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  1. input/GEO/Type_1_Diabetes/GSE71799/GSE71799_family.soft.gz +3 -0
  2. input/GEO/Vitamin_D_Levels/GSE33544/GSE33544_series_matrix.txt.gz +3 -0
  3. input/GEO/Vitamin_D_Levels/GSE35925/GSE35925_series_matrix.txt.gz +3 -0
  4. input/GEO/Werner_Syndrome/GSE48761/GSE48761_family.soft.gz +3 -0
  5. output/regress/Epilepsy/significant_genes_condition_Depression.json +1746 -0
  6. output/regress/Epilepsy/significant_genes_condition_Fibromyalgia.json +2217 -0
  7. output/regress/Epilepsy/significant_genes_condition_Gender.json +1788 -0
  8. p1/preprocess/Pheochromocytoma_and_Paraganglioma/gene_data/GSE64957.csv +153 -0
  9. p1/preprocess/Polycystic_Kidney_Disease/GSE74451.csv +0 -0
  10. p1/preprocess/Polycystic_Kidney_Disease/clinical_data/GSE74451.csv +3 -0
  11. p1/preprocess/Polycystic_Kidney_Disease/code/GSE74451.py +236 -0
  12. p1/preprocess/Polycystic_Kidney_Disease/code/GSE74453.py +228 -0
  13. p1/preprocess/Polycystic_Kidney_Disease/code/TCGA.py +74 -0
  14. p1/preprocess/Polycystic_Kidney_Disease/cohort_info.json +1 -0
  15. p1/preprocess/Polycystic_Kidney_Disease/gene_data/GSE74451.csv +0 -0
  16. p1/preprocess/Polycystic_Ovary_Syndrome/GSE43322.csv +0 -0
  17. p1/preprocess/Polycystic_Ovary_Syndrome/GSE87435.csv +0 -0
  18. p1/preprocess/Polycystic_Ovary_Syndrome/clinical_data/GSE43322.csv +3 -0
  19. p1/preprocess/Polycystic_Ovary_Syndrome/clinical_data/GSE87435.csv +3 -0
  20. p1/preprocess/Polycystic_Ovary_Syndrome/code/GSE151158.py +104 -0
  21. p1/preprocess/Polycystic_Ovary_Syndrome/code/GSE43322.py +221 -0
  22. p1/preprocess/Polycystic_Ovary_Syndrome/code/GSE87435.py +269 -0
  23. p1/preprocess/Polycystic_Ovary_Syndrome/code/TCGA.py +135 -0
  24. p1/preprocess/Polycystic_Ovary_Syndrome/cohort_info.json +1 -0
  25. p1/preprocess/Polycystic_Ovary_Syndrome/gene_data/GSE43322.csv +0 -0
  26. p1/preprocess/Polycystic_Ovary_Syndrome/gene_data/GSE87435.csv +0 -0
  27. p1/preprocess/Post-Traumatic_Stress_Disorder/GSE199841.csv +0 -0
  28. p1/preprocess/Post-Traumatic_Stress_Disorder/code/GSE52875.py +216 -0
  29. p1/preprocess/Post-Traumatic_Stress_Disorder/code/GSE63878.py +216 -0
  30. p1/preprocess/Post-Traumatic_Stress_Disorder/code/GSE64814.py +222 -0
  31. p1/preprocess/Post-Traumatic_Stress_Disorder/code/GSE67663.py +253 -0
  32. p1/preprocess/Post-Traumatic_Stress_Disorder/code/GSE77164.py +210 -0
  33. p1/preprocess/Post-Traumatic_Stress_Disorder/code/GSE81761.py +252 -0
  34. p1/preprocess/Post-Traumatic_Stress_Disorder/code/TCGA.py +74 -0
  35. p1/preprocess/Post-Traumatic_Stress_Disorder/cohort_info.json +1 -0
  36. p1/preprocess/Prostate_Cancer/clinical_data/GSE192817.csv +2 -0
  37. p1/preprocess/Prostate_Cancer/clinical_data/GSE200879.csv +2 -0
  38. p1/preprocess/Prostate_Cancer/clinical_data/GSE206793.csv +3 -0
  39. p1/preprocess/Prostate_Cancer/clinical_data/GSE248619.csv +2 -0
  40. p1/preprocess/Prostate_Cancer/clinical_data/TCGA.csv +551 -0
  41. p1/preprocess/Prostate_Cancer/code/GSE125341.py +150 -0
  42. p1/preprocess/Prostate_Cancer/code/GSE178631.py +139 -0
  43. p1/preprocess/Prostate_Cancer/code/GSE192817.py +137 -0
  44. p1/preprocess/Prostate_Cancer/code/GSE200879.py +157 -0
  45. p1/preprocess/Psoriasis/GSE182740.csv +75 -0
  46. p1/preprocess/Psoriasis/GSE183134.csv +35 -0
  47. p1/preprocess/Psoriasis/GSE226244.csv +69 -0
  48. p1/preprocess/Psoriasis/clinical_data/GSE123086.csv +4 -0
  49. p1/preprocess/Psoriasis/clinical_data/GSE123088.csv +4 -0
  50. p1/preprocess/Psoriasis/clinical_data/GSE162998.csv +2 -0
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+ "CYP4A22",
354
+ "FKSG29",
355
+ "SNORA69",
356
+ "LINC00469",
357
+ "GUCY2GP",
358
+ "ACTC1",
359
+ "GP2",
360
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361
+ "MBD3L5",
362
+ "RAET1G",
363
+ "RHEBL1",
364
+ "NKAPP1",
365
+ "GPR151",
366
+ "PSG5",
367
+ "MRI1",
368
+ "SMN2",
369
+ "FAM47A",
370
+ "OR1K1",
371
+ "OR5AN1",
372
+ "TMEM244",
373
+ "MID2",
374
+ "GAGE1",
375
+ "ACSM6",
376
+ "PNPLA1",
377
+ "APOBEC3A",
378
+ "FLG2",
379
+ "MSGN1",
380
+ "DPPA3",
381
+ "STRA8",
382
+ "ITLN1",
383
+ "TOPAZ1",
384
+ "PRORSD1P",
385
+ "TTTY4C",
386
+ "ARL14",
387
+ "SLC22A16",
388
+ "SCGB2B2",
389
+ "OR2M5",
390
+ "PKD1L3",
391
+ "OR2T33",
392
+ "LOC730101",
393
+ "FCRL3",
394
+ "LOC642929",
395
+ "H3-4",
396
+ "ARRDC5",
397
+ "CYCSP52",
398
+ "SCP2D1",
399
+ "CLP1",
400
+ "ADGRF4",
401
+ "LINC00479",
402
+ "LRRC69",
403
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+ "DEFB115",
405
+ "DAOA",
406
+ "DEFA6",
407
+ "CRCT1",
408
+ "OR10P1",
409
+ "WTIP",
410
+ "DPPA2",
411
+ "OR2T10",
412
+ "KRTAP4-3",
413
+ "EMC10",
414
+ "OR5M3",
415
+ "LCE2C",
416
+ "GATA6",
417
+ "KRTAP1-1",
418
+ "AOX2P",
419
+ "SNORA16B",
420
+ "JAML",
421
+ "CD300LD-AS1",
422
+ "FRG2",
423
+ "HLA-DQA1",
424
+ "HSFY1P1",
425
+ "SYCP3",
426
+ "GSTA5",
427
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428
+ "VWDE",
429
+ "SLC26A5",
430
+ "GBP4",
431
+ "TMEM179",
432
+ "MGC27382",
433
+ "LINC00588",
434
+ "SFTPA1",
435
+ "OR1B1",
436
+ "TTTY22",
437
+ "ULK2",
438
+ "SFTA2",
439
+ "PCDHB4",
440
+ "PAGE3",
441
+ "CD34",
442
+ "GNAT3",
443
+ "LACRT",
444
+ "EZR",
445
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446
