Add files using upload-large-folder tool

.gitattributes

@@ -1148,3 +1148,27 @@ p1/preprocess/Hemochromatosis/gene_data/GSE50579.csv filter=lfs diff=lfs merge=l
 p1/preprocess/Heart_rate/gene_data/GSE34788.csv filter=lfs diff=lfs merge=lfs -text
 p1/preprocess/Height/gene_data/GSE97475.csv filter=lfs diff=lfs merge=lfs -text
 p1/preprocess/Height/gene_data/GSE101710.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/HIV_Resistance/GSE46599.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Height/gene_data/GSE101709.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/HIV_Resistance/gene_data/GSE46599.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/HIV_Resistance/GSE33580.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Huntingtons_Disease/GSE26927.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Huntingtons_Disease/GSE34201.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/HIV_Resistance/gene_data/GSE33580.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Huntingtons_Disease/gene_data/GSE26927.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Huntingtons_Disease/gene_data/GSE34201.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Huntingtons_Disease/gene_data/GSE95843.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Hypertension/GSE77627.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Hypertension/GSE161533.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Hypertension/GSE128381.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Huntingtons_Disease/GSE34721.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Huntingtons_Disease/GSE135589.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Huntingtons_Disease/gene_data/GSE34721.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Hypertension/GSE117261.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Head_and_Neck_Cancer/gene_data/GSE156915.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Hypertension/gene_data/GSE128381.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Hypertension/gene_data/GSE161533.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Hypertension/gene_data/GSE117261.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Huntingtons_Disease/gene_data/GSE135589.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Hypertension/gene_data/GSE71994.csv filter=lfs diff=lfs merge=lfs -text
+p1/preprocess/Hypertension/gene_data/GSE77627.csv filter=lfs diff=lfs merge=lfs -text

p1/preprocess/Atherosclerosis/GSE109048.csv

@@ -0,0 +1,57 @@
+,Atherosclerosis,OR4F16,OR4F17,OR4F21,OR4F29,OR4F2P,OR4F3,OR4F4,OR4F5,PCMTD2,SEPT14
+GSM2928448,1.0,2.5420604,3.65479775,2.231649,2.5420604,4.758553,2.5420604,1.38304675,2.2798885,0.810600625,2.17855
+GSM2928449,1.0,2.4884850000000003,3.27546875,2.0615325,2.4884850000000003,4.9502,2.4884850000000003,1.20231025,2.0982985,0.81653775,2.162189
+GSM2928450,1.0,4.3142904,5.825601499999999,3.3020445,4.3142904,9.321156,4.3142904,2.120111,4.1088135,0.685753375,2.8326535
+GSM2928451,1.0,3.6649840000000005,5.07030375,2.9357545,3.6649840000000005,6.566074,3.6649840000000005,2.01954975,3.4257805,0.662289625,2.492945
+GSM2928452,1.0,2.7035954,3.4557615000000004,2.093821,2.7035954,4.396016,2.7035954,1.321109,2.2786165,0.726929125,2.245447
+GSM2928453,1.0,2.5767966,3.383338,2.039704,2.5767966,4.225586,2.5767966,1.218445,1.996304,0.82775175,2.248301
+GSM2928454,1.0,2.6242086,3.1713899999999997,1.8293115,2.6242086,4.673549,2.6242086,1.17363,2.0012975,0.879793125,2.0694535
+GSM2928455,1.0,2.6959218,3.5346985,2.067562,2.6959218,4.997999,2.6959218,1.2968385,2.323042,0.831703375,2.2097125
+GSM2928456,1.0,2.771231,3.4166965,1.943375,2.771231,4.648803,2.771231,1.2629695,1.894126,0.765487,2.1018975
+GSM2928457,1.0,2.7009044,3.66845175,2.095118,2.7009044,4.818265,2.7009044,1.39708575,2.1543515,0.813898875,2.1716845
+GSM2928458,1.0,3.306057,3.341109,2.232502,3.306057,5.393229,3.306057,1.26369,2.0387285,0.98346925,2.184913
+GSM2928459,1.0,2.6292988,3.29447275,1.880212,2.6292988,4.6119,2.6292988,1.18038225,2.054807,0.8274695,2.134051
+GSM2928460,1.0,2.757701,3.4095959999999996,2.1980815,2.757701,5.195738,2.757701,1.257182,2.1537735,0.766849375,2.1716845
+GSM2928461,1.0,2.4774882000000003,3.3347825,1.8689905,2.4774882000000003,4.148212,2.4774882000000003,1.194427,1.9374215,0.746335375,2.121092
+GSM2928462,1.0,2.3687974,3.28110825,1.80633,2.3687974,4.141176,2.3687974,1.17288925,1.891764,0.753330875,2.232192
+GSM2928463,1.0,3.0569956000000005,3.4918975,2.324901,3.0569956000000005,4.521327,3.0569956000000005,1.3071585,2.130644,1.009245875,2.1715735
+GSM2928464,1.0,2.5827929999999997,3.5923557500000003,2.0716685,2.5827929999999997,4.787729,2.5827929999999997,1.32365525,2.124767,0.759927125,2.2004
+GSM2928465,1.0,2.6457842,3.23555975,1.942506,2.6457842,3.952442,2.6457842,1.19855525,2.135245,0.907258625,2.060387
+GSM2928466,0.0,2.8135651999999998,3.439068,2.3132095,2.8135651999999998,5.251666,2.8135651999999998,1.303069,1.9293785,0.745672375,2.2972555
+GSM2928467,0.0,3.168524,4.2946884999999995,2.262299,3.168524,5.773007,3.168524,1.62915,2.7884615,0.813817375,2.1391435
+GSM2928468,0.0,2.7513798,3.25981775,2.0615325,2.7513798,4.86418,2.7513798,1.13342775,2.0955745,0.7726135,2.067177
+GSM2928469,0.0,2.5093813999999997,3.3532319999999998,1.991198,2.5093813999999997,4.365879,2.5093813999999997,1.249809,1.997777,0.81468775,2.0609845
+GSM2928470,0.0,2.4695346,3.33809325,1.9151765,2.4695346,4.468233,2.4695346,1.18056675,1.9506505,0.711847375,2.099157
+GSM2928471,0.0,3.3407572,3.4241485000000003,2.3500865,3.3407572,5.94066,3.3407572,1.2671235,2.354982,0.834132375,2.1261395
+GSM2928472,0.0,2.6126698,3.48046525,1.841398,2.6126698,4.181142,2.6126698,1.23700425,1.9456,0.890139125,2.1816385
+GSM2928473,0.0,2.6948912,3.5117607499999997,1.934662,2.6948912,4.18371,2.6948912,1.20138725,2.2128505,0.813817375,2.0825065
+GSM2928474,0.0,2.9736062,4.59117625,2.5516425,2.9736062,6.062913,2.9736062,1.67318075,2.5474205,0.781132375,2.292387
+GSM2928475,0.0,2.9090552,3.2860292499999995,2.278318,2.9090552,5.894261,2.9090552,1.22066825,1.953684,0.9179525,2.060557
+GSM2928476,0.0,2.9485638,3.152998,2.222638,2.9485638,5.296056,2.9485638,1.152967,1.9991375,0.870419625,2.230037
+GSM2928477,0.0,3.309335,3.5573942499999998,2.575608,3.309335,6.093548,3.309335,1.24119575,2.3456995,0.86659075,2.329013
+GSM2928478,0.0,2.6824026,3.26352475,1.950054,2.6824026,4.186232,2.6824026,1.24022125,1.956644,0.90947,2.206893
+GSM2928479,0.0,2.5460307999999996,3.3428645,1.858947,2.5460307999999996,5.176661,2.5460307999999996,1.2761825,2.093103,0.736467625,2.191881
+GSM2928480,0.0,2.6424936,3.2651335,1.9674515,2.6424936,5.057247,2.6424936,1.181065,1.9348415,0.786284125,2.098356
+GSM2928481,0.0,2.752814,4.32833475,2.108439,2.752814,5.536383,2.752814,1.69500975,2.6648425,0.847237375,2.252621
+GSM2928482,0.0,3.290865,3.5935732500000004,2.1767895,3.290865,5.303023,3.290865,1.28921525,2.4097135,0.836028625,2.3272935
+GSM2928483,0.0,2.5296848,3.26786575,2.056688,2.5296848,4.89301,2.5296848,1.17821475,2.053762,0.791918875,2.1489915
+GSM2928484,0.0,3.0717722,3.2239020000000003,2.281916,3.0717722,5.394821,3.0717722,1.0443655,2.2254245,0.748545125,2.081123
+GSM2928485,1.0,3.113311,4.519833,2.5075515,3.113311,6.327871,3.113311,1.6767395,3.1057465,0.80189475,2.3888375
+GSM2928486,1.0,3.4363840000000003,4.74429125,2.5109465,3.4363840000000003,6.398536,3.4363840000000003,1.79011025,3.007653,0.742974,2.1884905
+GSM2928487,1.0,4.5301925999999995,5.1355830000000005,3.446703,4.5301925999999995,8.535204,4.5301925999999995,1.9191305,3.9471825,0.789322875,2.671726
+GSM2928488,1.0,2.6978723999999996,3.5155227499999997,1.882676,2.6978723999999996,4.687723,2.6978723999999996,1.22435725,2.51739,1.10600525,1.997512
+GSM2928489,1.0,2.6916752,3.4039415,1.7044375,2.6916752,4.368939,2.6916752,1.270769,1.936295,0.798266625,2.1001165
+GSM2928490,1.0,2.493736,3.35090175,1.8744285,2.493736,4.752713,2.493736,1.22174425,1.8578505,0.757701375,2.1136565
+GSM2928491,1.0,2.6510559999999996,3.011616,1.7931265,2.6510559999999996,4.212369,2.6510559999999996,1.0122205,1.8678855,1.116016875,1.9728775
+GSM2928492,1.0,2.8423382000000004,3.7048982500000003,2.0016285,2.8423382000000004,4.573951,2.8423382000000004,1.27551175,2.51694,0.82425225,2.247512
+GSM2928493,1.0,2.7169974,3.3571549999999997,1.9909035,2.7169974,4.463247,2.7169974,1.200808,1.972249,0.828520625,2.301859
+GSM2928494,1.0,3.3428152,3.88471425,2.5397905,3.3428152,6.470191,3.3428152,1.41222475,2.1082285,0.868575125,2.3252815
+GSM2928495,1.0,2.6144125999999996,3.3958907499999995,1.9478135,2.6144125999999996,4.54277,2.6144125999999996,1.23197275,1.900837,0.801149625,2.0400895
+GSM2928496,1.0,2.4906658,3.1648735,2.037298,2.4906658,4.85036,2.4906658,1.1035075,1.9252805,0.831748375,2.0955045
+GSM2928497,1.0,2.6306766,3.4328640000000004,1.984265,2.6306766,4.598246,2.6306766,1.2184025,1.894601,0.90378275,2.0555765
+GSM2928498,1.0,2.564085,3.28346875,1.695501,2.564085,4.118382,2.564085,1.14281325,1.9206615,0.72030175,2.238452
+GSM2928499,1.0,2.6168648,4.048498,1.985344,2.6168648,4.607283,2.6168648,1.4650995,2.2667855,0.6958105,2.1656145
+GSM2928500,1.0,2.6084864,3.31749575,2.0490825,2.6084864,4.495167,2.6084864,1.18534625,2.0172355,0.691177,2.1241635
+GSM2928501,1.0,2.7091269999999996,3.6807687500000004,2.035695,2.7091269999999996,5.192202,2.7091269999999996,1.34093775,2.3269145,0.813817375,2.1716845
+GSM2928502,1.0,2.6708324,3.4197455,1.9485745,2.6708324,5.204194,2.6708324,1.1358935,2.357783,0.820799375,2.1716845
+GSM2928503,1.0,2.867071,3.36906,2.3101085,2.867071,4.768052,2.867071,1.181737,2.1676035,0.79238225,2.126671

p1/preprocess/Atherosclerosis/GSE57691.csv

@@ -0,0 +1,69 @@
+,Atherosclerosis,OR4F16,OR4F21,OR4F3,OR4F4,OR4F5,PCMTD2
+GSM1386783,1.0,0.23883915,0.53942394,0.53858614,0.867682,0.78131913,1.1286402
+GSM1386784,1.0,-1.1944451,-2.10584734,-0.8894272,-2.0234218000000004,-1.3879666,0.84970903
+GSM1386785,1.0,0.3841734,1.1489543599999998,0.48546124,0.7820367800000001,1.04515505,1.0291362
+GSM1386786,1.0,-1.0211043,-2.33030034,-0.89872694,-2.3838234,-1.9033642,1.4199948
+GSM1386787,1.0,-1.4896231,-2.4131308000000002,-0.61812305,-2.5144706,-2.4734469,0.85770464
+GSM1386788,1.0,0.094777584,0.37066603,0.029472828,0.067504883,0.27933883,-0.21805048
+GSM1386789,1.0,-0.09707928,0.36506319,0.23913574,0.29710006,0.15818691099999999,0.027359962
+GSM1386790,1.0,0.13585997,0.40356731,0.022135258,0.204932213,0.229414936,-0.07426643
+GSM1386791,1.0,-0.28459454,-0.11457395199999999,-0.1816411,0.024843692,-0.35184574,0.0030756
+GSM1386792,1.0,-0.08352995,0.04900169499999999,-0.041664124,-0.3518076,-0.2970314,0.23399305
+GSM1386793,1.0,0.17742443,-0.035991668,-0.062768936,0.067766666,-0.061686039,0.34554863
+GSM1386794,1.0,-0.06287432,-0.3546939,-0.09017897,-0.49099494,-0.259068021,-0.25925875
+GSM1386795,1.0,0.7361469,0.9561801,0.53167295,0.82645132,1.26809405,-0.9356046
+GSM1386796,1.0,0.32778645,0.37000036,-0.004978657,0.2341361,0.083415031,-0.6476259
+GSM1386797,1.0,0.22304153,0.87560559,0.28741312,0.5356359500000001,1.0647583,-0.8021169
+GSM1386798,1.0,-0.09680271,-0.53307485,-0.19589424,-0.59719944,-0.82204247,0.25326347
+GSM1386799,1.0,0.44625282,0.61146545,0.027667522,0.43883799999999995,0.42215537000000003,-0.0030756
+GSM1386800,1.0,0.14393425,0.19690037200000002,0.08336258,-0.009117600000000003,0.23794602999999998,-0.09946108
+GSM1386801,1.0,0.34312534,0.17539215,0.13261986,-0.025274755999999995,0.28540564,-0.7215929
+GSM1386802,1.0,0.14683247,-0.025328160000000002,0.004978657,0.12526941,0.094993111,0.19180107
+GSM1386803,1.0,0.2962079,0.364687916,0.44305038,0.53017902,0.9258027,0.8715625
+GSM1386804,1.0,-1.4721146,-2.3540092,-0.92245007,-1.94606256,-1.83000425,0.74155474
+GSM1386805,1.0,0.51871777,1.1359124,0.65173435,0.86696577,1.07737543,0.60939455
+GSM1386806,1.0,-1.2463741,-2.4943362000000002,-0.9899926,-2.2423535,-2.5412082,1.1570144
+GSM1386807,1.0,-0.71378565,-1.30321694,-0.6754422,-1.6626921000000001,-1.1611657000000002,1.2271285
+GSM1386808,1.0,-0.5739317,-1.03041985,-0.55668163,-1.2853932000000001,-0.79418707,1.1129279
+GSM1386809,1.0,-1.1615801,-1.39879942,-0.8540139,-2.1464204999999996,-1.88598825,0.34160185
+GSM1386810,1.0,-1.2588696,-1.82799913,-1.0363269,-2.04504534,-1.7896041999999999,1.220819
+GSM1386811,1.0,-0.17679119,-0.82144833,-0.02175188,-1.0187483,-0.38675689,0.38297415
+GSM1386812,1.0,0.26977968,0.91326046,0.5196619,0.9127726500000001,1.27010295,0.7410984
+GSM1386813,1.0,0.506701,0.7822785400000001,0.519248,0.92507696,1.18292617,0.78384686
+GSM1386814,1.0,-1.0470796,-1.9369846000000002,-0.2801366,-1.9822397600000001,-1.561553,1.0111966
+GSM1386815,1.0,-1.4382753,-2.4763994,-1.1996527,-2.18464804,-1.93124727,1.1602397
+GSM1386816,1.0,-0.2334876,-0.34955073,-0.26493597,-0.5213356060000001,-0.53920459,0.6664443
+GSM1386817,1.0,0.0,0.23737144999999998,0.0,0.10456371,0.288816452,0.26579142
+GSM1386818,1.0,0.17134523,0.55437803,0.13288546,0.40646791,0.41361999,-0.20508003
+GSM1386819,1.0,0.2386918,0.49894714,0.005102634,0.45970249,0.43051863,-0.8014412
+GSM1386820,1.0,0.7031131,1.0046558399999999,0.533257,1.14076184,1.2745328,-0.49083138
+GSM1386821,1.0,-0.000496,-0.11847448,-0.15684175,-0.030855180000000003,-0.15421391,0.052060127
+GSM1386822,1.0,0.3597355,0.74723577,0.31128645,0.41821717999999997,0.6909670800000001,-0.90626955
+GSM1386823,1.0,-0.038836956,-0.47126674999999996,-0.18481445,-0.54772091,-0.31615019,0.111257076
+GSM1386824,1.0,0.12692833,0.41289759,0.23889208,0.10900831,0.268902302,-0.6948824
+GSM1386825,1.0,0.61382675,0.98817347,0.4191122,1.03412964,1.04204944,-0.45151377
+GSM1386826,1.0,0.28687286,0.46688891,0.47374392,0.189020158,0.123023514,-0.12901115
+GSM1386827,1.0,0.24881172,0.3971858,0.011720181,-0.197118286,0.07942772199999999,-0.13073301
+GSM1386828,1.0,0.31893158,0.71583318,0.15642977,0.6945042699999999,0.63826847,-0.81514025
+GSM1386829,1.0,-0.31258392,-0.60973311,-0.13690042,-0.39424276,-0.132316114,-0.005393028
+GSM1386830,1.0,0.57070637,1.14602615,0.44452953,1.1163883000000001,0.92213107,-0.2615714
+GSM1386831,1.0,0.612679,1.6355553,0.7136893,1.2421703499999999,1.4493804,-0.8501253
+GSM1386832,1.0,0.38011026,0.73755741,0.36495543,0.7642345500000001,0.89166452,0.0
+GSM1386833,1.0,0.22704124,0.40208959,0.09160614,0.42066669,0.366199985,-0.21306086
+GSM1386834,1.0,0.6170387,1.2158646499999999,0.46519995,1.2336998000000001,1.1475,-0.25943708
+GSM1386835,1.0,0.23919868,0.0,0.08466911,0.07517480599999998,0.023037430000000005,0.21249485
+GSM1386836,1.0,0.25013638,0.83558608,0.3518448,0.9479188999999999,0.9710945999999999,-0.31624937
+GSM1386837,1.0,0.058721542,0.187199115,0.31102705,-0.12496567,-0.0029811860000000003,-0.45074034
+GSM1386838,1.0,-0.09590149,-0.29230594,-0.42019033,0.05941199999999999,-0.18297672,0.42432547
+GSM1386839,1.0,0.0,0.32783842,0.241745,0.30297326999999996,0.58085347,-0.1091094
+GSM1386840,1.0,0.5643606,0.87081147,0.308362,1.1834692900000001,0.9477806200000001,-0.5802765
+GSM1386841,0.0,-0.5710311,-0.81707047,-0.27139902,-0.52866888,-0.58560992,0.20727634
+GSM1386842,0.0,0.12207651,0.10698557,0.27159643,0.34318924,0.15613937,-0.071946144
+GSM1386843,0.0,0.14912271,0.6065440200000001,0.0,0.53131725,0.46221972,0.08834982
+GSM1386844,0.0,-0.09638739,-0.085456371,-0.0449214,-0.19595909,-0.10737276,-0.24069166
+GSM1386845,0.0,-0.6957979,-1.05188035,-0.33716965,-0.93917752,-1.27701185,0.7654357
+GSM1386846,0.0,-0.29966164,-0.50029516,-0.07559013,-0.19395112999999997,-0.27339507,0.32654858
+GSM1386847,0.0,-0.19005346,-0.32417202,-0.16873789,-0.028076649,-0.053367615,-0.297359
+GSM1386848,0.0,-0.46812296,-0.6530427999999999,-0.23539066,-0.58253003,-0.57691431,1.0086036
+GSM1386849,0.0,-0.27124596,-0.6445465,-0.36366415,-0.42548752,-0.6586442,0.18150377
+GSM1386850,0.0,-0.33076286,-0.45045233,-0.15522003,-0.058813093999999996,-0.3830862,0.41234016

p1/preprocess/Atherosclerosis/clinical_data/GSE109048.csv

@@ -0,0 +1,2 @@
+GSM2928447,GSM2928448,GSM2928449,GSM2928450,GSM2928451,GSM2928452,GSM2928453,GSM2928454,GSM2928455,GSM2928456,GSM2928457,GSM2928458,GSM2928459,GSM2928460,GSM2928461,GSM2928462,GSM2928463,GSM2928464,GSM2928465,GSM2928466,GSM2928467,GSM2928468,GSM2928469,GSM2928470,GSM2928471,GSM2928472,GSM2928473,GSM2928474,GSM2928475,GSM2928476,GSM2928477,GSM2928478,GSM2928479,GSM2928480,GSM2928481,GSM2928482,GSM2928483,GSM2928484,GSM2928485,GSM2928486,GSM2928487,GSM2928488,GSM2928489,GSM2928490,GSM2928491,GSM2928492,GSM2928493,GSM2928494,GSM2928495,GSM2928496,GSM2928497,GSM2928498,GSM2928499,GSM2928500,GSM2928501,GSM2928502,GSM2928503
+1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

