ETS1,FADD,FAS,FCAR,FCER1A,FCER1G,FCGR1A/B,FCGR2A,FCGR2A/C,FCGR2B,FCGR3A/B,FCGRT,FKBP5,FN1,FOXP3,FYN,GATA3,GBP1,GBP5,GFI1,GNLY,GP1BB,GPI,GPR183,GZMA,GZMB,GZMK,HAMP,HAVCR2,HFE,HLA-A,HLA-B,HLA-C,HLA-DMA,HLA-DMB,HLA-DOB,HLA-DPA1,HLA-DPB1,HLA-DQA1,HLA-DQB1,HLA-DRA,HLA-DRB1,HLA-DRB3,HRAS,ICAM1,ICAM2,ICAM3,ICAM4,ICAM5,ICOS,ICOSLG,IDO1,IFI16,IFI35,IFIH1,IFIT2,IFITM1,IFNA1/13,IFNA2,IFNAR1,IFNAR2,IFNB1,IFNG,IFNGR1,IGF2R,IKBKAP,IKBKB,IKBKE,IKBKG,IKZF1,IKZF2,IKZF3,IL10,IL10RA,IL11RA,IL12A,IL12B,IL12RB1,IL13,IL13RA1,IL15,IL16,IL17A,IL17B,IL17F,IL18,IL18R1,IL18RAP,IL19,IL1A,IL1B,IL1R1,IL1R2,IL1RAP,IL1RL1,IL1RL2,IL1RN,IL2,IL20,IL21,IL21R,IL22,IL22RA2,IL23A,IL23R,IL26,IL27,IL28A,IL28A/B,IL29,IL2RA,IL2RB,IL2RG,IL3,IL32,IL4,IL4R,IL5,IL6,IL6R,IL6ST,IL7,IL7R,IL8,IL9,ILF3,IRAK1,IRAK2,IRAK3,IRAK4,IRF1,IRF3,IRF4,IRF5,IRF7,IRF8,IRGM,ITGA2B,ITGA4,ITGA5,ITGA6,ITGAE,ITGAL,ITGAM,ITGAX,ITGB1,ITGB2,ITLN1,ITLN2,JAK1,JAK2,JAK3,KCNJ2,KIR3DL1,KIR3DL2,KIR3DL3,KIR_Activating_Subgroup_1,KIR_Activating_Subgroup_2,KIR_Inhibiting_Subgroup_1,KIR_Inhibiting_Subgroup_2,KIT,KLRAP1,KLRB1,KLRC1,KLRC2,KLRC3,KLRC4,KLRD1,KLRF1,KLRF2,KLRG1,KLRG2,KLRK1,LAG3,LAIR1,LAMP3,LCK,LCP2,LEF1,LGALS3,LIF,LILRA1,LILRA2,LILRA3,LILRA4,LILRA5,LILRA6,LILRB1,LILRB2,LILRB3,LILRB4,LILRB5,LITAF,LTA,LTB4R,LTB4R2,LTBR,LTF,LY96,MAF,MALT1,MAP4K1,MAP4K2,MAP4K4,MAPK1,MAPK11,MAPK14,MAPKAPK2,MARCO,MASP1,MASP2,MBL2,MBP,MCL1,MIF,MME,MR1,MRC1,MS4A1,MSR1,MUC1,MX1,MYD88,NCAM1,NCF4,NCR1,NFATC1,NFATC2,NFATC3,NFIL3,NFKB1,NFKB2,NFKBIA,NFKBIZ,NLRP3,NOD1,NOD2,NOS2,NOTCH1,NOTCH2,NT5E,PAX5,PDCD1,PDCD1LG2,PDCD2,PDGFB,PDGFRB,PECAM1,PIGR,PLA2G2A,PLA2G2E,PLAU,PLAUR,PML,POU2F2,PPARG,PPBP,PRDM1,PRF1,PRKCD,PSMB10,PSMB5,PSMB7,PSMB8,PSMB9,PSMC2,PSMD7,PTAFR,PTGER4,PTGS2,PTK2,PTPN2,PTPN22,PTPN6,PTPRC_all,PYCARD,RAF1,RAG1,RAG2,RARRES3,RELA,RELB,RORC,RUNX1,S100A8,S100A9,S1PR1,sCTLA4,SELE,SELL,SELPLG,SERPING1,SH2D1A,SIGIRR,SKI,SLAMF1,SLAMF6,SLAMF7,SLC2A1,SMAD3,SMAD5,SOCS1,SOCS3,SPP1,SRC,STAT1,STAT2,STAT3,STAT4,STAT5A,STAT5B,STAT6,SYK,TAGAP,TAL1,TAP1,TAP2,TAPBP,TBK1,TBX21,TCF4,TCF7,TFRC,TGFB1,TGFBI,TGFBR1,TGFBR2,THY1,TICAM1,TIGIT,TIRAP,TLR1,TLR2,TLR3,TLR4,TLR5,TLR7,TLR8,TLR9,TMEM173,TNF,TNFAIP3,TNFAIP6,TNFRSF10C,TNFRSF11A,TNFRSF13B,TNFRSF13C,TNFRSF14,TNFRSF17,TNFRSF1B,TNFRSF4,TNFRSF8,TNFRSF9,TNFSF10,TNFSF11,TNFSF12,TNFSF13B,TNFSF15,TNFSF4,TNFSF8,TOLLIP,TP53,TRAF1,TRAF2,TRAF3,TRAF4,TRAF5,TRAF6,TYK2,UBE2L3,VCAM1,VTN,XBP1,XCL1,XCR1,ZAP70,ZBTB16,ZEB1

p3/preprocess/COVID-19/gene_data/GSE243348.csv

@@ -0,0 +1 @@
+ID,GSM7783810,GSM7783811,GSM7783812,GSM7783813,GSM7783814,GSM7783815,GSM7783816,GSM7783817,GSM7783818,GSM7783819,GSM7783820,GSM7783821,GSM7783822,GSM7783823,GSM7783824,GSM7783825,GSM7783826,GSM7783827,GSM7783828,GSM7783829,GSM7783830,GSM7783831,GSM7783832,GSM7783833,GSM7783834,GSM7783835,GSM7783836,GSM7783837,GSM7783838,GSM7783839,GSM7783840,GSM7783841,GSM7783842,GSM7783843,GSM7783844,GSM7783845,GSM7783846,GSM7783847,GSM7783848,GSM7783849,GSM7783850,GSM7783851,GSM7783852,GSM7783853,GSM7783854,GSM7783855,GSM7783856,GSM7783857,GSM7783858,GSM7783859,GSM7783860,GSM7783861,GSM7783862,GSM7783863,GSM7783864,GSM7783865,GSM7783866,GSM7783867,GSM7783868,GSM7783869,GSM7783870,GSM7783871,GSM7783872,GSM7783873,GSM7783874,GSM7783875,GSM7783876,GSM7783877,GSM7783878,GSM7783879,GSM7783880,GSM7783881,GSM7783882,GSM7783883,GSM7783884,GSM7783885,GSM7783886,GSM7783887,GSM7783888,GSM7783889,GSM7783890,GSM7783891,GSM7783892,GSM7783893,GSM7783894,GSM7783895,GSM7783896,GSM7783897,GSM7783898,GSM7783899,GSM7783900,GSM7783901,GSM7783902,GSM7783903,GSM7783904,GSM7783905,GSM7783906,GSM7783907,GSM7783908,GSM7783909,GSM7783910,GSM7783911,GSM7783912,GSM7783913,GSM7783914,GSM7783915,GSM7783916,GSM7783917,GSM7783918,GSM7783919,GSM7783920,GSM7783921,GSM7783922,GSM7783923,GSM7783924,GSM7783925,GSM7783926,GSM7783927,GSM7783928,GSM7783929,GSM7783930,GSM7783931,GSM7783932,GSM7783933,GSM7783934,GSM7783935,GSM7783936,GSM7783937,GSM7783938,GSM7783939,GSM7783940,GSM7783941,GSM7783942,GSM7783943,GSM7783944,GSM7783945,GSM7783946,GSM7783947,GSM7783948,GSM7783949,GSM7783950,GSM7783951,GSM7783952,GSM7783953,GSM7783954,GSM7783955,GSM7783956,GSM7783957,GSM7783958,GSM7783959,GSM7783960,GSM7783961,GSM7783962,GSM7783963,GSM7783964,GSM7783965,GSM7783966,GSM7783967,GSM7783968,GSM7783969,GSM7783970,GSM7783971,GSM7783972,GSM7783973,GSM7783974,GSM7783975,GSM7783976,GSM7783977,GSM7783978,GSM7783979,GSM7783980,GSM7783981,GSM7783982,GSM7783983,GSM7783984,GSM7783985,GSM7783986,GSM7783987,GSM7783988,GSM7783989,GSM7783990,GSM7783991,GSM7783992,GSM7783993,GSM7783994,GSM7783995,GSM7783996,GSM7783997,GSM7783998,GSM7783999,GSM7784000,GSM7784001,GSM7784002,GSM7784003,GSM7784004,GSM7784005,GSM7784006,GSM7784007,GSM7784008,GSM7784009,GSM7784010,GSM7784011,GSM7784012,GSM7784013,GSM7784014,GSM7784015,GSM7784016,GSM7784017,GSM7784018,GSM7784019,GSM7784020,GSM7784021,GSM7784022,GSM7784023,GSM7784024,GSM7784025,GSM7784026,GSM7784027,GSM7784028,GSM7784029,GSM7784030,GSM7784031,GSM7784032,GSM7784033,GSM7784034,GSM7784035,GSM7784036,GSM7784037,GSM7784038,GSM7784039,GSM7784040,GSM7784041,GSM7784042,GSM7784043,GSM7784044,GSM7784045,GSM7784046