+ "LCN1",
447
+ "ASIP",
448
+ "L1TD1",
449
+ "CASR",
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+ "SLC2A2",
452
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454
+ "SNORA79",
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456
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+ "OR51G2",
458
+ "GJB4",
459
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460
+ "GLMP",
461
+ "OR10AG1",
462
+ "TUBB7P",
463
+ "TTC22",
464
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465
+ "OR2A12",
466
+ "OR2M3",
467
+ "PHKA2",
468
+ "CT45A6",
469
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470
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471
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472
+ "LINC02694",
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+ "RAD51AP2",
474
+ "WFDC10B",
475
+ "SAYSD1",
476
+ "BTLA",
477
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478
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479
+ "ZP4",
480
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482
+ "MRPL42P5",
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+ "NANOS1",
484
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485
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497
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506
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507
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+ "SLC23A1",
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+ "ESPNL",
512
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514
+ "MSMB",
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+ "SERPINC1",
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+ "TAS2R8",
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+ "CT45A1"
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+ ],
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+ "Coefficient": [
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output/regress/Epilepsy/significant_genes_condition_Fibromyalgia.json ADDED
@@ -0,0 +1,2217 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "significant_genes": {
3
+ "Variable": [
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+ "ULK4P2",
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278
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281
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283
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284
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285
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286
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288
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290
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382
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385
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395
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412
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413
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415
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416
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462
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463
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466
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470
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471
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473
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474
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476
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478
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479
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480
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481
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482
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483
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484
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485
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486
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487
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488
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489
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490
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491
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492
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493
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494
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496
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497
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502
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503
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504
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505
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506
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507
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235
+ "DYNAP",
236
+ "MESD",
237
+ "TM6SF2",
238
+ "ZNF880",
239
+ "SLC22A20P",
240
+ "OR11H1",
241
+ "SNORD67",
242
+ "KLHL14",
243
+ "SLC26A5",
244
+ "GOLGA8DP",
245
+ "PCSK9",
246
+ "CRISP3",
247
+ "DPRX",
248
+ "KRTAP2-1",
249
+ "RPA4",
250
+ "LY6S-AS1",
251
+ "HTR1A",
252