p1/preprocess/Atherosclerosis/clinical_data/GSE123086.csv

@@ -0,0 +1,4 @@
+GSM3494884,GSM3494885,GSM3494886,GSM3494887,GSM3494888,GSM3494889,GSM3494890,GSM3494891,GSM3494892,GSM3494893,GSM3494894,GSM3494895,GSM3494896,GSM3494897,GSM3494898,GSM3494899,GSM3494900,GSM3494901,GSM3494902,GSM3494903,GSM3494904,GSM3494905,GSM3494906,GSM3494907,GSM3494908,GSM3494909,GSM3494910,GSM3494911,GSM3494912,GSM3494913,GSM3494914,GSM3494915,GSM3494916,GSM3494917,GSM3494918,GSM3494919,GSM3494920,GSM3494921,GSM3494922,GSM3494923,GSM3494924,GSM3494925,GSM3494926,GSM3494927,GSM3494928,GSM3494929,GSM3494930,GSM3494931,GSM3494932,GSM3494933,GSM3494934,GSM3494935,GSM3494936,GSM3494937,GSM3494938,GSM3494939,GSM3494940,GSM3494941,GSM3494942,GSM3494943,GSM3494944,GSM3494945,GSM3494946,GSM3494947,GSM3494948,GSM3494949,GSM3494950,GSM3494951,GSM3494952,GSM3494953,GSM3494954,GSM3494955,GSM3494956,GSM3494957,GSM3494958,GSM3494959,GSM3494960,GSM3494961,GSM3494962,GSM3494963,GSM3494964,GSM3494965,GSM3494966,GSM3494967,GSM3494968,GSM3494969,GSM3494970,GSM3494971,GSM3494972,GSM3494973,GSM3494974,GSM3494975,GSM3494976,GSM3494977,GSM3494978,GSM3494979,GSM3494980,GSM3494981,GSM3494982,GSM3494983,GSM3494984,GSM3494985,GSM3494986,GSM3494987,GSM3494988,GSM3494989,GSM3494990,GSM3494991,GSM3494992,GSM3494993,GSM3494994,GSM3494995,GSM3494996,GSM3494997,GSM3494998,GSM3494999,GSM3495000,GSM3495001,GSM3495002,GSM3495003,GSM3495004,GSM3495005,GSM3495006,GSM3495007,GSM3495008,GSM3495009,GSM3495010,GSM3495011,GSM3495012,GSM3495013,GSM3495014,GSM3495015,GSM3495016,GSM3495017,GSM3495018,GSM3495019,GSM3495020,GSM3495021,GSM3495022,GSM3495023,GSM3495024,GSM3495025,GSM3495026,GSM3495027,GSM3495028,GSM3495029,GSM3495030,GSM3495031,GSM3495032,GSM3495033,GSM3495034,GSM3495035,GSM3495036,GSM3495037,GSM3495038,GSM3495039,GSM3495040,GSM3495041,GSM3495042,GSM3495043,GSM3495044,GSM3495045,GSM3495046,GSM3495047,GSM3495048,GSM3495049
+0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+56.0,,20.0,51.0,37.0,61.0,,31.0,56.0,41.0,61.0,,80.0,53.0,61.0,73.0,60.0,76.0,77.0,74.0,69.0,77.0,81.0,70.0,82.0,69.0,82.0,67.0,67.0,78.0,67.0,74.0,,51.0,72.0,66.0,80.0,36.0,67.0,31.0,31.0,45.0,56.0,65.0,53.0,48.0,50.0,76.0,,24.0,42.0,76.0,22.0,,23.0,34.0,43.0,47.0,24.0,55.0,48.0,58.0,30.0,28.0,41.0,63.0,55.0,55.0,67.0,47.0,46.0,49.0,23.0,68.0,39.0,24.0,36.0,58.0,38.0,27.0,67.0,61.0,69.0,63.0,60.0,17.0,10.0,9.0,13.0,10.0,13.0,15.0,12.0,13.0,81.0,94.0,51.0,40.0,,97.0,23.0,93.0,58.0,28.0,54.0,15.0,8.0,11.0,12.0,8.0,14.0,8.0,10.0,14.0,13.0,40.0,52.0,42.0,29.0,43.0,41.0,54.0,42.0,49.0,45.0,56.0,64.0,71.0,48.0,20.0,53.0,32.0,26.0,28.0,47.0,24.0,48.0,,19.0,41.0,38.0,,15.0,12.0,13.0,,11.0,,16.0,11.0,,35.0,26.0,39.0,46.0,42.0,20.0,69.0,69.0,47.0,47.0,56.0,54.0,53.0,50.0,22.0
+1.0,,0.0,0.0,1.0,1.0,,1.0,0.0,0.0,0.0,,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,,0.0,0.0,1.0,1.0,,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,0.0,0.0,0.0,,0.0,1.0,0.0,,1.0,,1.0,1.0,,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0

p1/preprocess/Atherosclerosis/clinical_data/GSE123088.csv

@@ -0,0 +1,4 @@
+,GSM3494884,GSM3494885,GSM3494886,GSM3494887,GSM3494888,GSM3494889,GSM3494890,GSM3494891,GSM3494892,GSM3494893,GSM3494894,GSM3494895,GSM3494896,GSM3494897,GSM3494898,GSM3494899,GSM3494900,GSM3494901,GSM3494902,GSM3494903,GSM3494904,GSM3494905,GSM3494906,GSM3494907,GSM3494908,GSM3494909,GSM3494910,GSM3494911,GSM3494912,GSM3494913,GSM3494914,GSM3494915,GSM3494916,GSM3494917,GSM3494918,GSM3494919,GSM3494920,GSM3494921,GSM3494922,GSM3494923,GSM3494924,GSM3494925,GSM3494926,GSM3494927,GSM3494928,GSM3494929,GSM3494930,GSM3494931,GSM3494932,GSM3494933,GSM3494934,GSM3494935,GSM3494936,GSM3494937,GSM3494938,GSM3494939,GSM3494940,GSM3494941,GSM3494942,GSM3494943,GSM3494944,GSM3494945,GSM3494946,GSM3494947,GSM3494948,GSM3494949,GSM3494950,GSM3494951,GSM3494952,GSM3494953,GSM3494954,GSM3494955,GSM3494956,GSM3494957,GSM3494958,GSM3494959,GSM3494960,GSM3494961,GSM3494962,GSM3494963,GSM3494964,GSM3494965,GSM3494966,GSM3494967,GSM3494968,GSM3494969,GSM3494970,GSM3494971,GSM3494972,GSM3494973,GSM3494974,GSM3494975,GSM3494976,GSM3494977,GSM3494978,GSM3494979,GSM3494980,GSM3494981,GSM3494982,GSM3494983,GSM3494984,GSM3494985,GSM3494986,GSM3494987,GSM3494988,GSM3494989,GSM3494990,GSM3494991,GSM3494992,GSM3494993,GSM3494994,GSM3494995,GSM3494996,GSM3494997,GSM3494998,GSM3494999,GSM3495000,GSM3495001,GSM3495002,GSM3495003,GSM3495004,GSM3495005,GSM3495006,GSM3495007,GSM3495008,GSM3495009,GSM3495010,GSM3495011,GSM3495012,GSM3495013,GSM3495014,GSM3495015,GSM3495016,GSM3495017,GSM3495018,GSM3495019,GSM3495020,GSM3495021,GSM3495022,GSM3495023,GSM3495024,GSM3495025,GSM3495026,GSM3495027,GSM3495028,GSM3495029,GSM3495030,GSM3495031,GSM3495032,GSM3495033,GSM3495034,GSM3495035,GSM3495036,GSM3495037,GSM3495038,GSM3495039,GSM3495040,GSM3495041,GSM3495042,GSM3495043,GSM3495044,GSM3495045,GSM3495046,GSM3495047,GSM3495048,GSM3495049,GSM3495050,GSM3495051,GSM3495052,GSM3495053,GSM3495054,GSM3495055,GSM3495056,GSM3495057,GSM3495058,GSM3495059,GSM3495060,GSM3495061,GSM3495062,GSM3495063,GSM3495064,GSM3495065,GSM3495066,GSM3495067,GSM3495068,GSM3495069,GSM3495070,GSM3495071,GSM3495072,GSM3495073,GSM3495074,GSM3495075,GSM3495076,GSM3495077,GSM3495078,GSM3495079,GSM3495080,GSM3495081,GSM3495082,GSM3495083,GSM3495084,GSM3495085,GSM3495086,GSM3495087
+Atherosclerosis,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Age,56.0,,20.0,51.0,37.0,61.0,,31.0,56.0,41.0,61.0,,80.0,53.0,61.0,73.0,60.0,76.0,77.0,74.0,69.0,77.0,81.0,70.0,82.0,69.0,82.0,67.0,67.0,78.0,67.0,74.0,,51.0,72.0,66.0,80.0,36.0,67.0,31.0,31.0,45.0,56.0,65.0,53.0,48.0,50.0,76.0,,24.0,42.0,76.0,22.0,,23.0,34.0,43.0,47.0,24.0,55.0,48.0,58.0,30.0,28.0,41.0,63.0,55.0,55.0,67.0,47.0,46.0,49.0,23.0,68.0,39.0,24.0,36.0,58.0,38.0,27.0,67.0,61.0,69.0,63.0,60.0,17.0,10.0,9.0,13.0,10.0,13.0,15.0,12.0,13.0,81.0,94.0,51.0,40.0,,97.0,23.0,93.0,58.0,28.0,54.0,15.0,8.0,11.0,12.0,8.0,14.0,8.0,10.0,14.0,13.0,40.0,52.0,42.0,29.0,43.0,41.0,54.0,42.0,49.0,45.0,56.0,64.0,71.0,48.0,20.0,53.0,32.0,26.0,28.0,47.0,24.0,48.0,,19.0,41.0,38.0,,15.0,12.0,13.0,,11.0,,16.0,11.0,,35.0,26.0,39.0,46.0,42.0,20.0,69.0,69.0,47.0,47.0,56.0,54.0,53.0,50.0,22.0,62.0,74.0,57.0,47.0,70.0,50.0,52.0,43.0,57.0,53.0,70.0,41.0,61.0,39.0,58.0,55.0,63.0,60.0,43.0,68.0,67.0,50.0,67.0,51.0,59.0,44.0,35.0,83.0,78.0,88.0,41.0,60.0,72.0,53.0,73.0,56.0,38.0,53.0
+Gender,1.0,,0.0,0.0,1.0,1.0,,1.0,0.0,0.0,0.0,,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,,0.0,0.0,1.0,1.0,,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,0.0,0.0,0.0,,0.0,1.0,0.0,,1.0,,1.0,1.0,,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

p1/preprocess/Atherosclerosis/clinical_data/GSE57691.csv

@@ -0,0 +1,2 @@
+GSM1386783,GSM1386784,GSM1386785,GSM1386786,GSM1386787,GSM1386788,GSM1386789,GSM1386790,GSM1386791,GSM1386792,GSM1386793,GSM1386794,GSM1386795,GSM1386796,GSM1386797,GSM1386798,GSM1386799,GSM1386800,GSM1386801,GSM1386802,GSM1386803,GSM1386804,GSM1386805,GSM1386806,GSM1386807,GSM1386808,GSM1386809,GSM1386810,GSM1386811,GSM1386812,GSM1386813,GSM1386814,GSM1386815,GSM1386816,GSM1386817,GSM1386818,GSM1386819,GSM1386820,GSM1386821,GSM1386822,GSM1386823,GSM1386824,GSM1386825,GSM1386826,GSM1386827,GSM1386828,GSM1386829,GSM1386830,GSM1386831,GSM1386832,GSM1386833,GSM1386834,GSM1386835,GSM1386836,GSM1386837,GSM1386838,GSM1386839,GSM1386840,GSM1386841,GSM1386842,GSM1386843,GSM1386844,GSM1386845,GSM1386846,GSM1386847,GSM1386848,GSM1386849,GSM1386850
+1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

p1/preprocess/Atherosclerosis/code/GSE109048.py

@@ -0,0 +1,216 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+cohort = "GSE109048"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atherosclerosis"
+in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE109048"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Atherosclerosis/GSE109048.csv"
+out_gene_data_file = "./output/preprocess/1/Atherosclerosis/gene_data/GSE109048.csv"
+out_clinical_data_file = "./output/preprocess/1/Atherosclerosis/clinical_data/GSE109048.csv"
+json_path = "./output/preprocess/1/Atherosclerosis/cohort_info.json"
+
+# STEP 1: Initial Data Loading
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    prefixes_a=background_prefixes,
+    prefixes_b=clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on the background info indicating gene expression profiling (platelet mRNA).
+
+# 2. Variable Availability and Data Type Conversion
+# Examine sample characteristics: {0: ['tissue: Platelets'], 1: ['diagnosis: sCAD', 'diagnosis: healthy', 'diagnosis: STEMI']}
+# We see diagnosis info in row 1. We'll interpret "sCAD" or "STEMI" as having Atherosclerosis (1) and "healthy" as (0).
+trait_row = 1
+age_row = None
+gender_row = None
+
+# Define conversion functions
+def convert_trait(value: str) -> Optional[int]:
+    """
+    Convert the diagnosis info (sCAD, STEMI, healthy) to a binary code:
+    1 for atherosclerosis (sCAD or STEMI), 0 for healthy, None if unknown.
+    """
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val in ["scad", "stemi"]:
+        return 1
+    elif val == "healthy":
+        return 0
+    else:
+        return None
+
+def convert_age(value: str) -> Optional[float]:
+    """
+    No age data available, so we simply return None.
+    """
+    return None
+
+def convert_gender(value: str) -> Optional[int]:
+    """
+    No gender data available, so we simply return None.
+    """
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+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=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if trait data is available)
+if trait_row is not None:
+    # Assume 'clinical_data' DataFrame is already loaded in the environment
+    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
+    preview_data = preview_df(selected_clinical_df)
+    print("Preview of extracted clinical features:", preview_data)
+
+    # Save the clinical data to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on observation, these identifiers (e.g., '2824546_st') appear to be microarray probe IDs, not typical human gene symbols.
+# Therefore, they require mapping to gene symbols.
+
+print("They are microarray probe IDs and require further mapping to standard gene symbols.")
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP 6: Gene Identifier Mapping
+
+# We have probe IDs in the gene expression data that look like "2824546_st",
+# but the annotation has columns "ID" and "probeset_id" with values like "TC01000001.hg.1".
+# The library function 'get_gene_mapping' expects the probe column to be named "ID",
+# and will raise a KeyError if we pass in a different column name (e.g., "probeset_id").
+# Here, we manually build the mapping DataFrame to avoid the KeyError.
+
+# Define the columns in the annotation DataFrame that correspond to probe ID and gene info
+prob_col = "probeset_id"
+gene_col = "gene_assignment"
+
+# 1. Manually build the mapping DataFrame to avoid the mismatch with the library function.
+if prob_col not in gene_annotation.columns or gene_col not in gene_annotation.columns:
+    print(f"Columns '{prob_col}' or '{gene_col}' not found in annotation. Skipping mapping.")
+else:
+    mapping_df = gene_annotation.loc[:, [prob_col, gene_col]].dropna().copy()
+    # Rename to "ID" and "Gene" for downstream consistency
+    mapping_df = mapping_df.rename(columns={prob_col: 'ID', gene_col: 'Gene'})
+    mapping_df['ID'] = mapping_df['ID'].astype(str)
+
+    # 2. Check overlap between annotation IDs and expression data index
+    common_ids = set(mapping_df['ID']).intersection(set(gene_data.index))
+    if not common_ids:
+        print("No matching probe IDs found between gene_data and annotation. Skipping mapping.")
+    else:
+        # 3. Apply the mapping to convert probe-level data to gene-level data
+        gene_data = apply_gene_mapping(gene_data, mapping_df)
+        print("Mapped gene_data shape:", gene_data.shape)
+        print(gene_data.head())
+import os
+import pandas as pd
+
+# STEP 7
+
+# 1. Normalize the gene expression data to standard gene symbols.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print("Normalized gene expression data saved to:", out_gene_data_file)
+
+# Check if clinical data exists before linking
+if not os.path.exists(out_clinical_data_file):
+    # Without clinical data, we cannot do trait-based analysis
+    dummy_df = pd.DataFrame()
+    trait_biased = True  # Mark as unusable because we lack trait information
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=trait_biased,
+        df=dummy_df,
+        note="No trait data found. This dataset is not usable for final analysis."
+    )
+    print("Clinical data file not found. Skipping linking and final data export.")
+else:
+    # 2. Link the clinical and genetic data
+    # Read the clinical CSV with index_col=0 to preserve the feature name (trait row label)
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, header=0, index_col=0)
+    
+    # If there's exactly one row (our trait row), rename it to 'trait'
+    if selected_clinical_df.shape[0] == 1:
+        selected_clinical_df.index = [trait]
+
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values
+    df = handle_missing_values(linked_data, trait)
+
+    # 4. Determine whether the trait or demographic features are biased
+    trait_biased, df = judge_and_remove_biased_features(df, trait)
+
+    # 5. Perform final validation
+    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=df,
+        note="Final step with linking, missing-value handling, and bias checks."
+    )
+
+    # 6. If the data is usable, save the final linked data
+    if is_usable:
+        df.to_csv(out_data_file)
+        print(f"Final linked data saved to: {out_data_file}")
+    else:
+        print("Dataset is not usable or severely biased. No final data saved.")

p1/preprocess/Atherosclerosis/code/GSE123086.py

@@ -0,0 +1,190 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+cohort = "GSE123086"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atherosclerosis"
+in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE123086"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Atherosclerosis/GSE123086.csv"
+out_gene_data_file = "./output/preprocess/1/Atherosclerosis/gene_data/GSE123086.csv"
+out_clinical_data_file = "./output/preprocess/1/Atherosclerosis/clinical_data/GSE123086.csv"
+json_path = "./output/preprocess/1/Atherosclerosis/cohort_info.json"
+
+# STEP 1: Initial Data Loading
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    prefixes_a=background_prefixes,
+    prefixes_b=clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Determine gene expression data availability
+is_gene_available = True  # Based on the microarray gene expression description
+
+# 2. Identify rows and define conversion functions
+trait_row = 1    # Row containing primary diagnoses info including ATHEROSCLEROSIS
+age_row = 3      # Row containing various "age: ..." entries
+gender_row = 2   # Row containing "Sex: Male" or "Sex: Female" entries
+
+def convert_trait(value: str):
+    """Convert to binary: 1 if contains 'ATHEROSCLEROSIS', else 0."""
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().upper()
+    return 1 if 'ATHEROSCLEROSIS' in val else 0
+
+def convert_age(value: str):
+    """Convert to continuous age in years. Unknown or non-numeric => None."""
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip()
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    """Convert to binary: Female => 0, Male => 1, else None."""
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().upper()
+    if val == 'MALE':
+        return 1
+    elif val == 'FEMALE':
+        return 0
+    return None
+
+# 3. Initial filtering and metadata save
+is_trait_available = (trait_row is not None)
+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=is_trait_available
+)
+
+# 4. Clinical feature extraction and preview if trait data is available
+if trait_row is not None:
+    extracted_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 = preview_df(extracted_clinical_df)
+    print("Preview of extracted clinical features:", preview)
+    extracted_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the numeric format, these are not typical human gene symbols and likely require mapping.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# Gene Identifier Mapping
+prob_col = "ID"       # The column in gene_annotation that matches the gene_data index
+gene_col = "ENTREZ_GENE_ID"  # The column in gene_annotation to treat as the gene symbol for mapping
+
+# 1. Get the gene mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)
+
+# 2. Convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# 3. Preview mapped gene_data
+print("Preview of gene_data after mapping:")
+print(preview_df(gene_data))
+import os
+import pandas as pd
+
+# STEP 7
+
+# 1. Normalize the gene expression data to standard gene symbols.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print("Normalized gene expression data saved to:", out_gene_data_file)
+
+# Check if clinical data exists. If not, we cannot link or proceed with trait-based analysis.
+if not os.path.exists(out_clinical_data_file):
+    # We must perform final validation so that the cohort is recorded as unusable (missing trait data).
+    dummy_df = pd.DataFrame()
+    trait_biased = True  # Mark as biased or unusable because we lack any trait information
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=trait_biased,
+        df=dummy_df,
+        note="No trait data found. This dataset is not usable for final analysis."
+    )
+    print("Clinical data file not found. Skipping linking and final data export.")
+else:
+    # 2. Read the clinical data without using index_col; assign the correct row index manually.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, header=0)
+    # We have exactly three rows: trait, Age, Gender
+    selected_clinical_df.index = [trait, "Age", "Gender"]
+    
+    # Link the clinical data with genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values systematically.
+    df = handle_missing_values(linked_data, trait)
+
+    # 4. Determine whether the trait or demographic features are biased; remove biased demographic features.
+    trait_biased, df = judge_and_remove_biased_features(df, trait)
+
+    # 5. Perform final validation with full dataset information.
+    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=df,
+        note="Final step with linking, missing-value handling, bias checks."
+    )
+
+    # 6. If the data is usable, save the final linked data.
+    if is_usable:
+        df.to_csv(out_data_file)
+        print(f"Final linked data saved to: {out_data_file}")
+    else:
+        print("Dataset is not usable or is severely biased. No final data saved.")