p3/preprocess/COVID-19/gene_data/GSE273225.csv

@@ -0,0 +1 @@
+ID,GSM8424381,GSM8424382,GSM8424383,GSM8424384,GSM8424385,GSM8424386,GSM8424387,GSM8424388,GSM8424389,GSM8424390,GSM8424391,GSM8424392,GSM8424393,GSM8424394,GSM8424395,GSM8424396,GSM8424397,GSM8424398,GSM8424399,GSM8424400,GSM8424401,GSM8424402,GSM8424403,GSM8424404,GSM8424405,GSM8424406,GSM8424407,GSM8424408,GSM8424409,GSM8424410,GSM8424411,GSM8424412,GSM8424413,GSM8424414,GSM8424415,GSM8424416,GSM8424417,GSM8424418,GSM8424419,GSM8424420,GSM8424421,GSM8424422,GSM8424423,GSM8424424,GSM8424425,GSM8424426,GSM8424427,GSM8424428,GSM8424429,GSM8424430,GSM8424431,GSM8424432,GSM8424433,GSM8424434,GSM8424435,GSM8424436,GSM8424437,GSM8424438,GSM8424439,GSM8424440,GSM8424441,GSM8424442,GSM8424443,GSM8424444,GSM8424445,GSM8424446,GSM8424447,GSM8424448,GSM8424449,GSM8424450,GSM8424451,GSM8424452,GSM8424453,GSM8424454,GSM8424455,GSM8424456,GSM8424457,GSM8424458,GSM8424459,GSM8424460,GSM8424461,GSM8424462,GSM8424463,GSM8424464,GSM8424465,GSM8424466,GSM8424467,GSM8424468,GSM8424469,GSM8424470,GSM8424471,GSM8424472,GSM8424473,GSM8424474,GSM8424475,GSM8424476,GSM8424477,GSM8424478,GSM8424479,GSM8424480,GSM8424481,GSM8424482,GSM8424483,GSM8424484,GSM8424485,GSM8424486,GSM8424487,GSM8424488,GSM8424489,GSM8424490,GSM8424491,GSM8424492,GSM8424493,GSM8424494,GSM8424495,GSM8424496,GSM8424497,GSM8424498

p3/preprocess/COVID-19/gene_data/GSE275334.csv

@@ -0,0 +1 @@
+ID,GSM8475033,GSM8475034,GSM8475035,GSM8475036,GSM8475037,GSM8475038,GSM8475039,GSM8475040,GSM8475041,GSM8475042,GSM8475043,GSM8475044,GSM8475045,GSM8475046,GSM8475047,GSM8475048,GSM8475049,GSM8475050,GSM8475051,GSM8475052,GSM8475053,GSM8475054,GSM8475055,GSM8475056,GSM8475057,GSM8475058,GSM8475059,GSM8475060,GSM8475061,GSM8475062,GSM8475063,GSM8475064,GSM8475065,GSM8475066,GSM8475067,GSM8475068,GSM8475069,GSM8475070,GSM8475071,GSM8475072,GSM8475073,GSM8475074,GSM8475075,GSM8475076,GSM8475077,GSM8475078,GSM8475079

p3/preprocess/Chronic_Fatigue_Syndrome/GSE67311.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3fd88a7a15344cf19a61c9934d351b294f98b44901a486e7d317fd15b9567a09
+size 31000745

p3/preprocess/Chronic_Fatigue_Syndrome/gene_data/GSE67311.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fdc80550137956d622d2ec9a5baefec581baacd4c28f983c456b9d007fbd70b7
+size 33088822

p3/preprocess/Chronic_kidney_disease/gene_data/GSE104948.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b93ba7a4047baf59fdec451c2020d5d5231e8af80967764436635698582bf94c
+size 17860760

p3/preprocess/Chronic_kidney_disease/gene_data/GSE104954.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bc3a9f0687529d57f9d8a3f651c165f08f173b4bef010a0eb2b694d1dc0ac4c9
+size 19283622

p3/preprocess/Chronic_kidney_disease/gene_data/GSE66494.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d9524c8b61ad1579c84e5d7699ddb3ed40d81d05398389382d88b146aa42c5ff
+size 14315787

p3/preprocess/Colon_and_Rectal_Cancer/GSE46517.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:877b4ebb87ccb0f76768134478d83443b3bf0060c66dcda826422e9c3af688ea
+size 21212631

p3/preprocess/Colon_and_Rectal_Cancer/GSE46862.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3683edd2ed6fb481389c4b14402e3baee14adaa30ed41c33150ea01d144af436
+size 26191070

p3/preprocess/Colon_and_Rectal_Cancer/GSE56699.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a3bde3e89f4901985e9b3208f9c1b2ad04405a012b83da94ced54695e51bc54d
+size 14908240