+ "VN1R4",
253
+ "OR4M2",
254
+ "FAM138D",
255
+ "IFNK",
256
+ "EZR",
257
+ "ALOX15",
258
+ "GTF2IRD2",
259
+ "MBD3L5",
260
+ "LINC00469",
261
+ "OR2T33",
262
+ "ANTXRL",
263
+ "OR2M5",
264
+ "PCDHB6",
265
+ "RHAG",
266
+ "PAGE5",
267
+ "PPP1R14D",
268
+ "CYP4A22",
269
+ "RPL21P44",
270
+ "PABPC1P2",
271
+ "USH1G",
272
+ "OR2M1P",
273
+ "TNF",
274
+ "KRTAP6-2",
275
+ "ATP2A1",
276
+ "FAM138B",
277
+ "OR4C6",
278
+ "CCL1",
279
+ "NT5C1B",
280
+ "OR6T1",
281
+ "RUNX1-IT1",
282
+ "IHO1",
283
+ "NAPSB",
284
+ "YIPF7",
285
+ "TSBP1",
286
+ "HSF5",
287
+ "CCDC38",
288
+ "XCR1",
289
+ "IGDCC4",
290
+ "PAGE3",
291
+ "CATSPER4",
292
+ "GNAT3",
293
+ "HOXD8",
294
+ "OR5M3",
295
+ "OR2AT4",
296
+ "TAB3",
297
+ "CT47B1",
298
+ "CCL15",
299
+ "SPATA18",
300
+ "UCP1",
301
+ "SNORA79",
302
+ "TBC1D26",
303
+ "NPHP3-AS1",
304
+ "TDRD12",
305
+ "OR2T6",
306
+ "RAET1G",
307
+ "FOXA3",
308
+ "OR2A12",
309
+ "POU4F3",
310
+ "ADGRF1",
311
+ "TMEM72",
312
+ "HSP90AB4P",
313
+ "SMN2",
314
+ "SNORA36B",
315
+ "MAGEB4",
316
+ "ITLN1",
317
+ "SERPINF2",
318
+ "LDLRAD1",
319
+ "TTTY22",
320
+ "PAX8",
321
+ "OR2L1P",
322
+ "BTC",
323
+ "KCNK10",
324
+ "PCGEM1",
325
+ "PCDHGB1",
326
+ "HS3ST6",
327
+ "GAGE12D",
328
+ "STRA8",
329
+ "OR1N2",
330
+ "ABCB5",
331
+ "MGC27382",
332
+ "MOGAT3",
333
+ "MAGEA5P",
334
+ "SNORA5C",
335
+ "RAG2",
336
+ "CST9",
337
+ "ADGRF3",
338
+ "LCN1",
339
+ "LGALS9B",
340
+ "LACRT",
341
+ "GSTA5",
342
+ "FAM99A",
343
+ "SNORA69",
344
+ "DEFB129",
345
+ "OR10G3",
346
+ "CSTPP1",
347
+ "MID2",
348
+ "SFTPA1",
349
+ "A4GNT",
350
+ "SLC15A1",
351
+ "RAET1L",
352
+ "GAGE1",
353
+ "KRTAP4-7",
354
+ "TUBB7P",
355
+ "TMEM244",
356
+ "MUCL1",
357
+ "SNORA16B",
358
+ "TBC1D29P",
359
+ "FSHR",
360
+ "OR10S1",
361
+ "ASCL3",
362
+ "TMEM179",
363
+ "CHST13",
364
+ "PEDS1-UBE2V1",
365
+ "PTH2",
366
+ "SNORA38B",
367
+ "NANOS2",
368
+ "MEOX1",
369
+ "ULK2",
370
+ "FAM47A",
371
+ "SIGLEC6",
372
+ "ING1",
373
+ "SOX3",
374
+ "IL21",
375
+ "RSPH6A",
376
+ "BCL11A",
377
+ "OR5AN1",
378
+ "PPAN-P2RY11",
379
+ "FKSG29",
380
+ "OCLN",
381
+ "CASR",
382
+ "CYP11B2",
383
+ "SFTA2",
384
+ "OR2B3",
385
+ "OR10G2",
386
+ "INTS4P2",
387
+ "ADGRF2P",
388
+ "RBM48",
389
+ "LINC00200",
390
+ "C1orf185",
391
+ "WTIP",
392
+ "TMPRSS11BNL",
393
+ "LCE3D",
394
+ "GUCY2F",
395
+ "TAS2R3",
396
+ "GPR151",
397
+ "GDF7",
398
+ "SEPTIN14",
399
+ "POR",
400
+ "PCDHB4",
401
+ "ALDH1A1",
402
+ "GJB4",
403
+ "FOXD4L5",
404
+ "FGA",
405
+ "PRSS38",
406
+ "NEUROG2",
407
+ "UBE2DNL",
408
+ "NUP210L",
409
+ "ACTC1",
410
+ "GP2",
411
+ "HELT",
412
+ "OR10P1",
413
+ "KRT4",
414
+ "OR2M3",
415
+ "FLACC1",
416
+ "CPXCR1",
417
+ "IL27",
418
+ "MAGEB18",
419
+ "RGS13",
420
+ "PNPLA1",
421
+ "NANOS1",
422
+ "SSX8P",
423
+ "INHBA",
424
+ "CIMIP2B",
425
+ "SLFN14",
426
+ "OR10X1",
427
+ "LINC01565",
428
+ "LINC00588",
429
+ "C1orf105",
430
+ "DEFB110",
431
+ "GBP4",
432
+ "DEFB115",
433
+ "ACSM6",
434
+ "TOPAZ1",
435
+ "GARIN6",
436
+ "KRTAP20-4",
437
+ "ADAMTS16",
438
+ "AOX2P",
439
+ "OR1B1",
440
+ "GUCY2GP",
441
+ "CD300LD",
442
+ "OR2T35",
443
+ "RPL23AP7",
444
+ "PRORSD1P",
445
+ "TUBAL3",
446
+ "ADH1B",
447
+ "TTC22",
448
+ "PSG5",
449
+ "SCGB2B2",
450
+ "SEMG1",
451
+ "CYCSP52",
452
+ "OR51T1",
453
+ "SPINK5",
454
+ "MRI1",
455
+ "SPINK4",
456
+ "PCDHGA8",
457
+ "APOBEC3A",
458
+ "EPCIP-AS1",
459
+ "SNORA18",
460
+ "WNT6",
461
+ "OR2T12",
462
+ "PSG3",
463
+ "CDH19",
464
+ "KRTAP4-3",
465
+ "OR2F2",
466
+ "USP26",
467
+ "PRR30",
468
+ "PRAMEF11",
469
+ "PLEKHG4",
470
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471
+ "LRRC74A",
472
+ "HSFY1P1",
473
+ "MS4A2",
474
+ "ZNF594",
475
+ "DAZL",
476
+ "TTTY4C",
477
+ "SUSD1",
478
+ "KRTAP5-5",
479
+ "SERPINB10",
480
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481
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482
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483
+ "OR4F4",
484
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485
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486
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487
+ "GOLGA8B",
488
+ "ZFP91-CNTF",
489
+ "ESPNP",
490
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491
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492
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493
+ "ZBED1",
494
+ "DPPA3",
495
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496
+ "PPP1R2B",
497
+ "CRCT1",
498
+ "SPIB",
499
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500
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501
+ "DUSP21",
502
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503
+ "DGAT2L6",
504
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505
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506
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507
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508
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509
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510
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511
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512
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513
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514
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515
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516