p1/preprocess/Atherosclerosis/code/GSE123088.py

@@ -0,0 +1,213 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+cohort = "GSE123088"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atherosclerosis"
+in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE123088"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Atherosclerosis/GSE123088.csv"
+out_gene_data_file = "./output/preprocess/1/Atherosclerosis/gene_data/GSE123088.csv"
+out_clinical_data_file = "./output/preprocess/1/Atherosclerosis/clinical_data/GSE123088.csv"
+json_path = "./output/preprocess/1/Atherosclerosis/cohort_info.json"
+
+# STEP 1: Initial Data Loading
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    prefixes_a=background_prefixes,
+    prefixes_b=clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on the provided information, it appears to be gene expression data.
+
+# 2. Variable Availability and Conversions
+#    Observations from the sample characteristics:
+#    - trait ("Atherosclerosis") is found in row 1 under "primary diagnosis: ATHEROSCLEROSIS".
+#    - age values are predominantly found in row 3 (e.g., "age: 56", "age: 20", etc.).
+#    - gender is found in row 2 (e.g., "Sex: Male", "Sex: Female"), although it also appears elsewhere.
+
+trait_row = 1
+age_row = 3
+gender_row = 2
+
+def convert_trait(value: str) -> int:
+    """
+    Convert the trait field to a binary: 1 if 'ATHEROSCLEROSIS', otherwise 0.
+    """
+    # Split by colon and strip
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().upper()
+    if val == "ATHEROSCLEROSIS":
+        return 1
+    else:
+        return 0
+
+def convert_age(value: str) -> float:
+    """
+    Convert age to a float. 
+    If parsing fails or the entry is not an age, return None.
+    """
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip()
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(value: str) -> int:
+    """
+    Convert gender to binary: 0 = female, 1 = male.
+    If parsing fails or the entry is unknown, return None.
+    """
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().upper()
+    if val == "MALE":
+        return 1
+    elif val == "FEMALE":
+        return 0
+    else:
+        return None
+
+# 3. Initial Filtering and Save Metadata
+is_trait_available = (trait_row is not None)
+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=is_trait_available
+)
+
+# 4. Clinical Feature Extraction and Preview, if trait is available
+if trait_row is not None:
+    selected_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_result = preview_df(selected_clinical_df)
+    print("Preview of selected clinical features:", preview_result)
+
+    # Save the extracted clinical features
+    selected_clinical_df.to_csv(out_clinical_data_file)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Observing the numeric identifiers (1, 2, 3, 9, 10, etc.), these do not resemble standard human gene symbols.
+# Therefore, we conclude that gene mapping is required.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+# The original approach resulted in an empty DataFrame because numeric Entrez IDs do not match the built-in
+# extract_human_gene_symbols pattern. Below, we prepend an 'E' to each numeric ID so they become valid strings
+# (e.g., "E1", "E2"), which pass the pattern check. This way, they won't be discarded.
+
+# 1. Modify the "ENTREZ_GENE_ID" column to prepend an 'E' to each numeric ID
+gene_annotation["ENTREZ_GENE_ID"] = gene_annotation["ENTREZ_GENE_ID"].apply(
+    lambda x: f"E{x}" if pd.notnull(x) else x
+)
+
+# 2. Identify the columns that match the gene expression data (ID) and the modified gene identifier (ENTREZ_GENE_ID).
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="ENTREZ_GENE_ID")
+
+# 3. Apply the gene mapping to convert probe-level measurements to gene-level expression.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional) Print a quick check of the mapped gene_data
+print("After mapping, gene_data shape:", gene_data.shape)
+print("First 10 gene symbols:", gene_data.index[:10])
+import os
+import pandas as pd
+
+# STEP 7
+
+# 1. Normalize the gene expression data to standard gene symbols.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print("Normalized gene expression data saved to:", out_gene_data_file)
+
+# Check if clinical data exists. If not, we cannot link or proceed with trait-based analysis.
+if not os.path.exists(out_clinical_data_file):
+    # We must perform final validation so that the cohort is recorded as unusable (missing trait data).
+    dummy_df = pd.DataFrame()
+    trait_biased = True  # Mark as biased or unusable because we lack any trait information
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=trait_biased,
+        df=dummy_df,
+        note="No trait data found. This dataset is not usable for final analysis."
+    )
+    print("Clinical data file not found. Skipping linking and final data export.")
+else:
+    # 2. Link the clinical data with genetic data
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values systematically.
+    df = handle_missing_values(linked_data, trait)
+
+    # 4. Determine whether the trait or demographic features are biased; remove biased demographic features.
+    trait_biased, df = judge_and_remove_biased_features(df, trait)
+
+    # 5. Perform final validation with full dataset information.
+    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=df,
+        note="Final step with linking, missing-value handling, bias checks."
+    )
+
+    # 6. If the data is usable, save the final linked data.
+    if is_usable:
+        df.to_csv(out_data_file)
+        print(f"Final linked data saved to: {out_data_file}")
+    else:
+        print("Dataset is not usable or severely biased. No final data saved.")

p1/preprocess/Atherosclerosis/code/GSE125771.py

@@ -0,0 +1,182 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+cohort = "GSE125771"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atherosclerosis"
+in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE125771"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Atherosclerosis/GSE125771.csv"
+out_gene_data_file = "./output/preprocess/1/Atherosclerosis/gene_data/GSE125771.csv"
+out_clinical_data_file = "./output/preprocess/1/Atherosclerosis/clinical_data/GSE125771.csv"
+json_path = "./output/preprocess/1/Atherosclerosis/cohort_info.json"
+
+# STEP 1: Initial Data Loading
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    prefixes_a=background_prefixes,
+    prefixes_b=clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+import pandas as pd
+from typing import Optional, Callable
+
+# 1. Determine gene expression availability
+is_gene_available = True  # Based on the background info ("RNA expression data")
+
+# 2. Determine variable availability and define converter functions
+#    Inspecting the dictionary:
+#      0 -> ['tissue: carotid-atherosclerotic-plaque']  (only one unique value, not useful for association)
+#      1 -> ['ID: ...']                                (sample IDs, not needed)
+#      2 -> ['Sex: Male', 'Sex: Female']               (gender info)
+#      3 -> ['age: 73', 'age: 60', ...]                (age info)
+
+trait_row = None  # Only a single unique value in row 0, so treat trait as not available
+age_row = 3       # Multiple unique values
+gender_row = 2    # Contains both "Male" and "Female"
+
+# Data type conversion functions
+def convert_trait(value: str) -> Optional[int]:
+    """No trait data in this dataset (None). Function provided for completeness."""
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    """Extract numeric age from the string after 'age: '. Unknown/invalid -> None."""
+    try:
+        val_str = value.split(':', 1)[1].strip()
+        return float(val_str)
+    except:
+        return None
+
+def convert_gender(value: str) -> Optional[int]:
+    """
+    Convert gender to binary.
+    - "Female" -> 0
+    - "Male" -> 1
+    Unknown -> None
+    """
+    try:
+        val_str = value.split(':', 1)[1].strip().lower()
+        if val_str == 'male':
+            return 1
+        elif val_str == 'female':
+            return 0
+        else:
+            return None
+    except:
+        return None
+
+# 3. Conduct initial filtering on usability
+#    Trait data is unavailable since trait_row is None
+is_trait_available = (trait_row is not None)
+
+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=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+#    Only proceed if trait_row is not None. Here, it is None, so we skip.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the listed identifiers (e.g., "TC01000001.hg.1"), these are not recognized human gene symbols.
+# They appear to be proprietary or custom probe identifiers that likely require mapping to standard gene symbols.
+print("These gene identifiers are not standard human gene symbols.\nrequires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the matching column for the probe identifiers ("ID") and the column containing gene symbol information ("gene_assignment").
+# 2. Obtain the mapping dataframe.
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="gene_assignment")
+
+# 3. Apply mapping to convert the probe-level expression to gene-level expression.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+import os
+import pandas as pd
+
+# STEP 7
+
+# 1. Normalize the gene expression data to standard gene symbols.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print("Normalized gene expression data saved to:", out_gene_data_file)
+
+# Check if clinical data exists. If not, we cannot link or proceed with trait-based analysis.
+if not os.path.exists(out_clinical_data_file):
+    # We must perform final validation so that the cohort is recorded as unusable (missing trait data).
+    dummy_df = pd.DataFrame()
+    trait_biased = True  # Mark as biased or unusable because we lack any trait information
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=trait_biased,
+        df=dummy_df,
+        note="No trait data found. This dataset is not usable for final analysis."
+    )
+    print("Clinical data file not found. Skipping linking and final data export.")
+else:
+    # 2. Link the clinical data with genetic data
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, header=0)
+    # By design, each row in this CSV might represent a clinical feature (e.g., trait, age, gender).
+    # Since trait_row was None, we typically wouldn't have a valid trait row, but let's proceed safely:
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values systematically.
+    df = handle_missing_values(linked_data, trait)
+
+    # 4. Determine whether the trait or demographic features are biased; remove biased demographic features.
+    trait_biased, df = judge_and_remove_biased_features(df, trait)
+
+    # 5. Perform final validation with full dataset information.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,  # We do have a clinical file now
+        is_biased=trait_biased,
+        df=df,
+        note="Final step with linking, missing-value handling, bias checks."
+    )
+
+    # 6. If the data is usable, save the final linked data.
+    if is_usable:
+        df.to_csv(out_data_file)
+        print(f"Final linked data saved to: {out_data_file}")
+    else:
+        print("Dataset is not usable or severely biased. No final data saved.")

p1/preprocess/Atherosclerosis/code/GSE133601.py

@@ -0,0 +1,159 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+cohort = "GSE133601"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atherosclerosis"
+in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE133601"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Atherosclerosis/GSE133601.csv"
+out_gene_data_file = "./output/preprocess/1/Atherosclerosis/gene_data/GSE133601.csv"
+out_clinical_data_file = "./output/preprocess/1/Atherosclerosis/clinical_data/GSE133601.csv"
+json_path = "./output/preprocess/1/Atherosclerosis/cohort_info.json"
+
+# STEP 1: Initial Data Loading
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    prefixes_a=background_prefixes,
+    prefixes_b=clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Gene Expression Data Availability
+is_gene_available = True  # The background describes a transcriptional survey, so gene expression data is likely available.
+
+# 2) Variable Availability and Data Type Conversion
+# From the sample characteristics dictionary, no entries correspond to Atherosclerosis, age, or gender data.
+trait_row = None
+age_row = None
+gender_row = None
+
+def convert_trait(value: str) -> Optional[int]:
+    # No specific data for the trait is present, so return None.
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    # No age data found, so return None.
+    return None
+
+def convert_gender(value: str) -> Optional[int]:
+    # No gender data found, so return None.
+    return None
+
+# 3) Save Metadata (Initial Filtering)
+# If trait_row is None, then trait data is considered unavailable
+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
+# Since trait_row is None, we skip clinical feature extraction
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# The gene identifiers listed (e.g., '10000_at', '10001_at') are Affymetrix probe IDs, not standard human gene symbols.
+# Hence, they require mapping to official gene symbols.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1 & 2. Identify the columns in `gene_annotation` that match the probe IDs and the gene symbols.
+#    Based on the preview, 'ID' stores the probe identifiers (e.g., '10000_at'),
+#    while 'Description' appears to store gene descriptions/symbols.
+prob_col = 'ID'
+gene_col = 'Description'
+
+# Get the gene mapping dataframe from the annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_col)
+
+# 3. Convert probe-level measurements into gene-level measurements
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+import os
+import pandas as pd
+
+# STEP 7
+
+# 1. Normalize the gene expression data to standard gene symbols.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print("Normalized gene expression data saved to:", out_gene_data_file)
+
+# Check if clinical data exists. If not, we cannot link or proceed with trait-based analysis.
+if not os.path.exists(out_clinical_data_file):
+    # We must perform final validation so that the cohort is recorded as unusable (missing trait data).
+    dummy_df = pd.DataFrame()
+    trait_biased = True  # Mark as biased or unusable because we lack any trait information
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=trait_biased,
+        df=dummy_df,
+        note="No trait data found. This dataset is not usable for final analysis."
+    )
+    print("Clinical data file not found. Skipping linking and final data export.")
+else:
+    # 2. Link the clinical data with genetic data
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, header=0)
+    # By design, each row in this CSV might represent a clinical feature (e.g., trait, age, gender).
+    # Since trait_row was None, we typically wouldn't have a valid trait row, but let's proceed safely:
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values systematically.
+    df = handle_missing_values(linked_data, trait)
+
+    # 4. Determine whether the trait or demographic features are biased; remove biased demographic features.
+    trait_biased, df = judge_and_remove_biased_features(df, trait)
+
+    # 5. Perform final validation with full dataset information.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,  # We do have a clinical file now
+        is_biased=trait_biased,
+        df=df,
+        note="Final step with linking, missing-value handling, bias checks."
+    )
+
+    # 6. If the data is usable, save the final linked data.
+    if is_usable:
+        df.to_csv(out_data_file)
+        print(f"Final linked data saved to: {out_data_file}")
+    else:
+        print("Dataset is not usable or severely biased. No final data saved.")

p1/preprocess/Atherosclerosis/code/GSE154851.py

@@ -0,0 +1,175 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+cohort = "GSE154851"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atherosclerosis"
+in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE154851"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Atherosclerosis/GSE154851.csv"
+out_gene_data_file = "./output/preprocess/1/Atherosclerosis/gene_data/GSE154851.csv"
+out_clinical_data_file = "./output/preprocess/1/Atherosclerosis/clinical_data/GSE154851.csv"
+json_path = "./output/preprocess/1/Atherosclerosis/cohort_info.json"
+
+# STEP 1: Initial Data Loading
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    prefixes_a=background_prefixes,
+    prefixes_b=clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Decide gene expression data availability
+is_gene_available = True  # from background info: "Human Gene Expression 8x60K Microarray kit"
+
+# 2. Identify availability rows for trait, age, and gender
+trait_row = None  # No row indicates atherosclerosis status in the sample characteristics
+age_row = 2       # Row 2 has multiple age values
+gender_row = 1    # Row 1 has multiple gender values
+
+# 2.2 Define conversion functions
+
+def convert_trait(value: str):
+    # No actual data available, return None
+    return None
+
+def convert_age(value: str):
+    # Example: "age: 37y"
+    try:
+        # Split by colon, remove 'y', convert to float
+        parts = value.split(':')
+        if len(parts) < 2:
+            return None
+        age_str = parts[1].strip().lower().replace('y', '')
+        return float(age_str)
+    except:
+        return None
+
+def convert_gender(value: str):
+    # Example: "gender: male" -> 1, "gender: female" -> 0
+    try:
+        parts = value.split(':')
+        if len(parts) < 2:
+            return None
+        gender_str = parts[1].strip().lower()
+        if 'female' in gender_str:
+            return 0
+        elif 'male' in gender_str:
+            return 1
+        else:
+            return None
+    except:
+        return None
+
+# 3. Save metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+
+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=is_trait_available
+)
+
+# 4. Since trait_row is None, skip clinical feature extraction
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# The given IDs (1,2,3,...) do not match standard human gene symbol format,
+# so they likely need to be mapped to proper gene symbols
+print("These identifiers appear to be numeric, not standard human gene symbols.")
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns in the annotation that match the gene expression data's IDs and the gene symbol
+#    In this dataset, 'ID' corresponds to the probe identifiers, and 'GENE_SYMBOL' corresponds to the gene symbols.
+
+# 2. Get a gene mapping dataframe by extracting these two columns
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# 3. Convert probe-level measurements to gene-level measurements
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional) Print some info to verify the result
+print("Gene data shape after mapping:", gene_data.shape)
+print(gene_data.head())
+# STEP 7
+
+import os
+import pandas as pd
+
+# 1. Normalize the gene expression data to standard gene symbols.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print("Normalized gene expression data saved to:", out_gene_data_file)
+
+# Check if a clinical data file was ever generated (which indicates trait data was available).
+if not os.path.exists(out_clinical_data_file):
+    print("No clinical data file found. This implies trait data is unavailable.")
+    # In this scenario, we do a partial validation (is_final=False) because we cannot finalize without trait data.
+    is_usable = validate_and_save_cohort_info(
+        is_final=False,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False
+    )
+    print("Initial validation recorded for missing trait data. No final data will be saved.")
+else:
+    # 2. Link the clinical and genetic data on sample IDs
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values
+    df = handle_missing_values(linked_data, trait)
+
+    # 4. Determine whether the trait or demographic features are severely biased
+    trait_biased, df = judge_and_remove_biased_features(df, trait)
+
+    # 5. Perform final validation with full dataset
+    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=df,
+        note="Final step with linking, missing-value handling, bias checks."
+    )
+
+    # 6. If usable, save the final linked data
+    if is_usable:
+        df.to_csv(out_data_file)
+        print(f"Final linked data saved to: {out_data_file}")
+    else:
+        print("Dataset is not usable or severely biased. No final data saved.")

p1/preprocess/Atherosclerosis/code/GSE57691.py

@@ -0,0 +1,166 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+cohort = "GSE57691"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atherosclerosis"
+in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE57691"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Atherosclerosis/GSE57691.csv"
+out_gene_data_file = "./output/preprocess/1/Atherosclerosis/gene_data/GSE57691.csv"
+out_clinical_data_file = "./output/preprocess/1/Atherosclerosis/clinical_data/GSE57691.csv"
+json_path = "./output/preprocess/1/Atherosclerosis/cohort_info.json"
+
+# STEP 1: Initial Data Loading
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    prefixes_a=background_prefixes,
+    prefixes_b=clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Determine gene availability
+is_gene_available = True  # From the background info, it clearly states "Genome-wide expression analysis"
+
+# Step 2: Identify data availability and define row indices for trait, age, and gender
+# Based on inspection of the sample characteristics dictionary:
+trait_row = 0   # Matches the "disease state" field
+age_row = None  # Age info not provided
+gender_row = None  # Gender info not provided
+
+# Step 2 (continued): Define data type conversions
+def convert_trait(value: str):
+    # Extract the substring after colon
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[-1].strip().lower()
+    # Map "control" to 0; all other known disease states to 1
+    if 'control' in val:
+        return 0
+    elif 'aaa' in val or 'aod' in val:
+        return 1
+    return None
+
+# No age or gender data available, so define stubs that always return None
+def convert_age(value: str):
+    return None
+
+def convert_gender(value: str):
+    return None
+
+# Step 3: Conduct initial filtering and save metadata
+# Trait data is considered available if trait_row is not None
+is_trait_available = (trait_row is not None)
+
+# We are in the middle of preprocessing, so is_final=False
+# This function will record partial metadata if the dataset fails
+# or return to continue if it passes (with is_gene_available & is_trait_available).
+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=is_trait_available
+)
+
+# Step 4: Extract clinical features if trait data is available
+if trait_row is not None:
+    selected_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 the extracted clinical features
+    preview_data = preview_df(selected_clinical_df)
+    print("Clinical features preview:", preview_data)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify which columns in the gene annotation correspond to probe identifiers and gene symbols
+#    From the preview, "ID" contains "ILMN_..." probe identifiers, and "Symbol" holds gene symbols.
+
+# 2. Create a gene mapping dataframe from the annotation dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Symbol")
+
+# 3. Convert probe-level measurements to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optionally preview the result if desired)
+print("Mapped gene expression data shape:", gene_data.shape)
+print("First 5 genes in the mapped data:", gene_data.index[:5].tolist())
+# STEP 7
+
+import pandas as pd
+
+# 1. Normalize the gene expression data to standard gene symbols.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print("Normalized gene expression data saved to:", out_gene_data_file)
+
+# 2. Read back the clinical data, reassign its single row index to the trait name, and link with genetic data.
+selected_clinical_df = pd.read_csv(out_clinical_data_file, header=0)
+selected_clinical_df.index = [trait]  # Ensure the clinical row is labeled by the trait
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values systematically.
+df = handle_missing_values(linked_data, trait)
+
+# 4. Determine whether the trait or demographic features are biased; remove biased demographic features.
+trait_biased, df = judge_and_remove_biased_features(df, trait)
+
+# 5. Perform final validation with full dataset information.
+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=df,
+    note="Final step with linking, missing-value handling, bias checks."
+)
+
+# 6. If the data is usable, save the final linked data.
+if is_usable:
+    df.to_csv(out_data_file)
+    print(f"Final linked data saved to: {out_data_file}")
+else:
+    print("Dataset is not usable or severely biased. No final data saved.")

p1/preprocess/Atherosclerosis/code/GSE83500.py

@@ -0,0 +1,151 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+cohort = "GSE83500"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atherosclerosis"
+in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE83500"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Atherosclerosis/GSE83500.csv"
+out_gene_data_file = "./output/preprocess/1/Atherosclerosis/gene_data/GSE83500.csv"
+out_clinical_data_file = "./output/preprocess/1/Atherosclerosis/clinical_data/GSE83500.csv"
+json_path = "./output/preprocess/1/Atherosclerosis/cohort_info.json"
+
+# STEP 1: Initial Data Loading
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    prefixes_a=background_prefixes,
+    prefixes_b=clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on the microarray-based gene expression mention
+
+# 2.1 Variable Availability
+#    The entire cohort has atherosclerosis, so it does not vary => trait_row = None
+trait_row = None
+age_row = 1   # "age: ..."
+gender_row = 2  # "Sex: ..."
+
+# 2.2 Data Type Conversions
+def convert_trait(value: str):
+    # No trait variation in this dataset => return None
+    return None
+
+def convert_age(value: str):
+    # Example: "age: 69"
+    parts = value.split(":")
+    if len(parts) < 2:
+        return None
+    age_str = parts[1].strip()
+    try:
+        return float(age_str)
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    # Example: "Sex: Male" or "Sex: Female"
+    parts = value.split(":")
+    if len(parts) < 2:
+        return None
+    gender_str = parts[1].strip().lower()
+    if gender_str == 'male':
+        return 1
+    elif gender_str == 'female':
+        return 0
+    return None
+
+# 3. Save Metadata (initial filtering)
+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
+#    Skip because trait_row is None (trait not available)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# The provided gene identifiers (e.g., '11715100_at') are Affymetrix probe IDs, not human gene symbols.
+# Therefore, they require mapping to gene symbols.
+
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns for probe IDs and gene symbols in the annotation dataframe.
+#    From the preview, the "ID" column matches the probe identifiers in gene_data,
+#    and "Gene Symbol" column contains the actual gene symbols.
+
+# 2. Get the gene mapping dataframe using these columns
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Gene Symbol")
+
+# 3. Convert probe-level data into gene-level data using the mapping
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# For verification, print out the shape and first few gene symbols
+print("Gene data shape after mapping:", gene_data.shape)
+print("First 20 gene symbols in the mapped data:", list(gene_data.index[:20]))
+# STEP 7
+
+# Since trait data is unavailable (trait_row = None), we do NOT have any clinical data to link.
+# We'll only normalize the gene data, then finalize validation indicating no trait data.
+
+# 1. Normalize the gene expression data to standard gene symbols.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print("Normalized gene expression data saved to:", out_gene_data_file)
+
+# 2. Skip linking and missing value handling because we have no clinical data for trait-based analysis.
+
+# 3. For final validation, we must provide a DataFrame and an is_biased flag. Since there's no trait,
+#    we set is_trait_available=False, and use an empty DataFrame with is_biased=False.
+empty_df = pd.DataFrame()
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=False,  # Arbitrarily False; trait is missing anyway.
+    df=empty_df,
+    note="No trait data available; dataset cannot be used for trait-based analysis."
+)
+
+if is_usable:
+    print("Unexpectedly marked usable despite missing trait data.")
+else:
+    print("Dataset is not usable due to missing trait data. No final data saved.")