p3/preprocess/Colon_and_Rectal_Cancer/clinical_data/TCGA.csv

@@ -0,0 +1,737 @@
+sampleID,Colon_and_Rectal_Cancer,Age,Gender
+TCGA-3L-AA1B-01,1,61.0,0.0
+TCGA-4N-A93T-01,1,67.0,1.0
+TCGA-4T-AA8H-01,1,42.0,0.0
+TCGA-5M-AAT4-01,1,74.0,1.0
+TCGA-5M-AAT5-01,1,,
+TCGA-5M-AAT6-01,1,40.0,0.0
+TCGA-5M-AATA-01,1,,
+TCGA-5M-AATE-01,1,76.0,1.0
+TCGA-A6-2670-01,1,45.0,1.0
+TCGA-A6-2671-01,1,85.0,1.0
+TCGA-A6-2671-11,0,85.0,1.0
+TCGA-A6-2672-01,1,82.0,0.0
+TCGA-A6-2672-11,0,82.0,0.0
+TCGA-A6-2674-01,1,71.0,1.0
+TCGA-A6-2674-11,0,71.0,1.0
+TCGA-A6-2675-01,1,78.0,1.0
+TCGA-A6-2675-11,0,78.0,1.0
+TCGA-A6-2676-01,1,75.0,0.0
+TCGA-A6-2676-11,0,75.0,0.0
+TCGA-A6-2677-01,1,68.0,0.0
+TCGA-A6-2677-11,0,68.0,0.0
+TCGA-A6-2678-01,1,43.0,0.0
+TCGA-A6-2678-11,0,43.0,0.0
+TCGA-A6-2679-01,1,73.0,0.0
+TCGA-A6-2679-11,0,73.0,0.0
+TCGA-A6-2680-01,1,72.0,0.0
+TCGA-A6-2680-11,0,72.0,0.0
+TCGA-A6-2681-01,1,73.0,0.0
+TCGA-A6-2681-11,0,73.0,0.0
+TCGA-A6-2682-01,1,70.0,1.0
+TCGA-A6-2682-11,0,70.0,1.0
+TCGA-A6-2683-01,1,57.0,0.0
+TCGA-A6-2683-11,0,57.0,0.0
+TCGA-A6-2684-01,1,75.0,0.0
+TCGA-A6-2684-11,0,75.0,0.0
+TCGA-A6-2685-01,1,48.0,0.0
+TCGA-A6-2685-11,0,48.0,0.0
+TCGA-A6-2686-01,1,81.0,0.0
+TCGA-A6-2686-11,0,81.0,0.0
+TCGA-A6-3807-01,1,53.0,0.0
+TCGA-A6-3807-11,0,53.0,0.0
+TCGA-A6-3808-01,1,73.0,1.0
+TCGA-A6-3808-11,0,73.0,1.0
+TCGA-A6-3809-01,1,71.0,0.0
+TCGA-A6-3809-11,0,71.0,0.0
+TCGA-A6-3810-01,1,62.0,1.0
+TCGA-A6-3810-11,0,62.0,1.0
+TCGA-A6-4105-01,1,79.0,1.0
+TCGA-A6-4107-01,1,57.0,0.0
+TCGA-A6-4107-11,0,57.0,0.0
+TCGA-A6-5656-01,1,74.0,1.0
+TCGA-A6-5657-01,1,65.0,1.0
+TCGA-A6-5659-01,1,82.0,1.0
+TCGA-A6-5659-11,0,82.0,1.0
+TCGA-A6-5660-01,1,73.0,1.0
+TCGA-A6-5661-01,1,80.0,0.0
+TCGA-A6-5662-01,1,46.0,1.0
+TCGA-A6-5662-11,0,46.0,1.0
+TCGA-A6-5664-01,1,80.0,1.0
+TCGA-A6-5665-01,1,84.0,0.0
+TCGA-A6-5665-11,0,84.0,0.0
+TCGA-A6-5666-01,1,78.0,1.0
+TCGA-A6-5667-01,1,40.0,0.0
+TCGA-A6-5667-11,0,40.0,0.0
+TCGA-A6-6137-01,1,55.0,1.0
+TCGA-A6-6138-01,1,61.0,1.0
+TCGA-A6-6140-01,1,62.0,1.0
+TCGA-A6-6141-01,1,31.0,1.0
+TCGA-A6-6142-01,1,56.0,0.0
+TCGA-A6-6648-01,1,56.0,1.0
+TCGA-A6-6649-01,1,66.0,1.0
+TCGA-A6-6650-01,1,69.0,0.0
+TCGA-A6-6651-01,1,55.0,0.0
+TCGA-A6-6652-01,1,59.0,1.0
+TCGA-A6-6653-01,1,82.0,1.0
+TCGA-A6-6654-01,1,65.0,0.0
+TCGA-A6-6780-01,1,74.0,1.0
+TCGA-A6-6781-01,1,43.0,1.0
+TCGA-A6-6782-01,1,82.0,1.0
+TCGA-A6-A565-01,1,34.0,0.0
+TCGA-A6-A566-01,1,55.0,0.0
+TCGA-A6-A567-01,1,56.0,1.0
+TCGA-A6-A56B-01,1,57.0,1.0
+TCGA-A6-A5ZU-01,1,59.0,1.0
+TCGA-AA-3488-01,1,58.0,1.0
+TCGA-AA-3488-11,0,58.0,1.0
+TCGA-AA-3489-01,1,75.0,1.0
+TCGA-AA-3489-11,0,75.0,1.0
+TCGA-AA-3492-01,1,90.0,0.0
+TCGA-AA-3492-11,0,90.0,0.0
+TCGA-AA-3494-01,1,55.0,1.0
+TCGA-AA-3494-11,0,55.0,1.0
+TCGA-AA-3495-01,1,79.0,1.0
+TCGA-AA-3495-11,0,79.0,1.0
+TCGA-AA-3496-01,1,83.0,0.0
+TCGA-AA-3496-11,0,83.0,0.0
+TCGA-AA-3502-01,1,73.0,1.0
+TCGA-AA-3502-11,0,73.0,1.0
+TCGA-AA-3506-01,1,77.0,1.0
+TCGA-AA-3506-11,0,77.0,1.0
+TCGA-AA-3509-01,1,54.0,0.0
+TCGA-AA-3509-11,0,54.0,0.0
+TCGA-AA-3510-01,1,70.0,1.0
+TCGA-AA-3510-11,0,70.0,1.0
+TCGA-AA-3511-01,1,64.0,1.0
+TCGA-AA-3511-11,0,64.0,1.0
+TCGA-AA-3514-01,1,81.0,0.0
+TCGA-AA-3514-11,0,81.0,0.0
+TCGA-AA-3516-01,1,74.0,0.0
+TCGA-AA-3516-11,0,74.0,0.0
+TCGA-AA-3517-01,1,60.0,1.0
+TCGA-AA-3517-11,0,60.0,1.0
+TCGA-AA-3518-01,1,81.0,0.0
+TCGA-AA-3518-11,0,81.0,0.0
+TCGA-AA-3519-01,1,63.0,1.0
+TCGA-AA-3519-11,0,63.0,1.0
+TCGA-AA-3520-01,1,86.0,0.0
+TCGA-AA-3520-11,0,86.0,0.0
+TCGA-AA-3521-01,1,87.0,1.0
+TCGA-AA-3521-11,0,87.0,1.0
+TCGA-AA-3522-01,1,67.0,1.0
+TCGA-AA-3522-11,0,67.0,1.0
+TCGA-AA-3524-01,1,85.0,1.0
+TCGA-AA-3524-11,0,85.0,1.0
+TCGA-AA-3525-01,1,90.0,1.0
+TCGA-AA-3525-11,0,90.0,1.0
+TCGA-AA-3526-01,1,57.0,1.0
+TCGA-AA-3526-11,0,57.0,1.0
+TCGA-AA-3527-01,1,90.0,0.0
+TCGA-AA-3527-11,0,90.0,0.0
+TCGA-AA-3529-01,1,78.0,0.0
+TCGA-AA-3529-11,0,78.0,0.0
+TCGA-AA-3530-01,1,80.0,1.0
+TCGA-AA-3531-01,1,75.0,0.0
+TCGA-AA-3531-11,0,75.0,0.0
+TCGA-AA-3532-01,1,63.0,1.0
+TCGA-AA-3532-11,0,63.0,1.0
+TCGA-AA-3534-01,1,78.0,0.0
+TCGA-AA-3534-11,0,78.0,0.0
+TCGA-AA-3538-01,1,54.0,0.0
+TCGA-AA-3542-01,1,69.0,1.0
+TCGA-AA-3543-01,1,84.0,1.0
+TCGA-AA-3544-01,1,68.0,1.0
+TCGA-AA-3548-01,1,71.0,0.0