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517
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518
+ "RMDN2",
519
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520
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521
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522
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523
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524
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525
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526
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527
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528
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529
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530
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531
+ "KRT74",
532
+ "SELE",
533
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534
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535
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536
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537
+ "SNORA51",
538
+ "PKD1L3",
539
+ "MBD3L2",
540
+ "MT4",
541
+ "MSMB",
542
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543
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544
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545
+ "SLCO4C1",
546
+ "TTTY7",
547
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548
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549
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550
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551
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552
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553
+ "GALK2",
554
+ "SPTBN5",
555
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556
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557
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558
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559
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560
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561
+ "FAM99B",
562
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563
+ "FZD9",
564
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565
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+ "OR6C76",
567
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568
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+ "KPRP",
570
+ "GDPGP1",
571
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572
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573
+ "SDHAP3",
574
+ "NOBOX",
575
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576
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577
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578
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580
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584
+ "PCDH10",
585
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586
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588
+ "OR1F2P",
589
+ "OR11H4",
590
+ "HBG1",
591
+ "COL6A4P1"
592
+ ],
593
+ "Coefficient": [
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1782
+ "precision": 0.37084141410778665,
1783
+ "recall": 0.9374999999999998,
1784
+ "f1": 0.531418569257171,
1785
+ "jaccard": 0.26670881663987545
1786
+ }
1787
+ }
1788
+ }
p1/preprocess/Pheochromocytoma_and_Paraganglioma/gene_data/GSE64957.csv ADDED
@@ -0,0 +1,153 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Gene,GSM1584943,GSM1584944,GSM1584945,GSM1584946,GSM1584947,GSM1584948,GSM1584949,GSM1584950,GSM1584951,GSM1584952,GSM1584953,GSM1584954,GSM1584955,GSM1584956,GSM1584957,GSM1584958,GSM1584959,GSM1584960,GSM1584961,GSM1584962,GSM1584963,GSM1584964,GSM1584965,GSM1584966,GSM1584967,GSM1584968,GSM1584969,GSM1584970,GSM1584971,GSM1584972,GSM1584973,GSM1584974,GSM1584975,GSM1584976,GSM1584977,GSM1584978,GSM1584979,GSM1584980,GSM1584981,GSM1584982,GSM1584983,GSM1584984,GSM1584985,GSM1584986,GSM1584987,GSM1584988,GSM1584989,GSM1584990,GSM1584991,GSM1584992,GSM1584993,GSM1584994,GSM1584995,GSM1584996,GSM1584997
2
+ A4GALT,5.91165,5.92605,5.7631,5.90595,5.81135,5.9799,5.9725,6.10825,6.02815,6.09555,6.006,6.1496,6.0652,6.08715,5.9023,5.6933,5.9528,6.0292,6.0418,6.03205,6.02205,5.98705,6.08215,6.01645,6.1174,6.13775,6.0901,6.00135,6.0092,6.0143,5.84635,6.0358,6.00335,6.14995,6.07665,6.10235,6.1503,6.1531,5.908,6.03745,5.96925,5.9806,6.07675,6.06235,5.9906,6.0461,6.0946,6.07305,5.9819,6.07645,6.01495,6.06695,6.00555,5.72915,5.89335
3
+ AKR1C1,2.643035,1.90305,1.720845,2.40886,2.6956,2.496125,2.35898,2.14994,2.639025,2.390345,2.690365,2.08485,2.286345,1.71097,2.449515,1.889835,3.443135,1.55817,2.288405,2.45671,3.268885,2.369585,2.845685,2.11081,2.423075,1.74085,2.271945,1.76453,2.00136,2.078245,1.868205,2.001955,2.239195,2.366785,1.86909,2.26226,2.249455,2.32646,1.914715,2.528375,1.874005,1.64005,2.068245,2.774145,2.14915,2.10078,2.358065,2.69777,2.083615,1.928025,1.879935,2.79925,2.05766,2.13601,2.136445
4
+ ALPP,4.26697,4.11652,4.23139,4.32974,4.24077,3.77836,4.02446,4.03295,4.15732,3.94692,3.80436,3.6614,4.00887,4.00493,3.25399,4.62448,3.57406,4.33075,4.09879,4.39965,3.99204,3.83073,3.79965,4.05296,4.05439,3.70552,3.94326,4.27327,3.75436,3.92143,3.95816,4.07621,4.115,3.78698,4.12376,4.01681,4.09011,3.78604,4.39851,4.37114,4.27151,4.05403,3.84306,3.55133,4.15157,4.04974,4.23637,4.06171,4.16613,3.72924,3.89055,3.92914,3.64376,3.90763,4.11305