p1/preprocess/Atherosclerosis/code/GSE87005.py

@@ -0,0 +1,168 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+cohort = "GSE87005"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atherosclerosis"
+in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE87005"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Atherosclerosis/GSE87005.csv"
+out_gene_data_file = "./output/preprocess/1/Atherosclerosis/gene_data/GSE87005.csv"
+out_clinical_data_file = "./output/preprocess/1/Atherosclerosis/clinical_data/GSE87005.csv"
+json_path = "./output/preprocess/1/Atherosclerosis/cohort_info.json"
+
+# STEP 1: Initial Data Loading
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    prefixes_a=background_prefixes,
+    prefixes_b=clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on the transcriptomic profiling info
+
+# 2. Variable Availability
+trait_row = None   # No column found that corresponds to "Atherosclerosis"
+age_row = None     # No column found for age
+gender_row = None  # No column found for gender
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> Optional[float]:
+    parts = value.split(':', 1)
+    val = parts[-1].strip() if len(parts) > 1 else value.strip()
+    return None  # No valid trait data; returning None for all inputs
+
+def convert_age(value: str) -> Optional[float]:
+    parts = value.split(':', 1)
+    val = parts[-1].strip() if len(parts) > 1 else value.strip()
+    return None  # No valid age data; returning None for all inputs
+
+def convert_gender(value: str) -> Optional[int]:
+    parts = value.split(':', 1)
+    val = parts[-1].strip().lower() if len(parts) > 1 else value.strip().lower()
+    return None  # No valid gender data; returning None for all inputs
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+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=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Skip this step because trait_row is None (trait data not available)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# The gene identifiers appear to be microarray probe IDs rather than standard human gene symbols.
+print("These appear to be microarray probe IDs that require mapping to gene symbols.")
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP 6: Gene Identifier Mapping
+
+# 1) From the annotation preview, we see the same kind of IDs are stored in column "ID",
+#    and the gene symbols are in column "GENE_SYMBOL".
+# 2) Get the mapping dataframe.
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation,
+    prob_col='ID',
+    gene_col='GENE_SYMBOL'
+)
+
+# 3) Convert probe-level measurements to gene-level data.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP 7
+
+import pandas as pd
+import os
+
+# 1. Normalize the gene expression data to standard gene symbols.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print("Normalized gene expression data saved to:", out_gene_data_file)
+
+# Check if the clinical file actually exists
+if not os.path.exists(out_clinical_data_file):
+    # Trait data was not available, so no clinical file was ever written
+    print("Clinical data file not found; trait data not available.")
+
+    # Perform final validation indicating the trait is missing, and provide is_biased=False
+    # plus an empty DataFrame to fulfill the function signature for final validation.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=False,   # Must be a boolean, even though trait isn't available
+        df=pd.DataFrame(), # Provide an empty DataFrame to finalize
+        note="No trait data available to finish pipeline."
+    )
+    if not is_usable:
+        print("No final data saved.")
+    else:
+        print("Data unexpectedly marked usable despite no trait data.")
+else:
+    # 2. Read the clinical data file and link with genetic data
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, header=0)
+    # If there's only one row, label its index with the trait name
+    if len(selected_clinical_df) == 1:
+        selected_clinical_df.index = [trait]
+
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values
+    df = handle_missing_values(linked_data, trait)
+
+    # 4. Determine whether the trait or demographic features are biased; remove biased demographic features.
+    trait_biased, df = judge_and_remove_biased_features(df, trait)
+
+    # 5. Perform final validation with full dataset
+    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=df,
+        note="Final step with linking, missing-value handling, bias checks."
+    )
+
+    # 6. If data is usable, save the final linked data.
+    if is_usable:
+        df.to_csv(out_data_file)
+        print(f"Final linked data saved to: {out_data_file}")
+    else:
+        print("Dataset is not usable or severely biased. No final data saved.")

p1/preprocess/Atherosclerosis/code/GSE90074.py

@@ -0,0 +1,170 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+cohort = "GSE90074"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atherosclerosis"
+in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE90074"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Atherosclerosis/GSE90074.csv"
+out_gene_data_file = "./output/preprocess/1/Atherosclerosis/gene_data/GSE90074.csv"
+out_clinical_data_file = "./output/preprocess/1/Atherosclerosis/clinical_data/GSE90074.csv"
+json_path = "./output/preprocess/1/Atherosclerosis/cohort_info.json"
+
+# STEP 1: Initial Data Loading
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    prefixes_a=background_prefixes,
+    prefixes_b=clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True
+
+# 2.1 Data Availability
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2 Data Type Conversions
+def convert_trait(value: str):
+    if not value:
+        return None
+    val = value.split(":", 1)[-1].strip().lower()
+    # Example conversion logic:
+    if "atherosclerosis" in val:
+        return 1
+    elif "control" in val:
+        return 0
+    return None
+
+def convert_age(value: str):
+    if not value:
+        return None
+    val = value.split(":", 1)[-1].strip()
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    if not value:
+        return None
+    val = value.split(":", 1)[-1].strip().lower()
+    if val == "male":
+        return 1
+    elif val == "female":
+        return 0
+    return None
+
+# 3. Save Metadata
+is_trait_available = (trait_row is not None)
+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=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Skip this step because trait_row is None
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Based on the preview of gene_annotation and the row IDs in gene_data, 
+#    we identify 'ID' as the probe identifier column and 'GENE_SYMBOL' as the gene symbol column.
+
+# 2. Obtain gene mapping from the annotation dataframe.
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# 3. Convert probe-level expression to gene-level expression.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional) Print a summary of the resulting gene_data for verification
+print("Resulting gene_data shape:", gene_data.shape)
+print("Resulting gene_data head:\n", gene_data.head())
+import os
+import pandas as pd
+
+# STEP 7
+
+# 1. Normalize the gene expression data to standard gene symbols.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print("Normalized gene expression data saved to:", out_gene_data_file)
+
+# Check whether we actually have a clinical data file (which implies trait data was extracted).
+if os.path.exists(out_clinical_data_file):
+    # 2. Link the clinical and genetic data on sample IDs.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, header=0, index_col=0)
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data.
+    df = handle_missing_values(linked_data, trait)
+
+    # 4. Determine whether the trait or demographic features are biased; remove biased demographic features.
+    trait_biased, df = judge_and_remove_biased_features(df, trait)
+
+    # 5. Perform final validation with full dataset information.
+    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=df,
+        note="Final step with linking, missing-value handling, and bias checks."
+    )
+
+    # 6. If the data is usable, save the final linked data.
+    if is_usable:
+        df.to_csv(out_data_file)
+        print(f"Final linked data saved to: {out_data_file}")
+    else:
+        print("Dataset is not usable or is severely biased. No final data saved.")
+
+else:
+    # Trait data was never extracted, so record it as unavailable with is_final=False
+    is_usable = validate_and_save_cohort_info(
+        is_final=False,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        note="No clinical data available; trait data missing."
+    )
+    print("No clinical data file found. Skipping linking and final data save.")

p1/preprocess/Atherosclerosis/code/TCGA.py

@@ -0,0 +1,62 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Atherosclerosis/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Atherosclerosis/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Atherosclerosis/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Atherosclerosis/cohort_info.json"
+
+import os
+
+# Step 1: Identify subdirectory that might relate to our trait "Atherosclerosis"
+subdirs = [
+    'CrawlData.ipynb', '.DS_Store', 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)',
+    'TCGA_Uterine_Carcinosarcoma_(UCS)', 'TCGA_Thyroid_Cancer_(THCA)', 'TCGA_Thymoma_(THYM)',
+    'TCGA_Testicular_Cancer_(TGCT)', 'TCGA_Stomach_Cancer_(STAD)', 'TCGA_Sarcoma_(SARC)',
+    'TCGA_Rectal_Cancer_(READ)', 'TCGA_Prostate_Cancer_(PRAD)', 'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)',
+    'TCGA_Pancreatic_Cancer_(PAAD)', 'TCGA_Ovarian_Cancer_(OV)', 'TCGA_Ocular_melanomas_(UVM)',
+    'TCGA_Mesothelioma_(MESO)', 'TCGA_Melanoma_(SKCM)', 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)',
+    'TCGA_Lung_Cancer_(LUNG)', 'TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lower_Grade_Glioma_(LGG)',
+    'TCGA_Liver_Cancer_(LIHC)', 'TCGA_Large_Bcell_Lymphoma_(DLBC)', 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)',
+    'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)', 'TCGA_Kidney_Chromophobe_(KICH)', 'TCGA_Head_and_Neck_Cancer_(HNSC)',
+    'TCGA_Glioblastoma_(GBM)', 'TCGA_Esophageal_Cancer_(ESCA)', 'TCGA_Endometrioid_Cancer_(UCEC)',
+    'TCGA_Colon_and_Rectal_Cancer_(COADREAD)', 'TCGA_Colon_Cancer_(COAD)', 'TCGA_Cervical_Cancer_(CESC)',
+    'TCGA_Breast_Cancer_(BRCA)', 'TCGA_Bladder_Cancer_(BLCA)', 'TCGA_Bile_Duct_Cancer_(CHOL)',
+    'TCGA_Adrenocortical_Cancer_(ACC)', 'TCGA_Acute_Myeloid_Leukemia_(LAML)'
+]
+
+suitable_subdir = None
+# Replace synonyms with atherosclerosis-related terms
+synonyms = ["atherosclerosis"]
+
+for sd in subdirs:
+    if any(term in sd.lower() for term in synonyms):
+        suitable_subdir = sd
+        break
+
+if not suitable_subdir:
+    print(f"No suitable subdirectory found for trait '{trait}'. Skipping this trait.")
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+else:
+    # Step 2: Identify clinical and genetic file paths
+    clinical_path, genetic_path = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, suitable_subdir))
+
+    # Step 3: Load data into dataframes
+    clinical_df = pd.read_csv(clinical_path, index_col=0, sep='\t')
+    genetic_df = pd.read_csv(genetic_path, index_col=0, sep='\t')
+
+    # Step 4: Print clinical data columns
+    print("Clinical Data Columns:", clinical_df.columns.tolist())

p1/preprocess/Atherosclerosis/cohort_info.json

@@ -0,0 +1 @@
+{"GSE90074": {"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}, "GSE87005": {"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": "No trait data available to finish pipeline."}, "GSE83500": {"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": "No trait data available; dataset cannot be used for trait-based analysis."}, "GSE57691": {"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": 68, "note": "Final step with linking, missing-value handling, bias checks."}, "GSE154851": {"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}, "GSE133601": {"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": "No trait data found. This dataset is not usable for final analysis."}, "GSE125771": {"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": "No trait data found. This dataset is not usable for final analysis."}, "GSE123088": {"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": "Final step with linking, missing-value handling, bias checks."}, "GSE123086": {"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": "Final step with linking, missing-value handling, bias checks."}, "GSE109048": {"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": 56, "note": "Final step with linking, missing-value handling, and bias checks."}, "TCGA": {"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}}

p1/preprocess/Atherosclerosis/gene_data/GSE109048.csv

@@ -0,0 +1,11 @@
+Gene,GSM2928447,GSM2928448,GSM2928449,GSM2928450,GSM2928451,GSM2928452,GSM2928453,GSM2928454,GSM2928455,GSM2928456,GSM2928457,GSM2928458,GSM2928459,GSM2928460,GSM2928461,GSM2928462,GSM2928463,GSM2928464,GSM2928465,GSM2928466,GSM2928467,GSM2928468,GSM2928469,GSM2928470,GSM2928471,GSM2928472,GSM2928473,GSM2928474,GSM2928475,GSM2928476,GSM2928477,GSM2928478,GSM2928479,GSM2928480,GSM2928481,GSM2928482,GSM2928483,GSM2928484,GSM2928485,GSM2928486,GSM2928487,GSM2928488,GSM2928489,GSM2928490,GSM2928491,GSM2928492,GSM2928493,GSM2928494,GSM2928495,GSM2928496,GSM2928497,GSM2928498,GSM2928499,GSM2928500,GSM2928501,GSM2928502,GSM2928503
+OR4F16,2.9743094,2.5420604,2.4884850000000003,4.3142904,3.6649840000000005,2.7035954,2.5767966,2.6242086,2.6959218,2.771231,2.7009044,3.306057,2.6292988,2.757701,2.4774882000000003,2.3687974,3.0569956000000005,2.5827929999999997,2.6457842,2.8135651999999998,3.168524,2.7513798,2.5093813999999997,2.4695346,3.3407572,2.6126698,2.6948912,2.9736062,2.9090552,2.9485638,3.309335,2.6824026,2.5460307999999996,2.6424936,2.752814,3.290865,2.5296848,3.0717722,3.113311,3.4363840000000003,4.5301925999999995,2.6978723999999996,2.6916752,2.493736,2.6510559999999996,2.8423382000000004,2.7169974,3.3428152,2.6144125999999996,2.4906658,2.6306766,2.564085,2.6168648,2.6084864,2.7091269999999996,2.6708324,2.867071
+OR4F17,3.56224725,3.65479775,3.27546875,5.825601499999999,5.07030375,3.4557615000000004,3.383338,3.1713899999999997,3.5346985,3.4166965,3.66845175,3.341109,3.29447275,3.4095959999999996,3.3347825,3.28110825,3.4918975,3.5923557500000003,3.23555975,3.439068,4.2946884999999995,3.25981775,3.3532319999999998,3.33809325,3.4241485000000003,3.48046525,3.5117607499999997,4.59117625,3.2860292499999995,3.152998,3.5573942499999998,3.26352475,3.3428645,3.2651335,4.32833475,3.5935732500000004,3.26786575,3.2239020000000003,4.519833,4.74429125,5.1355830000000005,3.5155227499999997,3.4039415,3.35090175,3.011616,3.7048982500000003,3.3571549999999997,3.88471425,3.3958907499999995,3.1648735,3.4328640000000004,3.28346875,4.048498,3.31749575,3.6807687500000004,3.4197455,3.36906
+OR4F21,2.1497145,2.231649,2.0615325,3.3020445,2.9357545,2.093821,2.039704,1.8293115,2.067562,1.943375,2.095118,2.232502,1.880212,2.1980815,1.8689905,1.80633,2.324901,2.0716685,1.942506,2.3132095,2.262299,2.0615325,1.991198,1.9151765,2.3500865,1.841398,1.934662,2.5516425,2.278318,2.222638,2.575608,1.950054,1.858947,1.9674515,2.108439,2.1767895,2.056688,2.281916,2.5075515,2.5109465,3.446703,1.882676,1.7044375,1.8744285,1.7931265,2.0016285,1.9909035,2.5397905,1.9478135,2.037298,1.984265,1.695501,1.985344,2.0490825,2.035695,1.9485745,2.3101085
+OR4F29,2.9743094,2.5420604,2.4884850000000003,4.3142904,3.6649840000000005,2.7035954,2.5767966,2.6242086,2.6959218,2.771231,2.7009044,3.306057,2.6292988,2.757701,2.4774882000000003,2.3687974,3.0569956000000005,2.5827929999999997,2.6457842,2.8135651999999998,3.168524,2.7513798,2.5093813999999997,2.4695346,3.3407572,2.6126698,2.6948912,2.9736062,2.9090552,2.9485638,3.309335,2.6824026,2.5460307999999996,2.6424936,2.752814,3.290865,2.5296848,3.0717722,3.113311,3.4363840000000003,4.5301925999999995,2.6978723999999996,2.6916752,2.493736,2.6510559999999996,2.8423382000000004,2.7169974,3.3428152,2.6144125999999996,2.4906658,2.6306766,2.564085,2.6168648,2.6084864,2.7091269999999996,2.6708324,2.867071
+OR4F2P,5.080413,4.758553,4.9502,9.321156,6.566074,4.396016,4.225586,4.673549,4.997999,4.648803,4.818265,5.393229,4.6119,5.195738,4.148212,4.141176,4.521327,4.787729,3.952442,5.251666,5.773007,4.86418,4.365879,4.468233,5.94066,4.181142,4.18371,6.062913,5.894261,5.296056,6.093548,4.186232,5.176661,5.057247,5.536383,5.303023,4.89301,5.394821,6.327871,6.398536,8.535204,4.687723,4.368939,4.752713,4.212369,4.573951,4.463247,6.470191,4.54277,4.85036,4.598246,4.118382,4.607283,4.495167,5.192202,5.204194,4.768052
+OR4F3,2.9743094,2.5420604,2.4884850000000003,4.3142904,3.6649840000000005,2.7035954,2.5767966,2.6242086,2.6959218,2.771231,2.7009044,3.306057,2.6292988,2.757701,2.4774882000000003,2.3687974,3.0569956000000005,2.5827929999999997,2.6457842,2.8135651999999998,3.168524,2.7513798,2.5093813999999997,2.4695346,3.3407572,2.6126698,2.6948912,2.9736062,2.9090552,2.9485638,3.309335,2.6824026,2.5460307999999996,2.6424936,2.752814,3.290865,2.5296848,3.0717722,3.113311,3.4363840000000003,4.5301925999999995,2.6978723999999996,2.6916752,2.493736,2.6510559999999996,2.8423382000000004,2.7169974,3.3428152,2.6144125999999996,2.4906658,2.6306766,2.564085,2.6168648,2.6084864,2.7091269999999996,2.6708324,2.867071
+OR4F4,1.27695275,1.38304675,1.20231025,2.120111,2.01954975,1.321109,1.218445,1.17363,1.2968385,1.2629695,1.39708575,1.26369,1.18038225,1.257182,1.194427,1.17288925,1.3071585,1.32365525,1.19855525,1.303069,1.62915,1.13342775,1.249809,1.18056675,1.2671235,1.23700425,1.20138725,1.67318075,1.22066825,1.152967,1.24119575,1.24022125,1.2761825,1.181065,1.69500975,1.28921525,1.17821475,1.0443655,1.6767395,1.79011025,1.9191305,1.22435725,1.270769,1.22174425,1.0122205,1.27551175,1.200808,1.41222475,1.23197275,1.1035075,1.2184025,1.14281325,1.4650995,1.18534625,1.34093775,1.1358935,1.181737
+OR4F5,1.97288,2.2798885,2.0982985,4.1088135,3.4257805,2.2786165,1.996304,2.0012975,2.323042,1.894126,2.1543515,2.0387285,2.054807,2.1537735,1.9374215,1.891764,2.130644,2.124767,2.135245,1.9293785,2.7884615,2.0955745,1.997777,1.9506505,2.354982,1.9456,2.2128505,2.5474205,1.953684,1.9991375,2.3456995,1.956644,2.093103,1.9348415,2.6648425,2.4097135,2.053762,2.2254245,3.1057465,3.007653,3.9471825,2.51739,1.936295,1.8578505,1.8678855,2.51694,1.972249,2.1082285,1.900837,1.9252805,1.894601,1.9206615,2.2667855,2.0172355,2.3269145,2.357783,2.1676035
+PCMTD2,0.84254025,0.810600625,0.81653775,0.685753375,0.662289625,0.726929125,0.82775175,0.879793125,0.831703375,0.765487,0.813898875,0.98346925,0.8274695,0.766849375,0.746335375,0.753330875,1.009245875,0.759927125,0.907258625,0.745672375,0.813817375,0.7726135,0.81468775,0.711847375,0.834132375,0.890139125,0.813817375,0.781132375,0.9179525,0.870419625,0.86659075,0.90947,0.736467625,0.786284125,0.847237375,0.836028625,0.791918875,0.748545125,0.80189475,0.742974,0.789322875,1.10600525,0.798266625,0.757701375,1.116016875,0.82425225,0.828520625,0.868575125,0.801149625,0.831748375,0.90378275,0.72030175,0.6958105,0.691177,0.813817375,0.820799375,0.79238225
+SEPT14,2.001906,2.17855,2.162189,2.8326535,2.492945,2.245447,2.248301,2.0694535,2.2097125,2.1018975,2.1716845,2.184913,2.134051,2.1716845,2.121092,2.232192,2.1715735,2.2004,2.060387,2.2972555,2.1391435,2.067177,2.0609845,2.099157,2.1261395,2.1816385,2.0825065,2.292387,2.060557,2.230037,2.329013,2.206893,2.191881,2.098356,2.252621,2.3272935,2.1489915,2.081123,2.3888375,2.1884905,2.671726,1.997512,2.1001165,2.1136565,1.9728775,2.247512,2.301859,2.3252815,2.0400895,2.0955045,2.0555765,2.238452,2.1656145,2.1241635,2.1716845,2.1716845,2.126671

p1/preprocess/Atherosclerosis/gene_data/GSE123086.csv

@@ -0,0 +1 @@
+Gene,GSM3494884,GSM3494885,GSM3494886,GSM3494887,GSM3494888,GSM3494889,GSM3494890,GSM3494891,GSM3494892,GSM3494893,GSM3494894,GSM3494895,GSM3494896,GSM3494897,GSM3494898,GSM3494899,GSM3494900,GSM3494901,GSM3494902,GSM3494903,GSM3494904,GSM3494905,GSM3494906,GSM3494907,GSM3494908,GSM3494909,GSM3494910,GSM3494911,GSM3494912,GSM3494913,GSM3494914,GSM3494915,GSM3494916,GSM3494917,GSM3494918,GSM3494919,GSM3494920,GSM3494921,GSM3494922,GSM3494923,GSM3494924,GSM3494925,GSM3494926,GSM3494927,GSM3494928,GSM3494929,GSM3494930,GSM3494931,GSM3494932,GSM3494933,GSM3494934,GSM3494935,GSM3494936,GSM3494937,GSM3494938,GSM3494939,GSM3494940,GSM3494941,GSM3494942,GSM3494943,GSM3494944,GSM3494945,GSM3494946,GSM3494947,GSM3494948,GSM3494949,GSM3494950,GSM3494951,GSM3494952,GSM3494953,GSM3494954,GSM3494955,GSM3494956,GSM3494957,GSM3494958,GSM3494959,GSM3494960,GSM3494961,GSM3494962,GSM3494963,GSM3494964,GSM3494965,GSM3494966,GSM3494967,GSM3494968,GSM3494969,GSM3494970,GSM3494971,GSM3494972,GSM3494973,GSM3494974,GSM3494975,GSM3494976,GSM3494977,GSM3494978,GSM3494979,GSM3494980,GSM3494981,GSM3494982,GSM3494983,GSM3494984,GSM3494985,GSM3494986,GSM3494987,GSM3494988,GSM3494989,GSM3494990,GSM3494991,GSM3494992,GSM3494993,GSM3494994,GSM3494995,GSM3494996,GSM3494997,GSM3494998,GSM3494999,GSM3495000,GSM3495001,GSM3495002,GSM3495003,GSM3495004,GSM3495005,GSM3495006,GSM3495007,GSM3495008,GSM3495009,GSM3495010,GSM3495011,GSM3495012,GSM3495013,GSM3495014,GSM3495015,GSM3495016,GSM3495017,GSM3495018,GSM3495019,GSM3495020,GSM3495021,GSM3495022,GSM3495023,GSM3495024,GSM3495025,GSM3495026,GSM3495027,GSM3495028,GSM3495029,GSM3495030,GSM3495031,GSM3495032,GSM3495033,GSM3495034,GSM3495035,GSM3495036,GSM3495037,GSM3495038,GSM3495039,GSM3495040,GSM3495041,GSM3495042,GSM3495043,GSM3495044,GSM3495045,GSM3495046,GSM3495047,GSM3495048,GSM3495049