+TCGA-AA-3549-01,1,69.0,1.0
+TCGA-AA-3552-01,1,85.0,1.0
+TCGA-AA-3553-01,1,61.0,0.0
+TCGA-AA-3554-01,1,62.0,0.0
+TCGA-AA-3555-01,1,81.0,0.0
+TCGA-AA-3556-01,1,78.0,1.0
+TCGA-AA-3558-01,1,70.0,1.0
+TCGA-AA-3560-01,1,72.0,0.0
+TCGA-AA-3561-01,1,72.0,1.0
+TCGA-AA-3562-01,1,82.0,1.0
+TCGA-AA-3655-01,1,68.0,1.0
+TCGA-AA-3655-11,0,68.0,1.0
+TCGA-AA-3660-01,1,51.0,0.0
+TCGA-AA-3660-11,0,51.0,0.0
+TCGA-AA-3662-01,1,80.0,0.0
+TCGA-AA-3662-11,0,80.0,0.0
+TCGA-AA-3663-01,1,42.0,1.0
+TCGA-AA-3663-11,0,42.0,1.0
+TCGA-AA-3664-01,1,74.0,0.0
+TCGA-AA-3666-01,1,68.0,1.0
+TCGA-AA-3667-01,1,36.0,0.0
+TCGA-AA-3672-01,1,90.0,0.0
+TCGA-AA-3673-01,1,53.0,0.0
+TCGA-AA-3675-01,1,84.0,1.0
+TCGA-AA-3678-01,1,60.0,0.0
+TCGA-AA-3679-01,1,59.0,1.0
+TCGA-AA-3680-01,1,67.0,0.0
+TCGA-AA-3681-01,1,77.0,0.0
+TCGA-AA-3684-01,1,65.0,0.0
+TCGA-AA-3685-01,1,69.0,1.0
+TCGA-AA-3688-01,1,80.0,1.0
+TCGA-AA-3692-01,1,47.0,0.0
+TCGA-AA-3693-01,1,77.0,0.0
+TCGA-AA-3695-01,1,63.0,0.0
+TCGA-AA-3696-01,1,75.0,0.0
+TCGA-AA-3697-01,1,77.0,1.0
+TCGA-AA-3697-11,0,77.0,1.0
+TCGA-AA-3710-01,1,80.0,0.0
+TCGA-AA-3712-01,1,65.0,1.0
+TCGA-AA-3712-11,0,65.0,1.0
+TCGA-AA-3713-01,1,68.0,1.0
+TCGA-AA-3713-11,0,68.0,1.0
+TCGA-AA-3715-01,1,77.0,1.0
+TCGA-AA-3811-01,1,84.0,0.0
+TCGA-AA-3812-01,1,82.0,0.0
+TCGA-AA-3814-01,1,85.0,0.0
+TCGA-AA-3815-01,1,65.0,0.0
+TCGA-AA-3818-01,1,78.0,0.0
+TCGA-AA-3819-01,1,41.0,0.0
+TCGA-AA-3821-01,1,81.0,0.0
+TCGA-AA-3831-01,1,66.0,1.0
+TCGA-AA-3833-01,1,63.0,0.0
+TCGA-AA-3837-01,1,67.0,1.0
+TCGA-AA-3841-01,1,66.0,1.0
+TCGA-AA-3842-01,1,51.0,1.0
+TCGA-AA-3844-01,1,78.0,0.0
+TCGA-AA-3845-01,1,86.0,0.0
+TCGA-AA-3846-01,1,74.0,0.0
+TCGA-AA-3848-01,1,82.0,0.0
+TCGA-AA-3850-01,1,74.0,1.0
+TCGA-AA-3851-01,1,74.0,1.0
+TCGA-AA-3852-01,1,88.0,1.0
+TCGA-AA-3854-01,1,71.0,0.0
+TCGA-AA-3855-01,1,72.0,1.0
+TCGA-AA-3856-01,1,59.0,1.0
+TCGA-AA-3858-01,1,67.0,1.0
+TCGA-AA-3860-01,1,53.0,0.0
+TCGA-AA-3861-01,1,72.0,1.0
+TCGA-AA-3862-01,1,82.0,1.0
+TCGA-AA-3864-01,1,71.0,1.0
+TCGA-AA-3866-01,1,78.0,0.0
+TCGA-AA-3867-01,1,74.0,1.0
+TCGA-AA-3869-01,1,76.0,1.0
+TCGA-AA-3870-01,1,71.0,0.0
+TCGA-AA-3872-01,1,45.0,1.0
+TCGA-AA-3875-01,1,78.0,0.0
+TCGA-AA-3877-01,1,83.0,0.0
+TCGA-AA-3930-01,1,66.0,1.0
+TCGA-AA-3939-01,1,83.0,1.0
+TCGA-AA-3941-01,1,84.0,0.0
+TCGA-AA-3947-01,1,60.0,0.0
+TCGA-AA-3949-01,1,87.0,0.0
+TCGA-AA-3950-01,1,79.0,0.0
+TCGA-AA-3952-01,1,68.0,1.0
+TCGA-AA-3955-01,1,38.0,1.0
+TCGA-AA-3956-01,1,65.0,1.0
+TCGA-AA-3966-01,1,89.0,0.0
+TCGA-AA-3968-01,1,55.0,0.0
+TCGA-AA-3970-01,1,65.0,1.0
+TCGA-AA-3971-01,1,58.0,1.0
+TCGA-AA-3972-01,1,72.0,1.0
+TCGA-AA-3973-01,1,69.0,1.0
+TCGA-AA-3975-01,1,80.0,1.0
+TCGA-AA-3976-01,1,70.0,1.0
+TCGA-AA-3977-01,1,65.0,1.0
+TCGA-AA-3979-01,1,84.0,1.0
+TCGA-AA-3980-01,1,89.0,0.0
+TCGA-AA-3982-01,1,75.0,1.0
+TCGA-AA-3984-01,1,61.0,0.0
+TCGA-AA-3986-01,1,73.0,1.0
+TCGA-AA-3989-01,1,84.0,1.0
+TCGA-AA-3994-01,1,69.0,1.0
+TCGA-AA-A004-01,1,76.0,1.0
+TCGA-AA-A00A-01,1,80.0,1.0
+TCGA-AA-A00D-01,1,70.0,1.0
+TCGA-AA-A00E-01,1,65.0,1.0
+TCGA-AA-A00F-01,1,66.0,1.0
+TCGA-AA-A00J-01,1,80.0,1.0
+TCGA-AA-A00K-01,1,79.0,1.0
+TCGA-AA-A00L-01,1,66.0,1.0
+TCGA-AA-A00N-01,1,75.0,1.0
+TCGA-AA-A00O-01,1,83.0,0.0
+TCGA-AA-A00Q-01,1,66.0,0.0
+TCGA-AA-A00R-01,1,64.0,0.0
+TCGA-AA-A00U-01,1,50.0,1.0
+TCGA-AA-A00W-01,1,80.0,1.0
+TCGA-AA-A00Z-01,1,70.0,1.0
+TCGA-AA-A010-01,1,46.0,0.0
+TCGA-AA-A017-01,1,57.0,0.0
+TCGA-AA-A01C-01,1,75.0,1.0
+TCGA-AA-A01D-01,1,47.0,0.0
+TCGA-AA-A01F-01,1,72.0,1.0
+TCGA-AA-A01G-01,1,63.0,1.0
+TCGA-AA-A01I-01,1,76.0,1.0
+TCGA-AA-A01K-01,1,74.0,0.0
+TCGA-AA-A01P-01,1,80.0,0.0
+TCGA-AA-A01P-11,0,80.0,0.0
+TCGA-AA-A01Q-01,1,48.0,0.0
+TCGA-AA-A01R-01,1,47.0,1.0
+TCGA-AA-A01R-11,0,47.0,1.0
+TCGA-AA-A01S-01,1,47.0,0.0
+TCGA-AA-A01S-11,0,47.0,0.0
+TCGA-AA-A01T-01,1,63.0,0.0
+TCGA-AA-A01T-11,0,63.0,0.0
+TCGA-AA-A01V-01,1,59.0,1.0
+TCGA-AA-A01V-11,0,59.0,1.0
+TCGA-AA-A01X-01,1,80.0,0.0
+TCGA-AA-A01X-11,0,80.0,0.0
+TCGA-AA-A01Z-01,1,68.0,1.0
+TCGA-AA-A01Z-11,0,68.0,1.0
+TCGA-AA-A022-01,1,88.0,0.0
+TCGA-AA-A022-11,0,88.0,0.0
+TCGA-AA-A024-01,1,81.0,1.0
+TCGA-AA-A029-01,1,67.0,1.0
+TCGA-AA-A02E-01,1,82.0,0.0
+TCGA-AA-A02F-01,1,68.0,0.0
+TCGA-AA-A02H-01,1,74.0,0.0
+TCGA-AA-A02J-01,1,70.0,0.0