5
+ ANKRD36,5.14076,7.2714,7.13704,7.5337,4.6547,7.92188,6.8956,6.61146,7.33447,8.02638,6.90144,6.07013,7.3505,7.6264,7.24686,4.22365,6.75343,7.36182,7.63596,7.11283,6.62834,7.03219,6.26705,3.86435,6.477,7.67471,6.93092,8.70887,7.74004,7.41025,8.19823,6.1292,7.48142,6.375,6.55305,8.13465,7.4925,6.67253,5.42058,7.69956,7.43257,7.14944,7.63782,6.14027,7.71385,6.88457,7.81965,7.21851,7.32097,6.9369,5.80957,7.11895,7.03042,6.80984,7.46496
6
+ APOBEC3F,4.82159,4.4237,4.6974,4.64388,4.01818,5.03524,4.89076,5.17665,5.51437,6.05426,5.22467,7.43262,5.92425,4.18585,5.98825,3.31962,4.68059,6.97257,4.2714,6.42748,4.06422,5.93433,4.97857,7.86136,6.79772,4.68898,4.62304,4.77973,4.63794,4.83066,4.96978,3.87916,5.16858,5.55647,4.3578,6.05754,5.46293,6.8367,5.76312,5.31145,5.54858,3.91782,5.0741,3.92486,6.27663,6.83615,5.98236,4.74593,5.9053,6.98301,4.32719,5.78489,4.7778,7.92837,5.505
7
+ ARL4A,2.26902,2.178855,1.7545,1.43369,1.985865,2.22609,2.424605,2.02046,1.56223,1.97796,2.171175,1.98317,1.854795,1.848995,1.981955,1.823975,2.338985,1.226285,2.17243,1.536675,1.84844,2.17549,1.94056,1.400915,1.50331,1.29687,1.34066,1.17545,2.28608,1.961575,1.35528,2.002115,2.41207,2.5664,1.53879,2.07897,3.040225,2.32467,2.088465,1.835455,2.204465,1.553875,2.15479,1.73631,2.107065,1.43974,1.970765,1.82855,1.73674,2.634735,2.012335,1.975975,2.508165,2.446855,1.863735
8
+ ATF4,4.638085,4.2436,3.926335,4.25632,4.50491,4.468215,4.578495,4.51586,4.302865,4.54929,4.501675,4.677175,4.25565,4.53741,4.649485,4.40524,4.44988,4.56091,4.48375,4.6057,4.618195,4.65697,4.534585,4.28501,4.459255,4.625265,4.71877,4.46134,4.728755,4.48965,4.356985,4.62745,4.814355,4.634595,4.5685,4.668095,4.57277,4.690745,4.37932,4.55686,4.370055,4.466975,4.362185,4.532595,4.49446,4.50904,4.552225,4.458065,4.683535,4.40917,4.314845,4.544955,4.44533,4.59077,4.529165
9
+ BAGE,7.28535,6.35143,6.61548,6.12667,5.91633,5.74257,5.87593,5.76786,6.29858,6.04156,5.8058,6.51284,6.15104,6.23995,5.69712,6.94591,5.90399,6.93219,6.75323,7.1021,5.73463,5.41363,6.49679,5.67559,6.17534,6.06401,5.80158,6.29222,6.06165,6.06494,5.90962,5.65455,5.88259,6.26669,5.80434,6.75766,5.80491,5.99399,5.79148,6.05968,6.56613,5.98348,5.1911,6.75929,6.62161,6.52897,6.16338,6.07845,6.14927,6.13982,6.15738,5.07475,5.16308,6.1758,6.44938
10
+ BAGE2,4.92329,4.67226,4.67441,4.81378,4.11835,4.51226,4.56813,4.57805,4.79445,4.78622,4.76757,4.66964,4.77883,4.11742,4.44141,5.11576,4.44056,5.15889,4.45187,4.76962,4.47972,4.03842,4.65043,4.0073,5.1981,4.44783,4.58065,4.41271,4.83682,4.441,4.73675,4.81865,4.28804,5.17379,4.18381,5.00951,4.30014,5.16855,4.23937,4.71476,4.93314,4.36186,4.6947,5.24367,4.66178,4.37568,4.79341,4.63476,4.68671,4.18115,4.75852,3.88587,4.36873,4.62338,4.76219
11
+ BAGE3,4.47124,4.20488,4.27984,4.62923,3.90635,4.15005,4.19492,4.40029,4.50147,4.29787,4.17678,4.44283,4.17112,3.73317,4.02117,4.26471,4.0405,4.88579,4.04511,4.31461,4.15355,3.62834,4.36007,3.87326,4.69174,4.25403,4.37824,3.99678,4.41934,4.00537,4.39649,4.42409,3.89497,4.36363,3.70344,4.69677,3.89652,4.88223,4.0302,4.37517,4.55678,4.19517,4.24947,4.6033,4.30393,3.99583,4.32338,4.49254,4.3501,4.00763,4.08303,3.42698,3.99517,4.24539,4.38448
12
+ BAGE4,5.44653,5.17785,5.10323,5.13308,4.81175,5.01135,5.0858,5.13439,5.37137,5.13308,5.18699,5.13308,5.38583,4.88614,5.09257,6.09617,5.01354,5.75569,5.0237,5.34976,5.09781,4.76036,5.21058,4.46037,5.59712,4.83349,5.18541,4.96981,5.28154,4.96733,5.08512,5.26188,4.77235,5.61016,4.59432,5.42496,4.71284,5.46735,4.52296,5.11913,5.44767,5.14247,5.07481,5.88967,5.298,4.88967,5.19327,5.01661,5.23779,4.85538,5.36029,4.92582,4.95189,5.13308,5.24578
13
+ BAGE5,5.38406,5.12452,5.04098,5.20827,4.50506,4.67492,4.9078,5.13961,5.25084,4.97437,4.85716,5.07455,4.9192,4.7978,4.9891,5.20906,4.73272,5.809,4.78748,5.20507,4.675,4.49537,5.14248,4.50316,5.4072,4.87484,4.96664,4.68689,5.08064,4.79575,5.08738,4.95232,4.51444,5.45779,4.49719,5.27567,4.59961,5.45823,4.69305,5.04562,5.29198,4.91388,4.93196,5.46264,5.19416,4.77121,5.03474,4.98182,5.20143,4.76014,5.09779,4.10435,4.74357,4.97467,5.19793
14
+ BANF1,7.63959,7.57152,8.19928,6.84635,9.28603,8.90006,8.38754,8.57068,8.82865,8.36367,8.00144,9.34688,8.88744,8.91152,8.22168,8.22441,8.75701,8.93555,8.86756,8.63656,8.08538,9.28576,8.88448,8.15059,8.87923,8.99501,8.59577,8.20485,8.62195,8.23232,8.47598,9.76876,8.26986,8.50152,8.92822,7.90146,8.76038,9.61685,7.94778,9.0133,9.15408,9.06752,8.85325,8.72881,8.35896,8.88299,8.69693,8.59596,8.23348,8.47757,8.9124,7.82129,8.18077,8.27567,8.01341
15
+ BCL2L15,1.85681,2.2928,2.10349,2.03314,1.85044,1.64304,1.40254,2.25499,2.63498,2.1714,1.51484,1.90487,1.57016,2.38094,2.54899,3.80899,1.6422,2.24429,2.17709,1.795,2.03682,1.85171,2.59536,2.57515,2.43747,1.59755,2.25838,1.87881,2.34602,2.65975,2.39002,2.09053,2.26384,2.10947,2.27296,1.85851,2.17607,2.66334,2.13147,2.51364,1.98528,1.6481,2.57118,2.18177,2.86132,2.14579,2.69725,1.85693,2.39051,1.93397,2.76553,2.63006,2.176,1.76557,2.64742
16