p1/preprocess/Atherosclerosis/gene_data/GSE123088.csv

@@ -0,0 +1 @@
+Gene,GSM3494884,GSM3494885,GSM3494886,GSM3494887,GSM3494888,GSM3494889,GSM3494890,GSM3494891,GSM3494892,GSM3494893,GSM3494894,GSM3494895,GSM3494896,GSM3494897,GSM3494898,GSM3494899,GSM3494900,GSM3494901,GSM3494902,GSM3494903,GSM3494904,GSM3494905,GSM3494906,GSM3494907,GSM3494908,GSM3494909,GSM3494910,GSM3494911,GSM3494912,GSM3494913,GSM3494914,GSM3494915,GSM3494916,GSM3494917,GSM3494918,GSM3494919,GSM3494920,GSM3494921,GSM3494922,GSM3494923,GSM3494924,GSM3494925,GSM3494926,GSM3494927,GSM3494928,GSM3494929,GSM3494930,GSM3494931,GSM3494932,GSM3494933,GSM3494934,GSM3494935,GSM3494936,GSM3494937,GSM3494938,GSM3494939,GSM3494940,GSM3494941,GSM3494942,GSM3494943,GSM3494944,GSM3494945,GSM3494946,GSM3494947,GSM3494948,GSM3494949,GSM3494950,GSM3494951,GSM3494952,GSM3494953,GSM3494954,GSM3494955,GSM3494956,GSM3494957,GSM3494958,GSM3494959,GSM3494960,GSM3494961,GSM3494962,GSM3494963,GSM3494964,GSM3494965,GSM3494966,GSM3494967,GSM3494968,GSM3494969,GSM3494970,GSM3494971,GSM3494972,GSM3494973,GSM3494974,GSM3494975,GSM3494976,GSM3494977,GSM3494978,GSM3494979,GSM3494980,GSM3494981,GSM3494982,GSM3494983,GSM3494984,GSM3494985,GSM3494986,GSM3494987,GSM3494988,GSM3494989,GSM3494990,GSM3494991,GSM3494992,GSM3494993,GSM3494994,GSM3494995,GSM3494996,GSM3494997,GSM3494998,GSM3494999,GSM3495000,GSM3495001,GSM3495002,GSM3495003,GSM3495004,GSM3495005,GSM3495006,GSM3495007,GSM3495008,GSM3495009,GSM3495010,GSM3495011,GSM3495012,GSM3495013,GSM3495014,GSM3495015,GSM3495016,GSM3495017,GSM3495018,GSM3495019,GSM3495020,GSM3495021,GSM3495022,GSM3495023,GSM3495024,GSM3495025,GSM3495026,GSM3495027,GSM3495028,GSM3495029,GSM3495030,GSM3495031,GSM3495032,GSM3495033,GSM3495034,GSM3495035,GSM3495036,GSM3495037,GSM3495038,GSM3495039,GSM3495040,GSM3495041,GSM3495042,GSM3495043,GSM3495044,GSM3495045,GSM3495046,GSM3495047,GSM3495048,GSM3495049,GSM3495050,GSM3495051,GSM3495052,GSM3495053,GSM3495054,GSM3495055,GSM3495056,GSM3495057,GSM3495058,GSM3495059,GSM3495060,GSM3495061,GSM3495062,GSM3495063,GSM3495064,GSM3495065,GSM3495066,GSM3495067,GSM3495068,GSM3495069,GSM3495070,GSM3495071,GSM3495072,GSM3495073,GSM3495074,GSM3495075,GSM3495076,GSM3495077,GSM3495078,GSM3495079,GSM3495080,GSM3495081,GSM3495082,GSM3495083,GSM3495084,GSM3495085,GSM3495086,GSM3495087

p1/preprocess/Atherosclerosis/gene_data/GSE125771.csv

@@ -0,0 +1,11 @@
+Gene,GSM3581706,GSM3581707,GSM3581708,GSM3581709,GSM3581710,GSM3581711,GSM3581712,GSM3581713,GSM3581714,GSM3581715,GSM3581716,GSM3581717,GSM3581718,GSM3581719,GSM3581720,GSM3581721,GSM3581722,GSM3581723,GSM3581724,GSM3581725,GSM3581726,GSM3581727,GSM3581728,GSM3581729,GSM3581730,GSM3581731,GSM3581732,GSM3581733,GSM3581734,GSM3581735,GSM3581736,GSM3581737,GSM3581738,GSM3581739,GSM3581740,GSM3581741,GSM3581742,GSM3581743,GSM3581744,GSM3581745
+OR4F16,2.698,2.65,2.784,2.792,2.9859999999999998,2.322,2.694,2.512,2.4699999999999998,2.63,2.488,2.792,2.192,2.734,2.338,2.392,2.4240000000000004,2.3440000000000003,2.394,2.736,2.25,2.252,2.472,2.716,2.518,2.7119999999999997,2.364,2.678,3.036,2.6159999999999997,2.6639999999999997,2.988,2.678,2.558,2.722,2.838,2.496,3.062,2.43,2.644
+OR4F17,2.265,2.5075,2.4925,2.9074999999999998,2.63,2.5,2.53,2.3,2.45,2.9,2.445,2.7350000000000003,2.5075000000000003,2.7675,2.5575,2.41,2.3325,2.3,2.69,2.4875,2.565,2.5975,2.4625,2.46,2.3875,2.89,2.52,2.54,2.36,2.505,2.5025,2.5425,2.625,2.64,2.37,2.3024999999999998,2.2975,2.5625,2.7199999999999998,2.43
+OR4F21,2.085,1.83,1.7,1.75,1.95,1.79,1.99,1.775,1.63,1.62,1.79,1.625,1.665,1.645,1.56,1.78,1.765,1.63,1.78,2.065,1.755,1.535,1.46,1.9,1.57,2.02,1.605,1.935,1.94,1.95,1.91,2.21,2.1,1.765,1.69,2.02,1.975,2.215,1.55,1.635
+OR4F29,2.698,2.65,2.784,2.792,2.9859999999999998,2.322,2.694,2.512,2.4699999999999998,2.63,2.488,2.792,2.192,2.734,2.338,2.392,2.4240000000000004,2.3440000000000003,2.394,2.736,2.25,2.252,2.472,2.716,2.518,2.7119999999999997,2.364,2.678,3.036,2.6159999999999997,2.6639999999999997,2.988,2.678,2.558,2.722,2.838,2.496,3.062,2.43,2.644
+OR4F2P,4.65,4.74,4.19,4.56,5.16,3.92,4.65,4.47,4.53,4.12,4.57,4.43,3.63,4.21,4.72,4.68,3.82,4.18,5.3,4.92,4.39,3.73,4.13,4.59,4.42,4.62,4.32,4.62,4.65,3.97,4.44,5.01,4.59,4.57,4.09,4.69,4.23,4.97,4.14,4.06
+OR4F3,2.698,2.65,2.784,2.792,2.9859999999999998,2.322,2.694,2.512,2.4699999999999998,2.63,2.488,2.792,2.192,2.734,2.338,2.392,2.4240000000000004,2.3440000000000003,2.394,2.736,2.25,2.252,2.472,2.716,2.518,2.7119999999999997,2.364,2.678,3.036,2.6159999999999997,2.6639999999999997,2.988,2.678,2.558,2.722,2.838,2.496,3.062,2.43,2.644
+OR4F4,0.775,0.8175,0.8075,0.9575,0.965,0.835,0.84,0.725,0.815,1.005,0.89,0.925,0.8525,0.9175,0.8675,0.78,0.7675,0.735,0.885,0.8125,0.865,0.9025,0.9225,0.85,0.8025,0.995,0.83,0.885,0.795,0.885,0.8325,0.8525,0.85,0.895,0.785,0.7575,0.7575,0.8475,0.99,0.81
+OR4F5,1.45,1.65,1.685,1.71,1.73,1.575,1.705,1.64,1.57,1.57,1.515,1.49,1.58,1.565,1.455,1.635,1.535,1.51,1.465,1.61,1.785,1.645,1.64,1.78,1.47,1.515,1.595,1.53,1.755,1.685,1.62,1.525,1.585,1.655,1.78,1.575,1.465,1.625,1.8,1.49
+PCMTD2,1.07375,1.07125,1.1625,1.0725,1.1375,1.10625,1.09625,1.075,1.08,1.09125,1.0725,1.0725,1.06375,1.0675,1.065,1.08375,1.0725,1.08125,1.11375,1.06125,1.06875,1.06125,1.03625,1.0825,1.08125,1.0375,1.0675,1.085,1.10625,1.09625,1.08,1.0875,1.045,1.07,1.12125,1.075,1.07625,1.11375,1.065,1.07625
+SEPT14,1.84,1.76,1.805,1.83,1.9,1.84,1.805,1.81,1.88,1.935,1.81,1.8,1.85,1.77,1.865,1.83,1.79,1.83,1.84,1.9,1.84,1.8,1.785,1.965,1.81,1.845,1.83,1.92,1.845,1.89,1.89,1.855,1.82,1.845,1.77,1.81,1.79,1.815,1.875,1.825

p1/preprocess/Atherosclerosis/gene_data/GSE133601.csv

@@ -0,0 +1 @@
+Gene,GSM3912810,GSM3912811,GSM3912812,GSM3912813,GSM3912814,GSM3912815,GSM3912816,GSM3912817,GSM3912818,GSM3912819,GSM3912820,GSM3912821,GSM3912822,GSM3912823,GSM3912824,GSM3912825,GSM3912826,GSM3912827,GSM3912828,GSM3912829,GSM3912830,GSM3912831,GSM3912832,GSM3912833,GSM3912834,GSM3912835,GSM3912836,GSM3912837,GSM3912838,GSM3912839

p1/preprocess/Atherosclerosis/gene_data/GSE154851.csv

@@ -0,0 +1,6 @@
+Gene,GSM4681537,GSM4681538,GSM4681539,GSM4681540,GSM4681541,GSM4681542,GSM4681543,GSM4681544,GSM4681545,GSM4681546,GSM4681547,GSM4681548,GSM4681549,GSM4681550,GSM4681551,GSM4681552,GSM4681553,GSM4681554,GSM4681555,GSM4681556,GSM4681557,GSM4681558,GSM4681559,GSM4681560,GSM4681561,GSM4681562,GSM4681563,GSM4681564,GSM4681565,GSM4681566,GSM4681567,GSM4681568,GSM4681569,GSM4681570,GSM4681571,GSM4681572,GSM4681573,GSM4681574,GSM4681575,GSM4681576,GSM4681577,GSM4681578,GSM4681579,GSM4681580,GSM4681581,GSM4681582,GSM4681583,GSM4681584,GSM4681585,GSM4681586,GSM4681587,GSM4681588,GSM4681589,GSM4681590,GSM4681591,GSM4681592,GSM4681593,GSM4681594,GSM4681595,GSM4681596,GSM4681597,GSM4681598,GSM4681599,GSM4681600,GSM4681601,GSM4681602,GSM4681603,GSM4681604,GSM4681605,GSM4681606
+OR4F21,76.99630929,78.69674393,74.21884669,89.01765389,85.2011783,80.39581357,81.03871334,70.50138414,71.16548252,86.25233023,78.45779543,70.30777182,93.4129832,67.6911836,70.31651968,95.38636341,61.9550501,63.06979423,64.40599509,2391.013329,65.49107332,64.8602681,72.52725746,66.11689167,69.39993507,60.68631029,63.78833892,62.80407145,67.05138507,66.93799511,62.97999013,65.29222447,79.50100076,75.95287215,78.13459507,67.85257446,77.26606382,70.69440597,74.82658835,77.70516367,82.22647951,79.92856858,76.38130167,80.39461811,84.57592991,67.39843791,65.37587967,63.4153476,64.39083156,64.34552039,68.36794939,62.26281045,70.63187177,62.70930624,65.47827859,64.69453168,68.37242582,68.17545985,68.48481041,72.89052341,66.58051526,68.38723836,72.70139711,64.60864379,71.07279281,71.35978757,61.19104133,73.74894097,67.9526394,73.4051552
+OR4F29,79.49630929,88.69674393,67.71884669,73.51765389,73.2011783,70.39581357,76.03871334,69.50138414,71.16548252,70.75233023,67.95779543,66.30777182,77.4129832,67.6911836,68.31651968,84.38636341,53.45507695,60.06979423,63.40599509,1939.513329,62.49107332,74.8602681,69.52725746,66.11689167,70.89993507,65.18631029,57.78833892,62.80407145,64.55138507,67.93799511,68.97999013,66.79222447,79.50100076,68.95287215,71.63459507,64.35257446,73.76606382,63.69440597,74.82658835,78.70516367,75.22647951,70.42856858,71.88130167,81.39461811,79.57592991,63.39843791,67.37587967,64.4153476,70.39083156,68.84552039,61.36794939,65.76281045,70.63187177,63.70930624,66.47827859,63.69453168,66.37242582,62.17545985,64.48481041,81.39052341,79.58051526,71.38723836,70.70139711,67.60864379,69.07279281,66.35978757,71.19104133,67.74894097,69.4526394,73.9051552
+OR4F4,68.99630929,74.19674393,73.71884669,68.01765389,75.2011783,68.89581357,78.03871334,66.00138414,65.16548252,71.75233023,77.95779543,67.30777182,74.4129832,65.1911836,73.31651968,68.88636341,65.4550501,65.06979423,66.90599509,474.0133295,66.99107332,64.8602681,65.52725746,63.11689167,62.89993507,59.68631029,63.28833892,65.30407145,65.05138507,67.43799511,63.47999013,60.79222447,66.50100076,74.45287215,64.13459507,64.85257446,69.26606382,63.69440597,71.82658835,72.70516367,75.72647951,71.42856858,72.88130167,68.39461811,70.57592991,67.89843791,62.87587967,63.4153476,62.39083156,63.34552039,68.86794939,63.26281045,69.13187177,55.70930624,63.97827859,69.19453168,64.37242582,76.67545985,67.48481041,64.89052341,67.58051526,62.88723836,60.70139711,61.60864379,62.07279281,61.35978757,63.69104133,67.74894097,66.9526394,65.4051552
+PCMTD2,1072.9926186,955.3934878,1901.4376937000002,1158.0353078,1351.4023566,4514.2916276,2787.0774263,3064.5027681,1133.830965,1218.5046604,1125.9155908,1452.6155436,1562.8259662,1497.3823672,1937.6330397000002,1353.2727268,555.9101002,555.1395884,1042.3119902,1067.026659,1106.9821466,924.2205362,1215.054515,1233.2337834,730.2998702,377.3726206,345.0766778,767.1081429999999,997.6027701999999,1345.3759901,1164.4599802,873.084449,766.0020016,1647.4057442,2914.2691901,1492.705149,2248.0321278,2088.888812,1042.6531766,1193.4103274,923.452959,850.8571372,1108.7626034,1267.7892362,933.6518598,692.7968758,666.2517594,461.83069520000004,406.7816632,569.6910408,528.7358988,311.0256208,627.2637436,658.4186124,803.4565572,566.3890634,499.74485159999995,573.3509196,1207.4696208,1049.7810468,1475.1610306,956.2744768,1279.4027942,991.2172876,546.1455856,860.7195752,971.3820826000001,2643.497882,1125.9052788,509.81031040000005
+SEPT14,2787.97785549,3389.18046353,4656.31308049,2790.10592349,3263.2070695,10684.37488277,7850.23227899,6664.50830444,4323.49289552,3479.0139810299997,3634.24677203,4047.84663102,5307.9778988,3967.1471019999994,5153.399118779999,4926.81817961,1550.2303006,1463.91876523,3287.4359704900003,7117.07997545,3027.44643952,2234.6616086,3402.16354446,3581.20135047,1583.39961057,880.11786178,985.23003344,1499.82442895,3807.3083146999998,5333.12797041,5056.87994043,3993.75334647,2698.50600496,7389.21723255,11105.30757027,4982.61544596,9266.096383420001,8513.166435969999,3179.95952955,3602.73098247,2957.35887751,2792.07141198,2962.78781047,3855.86770851,3144.95557951,2055.39062741,2184.25527817,1035.4920855999999,772.3449894800001,1051.07312239,1503.20769639,856.0768625000001,1314.79123077,1639.25583729,1700.36967159,1209.66719016,1214.23455482,1216.0527589,4681.40886201,4451.84314001,6041.483090860001,3836.82342996,5644.20838231,3208.1518629899997,3174.4367570100003,3765.15872597,3125.64624753,7376.49364597,4783.2158356,3772.430931

p1/preprocess/Atherosclerosis/gene_data/GSE57691.csv

@@ -0,0 +1,7 @@
+Gene,GSM1386783,GSM1386784,GSM1386785,GSM1386786,GSM1386787,GSM1386788,GSM1386789,GSM1386790,GSM1386791,GSM1386792,GSM1386793,GSM1386794,GSM1386795,GSM1386796,GSM1386797,GSM1386798,GSM1386799,GSM1386800,GSM1386801,GSM1386802,GSM1386803,GSM1386804,GSM1386805,GSM1386806,GSM1386807,GSM1386808,GSM1386809,GSM1386810,GSM1386811,GSM1386812,GSM1386813,GSM1386814,GSM1386815,GSM1386816,GSM1386817,GSM1386818,GSM1386819,GSM1386820,GSM1386821,GSM1386822,GSM1386823,GSM1386824,GSM1386825,GSM1386826,GSM1386827,GSM1386828,GSM1386829,GSM1386830,GSM1386831,GSM1386832,GSM1386833,GSM1386834,GSM1386835,GSM1386836,GSM1386837,GSM1386838,GSM1386839,GSM1386840,GSM1386841,GSM1386842,GSM1386843,GSM1386844,GSM1386845,GSM1386846,GSM1386847,GSM1386848,GSM1386849,GSM1386850
+OR4F16,0.23883915,-1.1944451,0.3841734,-1.0211043,-1.4896231,0.094777584,-0.09707928,0.13585997,-0.28459454,-0.08352995,0.17742443,-0.06287432,0.7361469,0.32778645,0.22304153,-0.09680271,0.44625282,0.14393425,0.34312534,0.14683247,0.2962079,-1.4721146,0.51871777,-1.2463741,-0.71378565,-0.5739317,-1.1615801,-1.2588696,-0.17679119,0.26977968,0.506701,-1.0470796,-1.4382753,-0.2334876,0.0,0.17134523,0.2386918,0.7031131,-0.000496,0.3597355,-0.038836956,0.12692833,0.61382675,0.28687286,0.24881172,0.31893158,-0.31258392,0.57070637,0.612679,0.38011026,0.22704124,0.6170387,0.23919868,0.25013638,0.058721542,-0.09590149,0.0,0.5643606,-0.5710311,0.12207651,0.14912271,-0.09638739,-0.6957979,-0.29966164,-0.19005346,-0.46812296,-0.27124596,-0.33076286
+OR4F21,0.53942394,-2.10584734,1.1489543599999998,-2.33030034,-2.4131308000000002,0.37066603,0.36506319,0.40356731,-0.11457395199999999,0.04900169499999999,-0.035991668,-0.3546939,0.9561801,0.37000036,0.87560559,-0.53307485,0.61146545,0.19690037200000002,0.17539215,-0.025328160000000002,0.364687916,-2.3540092,1.1359124,-2.4943362000000002,-1.30321694,-1.03041985,-1.39879942,-1.82799913,-0.82144833,0.91326046,0.7822785400000001,-1.9369846000000002,-2.4763994,-0.34955073,0.23737144999999998,0.55437803,0.49894714,1.0046558399999999,-0.11847448,0.74723577,-0.47126674999999996,0.41289759,0.98817347,0.46688891,0.3971858,0.71583318,-0.60973311,1.14602615,1.6355553,0.73755741,0.40208959,1.2158646499999999,0.0,0.83558608,0.187199115,-0.29230594,0.32783842,0.87081147,-0.81707047,0.10698557,0.6065440200000001,-0.085456371,-1.05188035,-0.50029516,-0.32417202,-0.6530427999999999,-0.6445465,-0.45045233
+OR4F3,0.53858614,-0.8894272,0.48546124,-0.89872694,-0.61812305,0.029472828,0.23913574,0.022135258,-0.1816411,-0.041664124,-0.062768936,-0.09017897,0.53167295,-0.004978657,0.28741312,-0.19589424,0.027667522,0.08336258,0.13261986,0.004978657,0.44305038,-0.92245007,0.65173435,-0.9899926,-0.6754422,-0.55668163,-0.8540139,-1.0363269,-0.02175188,0.5196619,0.519248,-0.2801366,-1.1996527,-0.26493597,0.0,0.13288546,0.005102634,0.533257,-0.15684175,0.31128645,-0.18481445,0.23889208,0.4191122,0.47374392,0.011720181,0.15642977,-0.13690042,0.44452953,0.7136893,0.36495543,0.09160614,0.46519995,0.08466911,0.3518448,0.31102705,-0.42019033,0.241745,0.308362,-0.27139902,0.27159643,0.0,-0.0449214,-0.33716965,-0.07559013,-0.16873789,-0.23539066,-0.36366415,-0.15522003
+OR4F4,0.867682,-2.0234218000000004,0.7820367800000001,-2.3838234,-2.5144706,0.067504883,0.29710006,0.204932213,0.024843692,-0.3518076,0.067766666,-0.49099494,0.82645132,0.2341361,0.5356359500000001,-0.59719944,0.43883799999999995,-0.009117600000000003,-0.025274755999999995,0.12526941,0.53017902,-1.94606256,0.86696577,-2.2423535,-1.6626921000000001,-1.2853932000000001,-2.1464204999999996,-2.04504534,-1.0187483,0.9127726500000001,0.92507696,-1.9822397600000001,-2.18464804,-0.5213356060000001,0.10456371,0.40646791,0.45970249,1.14076184,-0.030855180000000003,0.41821717999999997,-0.54772091,0.10900831,1.03412964,0.189020158,-0.197118286,0.6945042699999999,-0.39424276,1.1163883000000001,1.2421703499999999,0.7642345500000001,0.42066669,1.2336998000000001,0.07517480599999998,0.9479188999999999,-0.12496567,0.05941199999999999,0.30297326999999996,1.1834692900000001,-0.52866888,0.34318924,0.53131725,-0.19595909,-0.93917752,-0.19395112999999997,-0.028076649,-0.58253003,-0.42548752,-0.058813093999999996
+OR4F5,0.78131913,-1.3879666,1.04515505,-1.9033642,-2.4734469,0.27933883,0.15818691099999999,0.229414936,-0.35184574,-0.2970314,-0.061686039,-0.259068021,1.26809405,0.083415031,1.0647583,-0.82204247,0.42215537000000003,0.23794602999999998,0.28540564,0.094993111,0.9258027,-1.83000425,1.07737543,-2.5412082,-1.1611657000000002,-0.79418707,-1.88598825,-1.7896041999999999,-0.38675689,1.27010295,1.18292617,-1.561553,-1.93124727,-0.53920459,0.288816452,0.41361999,0.43051863,1.2745328,-0.15421391,0.6909670800000001,-0.31615019,0.268902302,1.04204944,0.123023514,0.07942772199999999,0.63826847,-0.132316114,0.92213107,1.4493804,0.89166452,0.366199985,1.1475,0.023037430000000005,0.9710945999999999,-0.0029811860000000003,-0.18297672,0.58085347,0.9477806200000001,-0.58560992,0.15613937,0.46221972,-0.10737276,-1.27701185,-0.27339507,-0.053367615,-0.57691431,-0.6586442,-0.3830862
+PCMTD2,1.1286402,0.84970903,1.0291362,1.4199948,0.85770464,-0.21805048,0.027359962,-0.07426643,0.0030756,0.23399305,0.34554863,-0.25925875,-0.9356046,-0.6476259,-0.8021169,0.25326347,-0.0030756,-0.09946108,-0.7215929,0.19180107,0.8715625,0.74155474,0.60939455,1.1570144,1.2271285,1.1129279,0.34160185,1.220819,0.38297415,0.7410984,0.78384686,1.0111966,1.1602397,0.6664443,0.26579142,-0.20508003,-0.8014412,-0.49083138,0.052060127,-0.90626955,0.111257076,-0.6948824,-0.45151377,-0.12901115,-0.13073301,-0.81514025,-0.005393028,-0.2615714,-0.8501253,0.0,-0.21306086,-0.25943708,0.21249485,-0.31624937,-0.45074034,0.42432547,-0.1091094,-0.5802765,0.20727634,-0.071946144,0.08834982,-0.24069166,0.7654357,0.32654858,-0.297359,1.0086036,0.18150377,0.41234016