+TCGA-AA-A02K-01,1,50.0,1.0
+TCGA-AA-A02O-01,1,82.0,1.0
+TCGA-AA-A02O-11,0,82.0,1.0
+TCGA-AA-A02R-01,1,84.0,0.0
+TCGA-AA-A02W-01,1,73.0,0.0
+TCGA-AA-A02Y-01,1,73.0,1.0
+TCGA-AA-A03F-01,1,90.0,0.0
+TCGA-AA-A03F-11,0,90.0,0.0
+TCGA-AA-A03J-01,1,65.0,0.0
+TCGA-AA-A03J-11,0,65.0,0.0
+TCGA-AD-5900-01,1,67.0,1.0
+TCGA-AD-6548-01,1,81.0,0.0
+TCGA-AD-6888-01,1,73.0,1.0
+TCGA-AD-6889-01,1,76.0,1.0
+TCGA-AD-6890-01,1,65.0,1.0
+TCGA-AD-6895-01,1,84.0,1.0
+TCGA-AD-6899-01,1,84.0,1.0
+TCGA-AD-6901-01,1,78.0,1.0
+TCGA-AD-6963-01,1,58.0,1.0
+TCGA-AD-6964-01,1,58.0,1.0
+TCGA-AD-6965-01,1,62.0,1.0
+TCGA-AD-A5EJ-01,1,74.0,0.0
+TCGA-AD-A5EK-01,1,51.0,1.0
+TCGA-AF-2687-01,1,57.0,1.0
+TCGA-AF-2689-01,1,41.0,0.0
+TCGA-AF-2689-11,0,41.0,0.0
+TCGA-AF-2690-01,1,76.0,0.0
+TCGA-AF-2691-01,1,48.0,0.0
+TCGA-AF-2691-11,0,48.0,0.0
+TCGA-AF-2692-01,1,54.0,0.0
+TCGA-AF-2692-11,0,54.0,0.0
+TCGA-AF-2693-01,1,75.0,1.0
+TCGA-AF-3400-01,1,54.0,1.0
+TCGA-AF-3400-11,0,54.0,1.0
+TCGA-AF-3911-01,1,48.0,1.0
+TCGA-AF-3913-01,1,60.0,1.0
+TCGA-AF-3913-11,0,60.0,1.0
+TCGA-AF-3914-01,1,39.0,1.0
+TCGA-AF-4110-01,1,77.0,1.0
+TCGA-AF-5654-01,1,73.0,0.0
+TCGA-AF-5654-11,0,73.0,0.0
+TCGA-AF-6136-01,1,72.0,0.0
+TCGA-AF-6655-01,1,66.0,1.0
+TCGA-AF-6672-01,1,43.0,1.0
+TCGA-AF-6784-01,1,,
+TCGA-AF-A56K-01,1,56.0,1.0
+TCGA-AF-A56L-01,1,48.0,0.0
+TCGA-AF-A56N-01,1,47.0,0.0
+TCGA-AG-3574-01,1,89.0,0.0
+TCGA-AG-3575-01,1,51.0,1.0
+TCGA-AG-3578-01,1,76.0,0.0
+TCGA-AG-3580-01,1,67.0,1.0
+TCGA-AG-3581-01,1,63.0,1.0
+TCGA-AG-3582-01,1,75.0,1.0
+TCGA-AG-3583-01,1,66.0,1.0
+TCGA-AG-3584-01,1,60.0,1.0
+TCGA-AG-3586-01,1,72.0,0.0
+TCGA-AG-3587-01,1,65.0,1.0
+TCGA-AG-3591-01,1,66.0,0.0
+TCGA-AG-3592-01,1,68.0,1.0
+TCGA-AG-3593-01,1,72.0,0.0
+TCGA-AG-3594-01,1,77.0,1.0
+TCGA-AG-3598-01,1,69.0,1.0
+TCGA-AG-3599-01,1,70.0,1.0
+TCGA-AG-3600-01,1,62.0,1.0
+TCGA-AG-3601-01,1,68.0,1.0
+TCGA-AG-3602-01,1,66.0,0.0
+TCGA-AG-3605-01,1,85.0,0.0
+TCGA-AG-3608-01,1,79.0,0.0
+TCGA-AG-3609-01,1,65.0,0.0
+TCGA-AG-3611-01,1,72.0,1.0
+TCGA-AG-3612-01,1,55.0,0.0
+TCGA-AG-3725-01,1,90.0,0.0
+TCGA-AG-3725-11,0,90.0,0.0
+TCGA-AG-3726-01,1,63.0,0.0
+TCGA-AG-3727-01,1,78.0,0.0
+TCGA-AG-3728-01,1,73.0,1.0
+TCGA-AG-3731-01,1,65.0,1.0
+TCGA-AG-3731-11,0,65.0,1.0
+TCGA-AG-3732-01,1,78.0,0.0
+TCGA-AG-3732-11,0,78.0,0.0
+TCGA-AG-3742-01,1,85.0,0.0
+TCGA-AG-3742-11,0,85.0,0.0
+TCGA-AG-3878-01,1,64.0,1.0
+TCGA-AG-3881-01,1,83.0,0.0
+TCGA-AG-3882-01,1,66.0,0.0
+TCGA-AG-3883-01,1,69.0,1.0
+TCGA-AG-3885-01,1,71.0,0.0
+TCGA-AG-3887-01,1,68.0,1.0
+TCGA-AG-3890-01,1,62.0,1.0
+TCGA-AG-3891-01,1,66.0,0.0
+TCGA-AG-3892-01,1,57.0,0.0
+TCGA-AG-3893-01,1,74.0,1.0
+TCGA-AG-3894-01,1,65.0,1.0
+TCGA-AG-3896-01,1,85.0,0.0
+TCGA-AG-3898-01,1,61.0,1.0
+TCGA-AG-3901-01,1,67.0,0.0
+TCGA-AG-3902-01,1,61.0,1.0
+TCGA-AG-3909-01,1,69.0,0.0
+TCGA-AG-3999-01,1,61.0,0.0
+TCGA-AG-4001-01,1,74.0,0.0
+TCGA-AG-4005-01,1,64.0,1.0
+TCGA-AG-4007-01,1,87.0,1.0
+TCGA-AG-4008-01,1,63.0,1.0
+TCGA-AG-4015-01,1,85.0,0.0
+TCGA-AG-4021-01,1,84.0,0.0
+TCGA-AG-4022-01,1,59.0,0.0
+TCGA-AG-A002-01,1,35.0,1.0
+TCGA-AG-A008-01,1,50.0,0.0
+TCGA-AG-A00C-01,1,49.0,0.0
+TCGA-AG-A00H-01,1,75.0,1.0
+TCGA-AG-A00Y-01,1,68.0,1.0
+TCGA-AG-A011-01,1,80.0,1.0
+TCGA-AG-A014-01,1,86.0,1.0
+TCGA-AG-A015-01,1,64.0,0.0
+TCGA-AG-A016-01,1,55.0,1.0
+TCGA-AG-A01J-01,1,59.0,0.0
+TCGA-AG-A01L-01,1,58.0,1.0
+TCGA-AG-A01N-01,1,68.0,0.0
+TCGA-AG-A01W-01,1,67.0,0.0
+TCGA-AG-A01W-11,0,67.0,0.0
+TCGA-AG-A01Y-01,1,49.0,0.0
+TCGA-AG-A01Y-11,0,49.0,0.0
+TCGA-AG-A020-01,1,57.0,0.0
+TCGA-AG-A020-11,0,57.0,0.0
+TCGA-AG-A023-01,1,62.0,0.0
+TCGA-AG-A025-01,1,62.0,0.0
+TCGA-AG-A026-01,1,66.0,1.0
+TCGA-AG-A02G-01,1,66.0,1.0
+TCGA-AG-A02N-01,1,67.0,1.0
+TCGA-AG-A02N-11,0,67.0,1.0
+TCGA-AG-A02X-01,1,77.0,1.0
+TCGA-AG-A032-01,1,68.0,1.0
+TCGA-AG-A036-01,1,71.0,1.0
+TCGA-AG-A036-11,0,71.0,1.0
+TCGA-AH-6544-01,1,60.0,1.0
+TCGA-AH-6547-01,1,79.0,0.0
+TCGA-AH-6549-01,1,66.0,1.0
+TCGA-AH-6643-01,1,50.0,1.0
+TCGA-AH-6643-11,0,50.0,1.0
+TCGA-AH-6644-01,1,73.0,1.0
+TCGA-AH-6897-01,1,48.0,1.0
+TCGA-AH-6903-01,1,46.0,1.0
+TCGA-AM-5820-01,1,59.0,0.0
+TCGA-AM-5821-01,1,68.0,0.0
+TCGA-AU-3779-01,1,80.0,0.0
+TCGA-AU-6004-01,1,69.0,0.0
+TCGA-AY-4070-01,1,50.0,0.0