+ BMP8B,4.66084,4.19761,4.60158,5.10846,5.06064,4.61917,4.46368,4.71408,4.92259,4.83607,4.5446,4.53148,4.46074,4.74617,4.22589,4.85387,4.38184,4.79182,4.76363,5.16028,4.71658,4.63724,4.81735,5.04821,4.96574,4.72699,4.41474,4.96768,4.09048,4.553,4.43225,4.84426,5.43586,4.1971,4.60006,4.64908,4.42761,4.1691,4.92319,4.90011,4.8538,5.04613,4.75308,4.87831,4.98457,5.05979,4.66308,4.76425,5.06432,4.50433,4.82381,4.57592,4.32204,5.05671,4.80052
17
+ BRWD1,2.330445,3.274275,3.48838,2.83536,3.22695,3.381965,3.35236,3.485915,2.976695,3.118135,3.443815,3.274275,3.274625,3.056295,2.60251,2.76576,3.42575,3.2619,3.282845,3.70403,3.22392,3.440005,3.33965,2.63525,3.67183,3.245635,2.91995,3.520135,3.27364,3.03944,3.415785,3.300075,3.35167,3.6158,3.177045,3.108855,3.325185,3.364005,2.863295,2.953975,3.53733,3.323875,3.19193,3.104835,3.470165,3.55104,3.255565,3.34841,3.51408,3.373255,3.514265,2.882855,2.91704,3.030965,3.14935
18
+ C3,9.925756666666667,9.432793333333333,9.621226666666667,9.463996666666667,9.620906666666666,9.49785,9.6738,9.983353333333334,9.279253333333333,9.803483333333332,10.117833333333333,10.237766666666667,9.777190000000001,9.739626666666666,9.81269,9.725933333333334,9.91506,9.815793333333332,9.99523,9.390943333333334,10.201266666666667,10.135133333333334,9.877613333333333,9.786023333333333,9.934660000000001,10.219966666666668,9.759689999999999,9.59639,9.532963333333333,9.978723333333333,9.74351,9.860473333333333,9.573193333333332,9.976099999999999,9.940873333333334,9.828233333333333,9.73548,9.900686666666667,9.55178,9.973743333333333,9.912886666666665,9.781846666666667,10.052546666666666,9.777786666666666,9.49371,9.759246666666668,9.75693,9.732776666666666,9.75967,9.9227,9.89989,10.081313333333334,9.84675,9.37607,9.670850000000002
19
+ CC2D2B,2.54965,2.19691,2.3451,3.56142,2.94988,2.55724,3.25256,2.60263,2.40942,3.77563,2.93379,2.68491,2.13416,2.50399,2.48313,2.92224,2.32505,3.07877,2.6803,2.42783,3.01705,2.07598,2.51088,2.53394,2.25673,2.42344,2.59901,2.85309,3.00381,2.37787,3.13374,2.79009,2.98409,3.09298,2.70121,2.18551,2.19035,2.3257,2.67591,2.81004,2.50742,2.10656,2.88522,2.17582,3.43261,2.27814,2.64483,2.60133,2.92928,2.36009,2.72943,2.66533,1.96905,3.19333,2.44398
20
+ CD82,11.073876666666667,10.978366666666666,11.484313333333333,11.95531,10.99843,10.980443333333334,10.890156666666666,10.945566666666666,10.633126666666666,10.475446666666667,10.941083333333333,10.90084,10.571793333333334,10.608006666666666,11.47834,11.24448,10.838233333333333,12.014226666666666,11.624443333333334,11.502986666666667,10.591146666666667,10.430716666666667,10.80039,10.976326666666667,10.592756666666666,11.324413333333332,11.379563333333333,11.931543333333334,10.680356666666666,11.288976666666667,10.71003,11.475836666666666,10.731286666666668,10.61866,11.275036666666667,11.737963333333333,11.124563333333333,11.179823333333333,10.841413333333334,11.45703,10.901316666666666,10.926046666666666,10.869333333333334,10.329296666666666,11.050666666666666,11.06095,11.436116666666667,11.333806666666666,11.597683333333334,10.723256666666666,10.95722,10.615876666666667,10.73671,11.035960000000001,11.00015
21
+ CDK11A,27.75171,23.743065,23.5068275,18.8332825,22.6794225,23.5443325,22.0794225,24.59972,25.00065,22.615195,21.211445,22.143205000000002,23.660017500000002,23.7996925,20.469285,24.0074125,20.8598,22.12786,21.3919775,20.1980675,21.863525000000003,23.443875,22.580337500000002,21.813775,23.563229999999997,23.1828825,23.009725,21.05899,23.248457499999997,23.605095,23.13147,23.129482499999998,22.6841975,22.195439999999998,23.526294999999998,20.83096,21.3401925,21.0008025,21.394132499999998,23.4770675,21.32042,21.861515,23.304515000000002,22.798175,21.2020075,21.6868625,22.834067500000003,23.40881,22.6877175,21.854565,22.491127499999997,22.6770775,22.131775,22.90835,22.138309999999997
22
+ CDK11B,32.08011,27.27588,27.173585,22.628525,26.775685,26.793255,25.069875,28.04416,29.18822,26.00416,24.28983,26.87689,26.971615,26.877275,23.18896,27.382514999999998,23.59306,25.37876,25.026135,23.279345,24.577090000000002,26.648780000000002,25.807335000000002,24.93665,26.87993,26.126015000000002,26.07313,24.18326,26.516315,27.08999,26.30268,26.128735,25.918435,25.24889,26.9971,23.920189999999998,23.670045000000002,23.811655000000002,24.976934999999997,27.996805000000002,24.688109999999998,25.09879,26.45134,26.22201,24.202015,24.011695,26.043375,27.17938,25.728995,24.16986,25.814335,25.714285,25.14584,26.02915,25.50901
23
+ CEP170,3.95566,5.63899,4.97099,4.34222,4.91561,4.7552,5.45339,6.19945,6.19101,6.0156,5.78352,5.89888,5.89557,5.18496,4.73373,5.03671,6.23227,5.43713,5.18375,5.2126,5.55207,5.60831,4.42995,4.7186,5.76187,5.47755,5.57417,4.94353,6.10809,5.92816,5.99581,5.27243,5.44018,5.79081,4.57769,6.17788,5.95246,6.00453,4.56084,5.71414,6.6429,4.94727,5.31188,5.49483,5.1189,4.90374,5.9574,5.53523,5.08506,6.37989,5.45339,5.17508,5.20047,5.8147,5.48415
24
+ CES1P1,3.95165,4.19008,4.00206,4.50658,4.17546,3.94725,3.77088,4.12714,4.03885,4.14012,4.47388,3.68473,4.17076,3.74392,3.98595,4.23147,4.0712,4.27955,4.32736,3.57312,4.40939,3.11509,4.31654,3.21244,3.71097,3.96539,3.89517,4.2567,4.13975,4.18424,3.82518,3.9586,4.56509,3.96202,4.68591,6.76064,4.07315,4.4082,3.90164,4.03885,4.022,3.89517,4.38476,4.52826,3.85384,4.40522,3.98624,4.32333,3.60589,3.86463,3.93676,4.21902,4.16846,4.07085,4.05946
25