p1/preprocess/Atherosclerosis/gene_data/GSE83500.csv

@@ -0,0 +1,9 @@
+Gene,GSM2204583,GSM2204584,GSM2204585,GSM2204586,GSM2204587,GSM2204588,GSM2204589,GSM2204590,GSM2204591,GSM2204592,GSM2204593,GSM2204594,GSM2204595,GSM2204596,GSM2204597,GSM2204598,GSM2204599,GSM2204600,GSM2204601,GSM2204602,GSM2204603,GSM2204604,GSM2204605,GSM2204606,GSM2204607,GSM2204608,GSM2204609,GSM2204610,GSM2204611,GSM2204612,GSM2204613,GSM2204614,GSM2204615,GSM2204616,GSM2204617,GSM2204618,GSM2204619
+OR4F16,0.73404282875,0.3740924585,0.6417244465,1.1723799365,0.633675524,0.508184537,0.41641832375,1.143497259,0.24259132625,0.526830854,0.36033071225,0.5309933575,0.9817117895,0.7126993795,0.47586142275,0.5899479995,0.52819003875,0.4484026745,0.5653469875,0.54013661375,0.50403557775,0.773723483,0.55774821325,0.470760079,0.43221755975,0.883782264,1.02579978875,0.67425360775,0.557528754,0.442916195,0.881916861,0.4335068825,0.8967029235,0.47660317975,0.38135789375,0.53448553975,1.11119684375
+OR4F17,1.435930195,1.6570465583333334,1.3007073310000001,1.0738727883333332,1.5612871863333335,0.8882332993333333,1.1802631416666667,1.3008281373333335,0.7565540956666666,1.2607606123333335,0.8480449673333333,0.77242003,1.3361036673333333,1.5208169723333331,1.6464558456666667,1.0737519816666665,1.5787638756666666,0.8436577013333334,0.8278770870000001,0.9472484106666667,1.6709513793333333,1.9853035016666667,1.128081073,1.66436419,2.061084817,1.717405383,1.1699813239999999,1.1362720993333333,0.849901211,1.249571753,1.0228292463333333,0.810378076,0.9064937576666666,1.1647974926666667,1.106744308,1.1983348773333333,1.0560802313333333
+OR4F21,0.73404282875,0.3740924585,0.6417244465,1.1723799365,0.633675524,0.508184537,0.41641832375,1.143497259,0.24259132625,0.526830854,0.36033071225,0.5309933575,0.9817117895,0.7126993795,0.47586142275,0.5899479995,0.52819003875,0.4484026745,0.5653469875,0.54013661375,0.50403557775,0.773723483,0.55774821325,0.470760079,0.43221755975,0.883782264,1.02579978875,0.67425360775,0.557528754,0.442916195,0.881916861,0.4335068825,0.8967029235,0.47660317975,0.38135789375,0.53448553975,1.11119684375
+OR4F29,0.73404282875,0.3740924585,0.6417244465,1.1723799365,0.633675524,0.508184537,0.41641832375,1.143497259,0.24259132625,0.526830854,0.36033071225,0.5309933575,0.9817117895,0.7126993795,0.47586142275,0.5899479995,0.52819003875,0.4484026745,0.5653469875,0.54013661375,0.50403557775,0.773723483,0.55774821325,0.470760079,0.43221755975,0.883782264,1.02579978875,0.67425360775,0.557528754,0.442916195,0.881916861,0.4335068825,0.8967029235,0.47660317975,0.38135789375,0.53448553975,1.11119684375
+OR4F3,0.73404282875,0.3740924585,0.6417244465,1.1723799365,0.633675524,0.508184537,0.41641832375,1.143497259,0.24259132625,0.526830854,0.36033071225,0.5309933575,0.9817117895,0.7126993795,0.47586142275,0.5899479995,0.52819003875,0.4484026745,0.5653469875,0.54013661375,0.50403557775,0.773723483,0.55774821325,0.470760079,0.43221755975,0.883782264,1.02579978875,0.67425360775,0.557528754,0.442916195,0.881916861,0.4335068825,0.8967029235,0.47660317975,0.38135789375,0.53448553975,1.11119684375
+OR4F4,1.435930195,1.6570465583333334,1.3007073310000001,1.0738727883333332,1.5612871863333335,0.8882332993333333,1.1802631416666667,1.3008281373333335,0.7565540956666666,1.2607606123333335,0.8480449673333333,0.77242003,1.3361036673333333,1.5208169723333331,1.6464558456666667,1.0737519816666665,1.5787638756666666,0.8436577013333334,0.8278770870000001,0.9472484106666667,1.6709513793333333,1.9853035016666667,1.128081073,1.66436419,2.061084817,1.717405383,1.1699813239999999,1.1362720993333333,0.849901211,1.249571753,1.0228292463333333,0.810378076,0.9064937576666666,1.1647974926666667,1.106744308,1.1983348773333333,1.0560802313333333
+OR4F5,1.435930195,1.6570465583333334,1.3007073310000001,1.0738727883333332,1.5612871863333335,0.8882332993333333,1.1802631416666667,1.3008281373333335,0.7565540956666666,1.2607606123333335,0.8480449673333333,0.77242003,1.3361036673333333,1.5208169723333331,1.6464558456666667,1.0737519816666665,1.5787638756666666,0.8436577013333334,0.8278770870000001,0.9472484106666667,1.6709513793333333,1.9853035016666667,1.128081073,1.66436419,2.061084817,1.717405383,1.1699813239999999,1.1362720993333333,0.849901211,1.249571753,1.0228292463333333,0.810378076,0.9064937576666666,1.1647974926666667,1.106744308,1.1983348773333333,1.0560802313333333
+PCMTD2,9.212923561,10.901026242,6.225646578,5.569975695,6.507394873,5.672079986,6.590931174,5.6873980820000005,6.094362397,5.749752266,7.977994324,5.2838688089999994,6.4738160360000006,6.103696742,6.433895479,6.336095189,8.113219952,5.340052931000001,6.130209167,6.110925157,6.254565071,7.372225433,6.282741802,7.268279745,5.4205496900000005,7.2397629939999995,7.858998548000001,8.314691785,5.718291504,8.122213357,8.7607611,7.1395928259999994,7.928611752,6.355009261,6.777270917999999,7.5501807549999995,9.022260618

p1/preprocess/Atherosclerosis/gene_data/GSE87005.csv

@@ -0,0 +1,4 @@
+Gene,GSM2318735,GSM2318736,GSM2318737,GSM2318738,GSM2318739,GSM2318740,GSM2318741,GSM2318742,GSM2318743,GSM2318744,GSM2318745,GSM2318746,GSM2318747,GSM2318748,GSM2318749,GSM2318750,GSM2318751,GSM2318752,GSM2318753,GSM2318754,GSM2601251,GSM2601252,GSM2601253,GSM2601254,GSM2601255,GSM2601256,GSM2601257,GSM2601258,GSM2601259,GSM2601260,GSM2601261,GSM2601262,GSM2601263,GSM2601264,GSM2601265,GSM2601266,GSM2601267,GSM2601268,GSM2601269,GSM2601270
+OR4F4,0.076086229,0.02122112,-0.014369964,-0.055314817,0.059367588,0.055313302,0.224541259,0.110152731,0.138016898,0.119488305,0.040423805,-0.015925659,0.017290977,-0.006090175,-0.074039422,0.176064838,-0.163149766,-0.211046351,0.032952149,0.069816381,0.007571743,-0.126856466,-0.274569688,-0.204298043,-0.11417227,-0.00603894,-0.103359563,-0.040022569,-0.043785947,0.064915781,-0.250219889,0.057375259,-0.01235037,-0.143792149,0.07217294,0.109868673,-0.133427067,0.067832201,-0.042262921,-0.149312185
+PCMTD2,0.0790916140000002,-1.330291535,-1.236856128,-2.0265615439999998,-1.8567810380000003,-1.247069341,-3.570309922,-2.66986745,-2.527290838,-2.1946688180000002,0.4104525520000001,-0.839647722,-1.4501600510000001,-1.3768209489999998,-1.814288625,-1.625312251,-1.6283604889999999,-2.787836945,-2.9168896560000004,-3.119051445,-0.32359645000000004,-1.171103067,-1.60992523,-1.450535541,-1.696539423,-1.811260978,-1.730542509,-1.369552595,-1.544923328,-2.274459026,-2.003422378,-2.074468398,-3.246823746,-1.471335315,-1.655066818,-3.021275105,-0.867779854,-1.836813931,-2.2971573320000003,-2.52382086
+SEPT14,-0.905542023,-0.917648746,-1.020976033,-1.259251759,-0.953311755,-0.654694816,-1.562520564,-1.073399137,-0.960422999,-0.839669583,-0.291180927,-0.763015417,-0.848288334,-0.725314267,-0.698915938,-0.827800233,-0.952698546,-1.409647978,-1.228737984,-1.001402098,-0.575602446,-0.721196017,-0.96733955,-0.709816386,-1.054288474,-0.754229189,-0.955637886,-1.001788312,-0.690354482,-1.114924244,-1.200054,-1.352219632,-1.527637284,-0.892108319,-0.856849267,-1.503430049,-0.842188496,-1.054300112,-1.203958807,-0.973160319

p1/preprocess/Atherosclerosis/gene_data/GSE90074.csv

@@ -0,0 +1,4 @@
+Gene,GSM2397158,GSM2397159,GSM2397160,GSM2397161,GSM2397162,GSM2397163,GSM2397164,GSM2397165,GSM2397166,GSM2397167,GSM2397168,GSM2397169,GSM2397170,GSM2397171,GSM2397172,GSM2397173,GSM2397174,GSM2397175,GSM2397176,GSM2397177,GSM2397178,GSM2397179,GSM2397180,GSM2397181,GSM2397182,GSM2397183,GSM2397184,GSM2397185,GSM2397186,GSM2397187,GSM2397188,GSM2397189,GSM2397190,GSM2397191,GSM2397192,GSM2397193,GSM2397194,GSM2397195,GSM2397196,GSM2397197,GSM2397198,GSM2397199,GSM2397200,GSM2397201,GSM2397202,GSM2397203,GSM2397204,GSM2397205,GSM2397206,GSM2397207,GSM2397208,GSM2397209,GSM2397210,GSM2397211,GSM2397212,GSM2397213,GSM2397214,GSM2397215,GSM2397216,GSM2397217,GSM2397218,GSM2397219,GSM2397220,GSM2397221,GSM2397222,GSM2397223,GSM2397224,GSM2397225,GSM2397226,GSM2397227,GSM2397228,GSM2397229,GSM2397230,GSM2397231,GSM2397232,GSM2397233,GSM2397234,GSM2397235,GSM2397236,GSM2397237,GSM2397238,GSM2397239,GSM2397240,GSM2397241,GSM2397242,GSM2397243,GSM2397244,GSM2397245,GSM2397246,GSM2397247,GSM2397248,GSM2397249,GSM2397250,GSM2397251,GSM2397252,GSM2397253,GSM2397254,GSM2397255,GSM2397256,GSM2397257,GSM2397258,GSM2397259,GSM2397260,GSM2397261,GSM2397262,GSM2397263,GSM2397264,GSM2397265,GSM2397266,GSM2397267,GSM2397268,GSM2397269,GSM2397270,GSM2397271,GSM2397272,GSM2397273,GSM2397274,GSM2397275,GSM2397276,GSM2397277,GSM2397278,GSM2397279,GSM2397280,GSM2397281,GSM2397282,GSM2397283,GSM2397284,GSM2397285,GSM2397286,GSM2397287,GSM2397288,GSM2397289,GSM2397290,GSM2397291,GSM2397292,GSM2397293,GSM2397294,GSM2397295,GSM2397296,GSM2397297,GSM2397298,GSM2397299,GSM2397300
+OR4F4,0.1569922,0.18030073,0.26213858,0.16803163,0.12651119,-0.05892077,0.041191563,0.10425018,0.09232132,-0.42954063,0.15294194,0.1040516,0.14770661,0.45683905,0.12147408,0.2047074,0.0655233,1.2849067,0.17395858,0.5854437,-0.067022786,1.0149486,0.07133813,0.3259787,0.12612104,0.19800374,0.16294952,0.06040123,0.08450462,0.13821602,0.057299837,0.096548684,-0.13347954,-0.8102703,0.8765347,0.068968974,0.1599166,0.2235326,0.034039088,0.98754305,0.070015274,0.20080808,-0.7166943,0.1440492,0.119819306,0.1093381,0.6254189,-0.7876895,-0.27751273,0.058492213,0.055436354,0.11770049,0.025675232,0.07084322,0.10926301,0.6922485,0.08429553,0.10010086,0.13768318,-0.3138606,1.054923,0.043069016,-0.10258953,0.22956711,0.79885596,0.104961,0.007241527,0.08528994,0.108226284,-0.7591176,-0.40155497,-0.17342688,0.20381382,0.16590142,0.24133192,0.24995989,1.0026032,0.18403223,0.16822481,0.12843744,0.1742735,0.002347887,0.94526196,0.41733,0.10293699,-0.022680854,0.2556183,0.15876822,0.21670753,-0.036190115,0.05725769,0.030792225,0.38771433,0.14055863,0.09163618,1.0645009,1.3219557,0.047081467,-0.060768206,0.10277972,0.0879257,-0.4267942,0.005198944,0.08440324,0.22137722,0.13186763,0.051485613,0.17329788,0.07887657,0.19015668,0.09957287,0.1768692,0.3427427,0.11901281,0.45452645,0.11447646,-0.4740777,0.00042,0.19828075,0.62180907,0.13817976,0.049551457,0.060067043,0.039967235,0.03546447,0.1018034,0.20030282,-0.4627527,0.14429757,1.0812862,1.3417456,0.04462391,0.41811228,0.041845944,0.6200126,-0.21184137,0.061717436,-0.08388565,-0.4738927,0.05350634,0.13472304,0.041450445,1.3234339
+PCMTD2,3.7170438,5.7394513400000005,5.0691242,2.8779832,4.849693,3.91820693,6.2330743,4.1792154,4.9238281,5.4179707,1.18420676,7.7726501,6.28544534,5.79604722,4.94735304,3.3857822000000004,7.0569048599999995,2.02431482,4.5906345,6.11548667,5.37297826,6.76595315,3.8379261000000002,2.33129211,2.597535,3.0572234,5.8234559,4.82599446,2.24042121,4.4635400999999995,5.0691185,5.42082677,2.9833364700000002,4.1095410999999995,6.9280997,2.34710789,5.6189792999999995,2.19875703,5.79014877,4.977993100000001,8.567978199999999,4.6535996,4.4280163,4.61723044,4.0677766,3.4899994999999997,4.0776347,5.45269517,4.13614756,4.1319848,4.9521351,4.1096801,6.17432374,7.87908528,4.3619806,5.9498879,6.2944402,4.00003446,2.7057637999999997,4.40082275,7.864989400000001,3.4554595399999997,4.1464032,3.8281696,5.37355591,4.9010639000000005,5.5096521,4.65151978,4.5628373,5.1266422,4.51598162,5.1570034,5.44732086,5.88902736,4.0507677300000005,5.2614500500000005,4.34159844,4.46987046,3.20363612,8.217065300000002,1.35675172,5.5091687,3.85779206,5.8044424,1.6680213,5.4335442,4.73256797,4.9656188,5.3279981,4.541894,4.4389463000000005,5.9003067,4.4154331,3.9468680000000003,4.3396169,6.19490332,5.7508488,6.82960101,4.94433104,5.1161271,5.9150571,5.1201973,7.565666,7.36788256,2.7934169200000003,4.88216207,8.42870013,3.83663575,5.4414356,5.2613932000000005,2.15420543,4.0315160599999995,4.6058757,2.45660495,5.4387283,3.6173134,5.267163,5.59436423,4.33286667,5.1122292,8.99571983,4.7112298,4.1137886,4.3482574,6.16523654,6.079329700000001,4.16834125,6.5170724,5.38267025,3.45613556,4.22255805,5.1597738,4.3919755,4.7945604,5.8581163400000005,1.9114534,1.7085276600000001,7.81648347,3.5104808,5.4308452,4.76883224,5.1048861400000005,4.543476
+SEPT14,1.8054761,2.5647285,2.3700705,1.0872518,2.0581245,1.6617641,2.8200188,1.4232941,2.2310562,2.337101,0.03097336,3.3967571,2.8089628,2.7204094,2.0849905,1.3007163,3.135336,0.4974451,1.9368963,2.7897606,2.0701778,2.860612,1.5698452,0.70171267,1.1389438,1.1740464,2.9345121,2.1756973,0.8836746,1.7313205,2.1733227,2.3112977,1.4431964,1.8790188,2.8160105,0.91933155,2.2771769,0.55466837,2.309167,2.1108093,3.9570198,1.9860867,1.9799052,1.9216928,1.6729728,1.4511862,1.6966066,2.664851,2.104968,1.919136,2.160738,1.7211236,2.98818,3.7533638,2.1755075,2.5496902,2.9050684,1.9276716,1.156763,1.8362826,3.4825485,1.6037579,1.6461153,1.7127322,2.5793324,2.101533,2.4793139,1.9778872,1.8531648,2.225501,2.0281334,2.242911,2.4049654,2.4450865,1.6774331,2.3353958,2.100977,2.116189,1.6340923,3.6797504,0.310753,2.5523596,1.6950006,2.4105482,0.32414812,2.370719,1.6457433,2.240822,2.1814985,1.9237756,1.6637211,2.2272987,1.611309,1.4830666,1.7179499,2.8281336,2.581727,3.220507,2.022605,2.3942728,2.57831,2.2837963,3.5562143,3.355173,1.3687648,2.3667183,3.8158197,1.6291418,2.7671459,2.2200346,0.6289138,1.834405,1.8482475,0.98682296,2.2964897,1.4622955,2.1530466,2.5293941,1.7060968,2.1470346,4.3947086,2.161786,1.6935062,1.9862622,2.5351334,2.7787933,1.7125903,3.0514097,2.2512465,1.4094306,1.6102237,2.302388,1.7685746,1.9679431,2.4054909,0.9238387,0.16716957,3.4868689,1.381855,2.3086376,2.2149973,1.9898739,2.1040087