+TCGA-AY-4071-01,1,63.0,0.0
+TCGA-AY-5543-01,1,65.0,0.0
+TCGA-AY-6196-01,1,47.0,1.0
+TCGA-AY-6197-01,1,60.0,1.0
+TCGA-AY-6386-01,1,66.0,0.0
+TCGA-AY-A54L-01,1,74.0,0.0
+TCGA-AY-A69D-01,1,55.0,0.0
+TCGA-AY-A71X-01,1,54.0,0.0
+TCGA-AY-A8YK-01,1,44.0,1.0
+TCGA-AZ-4308-01,1,47.0,0.0
+TCGA-AZ-4313-01,1,51.0,0.0
+TCGA-AZ-4315-01,1,61.0,1.0
+TCGA-AZ-4323-01,1,37.0,1.0
+TCGA-AZ-4614-01,1,71.0,0.0
+TCGA-AZ-4615-01,1,84.0,1.0
+TCGA-AZ-4616-01,1,82.0,0.0
+TCGA-AZ-4681-01,1,79.0,0.0
+TCGA-AZ-4682-01,1,61.0,1.0
+TCGA-AZ-4684-01,1,49.0,1.0
+TCGA-AZ-5403-01,1,43.0,1.0
+TCGA-AZ-5407-01,1,51.0,0.0
+TCGA-AZ-6598-01,1,77.0,0.0
+TCGA-AZ-6598-11,0,77.0,0.0
+TCGA-AZ-6599-01,1,72.0,1.0
+TCGA-AZ-6599-11,0,72.0,1.0
+TCGA-AZ-6600-01,1,64.0,1.0
+TCGA-AZ-6600-11,0,64.0,1.0
+TCGA-AZ-6601-01,1,68.0,1.0
+TCGA-AZ-6601-11,0,68.0,1.0
+TCGA-AZ-6603-01,1,77.0,0.0
+TCGA-AZ-6603-11,0,77.0,0.0
+TCGA-AZ-6605-01,1,77.0,1.0
+TCGA-AZ-6605-11,0,77.0,1.0
+TCGA-AZ-6606-01,1,81.0,1.0
+TCGA-AZ-6607-01,1,69.0,1.0
+TCGA-AZ-6608-01,1,55.0,0.0
+TCGA-BM-6198-01,1,73.0,1.0
+TCGA-CA-5254-01,1,42.0,0.0
+TCGA-CA-5255-01,1,45.0,1.0
+TCGA-CA-5256-01,1,54.0,0.0
+TCGA-CA-5796-01,1,52.0,0.0
+TCGA-CA-5797-01,1,56.0,1.0
+TCGA-CA-6715-01,1,63.0,1.0
+TCGA-CA-6716-01,1,65.0,1.0
+TCGA-CA-6717-01,1,57.0,1.0
+TCGA-CA-6718-01,1,46.0,1.0
+TCGA-CA-6719-01,1,77.0,1.0
+TCGA-CI-6619-01,1,41.0,1.0
+TCGA-CI-6620-01,1,41.0,0.0
+TCGA-CI-6621-01,1,63.0,1.0
+TCGA-CI-6622-01,1,74.0,1.0
+TCGA-CI-6623-01,1,44.0,1.0
+TCGA-CI-6624-01,1,53.0,0.0
+TCGA-CK-4947-01,1,46.0,0.0
+TCGA-CK-4948-01,1,45.0,0.0
+TCGA-CK-4950-01,1,68.0,0.0
+TCGA-CK-4951-01,1,79.0,0.0
+TCGA-CK-4952-01,1,48.0,0.0
+TCGA-CK-5912-01,1,81.0,1.0
+TCGA-CK-5913-01,1,58.0,0.0
+TCGA-CK-5914-01,1,81.0,1.0
+TCGA-CK-5915-01,1,63.0,1.0
+TCGA-CK-5916-01,1,71.0,0.0
+TCGA-CK-6746-01,1,84.0,0.0
+TCGA-CK-6747-01,1,87.0,0.0
+TCGA-CK-6748-01,1,45.0,0.0
+TCGA-CK-6751-01,1,88.0,0.0
+TCGA-CL-4957-01,1,79.0,0.0
+TCGA-CL-5917-01,1,71.0,0.0
+TCGA-CL-5918-01,1,90.0,0.0
+TCGA-CM-4743-01,1,69.0,1.0
+TCGA-CM-4744-01,1,69.0,1.0
+TCGA-CM-4746-01,1,61.0,1.0
+TCGA-CM-4747-01,1,47.0,1.0
+TCGA-CM-4748-01,1,53.0,1.0
+TCGA-CM-4750-01,1,34.0,0.0
+TCGA-CM-4751-01,1,62.0,1.0
+TCGA-CM-4752-01,1,58.0,1.0
+TCGA-CM-5341-01,1,82.0,0.0
+TCGA-CM-5344-01,1,39.0,0.0
+TCGA-CM-5348-01,1,72.0,1.0
+TCGA-CM-5349-01,1,68.0,0.0
+TCGA-CM-5860-01,1,44.0,1.0
+TCGA-CM-5861-01,1,63.0,0.0
+TCGA-CM-5862-01,1,80.0,1.0
+TCGA-CM-5863-01,1,60.0,0.0
+TCGA-CM-5864-01,1,60.0,1.0
+TCGA-CM-5868-01,1,59.0,0.0
+TCGA-CM-6161-01,1,36.0,0.0
+TCGA-CM-6162-01,1,48.0,0.0
+TCGA-CM-6163-01,1,74.0,1.0
+TCGA-CM-6164-01,1,46.0,0.0
+TCGA-CM-6165-01,1,74.0,1.0
+TCGA-CM-6166-01,1,48.0,0.0
+TCGA-CM-6167-01,1,57.0,0.0
+TCGA-CM-6168-01,1,84.0,0.0
+TCGA-CM-6169-01,1,67.0,1.0
+TCGA-CM-6170-01,1,73.0,0.0
+TCGA-CM-6171-01,1,77.0,0.0
+TCGA-CM-6172-01,1,70.0,0.0
+TCGA-CM-6674-01,1,39.0,1.0
+TCGA-CM-6675-01,1,35.0,1.0
+TCGA-CM-6676-01,1,82.0,1.0
+TCGA-CM-6677-01,1,75.0,0.0
+TCGA-CM-6678-01,1,63.0,0.0
+TCGA-CM-6679-01,1,58.0,1.0
+TCGA-CM-6680-01,1,78.0,0.0
+TCGA-D5-5537-01,1,83.0,1.0
+TCGA-D5-5538-01,1,60.0,0.0
+TCGA-D5-5539-01,1,60.0,1.0
+TCGA-D5-5540-01,1,73.0,1.0
+TCGA-D5-5541-01,1,63.0,1.0
+TCGA-D5-6529-01,1,69.0,1.0
+TCGA-D5-6530-01,1,53.0,1.0
+TCGA-D5-6531-01,1,75.0,1.0
+TCGA-D5-6532-01,1,61.0,1.0
+TCGA-D5-6533-01,1,68.0,0.0
+TCGA-D5-6534-01,1,62.0,0.0
+TCGA-D5-6535-01,1,80.0,0.0
+TCGA-D5-6536-01,1,73.0,1.0
+TCGA-D5-6537-01,1,64.0,1.0
+TCGA-D5-6538-01,1,79.0,0.0
+TCGA-D5-6539-01,1,45.0,0.0
+TCGA-D5-6540-01,1,66.0,1.0
+TCGA-D5-6541-01,1,49.0,1.0
+TCGA-D5-6898-01,1,51.0,0.0
+TCGA-D5-6920-01,1,77.0,0.0
+TCGA-D5-6922-01,1,76.0,1.0
+TCGA-D5-6923-01,1,57.0,1.0
+TCGA-D5-6924-01,1,68.0,1.0
+TCGA-D5-6926-01,1,65.0,1.0
+TCGA-D5-6927-01,1,34.0,1.0
+TCGA-D5-6928-01,1,80.0,1.0
+TCGA-D5-6929-01,1,49.0,0.0
+TCGA-D5-6930-01,1,67.0,1.0
+TCGA-D5-6931-01,1,77.0,1.0
+TCGA-D5-6932-01,1,69.0,1.0
+TCGA-D5-7000-01,1,79.0,0.0
+TCGA-DC-4745-01,1,49.0,0.0
+TCGA-DC-4749-01,1,57.0,1.0
+TCGA-DC-5337-01,1,69.0,1.0
+TCGA-DC-5869-01,1,62.0,0.0
+TCGA-DC-6154-01,1,57.0,0.0
+TCGA-DC-6155-01,1,31.0,0.0
+TCGA-DC-6156-01,1,62.0,1.0
+TCGA-DC-6157-01,1,48.0,1.0
+TCGA-DC-6158-01,1,70.0,1.0