+ CFHR1,2.701,2.45387,2.32748,2.426685,2.58703,2.529665,2.58648,2.319725,2.710945,2.87127,2.299745,2.53147,2.351445,2.45974,2.789935,2.757865,2.466555,2.342205,2.644,2.382035,2.685355,2.39344,2.6809,2.64123,2.697995,2.530675,2.61991,2.38959,2.43296,2.468955,2.398265,2.45571,2.806165,2.678275,2.35216,2.54428,2.68181,2.34454,2.56829,2.6986,2.37848,2.334695,2.52869,2.844875,2.547795,2.72305,2.55039,2.399305,2.69814,2.745695,2.965095,3.017355,2.821515,2.433235,2.525925
26
+ CFHR4,1.109,1.0448,1.104635,1.140605,1.09715,1.06962,0.919445,1.100155,1.06536,1.003925,0.974175,1.079725,1.11267,1.07168,0.938395,1.31677,1.004175,1.25041,1.087815,1.00774,1.238315,1.05476,0.992515,1.46153,1.10067,1.04544,0.97614,1.121185,1.010805,0.9944,1.209725,1.050485,0.96026,1.108785,1.05328,1.02205,1.232155,1.06982,0.972665,1.034325,1.101205,1.01777,1.349565,1.14894,1.093075,1.124685,1.11144,1.038575,1.078665,0.92035,1.08462,1.289125,1.0274,1.04318,1.00989
27
+ CHTF8,6.25437,3.49845,5.38408,6.8884,6.66055,6.0574,6.68959,5.60337,5.52348,6.62868,7.22277,6.87504,6.83583,5.09782,6.36764,4.77068,6.07032,4.71595,2.96391,6.08994,6.08863,6.00199,5.87748,6.13232,6.71691,6.67986,7.22138,4.18425,6.17123,7.54584,5.28098,5.97298,5.87227,8.49354,7.07097,6.43067,6.42522,6.8408,6.15271,6.80386,7.15458,8.17282,6.19889,5.63993,6.16845,5.68716,6.7478,8.3027,6.24636,5.52234,6.82199,4.41623,7.05632,4.93631,6.16923
28
+ CST12P,11.373615000000001,10.709415,11.609390000000001,11.624695,11.051925,10.82207,11.468555,11.349385,10.769555,11.989565,11.81821,11.523129999999998,11.246410000000001,11.211735000000001,11.29626,11.131129999999999,11.961215,11.620080000000002,11.225635,12.10586,10.8965,11.92885,10.72335,11.53399,11.17369,11.756765,11.738055,10.758865,11.923865,11.759855,12.243590000000001,11.297004999999999,11.634564999999998,11.406385,11.109639999999999,11.751505,11.85882,11.48518,10.63581,10.673685,11.282969999999999,11.492615,11.343795,12.20382,11.221975,11.396995,11.66045,11.388905,11.49726,11.00131,11.5421,11.90867,11.18577,10.750255,11.46408
29
+ CTBP2,2.821205,3.31774,3.695955,3.159535,2.96985,3.78498,3.296085,3.19422,3.267145,3.39514,3.675635,3.12534,3.52393,3.284235,3.27666,3.07969,3.323965,3.270445,3.344235,3.429535,3.307875,3.11414,3.557105,3.185945,3.38475,3.089865,3.671835,2.944235,3.398235,3.562815,3.401745,3.72977,3.545845,3.108395,3.739265,3.452555,3.518335,3.413705,3.03835,3.034015,3.20598,3.70573,3.093575,3.108975,3.360835,2.88884,3.06638,2.83392,3.071305,3.540535,2.905255,3.13514,3.004645,3.7941,3.99186
30
+ CYP2B6,6.440875,6.371685,6.7574749999999995,6.26338,6.696605,6.76825,7.041980000000001,6.986655,6.02397,6.68979,6.86886,6.4905100000000004,6.168675,6.59116,6.562189999999999,6.77427,6.45516,6.8964799999999995,6.589675,5.9330549999999995,6.4854,7.110675,6.621544999999999,6.782475,6.1944099999999995,6.53552,6.412685,6.638275,6.87147,6.530279999999999,6.70206,7.43294,6.5069300000000005,7.409644999999999,6.99481,5.96026,6.5415849999999995,6.5397,6.38594,6.36915,6.6210450000000005,6.22813,5.941995,6.281465,6.92607,6.6852800000000006,6.31227,6.36947,6.97853,6.379975,6.868205,6.058540000000001,6.593335,6.178095,6.83201
31
+ CYP2D6,2.57549,2.61737,2.616025,2.7729,2.713605,2.563245,2.666425,2.70908,2.669605,2.55579,2.395995,2.492865,2.541415,2.52027,2.45879,2.785815,2.59152,2.721075,2.669495,2.5986,2.5252,2.27452,2.614785,2.72417,2.63313,2.709975,2.520955,2.721295,2.46103,2.566795,2.71355,2.61708,2.58488,2.460555,2.81574,2.56774,2.548325,2.39079,2.74175,2.70861,2.480445,2.604585,2.364605,2.566915,2.68741,2.580225,2.60754,2.638765,2.63324,2.43396,2.54258,2.454285,2.48833,2.67012,2.82657
32
+ CYP51A1,3.865385,3.754315,4.14145,3.49048,3.983,4.205005,4.375555,4.277575,3.354365,4.134,4.472865,3.997645,3.62726,4.07089,4.1034,3.988455,3.86364,4.175405,3.92018,3.334455,3.9602,4.836155,4.00676,4.058305,3.56128,3.825545,3.89173,3.91698,4.41044,3.963485,3.98851,4.81586,3.92205,4.94909,4.17907,3.39252,3.99326,4.14891,3.64419,3.66054,4.1406,3.623545,3.57739,3.71455,4.23866,4.105055,3.70473,3.730705,4.34529,3.946015,4.325625,3.604255,4.105005,3.507975,4.00544
33
+ DDX19A,2.98094,2.934875,3.1534,2.971165,2.53398,3.29573,3.24688,3.11106,2.986085,2.96574,3.30054,3.1302,3.036965,3.044675,3.07675,3.00395,3.036105,3.49164,2.53454,3.17006,3.12004,3.211885,3.18038,2.750205,3.34764,3.461545,3.020275,3.144325,3.208695,3.11106,3.43277,3.06882,2.75315,3.186555,3.195745,3.25054,2.97756,3.210805,3.29799,2.605575,3.074725,3.29495,3.008925,2.992075,3.181885,3.38914,3.15354,3.285595,3.08824,3.079405,2.820575,2.751695,2.943755,2.86119,2.799995
34
+ DNAJB6,6.11357,7.04805,7.43904,6.85313,6.63296,7.39237,6.31156,7.44809,7.06706,7.17632,6.82447,7.33875,7.65413,7.28862,6.99416,7.28335,8.15322,5.70138,6.69833,6.53997,7.14456,7.58235,7.0413,5.66089,6.83569,6.84827,7.2783,6.72796,6.70252,6.9862,6.16936,7.01521,7.44849,7.18562,7.01811,6.41105,6.57601,6.9862,4.82272,6.25798,7.03257,7.49995,6.68086,7.18327,6.32732,6.96044,7.10204,7.2037,7.33571,7.58549,6.91146,7.27822,7.5909,5.94255,6.33168
35
+ DPPA3,2.72034,2.25461,2.31075,2.68029,2.63648,2.63979,2.603,2.35373,2.37474,2.40185,2.31733,2.19488,2.1396,2.37563,2.27067,2.70698,2.30291,2.49761,2.58416,2.40726,2.29295,2.59851,2.27463,2.65401,2.32675,2.54003,2.40614,2.94244,2.50934,2.32012,2.54868,2.5268,2.367,2.24436,2.57856,2.96008,2.49923,2.91932,2.81682,2.72304,2.43475,2.26259,2.20011,2.24588,2.63074,2.30233,2.33098,2.07971,2.32349,2.3096,2.19094,2.66342,2.54812,2.48294,2.2566