p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE162635.csv

@@ -0,0 +1,2 @@
+GSM4955871,GSM4955872,GSM4955873,GSM4955874,GSM4955875,GSM4955876,GSM4955877,GSM4955878,GSM4955879,GSM4955880,GSM4955881,GSM4955882,GSM4955883,GSM4955884,GSM4955885,GSM4955886,GSM4955887,GSM4955888,GSM4955889,GSM4955890,GSM4955891,GSM4955892,GSM4955893,GSM4955894,GSM4955895,GSM4955896,GSM4955897,GSM4955898,GSM4955899,GSM4955900,GSM4955901,GSM4955902,GSM4955903,GSM4955904,GSM4955905,GSM4955906,GSM4955907,GSM4955908,GSM4955909,GSM4955910,GSM4955911,GSM4955912,GSM4955913,GSM4955914,GSM4955915,GSM4955916,GSM4955917,GSM4955918,GSM4955919,GSM4955920,GSM4955921,GSM4955922,GSM4955923,GSM4955924,GSM4955925,GSM4955926,GSM4955927,GSM4955928,GSM4955929,GSM4955930,GSM4955931,GSM4955932,GSM4955933,GSM4955934,GSM4955935,GSM4955936,GSM4955937,GSM4955938,GSM4955939,GSM4955940,GSM4955941,GSM4955942,GSM4955943,GSM4955944,GSM4955945,GSM4955946,GSM4955947,GSM4955948,GSM4955949,GSM4955950,GSM4955951,GSM4955952,GSM4955953,GSM4955954,GSM4955955,GSM4955956,GSM4955957,GSM4955958,GSM4955959,GSM4955960,GSM4955961,GSM4955962,GSM4955963,GSM4955964,GSM4955965,GSM4955966,GSM4955967,GSM4955968,GSM4955969,GSM4955970,GSM4955971,GSM4955972,GSM4955973,GSM4955974,GSM4955975,GSM4955976,GSM4955977,GSM4955978,GSM4955979,GSM4955980,GSM4955981,GSM4955982,GSM4955983,GSM4955984,GSM4955985,GSM4955986,GSM4955987,GSM4955988,GSM4955989,GSM4955990,GSM4955991,GSM4955992,GSM4955993,GSM4955994,GSM4955995,GSM4955996,GSM4955997,GSM4955998,GSM4955999,GSM4956000,GSM4956001,GSM4956002,GSM4956003,GSM4956004,GSM4956005,GSM4956006,GSM4956007,GSM4956008,GSM4956009,GSM4956010,GSM4956011,GSM4956012,GSM4956013,GSM4956014,GSM4956015,GSM4956016,GSM4956017,GSM4956018,GSM4956019,GSM4956020,GSM4956021,GSM4956022,GSM4956023,GSM4956024,GSM4956025,GSM4956026,GSM4956027,GSM4956028,GSM4956029,GSM4956030,GSM4956031,GSM4956032,GSM4956033,GSM4956034,GSM4956035,GSM4956036,GSM4956037,GSM4956038,GSM4956039,GSM4956040,GSM4956041,GSM4956042,GSM4956043,GSM4956044,GSM4956045,GSM4956046,GSM4956047,GSM4956048,GSM4956049,GSM4956050,GSM4956051,GSM4956052,GSM4956053,GSM4956054,GSM4956055,GSM4956056,GSM4956057,GSM4956058,GSM4956059,GSM4956060,GSM4956061,GSM4956062,GSM4956063,GSM4956064,GSM4956065,GSM4956066,GSM4956067,GSM4956068,GSM4956069,GSM4956070,GSM4956071,GSM4956072,GSM4956073,GSM4956074,GSM4956075
+0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0

p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE208662.csv

@@ -0,0 +1,2 @@
+GSM6360778,GSM6360779,GSM6360780,GSM6360781,GSM6360782,GSM6360783,GSM6360784,GSM6360785,GSM6360786,GSM6360787,GSM6360788,GSM6360789,GSM6360790,GSM6360791,GSM6360792,GSM6360793,GSM6360794,GSM6360795,GSM6360796,GSM6360797,GSM6360798,GSM6360799,GSM6360800,GSM6360801,GSM6360802,GSM6360803,GSM6360804,GSM6360805,GSM6360806,GSM6360807,GSM6360808,GSM6360809
+1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0

p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE212331.csv

@@ -0,0 +1,4 @@
+,GSM6524456,GSM6524458,GSM6524459,GSM6524460,GSM6524462,GSM6524463,GSM6524465,GSM6524466,GSM6524468,GSM6524469,GSM6524470,GSM6524472,GSM6524473,GSM6524475,GSM6524476,GSM6524478,GSM6524479,GSM6524481,GSM6524482,GSM6524484,GSM6524485,GSM6524486,GSM6524488,GSM6524489,GSM6524490,GSM6524492,GSM6524493,GSM6524495,GSM6524496,GSM6524498,GSM6524499,GSM6524501,GSM6524502,GSM6524504,GSM6524505,GSM6524507,GSM6524508,GSM6524510,GSM6524511,GSM6524512,GSM6524514,GSM6524515,GSM6524517,GSM6524518,GSM6524520,GSM6524521,GSM6524523,GSM6524524,GSM6524525,GSM6524527,GSM6524528,GSM6524529,GSM6524531,GSM6524532,GSM6524534,GSM6524535,GSM6524537,GSM6524538,GSM6524540,GSM6524541,GSM6524543,GSM6524544,GSM6524545,GSM6524547,GSM6524548,GSM6524549,GSM6524551,GSM6524552,GSM6524554,GSM6524555,GSM6524557,GSM6524558,GSM6524560,GSM6524561,GSM6524563,GSM6524564,GSM6524566,GSM6524567,GSM6524568,GSM6524569,GSM6524571,GSM6524573,GSM6524574,GSM6524576,GSM6524577,GSM6524579,GSM6524580
+Trait,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Age,75.0,66.0,83.0,70.0,61.0,77.0,70.0,64.0,81.0,60.0,62.0,80.0,65.0,74.0,64.0,70.0,73.0,81.0,54.0,64.0,65.0,67.0,75.0,72.0,71.0,61.0,60.0,65.0,61.0,77.0,74.0,82.0,69.0,75.0,67.0,63.0,77.0,76.0,60.0,62.0,69.0,69.0,81.0,66.0,73.0,73.0,70.0,64.0,65.0,61.0,76.0,70.0,72.0,68.0,63.0,78.0,71.0,78.0,60.0,69.0,72.0,68.0,84.0,78.0,81.0,62.0,71.0,64.0,69.0,62.0,88.0,79.0,24.0,76.0,64.0,65.0,62.0,66.0,61.0,21.0,20.0,69.0,27.0,41.0,25.0,27.0,27.0
+Gender,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0

p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE21359.csv

@@ -0,0 +1,4 @@
+GSM101095,GSM101096,GSM101097,GSM101098,GSM101100,GSM101101,GSM101102,GSM101103,GSM101104,GSM101105,GSM101106,GSM101107,GSM101111,GSM101113,GSM101114,GSM101115,GSM101116,GSM114089,GSM114090,GSM190149,GSM190150,GSM190151,GSM190152,GSM190153,GSM190154,GSM190155,GSM190156,GSM252828,GSM252829,GSM252830,GSM252831,GSM252833,GSM252835,GSM252836,GSM252837,GSM252838,GSM252839,GSM252841,GSM252871,GSM252876,GSM252879,GSM252880,GSM252881,GSM252882,GSM252884,GSM252885,GSM254149,GSM254150,GSM254151,GSM254152,GSM254157,GSM254158,GSM254159,GSM254160,GSM254161,GSM254163,GSM254169,GSM254172,GSM254173,GSM254174,GSM254175,GSM254176,GSM298219,GSM298220,GSM298221,GSM298222,GSM298223,GSM298224,GSM298225,GSM298226,GSM298227,GSM298228,GSM298229,GSM298230,GSM298231,GSM298232,GSM298233,GSM298234,GSM298235,GSM298236,GSM298237,GSM298238,GSM298239,GSM298240,GSM298241,GSM298242,GSM298243,GSM298244,GSM298245,GSM298246,GSM298247,GSM300859,GSM302396,GSM302397,GSM302399,GSM350871,GSM350873,GSM350874,GSM350955,GSM350956,GSM350957,GSM350958,GSM364037,GSM364038,GSM364041,GSM364045,GSM364046,GSM364048,GSM410161,GSM410162,GSM410163,GSM410164,GSM410165,GSM434049,GSM434050,GSM434051,GSM434052,GSM434053,GSM434054,GSM434055,GSM434056,GSM434057,GSM434058,GSM434059,GSM434060,GSM434061,GSM434062,GSM434063,GSM434064,GSM458579,GSM458580,GSM458581,GSM458582,GSM469991,GSM470000
+0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0
+41.0,35.0,61.0,37.0,47.0,38.0,49.0,45.0,36.0,38.0,35.0,46.0,37.0,45.0,48.0,50.0,46.0,56.0,59.0,49.0,34.0,44.0,45.0,45.0,29.0,42.0,56.0,47.0,47.0,50.0,55.0,59.0,51.0,46.0,56.0,60.0,46.0,52.0,40.0,45.0,41.0,47.0,41.0,48.0,43.0,41.0,41.0,35.0,37.0,31.0,45.0,50.0,46.0,49.0,40.0,51.0,48.0,53.0,42.0,36.0,44.0,62.0,44.0,60.0,49.0,36.0,38.0,73.0,49.0,22.0,29.0,39.0,48.0,39.0,54.0,43.0,36.0,41.0,46.0,47.0,41.0,42.0,46.0,41.0,32.0,27.0,35.0,40.0,48.0,47.0,41.0,62.0,47.0,39.0,27.0,24.0,31.0,43.0,26.0,33.0,45.0,48.0,57.0,66.0,45.0,45.0,48.0,47.0,21.0,45.0,55.0,47.0,39.0,68.0,26.0,45.0,40.0,40.0,46.0,47.0,29.0,30.0,47.0,43.0,48.0,24.0,27.0,54.0,73.0,27.0,34.0,27.0,47.0,37.0,48.0
+1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0

p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE64599.csv

@@ -0,0 +1,2 @@
+GSM1575085,GSM1575086,GSM1575087,GSM1575088,GSM1575089,GSM1575090,GSM1575091,GSM1575092,GSM1575093,GSM1575094,GSM1575095,GSM1575096,GSM1575097,GSM1575098,GSM1575099,GSM1575100,GSM1575101,GSM1575102,GSM1575103,GSM1575104,GSM1575105,GSM1575106,GSM1575107,GSM1575108,GSM1575109,GSM1575110,GSM1575111,GSM1575112,GSM1575113,GSM1575114,GSM1575115,GSM1575116,GSM1575117,GSM1575118
+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,

p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/code/GSE162635.py

@@ -0,0 +1,157 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+cohort = "GSE162635"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)"
+in_cohort_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE162635"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/GSE162635.csv"
+out_gene_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE162635.csv"
+out_clinical_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE162635.csv"
+json_path = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Determine if the dataset contains gene expression data
+is_gene_available = True  # Based on the background info mentioning gene expression profiling
+
+# 2. Determine availability of trait, age, gender in the sample characteristics dictionary
+#    and define conversion functions.
+
+# After reviewing the sample characteristics:
+#  - The GOLD stage information ("gold.1: ...") at row 2 can be used to indicate COPD vs. healthy.
+#  - There is no apparent entry for age.
+#  - There is no apparent entry for gender.
+
+trait_row = 2   # "gold.1" data can be mapped to indicate COPD (I, II, III, IV) vs. healthy or O
+age_row = None
+gender_row = None
+
+def convert_trait(value: str):
+    """
+    Convert GOLD stage info to binary: 0 (healthy / stage O) or 1 (COPD stages I-IV).
+    """
+    if not isinstance(value, str):
+        return None
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[-1].strip().lower()
+    # healthy or O => 0, otherwise (I, II, III, IV) => 1
+    if val in ['healthy', 'o']:
+        return 0
+    elif val in ['i', 'ii', 'iii', 'iv']:
+        return 1
+    else:
+        return None
+
+# No data available for age or gender
+convert_age = None
+convert_gender = None
+
+# Since trait_row is not None, trait data is available
+is_trait_available = (trait_row is not None)
+
+# 3. Conduct initial filtering and record dataset info
+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=is_trait_available
+)
+
+# 4. If trait data is available, extract clinical features using the library function
+if is_trait_available:
+    selected_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 extracted features
+    print(preview_df(selected_clinical_df, n=5))
+    # Save to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns in the gene annotation that correspond to the probe IDs (matching those in 'gene_data.index')
+#    and the actual gene symbols. In this specific dataset, 'ID' holds the probe and 'Gene Symbol' holds the gene symbols.
+
+# 2. Get a gene mapping dataframe by extracting the two columns: 'ID' and 'Gene Symbol'.
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# 3. Convert probe-level measurements to gene-level expression data.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# As a quick check, print the shape of the resulting gene_data and its first few rows.
+print("Mapped gene expression data shape:", gene_data.shape)
+print(gene_data.head())
+# STEP7
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.
+#    Use 'selected_clinical_df' from the previous step instead of 'selected_clinical'.
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values in the linked data
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Conduct quality check and save the cohort information.
+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=is_trait_biased,
+    df=linked_data
+)
+
+# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.
+if is_usable:
+    unbiased_linked_data.to_csv(out_data_file)

p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/code/GSE175616.py

@@ -0,0 +1,146 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+cohort = "GSE175616"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)"
+in_cohort_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE175616"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/GSE175616.csv"
+out_gene_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE175616.csv"
+out_clinical_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE175616.csv"
+json_path = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # The background info clearly indicates nasal epithelium gene expression analysis
+
+# 2. Variable Availability and Data Type Conversion
+
+# Based on the sample characteristics, there is no row that mentions COPD or any indicator of it,
+# so the trait row is considered not available.
+trait_row = None
+
+# Multiple unique values for age are found under row 6
+age_row = 6
+
+# Multiple unique values for gender are found under row 5
+gender_row = 5
+
+# Define the conversion functions
+
+def convert_trait(x: str):
+    """
+    Since we determined trait is not recorded for this dataset,
+    return None for all inputs.
+    """
+    return None
+
+def convert_age(x: str):
+    """
+    Convert "age: 63" -> 63 (float or int).
+    Unknown or invalid values -> None.
+    """
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val_str = parts[1].strip()
+    try:
+        return float(val_str)
+    except ValueError:
+        return None
+
+def convert_gender(x: str):
+    """
+    Convert "Sex: male" -> 1, "Sex: female" -> 0, otherwise None.
+    """
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    gender_str = parts[1].strip().lower()
+    if gender_str == 'male':
+        return 1
+    elif gender_str == 'female':
+        return 0
+    else:
+        return None
+
+# 3. Save Metadata using the initial filtering (is_final=False)
+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=(trait_row is not None)
+)
+
+# 4. Since trait_row is None, we skip clinical feature extraction
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the identifiers like '100009676_at', '10000_at', etc., they appear to be Affymetrix probe IDs.
+# These are not direct human gene symbols, so they require mapping to gene symbols.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns in the annotation dataframe that correspond to probe IDs and gene symbols.
+#    From the preview, the 'ID' column matches the probe IDs in our gene_expression data,
+#    and the 'DESCRIPTION' column appears to store the gene symbol information (though it's descriptive text).
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='DESCRIPTION')
+
+# 2. Convert probe-level measurements to gene-level expression data by applying the gene mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# For inspection, let's print the shape of the new gene_data.
+print("Gene-level expression data shape:", gene_data.shape)
+# STEP7
+# Since trait_row is None (no trait data available), we cannot proceed with final validation that requires the trait.
+# Instead, we only perform partial validation to record that we cannot use this dataset for a trait-based analysis.
+
+# 1. Normalize the obtained gene data using the provided function.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Perform partial validation (is_final=False), indicating that trait data is not available.
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    note=f"No trait data is recorded in this dataset for '{trait}'."
+)
+
+# 3. Because the trait is unavailable, this dataset is not suitable for further trait-based analysis.
+#    Hence, no final linking or saving of a final merged dataset.

p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/code/GSE208662.py

@@ -0,0 +1,159 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+cohort = "GSE208662"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)"
+in_cohort_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE208662"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/GSE208662.csv"
+out_gene_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE208662.csv"
+out_clinical_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE208662.csv"
+json_path = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Determine if the dataset likely contains gene expression data
+is_gene_available = True  # Based on the "gene expression microarray" mention in the background
+
+# Step 2: Determine data availability and define data conversion functions
+
+# From the sample characteristics dictionary:
+# {0: ['disease state: COPD-IV', 'disease state: control'],
+#  1: ['treatment: LPS', 'treatment: Printex', 'treatment: Printex for 72h', 
+#      'treatment: Sham', 'treatment: Sham for 72h', 'treatment: Zn']}
+# We see that key "0" stores "disease state" data (COPD vs control). 
+# No apparent keys record age or gender data.
+
+trait_row = 0  # "disease state"
+age_row = None
+gender_row = None
+
+def convert_trait(value: str) -> Optional[int]:
+    """
+    Convert the 'disease state' raw string to a binary indicator (0 = control, 1 = COPD).
+    Unknown or unexpected patterns map to None.
+    """
+    parts = value.split(":", 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if "control" in val:
+        return 0
+    elif "copd" in val:
+        return 1
+    else:
+        return None
+
+# No age data found, so define a placeholder
+def convert_age(value: str) -> Optional[float]:
+    return None
+
+# No gender data found, so define a placeholder
+def convert_gender(value: str) -> Optional[int]:
+    return None
+
+# Step 3: Save metadata with initial filtering
+is_trait_available = (trait_row is not None)
+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=is_trait_available
+)
+
+# Step 4: Clinical Feature Extraction (only if trait data is available)
+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, n=5, max_items=200))
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the inspection of these IDs (e.g., "TC0100006437.hg.1"), they are not standard human gene symbols.
+# They appear to be some kind of probe/array identifiers. Therefore, we need to map them to gene symbols.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns in 'gene_annotation' that match the gene expression data's probe IDs and the actual gene symbols.
+#    From inspection, "ID" matches the probe identifiers in the expression data, and "SPOT_ID.1" contains gene symbol strings.
+
+# 2. Construct the gene mapping dataframe.
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="SPOT_ID.1")
+
+# 3. Convert probe-level data to gene-level data by applying the mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Print a brief preview of the resulting gene expression data.
+print("Mapped gene expression data shape:", gene_data.shape)
+print("Mapped gene expression data (first 5 rows):\n", gene_data.head())
+# STEP7
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.
+#    Use 'selected_clinical_df' from the previous step instead of 'selected_clinical'.
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values in the linked data
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Conduct quality check and save the cohort information.
+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=is_trait_biased,
+    df=linked_data
+)
+
+# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.
+if is_usable:
+    unbiased_linked_data.to_csv(out_data_file)

p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/code/GSE210272.py

@@ -0,0 +1,184 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+cohort = "GSE210272"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)"
+in_cohort_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE210272"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/GSE210272.csv"
+out_gene_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE210272.csv"
+out_clinical_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE210272.csv"
+json_path = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Determine if gene expression data is available
+# From the background info: "mRNA expression was profiled using Affymetrix Human Gene 1.0 ST Arrays."
+# => This dataset indeed contains gene expression data (not purely miRNA or methylation).
+is_gene_available = True
+
+# Step 2: Check availability of variables (trait, age, gender) and define converters
+
+# 2.1 - Identify rows for each variable and check if they're constant or missing
+
+# The trait is COPD. We do NOT see an explicit row for COPD status in the sample characteristics,
+# nor a consistent way to infer presence/absence from the dictionary.
+# Even though FEV1% predicted is relevant clinically, it does not provide a clear "has vs. no COPD" label,
+# and the background suggests "with and without COPD" but does not give a direct classification here.
+# Hence, treat trait data as unavailable.
+trait_row = None
+
+# Age data looks available in row 2, with multiple distinct values (57.6, 61, 66.3, etc.).
+age_row = 2
+
+# Gender data is available in row 1, with two distinct values ("Male", "Female").
+gender_row = 1
+
+# 2.2 - Define data type and converters
+
+def convert_trait(raw_value: str) -> int:
+    # Not used since trait_row is None, but define for completeness.
+    return None
+
+def convert_age(raw_value: str):
+    # Example: "age: 57.6" -> 57.6 as float, unknown -> None
+    parts = raw_value.split(":")
+    if len(parts) < 2:
+        return None
+    try:
+        return float(parts[1].strip())
+    except ValueError:
+        return None
+
+def convert_gender(raw_value: str):
+    # Example: "Sex: Male" -> 1, "Sex: Female" -> 0, otherwise -> None
+    parts = raw_value.split(":")
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == "male":
+        return 1
+    elif val == "female":
+        return 0
+    return None
+
+# Step 3: Save metadata with initial filtering
+# Trait data availability is determined by whether trait_row is None
+is_trait_available = (trait_row is not None)
+
+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=is_trait_available
+)
+
+# Step 4: Since trait_row is None, we SKIP the clinical feature extraction step.
+# (No extraction or CSV export for the trait variable.)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+print("These gene identifiers appear to be Ensembl transcript IDs, not standard human gene symbols.")
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP 6: Gene Identifier Mapping
+
+# 1. Observe that the "ID" column in gene_annotation matches the expression DataFrame index
+#    (e.g., "ENSG00000000003_at"), and "ORF" holds the Ensembl gene IDs (e.g., "ENSG00000000003").
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='ORF')
+
+# We'll define a custom function similar to the library's apply_gene_mapping, 
+# but preserving Ensembl IDs instead of extracting standard gene symbols:
+def apply_ensembl_mapping(expression_df: pd.DataFrame, mapping_df: pd.DataFrame) -> pd.DataFrame:
+    # Keep only the mapping rows that match expression_df's index
+    mapping_df = mapping_df[mapping_df['ID'].isin(expression_df.index)].copy()
+    mapping_df.drop_duplicates(subset=['ID', 'Gene'], inplace=True)
+
+    # Convert the 'Gene' column into list form (in case of multiple IDs in a single row)
+    # Here, we assume each row typically contains a single Ensembl ID string.
+    # Still, we handle multi-mapping if present.
+    mapping_df['Gene'] = mapping_df['Gene'].apply(lambda x: [x.strip()] if isinstance(x, str) else [])
+
+    # Count how many genes each probe maps to
+    mapping_df['num_genes'] = mapping_df['Gene'].apply(len)
+
+    # "explode" turns each list element into its own row
+    mapping_df = mapping_df.explode('Gene')
+    mapping_df.dropna(subset=['Gene'], inplace=True)
+
+    # Merge with expression data
+    mapping_df.set_index('ID', inplace=True)
+    merged_df = mapping_df.join(expression_df)
+
+    # Distribute expression by the number of genes when a probe maps to multiple genes
+    expr_cols = [col for col in merged_df.columns if col not in ['Gene', 'num_genes']]
+    merged_df[expr_cols] = merged_df[expr_cols].div(merged_df['num_genes'].replace(0, 1), axis=0)
+
+    # Sum values for each gene
+    gene_expression_df = merged_df.groupby('Gene')[expr_cols].sum()
+    return gene_expression_df
+
+# 2. Convert probe-level data into Ensembl gene-level data
+gene_data = apply_ensembl_mapping(gene_data, mapping_df)
+
+# Print out the final shape of the mapped gene_data
+print(f"Mapped gene_data shape: {gene_data.shape}")
+# STEP 7
+import pandas as pd
+
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Since the trait is unavailable (trait_row=None), we can't link clinical data or evaluate trait bias properly.
+# However, the library function validate_and_save_cohort_info requires 'df' and 'is_biased' when is_final=True.
+# We'll provide an empty DataFrame and set is_biased to True to mark the dataset as unusable.
+empty_df = pd.DataFrame()
+is_biased = True
+
+# 5. Conduct final quality validation, indicating the dataset is not usable without trait data.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=is_biased,
+    df=empty_df,
+    note="No trait data available; final validation indicates dataset is not usable for trait analysis."
+)
+
+# 6. Because the dataset is not usable, we do not save any further data.
+if is_usable:
+    pass