+TCGA-DC-6160-01,1,68.0,1.0
+TCGA-DC-6681-01,1,70.0,0.0
+TCGA-DC-6682-01,1,57.0,1.0
+TCGA-DC-6683-01,1,43.0,1.0
+TCGA-DM-A0X9-01,1,71.0,0.0
+TCGA-DM-A0XD-01,1,65.0,1.0
+TCGA-DM-A0XF-01,1,68.0,0.0
+TCGA-DM-A1D0-01,1,79.0,0.0
+TCGA-DM-A1D4-01,1,80.0,1.0
+TCGA-DM-A1D6-01,1,88.0,1.0
+TCGA-DM-A1D7-01,1,82.0,1.0
+TCGA-DM-A1D8-01,1,50.0,0.0
+TCGA-DM-A1D9-01,1,67.0,0.0
+TCGA-DM-A1DA-01,1,71.0,0.0
+TCGA-DM-A1DB-01,1,68.0,1.0
+TCGA-DM-A1HA-01,1,82.0,1.0
+TCGA-DM-A1HB-01,1,75.0,1.0
+TCGA-DM-A280-01,1,70.0,0.0
+TCGA-DM-A282-01,1,60.0,0.0
+TCGA-DM-A285-01,1,71.0,0.0
+TCGA-DM-A286-01,1,,
+TCGA-DM-A288-01,1,68.0,1.0
+TCGA-DM-A28A-01,1,78.0,1.0
+TCGA-DM-A28C-01,1,74.0,1.0
+TCGA-DM-A28E-01,1,72.0,0.0
+TCGA-DM-A28F-01,1,73.0,1.0
+TCGA-DM-A28G-01,1,75.0,1.0
+TCGA-DM-A28H-01,1,50.0,1.0
+TCGA-DM-A28K-01,1,75.0,1.0
+TCGA-DM-A28M-01,1,63.0,1.0
+TCGA-DT-5265-01,1,51.0,1.0
+TCGA-DY-A0XA-01,1,57.0,0.0
+TCGA-DY-A1DC-01,1,72.0,0.0
+TCGA-DY-A1DD-01,1,77.0,0.0
+TCGA-DY-A1DE-01,1,56.0,0.0
+TCGA-DY-A1DF-01,1,73.0,0.0
+TCGA-DY-A1DG-01,1,75.0,1.0
+TCGA-DY-A1H8-01,1,77.0,0.0
+TCGA-EF-5830-01,1,54.0,1.0
+TCGA-EF-5831-01,1,72.0,1.0
+TCGA-EI-6506-01,1,78.0,0.0
+TCGA-EI-6507-01,1,60.0,1.0
+TCGA-EI-6508-01,1,48.0,0.0
+TCGA-EI-6509-01,1,53.0,1.0
+TCGA-EI-6510-01,1,77.0,0.0
+TCGA-EI-6511-01,1,52.0,1.0
+TCGA-EI-6512-01,1,64.0,0.0
+TCGA-EI-6513-01,1,59.0,1.0
+TCGA-EI-6514-01,1,59.0,0.0
+TCGA-EI-6881-01,1,60.0,1.0
+TCGA-EI-6882-01,1,59.0,1.0
+TCGA-EI-6883-01,1,63.0,1.0
+TCGA-EI-6884-01,1,71.0,1.0
+TCGA-EI-6885-01,1,57.0,0.0
+TCGA-EI-6917-01,1,33.0,1.0
+TCGA-EI-7002-01,1,58.0,1.0
+TCGA-EI-7004-01,1,37.0,0.0
+TCGA-F4-6459-01,1,61.0,0.0
+TCGA-F4-6460-01,1,51.0,0.0
+TCGA-F4-6461-01,1,41.0,0.0
+TCGA-F4-6463-01,1,51.0,1.0
+TCGA-F4-6569-01,1,60.0,1.0
+TCGA-F4-6570-01,1,78.0,0.0
+TCGA-F4-6703-01,1,64.0,1.0
+TCGA-F4-6704-01,1,60.0,1.0
+TCGA-F4-6704-11,0,60.0,1.0
+TCGA-F4-6805-01,1,58.0,0.0
+TCGA-F4-6806-01,1,59.0,0.0
+TCGA-F4-6807-01,1,51.0,0.0
+TCGA-F4-6808-01,1,54.0,0.0
+TCGA-F4-6809-01,1,52.0,0.0
+TCGA-F4-6854-01,1,77.0,0.0
+TCGA-F4-6855-01,1,70.0,0.0
+TCGA-F4-6856-01,1,45.0,1.0
+TCGA-F4-6857-01,1,,
+TCGA-F5-6464-01,1,77.0,0.0
+TCGA-F5-6465-01,1,64.0,0.0
+TCGA-F5-6571-01,1,62.0,0.0
+TCGA-F5-6702-01,1,71.0,1.0
+TCGA-F5-6810-01,1,,
+TCGA-F5-6811-01,1,72.0,0.0
+TCGA-F5-6812-01,1,67.0,1.0
+TCGA-F5-6813-01,1,70.0,1.0
+TCGA-F5-6814-01,1,57.0,1.0
+TCGA-F5-6861-01,1,60.0,0.0
+TCGA-F5-6863-01,1,71.0,0.0
+TCGA-F5-6864-01,1,74.0,0.0
+TCGA-G4-6293-01,1,49.0,0.0
+TCGA-G4-6294-01,1,75.0,1.0
+TCGA-G4-6295-01,1,70.0,0.0
+TCGA-G4-6295-11,0,70.0,0.0
+TCGA-G4-6297-01,1,55.0,0.0
+TCGA-G4-6297-11,0,55.0,0.0
+TCGA-G4-6298-01,1,90.0,1.0
+TCGA-G4-6298-11,0,90.0,1.0
+TCGA-G4-6299-01,1,69.0,1.0
+TCGA-G4-6302-01,1,90.0,0.0
+TCGA-G4-6302-11,0,90.0,0.0
+TCGA-G4-6303-01,1,54.0,0.0
+TCGA-G4-6304-01,1,66.0,0.0
+TCGA-G4-6306-01,1,71.0,1.0
+TCGA-G4-6307-01,1,37.0,0.0
+TCGA-G4-6309-01,1,40.0,0.0
+TCGA-G4-6310-01,1,69.0,1.0
+TCGA-G4-6311-01,1,80.0,1.0
+TCGA-G4-6311-11,0,80.0,1.0
+TCGA-G4-6314-01,1,76.0,0.0
+TCGA-G4-6314-11,0,76.0,0.0
+TCGA-G4-6315-01,1,66.0,1.0
+TCGA-G4-6317-01,1,51.0,0.0
+TCGA-G4-6317-02,1,51.0,0.0
+TCGA-G4-6320-01,1,73.0,1.0
+TCGA-G4-6320-11,0,73.0,1.0
+TCGA-G4-6321-01,1,60.0,0.0
+TCGA-G4-6322-01,1,65.0,1.0
+TCGA-G4-6322-11,0,65.0,1.0
+TCGA-G4-6323-01,1,50.0,1.0
+TCGA-G4-6586-01,1,73.0,0.0
+TCGA-G4-6588-01,1,58.0,0.0
+TCGA-G4-6625-01,1,77.0,0.0
+TCGA-G4-6625-11,0,77.0,0.0
+TCGA-G4-6626-01,1,90.0,1.0
+TCGA-G4-6627-01,1,84.0,1.0
+TCGA-G4-6628-01,1,78.0,1.0
+TCGA-G5-6233-01,1,74.0,1.0
+TCGA-G5-6235-01,1,72.0,1.0
+TCGA-G5-6572-01,1,56.0,1.0
+TCGA-G5-6572-02,1,56.0,1.0
+TCGA-G5-6641-01,1,67.0,1.0
+TCGA-NH-A50T-01,1,68.0,0.0
+TCGA-NH-A50U-01,1,42.0,1.0
+TCGA-NH-A50V-01,1,69.0,1.0
+TCGA-NH-A5IV-01,1,90.0,0.0
+TCGA-NH-A6GA-01,1,58.0,1.0
+TCGA-NH-A6GB-01,1,71.0,0.0
+TCGA-NH-A6GC-01,1,66.0,0.0
+TCGA-NH-A8F7-01,1,53.0,0.0
+TCGA-NH-A8F7-06,1,53.0,0.0
+TCGA-NH-A8F8-01,1,79.0,1.0
+TCGA-QG-A5YV-01,1,64.0,0.0
+TCGA-QG-A5YW-01,1,55.0,0.0
+TCGA-QG-A5YX-01,1,61.0,0.0
+TCGA-QG-A5Z1-01,1,71.0,1.0
+TCGA-QG-A5Z2-01,1,61.0,1.0
+TCGA-QL-A97D-01,1,84.0,0.0
+TCGA-RU-A8FL-01,1,51.0,1.0
+TCGA-SS-A7HO-01,1,44.0,0.0
+TCGA-T9-A92H-01,1,82.0,1.0
+TCGA-WS-AB45-01,1,52.0,0.0