p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/code/GSE212331.py

@@ -0,0 +1,198 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+cohort = "GSE212331"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)"
+in_cohort_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE212331"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/GSE212331.csv"
+out_gene_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE212331.csv"
+out_clinical_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE212331.csv"
+json_path = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+# Based on the background info, gene expression data is indeed provided (not pure miRNA or methylation).
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+
+# From the sample characteristics dictionary:
+# - Row 1 contains "disease group: COPD" and "disease group: Healthy Control" => trait_row = 1
+# - Row 3 contains "age: ..." => age_row = 3
+# - Row 4 contains "gender: Female"/"gender: Male" => gender_row = 4
+trait_row = 1
+age_row = 3
+gender_row = 4
+
+# Define data type conversions. Each function should parse the string after the colon (":") if present.
+
+def convert_trait(x: str) -> int:
+    """
+    Convert COPD vs. Healthy Control to binary:
+    COPD -> 1, Healthy Control -> 0, otherwise None
+    """
+    # Example input: "disease group: COPD"
+    parts = x.split(':')
+    if len(parts) > 1:
+        val = parts[1].strip().lower()
+        if val == 'copd':
+            return 1
+        elif val == 'healthy control':
+            return 0
+    return None
+
+def convert_age(x: str) -> float:
+    """
+    Convert the age string to a float (continuous).
+    """
+    # Example input: "age: 75"
+    parts = x.split(':')
+    if len(parts) > 1:
+        val = parts[1].strip()
+        try:
+            return float(val)
+        except ValueError:
+            return None
+    return None
+
+def convert_gender(x: str) -> int:
+    """
+    Convert gender to binary:
+    Female -> 0, Male -> 1, otherwise None
+    """
+    # Example input: "gender: Female"
+    parts = x.split(':')
+    if len(parts) > 1:
+        val = parts[1].strip().lower()
+        if val == 'female':
+            return 0
+        elif val == 'male':
+            return 1
+    return None
+
+# 3. Save Metadata
+# We do initial filtering with is_final=False.
+# Trait data is available if trait_row is not None => True
+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 (only if trait_row is not None)
+if trait_row is not None:
+    # Suppose 'clinical_data' is already loaded or provided in the environment
+    # as instructed by the problem statement.
+    selected_clinical = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait="Trait",  # Just a placeholder name in final DF
+        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 selected clinical features
+    preview = preview_df(selected_clinical)
+    print("Preview of selected clinical features:", preview)
+
+    # Save the selected clinical features as a CSV file
+    selected_clinical.to_csv(out_clinical_data_file)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the observed identifiers (e.g., "ILMN_1343291"), these are Illumina probe identifiers, not standard human gene symbols.
+# Therefore, they require mapping to gene symbols.
+
+print("These ILMN_* entries are Illumina probe identifiers and need to be mapped to standard gene symbols.")
+print("\nrequires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns in the annotation dataframe for probe IDs and gene symbols.
+#    From the preview, the "ID" column matches the probe IDs (e.g., "ILMN_1343048"),
+#    and the "Symbol" column holds the gene symbols.
+prob_col = "ID"
+gene_col = "Symbol"
+
+# 2. Get a gene mapping dataframe with columns 'ID' and 'Gene'.
+mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)
+
+# 3. Convert probe-level measurements to gene-level expression:
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional) Preview a small portion of the resulting gene_data
+print("Preview of mapped gene_data:")
+preview_result = preview_df(gene_data, n=5)
+print(preview_result)
+# STEP7
+
+# Rename the row index "Trait" to the actual trait name so that after linking via transpose, we have a column
+# called "Chronic_obstructive_pulmonary_disease_(COPD)" in the final dataframe.
+selected_clinical = selected_clinical.rename(index={"Trait": trait})
+
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.
+linked_data = geo_link_clinical_genetic_data(selected_clinical, normalized_gene_data)
+
+# 3. Handle missing values in the linked data
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Conduct final quality check and save the cohort information.
+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=is_trait_biased,
+    df=linked_data
+)
+
+# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.
+if is_usable:
+    unbiased_linked_data.to_csv(out_data_file)

p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/code/GSE21359.py

@@ -0,0 +1,183 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+cohort = "GSE21359"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)"
+in_cohort_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE21359"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/GSE21359.csv"
+out_gene_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE21359.csv"
+out_clinical_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE21359.csv"
+json_path = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Determine if the dataset likely contains gene expression data
+is_gene_available = True  # Based on the background info ("Affymetrix arrays ..."), we consider this as gene expression data
+
+# Step 2.1: Determine data availability
+# Observing the sample characteristics dictionary, we see:
+#   - key=0 appears to contain various ages.
+#   - key=1 appears to contain M and F genders.
+#   - key=3 contains smoking status with COPD or non-COPD conditions.
+# We will use these keys for age, gender, and COPD trait, respectively.
+trait_row = 3
+age_row = 0
+gender_row = 1
+
+# Step 2.2: Data type conversions
+
+def convert_trait(value: str) -> int:
+    """
+    Convert the raw smoking status string to a binary trait:
+    1 = COPD
+    0 = non-COPD
+    Unknown/invalid -> None
+    """
+    # Typically the string has a pattern like "smoking status: COPD, GOLD-II, 60 pack-years"
+    # or "smoking status: non-smoker".
+    # Extract the portion after the first colon, then check if it contains "COPD".
+    parts = value.split(":", 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if "copd" in val:
+        return 1
+    elif "smoker" in val or "non-smoker" in val:
+        return 0
+    return None
+
+def convert_age(value: str) -> float:
+    """
+    Convert the raw age string to a numeric (continuous) age.
+    Unknown/invalid -> None
+    """
+    parts = value.split(":", 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip()
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(value: str) -> int:
+    """
+    Convert the raw gender string to a binary indicator:
+    0 = Female
+    1 = Male
+    Unknown/invalid -> None
+    """
+    parts = value.split(":", 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == 'm':
+        return 1
+    elif val == 'f':
+        return 0
+    return None
+
+# Step 3: Conduct initial filtering and save metadata
+is_trait_available = (trait_row is not None)
+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=is_trait_available
+)
+
+# Step 4: If trait data is available, 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
+    )
+    # Observe the extracted clinical features
+    preview_output = preview_df(selected_clinical)
+    print("Preview of selected clinical features:", preview_output)
+
+    # Save the clinical data
+    selected_clinical.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the identifiers (e.g., "1007_s_at", "1053_at", etc.), these are Affymetrix probe set IDs, not standard human gene symbols.
+# Therefore, they require mapping to gene symbols.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP6: Gene Identifier Mapping
+
+# 1. In the gene annotation DataFrame, "ID" matches the probe IDs in gene_data.index,
+#    and "Gene Symbol" is the column containing human gene symbols.
+# 2. Extract these columns to produce a mapping dataframe.
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Gene Symbol")
+
+# 3. Convert probe-level measurements into gene-level expression data.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP7
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.
+#    Use 'selected_clinical' variable from the previous step instead of 'selected_clinical_df'.
+linked_data = geo_link_clinical_genetic_data(selected_clinical, normalized_gene_data)
+
+# 3. Handle missing values in the linked data
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Conduct quality check and save the cohort information.
+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=is_trait_biased,
+    df=linked_data
+)
+
+# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.
+if is_usable:
+    unbiased_linked_data.to_csv(out_data_file)

p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/code/GSE64599.py

@@ -0,0 +1,211 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+cohort = "GSE64599"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)"
+in_cohort_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE64599"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/GSE64599.csv"
+out_gene_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE64599.csv"
+out_clinical_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE64599.csv"
+json_path = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Gene Expression Data Availability
+is_gene_available = True  # Based on the series description, we assume it's a gene expression dataset
+
+# 2) Variable Availability and Data Type Conversion
+#    We do not see any COPD, age, or gender data in the sample characteristics dictionary.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Define placeholder converter functions (they won't be used since all rows are None).
+def convert_trait(value: str):
+    return None
+
+def convert_age(value: str):
+    return None
+
+def convert_gender(value: str):
+    return None
+
+# 3) Save Metadata (Initial filtering)
+#    Trait data is considered not available if trait_row is None.
+is_trait_available = (trait_row is not None)
+
+# Perform the initial filtering and save info
+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=is_trait_available
+)
+
+# 4) Clinical Feature Extraction
+#    Since trait_row is None, we skip this step (no clinical data to extract).
+# 1) Determine if gene expression data is available
+is_gene_available = True  # Based on background info, we infer gene expression data is present
+
+# 2) Determine data availability for trait, age, and gender by checking the dataframe rows.
+#    If any row index is not valid or yields only 1 unique (non-null) value, set it to None.
+
+def check_row_availability(df: pd.DataFrame, row_idx: int) -> Optional[int]:
+    """Return the row index if valid with more than one unique-nonnull value; otherwise return None."""
+    if row_idx is None:
+        return None
+    if row_idx < 0 or row_idx >= len(df):
+        return None
+    vals = df.iloc[row_idx].dropna().unique()
+    if len(vals) <= 1:
+        return None
+    return row_idx
+
+trait_row_candidate = 2
+age_row_candidate = 3
+gender_row_candidate = 4
+
+trait_row = check_row_availability(clinical_data, trait_row_candidate)
+age_row = check_row_availability(clinical_data, age_row_candidate)
+gender_row = check_row_availability(clinical_data, gender_row_candidate)
+
+# 2.2) Data type conversion functions
+def _parse_after_colon(value: str) -> str:
+    parts = value.split(':', 1)
+    if len(parts) == 2:
+        return parts[1].strip().lower()
+    return parts[0].strip().lower()
+
+def convert_trait(value: str):
+    val = _parse_after_colon(value)
+    # Heuristic for COPD vs. control
+    if "copd" in val or "smoker" in val:
+        return 1
+    elif "control" in val or "normal" in val or "nonsmoker" in val:
+        return 0
+    return None  # unknown or not applicable
+
+def convert_age(value: str):
+    val = _parse_after_colon(value)
+    try:
+        return float(val)
+    except ValueError:
+        return None  # unknown
+
+def convert_gender(value: str):
+    val = _parse_after_colon(value)
+    if val.startswith('m'):
+        return 1
+    elif val.startswith('f'):
+        return 0
+    return None  # unknown or not applicable
+
+# 3) Save metadata by initial filtering with validate_and_save_cohort_info
+is_trait_available = (trait_row is not None)
+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=is_trait_available
+)
+
+# 4) If trait data is available, 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
+    )
+    preview_result = preview_df(selected_clinical_df)
+    print("Preview of selected clinical features:", preview_result)
+
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These identifiers appear to be Affymetrix probe set IDs rather than standard human gene symbols.
+# Therefore, they need to be mapped to their corresponding gene symbols.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns in the annotation that match the probe identifiers and gene symbols
+#    From the preview, the "ID" column matches our gene_data.index, and "Gene Symbol" stores gene symbols.
+probe_col = "ID"
+gene_symbol_col = "Gene Symbol"
+
+# 2. Create a mapping dataframe with two columns: probe identifier and gene symbol
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3. Convert probe-level data to gene-level expression values
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Print some info about the newly mapped gene_data for verification
+print("Mapped gene_data shape:", gene_data.shape)
+print("First 10 gene symbols in gene_data index:", gene_data.index[:10].tolist())
+# STEP8
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values in the linked data
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Conduct quality check and save the cohort information.
+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=is_trait_biased,
+    df=linked_data
+)
+
+# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.
+if is_usable:
+    unbiased_linked_data.to_csv(out_data_file)

p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/code/GSE84046.py

@@ -0,0 +1,156 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Chronic_obstructive_pulmonary_disease_(COPD)"
+cohort = "GSE84046"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)"
+in_cohort_dir = "../DATA/GEO/Chronic_obstructive_pulmonary_disease_(COPD)/GSE84046"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/GSE84046.csv"
+out_gene_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE84046.csv"
+out_clinical_data_file = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE84046.csv"
+json_path = "./output/preprocess/1/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # From the series description, it clearly involves "genome-wide gene expression analysis"
+
+# 2. Variable Availability and Data Type Conversion
+
+# Based on the sample characteristics, no row matches "COPD" or a case/control state for this trait
+trait_row = None
+
+# We can infer approximate "age" from the date of birth in row 5
+age_row = 5
+
+# Gender is in row 4
+gender_row = 4
+
+# Define the conversion functions
+
+def convert_trait(value: str):
+    """
+    Since 'trait_row' is None, we won't actually use this function.
+    Here, just return None.
+    """
+    return None
+
+def convert_age(value: str):
+    """
+    Convert a date of birth string (e.g., 'date of birth (dd-mm-yyyy): 1944-07-19')
+    into approximate age in years, assuming a reference year of 2015.
+    Return None if parsing fails.
+    """
+    try:
+        # Extract the date part after the colon
+        dob_str = value.split(':', 1)[1].strip()
+        year = int(dob_str.split('-')[0])
+        return 2015 - year
+    except:
+        return None
+
+def convert_gender(value: str):
+    """
+    Convert 'sexe: Male' or 'sexe: Female' to binary (male=1, female=0).
+    Return None if the string is unrecognized.
+    """
+    try:
+        gender_str = value.split(':', 1)[1].strip().lower()
+        if 'male' in gender_str:
+            return 1
+        elif 'female' in gender_str:
+            return 0
+        else:
+            return None
+    except:
+        return None
+
+# 3. Save Metadata (initial filtering)
+# Trait data is not available (trait_row is None => is_trait_available=False)
+is_trait_available = (trait_row is not None)
+
+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=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Since trait_row is None, we skip extracting clinical features for this dataset.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These identifiers look like microarray probe IDs, not standard human gene symbols.
+# Therefore, gene mapping to standard gene symbols is required.
+
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify which columns in the gene_annotation dataframe store the probe IDs (matching the gene_data index)
+#    and which store the gene symbols. From the preview, "ID" corresponds to the probe IDs,
+#    and "gene_assignment" contains gene symbol references.
+
+# 2. Create the mapping dataframe by extracting those columns
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="gene_assignment")
+
+# 3. Convert probe-level to gene-level expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# For a quick check of the transformed data, print its shape and a few gene names
+print(gene_data.shape)
+print(gene_data.index[:20])
+# STEP 7
+# In previous steps, we determined that trait data is not available (trait_row is None).
+# Hence, clinical extraction was skipped, and no "selected_clinical_data" was ever created.
+# We will still normalize the gene data, but since the trait is unavailable, the dataset
+# is not usable for trait-based analysis. Therefore, we conduct partial validation
+# indicating that the trait is not available, and skip linking or saving the combined dataset.
+
+# 1. Normalize the obtained gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Perform partial validation with "is_final=False" to record that trait data is unavailable.
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,      # Gene data is available
+    is_trait_available=False     # But trait is not available
+)
+
+# Because trait data is unavailable, we do not proceed with linking or final validation.
+# No further steps are taken for this dataset.

p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json

@@ -0,0 +1 @@
+{"GSE84046": {"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}, "GSE64599": {"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": ""}, "GSE64593": {"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": "No trait data detected; dataset is not suitable for trait association analysis."}, "GSE32030": {"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": "Trait data unavailable; only gene expression data was processed."}, "GSE21359": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 135, "note": ""}, "GSE212331": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 87, "note": ""}, "GSE210272": {"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": "No trait data available; final validation indicates dataset is not usable for trait analysis."}, "GSE208662": {"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": 32, "note": ""}, "GSE175616": {"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}, "GSE162635": {"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": 205, "note": ""}, "TCGA": {"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": "Preprocessing complete for Chronic_kidney_disease (TCGA)."}}

p1/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/gene_data/GSE210272.csv

@@ -0,0 +1 @@
+Gene,GSM6427241,GSM6427242,GSM6427243,GSM6427244,GSM6427245,GSM6427246,GSM6427247,GSM6427248,GSM6427249,GSM6427250,GSM6427251,GSM6427252,GSM6427253,GSM6427254,GSM6427255,GSM6427256,GSM6427257,GSM6427258,GSM6427259,GSM6427260,GSM6427261,GSM6427262,GSM6427263,GSM6427264,GSM6427265,GSM6427266,GSM6427267,GSM6427268,GSM6427269,GSM6427270,GSM6427271,GSM6427272,GSM6427273,GSM6427274,GSM6427275,GSM6427276,GSM6427277,GSM6427278,GSM6427279,GSM6427280,GSM6427281,GSM6427282,GSM6427283,GSM6427284,GSM6427285,GSM6427286,GSM6427287,GSM6427288,GSM6427289,GSM6427290,GSM6427291,GSM6427292,GSM6427293,GSM6427294,GSM6427295,GSM6427296,GSM6427297,GSM6427298,GSM6427299,GSM6427300,GSM6427301,GSM6427302,GSM6427303,GSM6427304,GSM6427305,GSM6427306,GSM6427307,GSM6427308,GSM6427309,GSM6427310,GSM6427311,GSM6427312,GSM6427313,GSM6427314,GSM6427315,GSM6427316,GSM6427317,GSM6427318,GSM6427319,GSM6427320,GSM6427321,GSM6427322,GSM6427323,GSM6427324,GSM6427325,GSM6427326,GSM6427327,GSM6427328,GSM6427329,GSM6427330,GSM6427331,GSM6427332,GSM6427333,GSM6427334,GSM6427335,GSM6427336,GSM6427337,GSM6427338,GSM6427339,GSM6427340,GSM6427341,GSM6427342,GSM6427343,GSM6427344,GSM6427345,GSM6427346,GSM6427347,GSM6427348,GSM6427349,GSM6427350,GSM6427351,GSM6427352,GSM6427353,GSM6427354,GSM6427355,GSM6427356,GSM6427357,GSM6427358,GSM6427359,GSM6427360,GSM6427361,GSM6427362,GSM6427363,GSM6427364,GSM6427365,GSM6427366,GSM6427367,GSM6427368,GSM6427369,GSM6427370,GSM6427371,GSM6427372,GSM6427373,GSM6427374,GSM6427375,GSM6427376,GSM6427377,GSM6427378,GSM6427379,GSM6427380,GSM6427381,GSM6427382,GSM6427383,GSM6427384,GSM6427385,GSM6427386,GSM6427387,GSM6427388,GSM6427389,GSM6427390,GSM6427391,GSM6427392,GSM6427393,GSM6427394,GSM6427395,GSM6427396,GSM6427397,GSM6427398,GSM6427399,GSM6427400,GSM6427401,GSM6427402,GSM6427403,GSM6427404,GSM6427405,GSM6427406,GSM6427407,GSM6427408,GSM6427409,GSM6427410,GSM6427411,GSM6427412,GSM6427413,GSM6427414,GSM6427415,GSM6427416,GSM6427417,GSM6427418,GSM6427419,GSM6427420,GSM6427421,GSM6427422,GSM6427423,GSM6427424,GSM6427425,GSM6427426,GSM6427427,GSM6427428,GSM6427429,GSM6427430,GSM6427431,GSM6427432,GSM6427433,GSM6427434,GSM6427435,GSM6427436,GSM6427437,GSM6427438,GSM6427439,GSM6427440,GSM6427441,GSM6427442,GSM6427443,GSM6427444,GSM6427445,GSM6427446,GSM6427447,GSM6427448,GSM6427449,GSM6427450,GSM6427451,GSM6427452,GSM6427453,GSM6427454,GSM6427455,GSM6427456,GSM6427457,GSM6427458,GSM6427459,GSM6427460,GSM6427461,GSM6427462,GSM6427463,GSM6427464,GSM6427465,GSM6427466,GSM6427467,GSM6427468,GSM6427469,GSM6427470,GSM6427471,GSM6427472,GSM6427473,GSM6427474,GSM6427475,GSM6427476,GSM6427477,GSM6427478

p1/preprocess/HIV_Resistance/GSE33580.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0765943151958fb8392662fbd94e440f6061dc568837bbbed1d2c8f7f407cbe6
+size 17574906

p1/preprocess/HIV_Resistance/GSE46599.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b71115cded6bc08963818d222d852020ec78b6bfb4fa526b28bbd530525a70d1
+size 11404923

p1/preprocess/HIV_Resistance/gene_data/GSE33580.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0511021b61ee52077846f8a26d1b4a3aba0da36e320fd0c6c78e33e164c1ec19
+size 17574551

p1/preprocess/HIV_Resistance/gene_data/GSE46599.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1f661e1f9ecddf85031728eb9eb0b8d5e16c1ec259db88715274af7b864f73f0
+size 11404720

p1/preprocess/Head_and_Neck_Cancer/gene_data/GSE156915.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f893270206b1788c43d42cd90337613a0c1f2fcb0dca19a85a481f724547402c
+size 97923881

p1/preprocess/Height/gene_data/GSE101709.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b4aa98212dd0007d265ccef684a3ff0b77bc1c64a4d5e3d9203f851aec4fffc1
+size 34315537