p3/preprocess/Colon_and_Rectal_Cancer/gene_data/GSE46517.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fd2d2b79a80f8697246aa18788edbad15d2ffe2ff4ba9c089dcb12af3f727919
+size 21209767

p3/preprocess/Colon_and_Rectal_Cancer/gene_data/GSE46862.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d79a8fdf66ba6462fed4b5a14927eb32e1f172afcf7c6323e258df0e28ddf6cd
+size 26190142

p3/preprocess/Colon_and_Rectal_Cancer/gene_data/GSE56699.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c1f8f30109dd1597ae686f6be87aa396216172b113188439c14dd85858ad336b
+size 17572338

p3/preprocess/Congestive_heart_failure/GSE182600.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:34a62110abaec4c7173911c9217a946438b1e514b780174d89b65ccc8eb074ca
+size 26036077

p3/preprocess/Congestive_heart_failure/clinical_data/GSE182600.csv

@@ -0,0 +1,4 @@
+,GSM5532093,GSM5532094,GSM5532095,GSM5532096,GSM5532097,GSM5532098,GSM5532099,GSM5532100,GSM5532101,GSM5532102,GSM5532103,GSM5532104,GSM5532105,GSM5532106,GSM5532107,GSM5532108,GSM5532109,GSM5532110,GSM5532111,GSM5532112,GSM5532113,GSM5532114,GSM5532115,GSM5532116,GSM5532117,GSM5532118,GSM5532119,GSM5532120,GSM5532121,GSM5532122,GSM5532123,GSM5532124,GSM5532125,GSM5532126,GSM5532127,GSM5532128,GSM5532129,GSM5532130,GSM5532131,GSM5532132,GSM5532133,GSM5532134,GSM5532135,GSM5532136,GSM5532137,GSM5532138,GSM5532139,GSM5532140,GSM5532141,GSM5532142,GSM5532143,GSM5532144,GSM5532145,GSM5532146,GSM5532147,GSM5532148,GSM5532149,GSM5532150,GSM5532151,GSM5532152,GSM5532153,GSM5532154,GSM5532155,GSM5532156,GSM5532157,GSM5532158,GSM5532159,GSM5532160,GSM5532161,GSM5532162,GSM5532163,GSM5532164,GSM5532165,GSM5532166,GSM5532167,GSM5532168,GSM5532169,GSM5532170
+Congestive_heart_failure,0.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Age,33.4,51.2,51.9,47.8,41.5,67.3,52.8,16.1,78.9,53.2,70.9,59.9,21.9,45.2,52.4,32.3,52.8,55.8,47.0,55.8,57.3,31.7,49.3,66.1,55.9,49.1,63.0,21.0,53.6,50.1,37.4,71.5,56.5,33.4,51.2,51.9,47.8,41.5,67.3,52.8,78.9,53.2,70.9,59.9,21.9,45.2,52.4,32.3,55.8,47.0,55.8,57.3,31.7,49.3,66.1,55.9,49.1,63.0,21.0,53.6,50.1,37.4,71.5,56.5,33.4,51.2,51.9,47.8,52.8,53.2,21.9,55.8,47.0,49.3,66.1,53.6,50.1,56.5
+Gender,0.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0

p3/preprocess/Congestive_heart_failure/clinical_data/GSE93101.csv

@@ -0,0 +1,4 @@
+,GSM2443799,GSM2443800,GSM2443801,GSM2443802,GSM2443803,GSM2443804,GSM2443805,GSM2443806,GSM2443807,GSM2443808,GSM2443809,GSM2443810,GSM2443811,GSM2443812,GSM2443813,GSM2443814,GSM2443815,GSM2443816,GSM2443817,GSM2443818,GSM2443819,GSM2443820,GSM2443821,GSM2443822,GSM2443823,GSM2443824,GSM2443825,GSM2443826,GSM2443827,GSM2443828,GSM2443829,GSM2443830,GSM2443831
+Congestive_heart_failure,0.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0
+Age,33.4,51.2,51.9,47.8,41.5,67.3,52.8,16.1,78.9,53.2,70.9,59.9,21.9,45.2,52.4,32.3,52.8,55.8,47.0,55.8,57.3,31.7,49.3,66.1,55.9,49.1,63.0,21.0,53.6,50.1,37.4,71.5,56.5
+Gender,0.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0

p3/preprocess/Congestive_heart_failure/code/GSE182600.py

@@ -0,0 +1,158 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Congestive_heart_failure"
+cohort = "GSE182600"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Congestive_heart_failure"
+in_cohort_dir = "../DATA/GEO/Congestive_heart_failure/GSE182600"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Congestive_heart_failure/GSE182600.csv"
+out_gene_data_file = "./output/preprocess/3/Congestive_heart_failure/gene_data/GSE182600.csv"
+out_clinical_data_file = "./output/preprocess/3/Congestive_heart_failure/clinical_data/GSE182600.csv"
+json_path = "./output/preprocess/3/Congestive_heart_failure/cohort_info.json"
+
+# Get paths to the SOFT and matrix files
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Get background info and clinical data from matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file)
+
+# Get unique values for each feature (row) in clinical data 
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("=== Dataset Background Information ===")
+print(background_info)
+print("\n=== Sample Characteristics ===")
+print(json.dumps(unique_values_dict, indent=2))
+# 1. Gene Expression Data Availability 
+# Based on background info showing genome-wide gene expression analysis, this cohort contains gene data
+is_gene_available = True
+
+# 2.1. Data Availability
+# Trait (heart failure outcome) is at key 3 with "Success" vs "Failure" values
+# Age is at key 1 with numeric age values
+# Gender is at key 2 with "F" vs "M" values
+trait_row = 3
+age_row = 1  
+gender_row = 2
+
+# 2.2. Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert Success/Failure to binary 0/1"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().lower()
+    if value == 'success':
+        return 0  # Success = 0
+    elif value in ['failure', 'fail']:
+        return 1  # Failure = 1
+    return None
+
+def convert_age(value: str) -> float:
+    """Convert age string to float"""
+    if not value or ':' not in value:
+        return None
+    try:
+        return float(value.split(':')[1].strip())
+    except:
+        return None
+
+def convert_gender(value: str) -> int:
+    """Convert F/M to binary 0/1"""
+    if not value or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().upper()
+    if value == 'F':
+        return 0  # Female = 0
+    elif value == 'M':
+        return 1  # Male = 1
+    return None
+
+# 3. Save Initial Metadata
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=trait_row is not None)
+
+# 4. Extract Clinical Features
+clinical_df = geo_select_clinical_features(clinical_data,
+                                         trait=trait,
+                                         trait_row=trait_row,
+                                         convert_trait=convert_trait,
+                                         age_row=age_row,
+                                         convert_age=convert_age, 
+                                         gender_row=gender_row,
+                                         convert_gender=convert_gender)
+
+# Preview and save clinical data
+print(preview_df(clinical_df))
+clinical_df.to_csv(out_clinical_data_file)
+# Extract gene expression data from matrix file
+genetic_df = get_genetic_data(matrix_file)
+
+# Print DataFrame shape and first 20 row IDs
+print("DataFrame shape:", genetic_df.shape)
+print("\nFirst 20 row IDs:")
+print(genetic_df.index[:20])
+
+print("\nPreview of first few rows and columns:")
+print(genetic_df.head().iloc[:, :5])
+# The identifiers start with "ILMN_" which indicates these are Illumina probe IDs, not gene symbols
+# Illumina probe IDs need to be mapped to standard gene symbols for analysis
+requires_gene_mapping = True
+# Extract gene annotation data, excluding control probe lines
+gene_metadata = get_gene_annotation(soft_file) 
+
+# Preview filtered annotation data
+print("Column names:")
+print(gene_metadata.columns)
+print("\nPreview of gene annotation data:")
+print(preview_df(gene_metadata))
+# Get mapping between probe IDs and gene symbols
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Symbol')
+
+# Convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(genetic_df, mapping_df)
+
+# Print info about the mapped data
+print("Gene expression data shape after mapping:", gene_data.shape)
+print("\nFirst few gene symbols:")
+print(gene_data.index[:10])
+print("\nPreview of gene expression data:")
+print(gene_data.head().iloc[:, :5])
+# 1. Normalize gene symbols and save
+gene_data = normalize_gene_symbols_in_index(gene_data) 
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True, 
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True, 
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Dataset contains gene expression from cardiogenic shock patients under ECMO, tracking outcome (Success vs Failure)"
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)