36
+ DYNC1I2,8.05003,8.14526,8.33282,7.01882,8.11142,8.15674,8.41605,8.45275,8.0762,8.28165,8.71019,8.67983,8.27332,8.20769,8.5944,7.82583,7.98504,8.08378,8.38702,7.63028,7.79521,8.432,8.39126,8.13727,8.1642,8.30443,8.13518,8.02653,8.23168,8.25712,7.70881,8.44455,7.92886,8.53176,8.55604,8.18757,8.45075,8.95687,8.01953,8.35452,7.97793,8.1727,8.26972,8.52413,8.44638,7.63028,8.1297,8.26737,8.53006,8.30265,8.48804,8.38059,8.60169,8.29205,7.39593
37
+ EIF3F,8.94872,8.46438,8.22087,9.30543,8.94613,8.94906,8.94659,9.50717,9.11662,9.40441,8.72155,9.36639,9.37181,8.81152,9.13161,8.85825,9.07676,9.07263,9.40567,9.43859,9.26036,9.37977,9.38106,8.89658,9.71732,9.26624,9.32647,8.9451,9.26258,9.35205,8.28939,9.21132,8.86069,9.04469,9.08351,9.27078,9.16435,9.65488,9.12729,9.22051,8.62978,9.03893,9.15479,9.51346,8.96764,9.6946,9.47347,9.07294,8.96876,9.0518,9.06956,9.73582,9.21014,8.65587,9.13154
38
+ ELMO1,5.69464,6.127,6.50701,5.2535,5.83385,5.82466,5.27803,5.08865,5.19545,5.76971,6.36911,6.80008,5.98774,5.2883,5.10318,4.98262,5.91626,6.21771,5.67675,6.00907,5.87764,5.36544,7.01877,5.39433,5.71051,5.24152,5.43295,8.37726,6.42589,5.94772,4.69859,4.66386,6.24503,6.47514,5.67061,6.08337,5.75315,5.65982,6.25113,5.66302,5.72853,6.58652,6.78189,5.84935,5.85315,5.96399,5.25849,4.76626,5.2112,5.94492,7.21553,5.87976,6.80776,5.42418,5.96325
39
+ ELOA,3.589855,3.084455,3.628265,3.246535,3.333835,3.28374,3.845485,3.819495,3.46168,3.57715,3.531595,3.794545,3.423115,3.514235,3.843225,3.04055,3.335295,3.74496,3.33177,3.834455,3.764335,3.960315,3.8425,3.967815,3.83053,3.395835,4.12458,3.369175,4.0003,3.222015,3.042455,4.234475,3.80096,3.97633,3.843015,3.626915,3.96947,4.131415,4.0096,4.037175,4.13307,3.95074,4.067355,4.0923,4.218405,3.41542,3.50238,3.65208,3.84888,3.76904,4.24361,3.891225,3.886165,2.926265,2.584595
40
+ ELOC,3.589855,3.084455,3.628265,3.246535,3.333835,3.28374,3.845485,3.819495,3.46168,3.57715,3.531595,3.794545,3.423115,3.514235,3.843225,3.04055,3.335295,3.74496,3.33177,3.834455,3.764335,3.960315,3.8425,3.967815,3.83053,3.395835,4.12458,3.369175,4.0003,3.222015,3.042455,4.234475,3.80096,3.97633,3.843015,3.626915,3.96947,4.131415,4.0096,4.037175,4.13307,3.95074,4.067355,4.0923,4.218405,3.41542,3.50238,3.65208,3.84888,3.76904,4.24361,3.891225,3.886165,2.926265,2.584595
41
+ ENSA,17.21465,19.259149999999998,16.399949999999997,13.48935,13.96291,17.77001,16.4331,16.29075,18.4939,17.209850000000003,16.95304,17.54683,14.47451,17.20304,16.04256,17.35116,17.2155,14.53126,19.28286,16.81521,14.83135,15.8186,17.45183,9.37523,17.4915,13.849969999999999,16.9456,15.21021,17.271630000000002,15.26714,14.9949,14.59647,16.50003,16.53208,16.90776,16.944679999999998,13.62055,14.987870000000001,12.27985,15.56662,16.244419999999998,13.619900000000001,15.87773,17.50495,19.93871,18.05957,14.33785,18.25308,15.5466,15.60805,17.98403,13.20665,14.31965,14.98594,18.41702
42
+ EPPK1,5.07276,5.10413,5.56165,5.00571,5.17366,5.17458,5.38208,4.3947,4.7931,4.80241,4.90299,4.84706,4.88507,5.10825,4.57666,5.67219,4.85951,5.04365,5.11056,5.35364,4.86963,4.58618,4.82922,5.06046,5.19122,5.02546,5.17611,4.83964,4.78271,5.12003,4.98435,4.53098,4.9038,4.55435,4.8736,4.93575,4.76431,4.45192,5.61401,5.35797,5.21403,4.77941,4.9618,5.2378,5.3474,4.95289,4.85603,4.51906,4.93424,4.66757,4.90459,4.88215,5.05922,4.78014,4.74678
43
+ GCOM1,6.30029,5.610213333333333,5.913286666666667,5.837983333333333,6.3026,7.459256666666667,6.246076666666667,6.443116666666667,5.957613333333333,6.36773,6.6468,6.981746666666666,6.701686666666667,6.888136666666666,6.03748,4.336126666666667,6.082813333333333,5.7887699999999995,6.560853333333333,5.526,6.106626666666666,7.020483333333333,6.703663333333333,5.2143266666666666,6.584496666666666,5.846213333333333,6.072886666666667,6.34993,6.4493,6.209943333333333,5.755043333333333,5.7847133333333325,6.445286666666666,6.897966666666667,4.930816666666667,6.641916666666667,6.37407,6.060573333333333,6.12831,6.02926,5.503243333333334,6.51363,6.120453333333334,5.344366666666667,5.611163333333334,5.451856666666666,6.2292000000000005,5.795366666666666,6.427989999999999,6.200056666666667,6.62475,6.88153,5.60927,6.229423333333333,6.42572
44
+ GH1,4.5983,4.28981,4.27192,4.782,4.6099,4.60466,4.02995,4.19854,4.33986,3.91507,3.83121,4.20597,4.09542,4.31852,3.97942,4.63486,4.39553,4.71674,4.68595,4.44772,3.88931,4.207,4.39272,4.18832,3.72922,4.27753,4.04401,4.18167,3.98449,4.29261,3.76651,4.07669,4.33462,3.92606,4.62098,4.01844,4.20424,4.07024,4.71693,4.29284,4.13565,4.61298,4.37765,4.231,4.39221,4.27247,3.86404,4.44566,4.03894,4.08974,4.35119,4.22084,4.47872,3.90825,4.33523
45
+ GNAQ,7.20059,7.35029,6.19155,6.62728,6.89074,5.99112,7.34132,8.17844,6.20381,8.12997,8.26219,7.60442,7.19634,7.30939,7.79598,6.22688,7.03234,6.63554,6.20501,6.98838,7.63575,7.62732,6.83206,5.97231,7.5697,7.2963,6.94298,7.23103,7.4684,7.68236,6.54111,7.67951,6.63637,8.00996,7.33293,6.53447,7.64702,7.69595,6.89462,5.44107,7.63007,6.77831,7.42963,7.53442,7.22074,7.58646,7.07645,7.19387,7.13244,7.13372,7.55863,6.90996,8.11487,7.58082,6.80966
46
+ GPHA2,1.26943,4.03923,1.362305,1.30044,1.05539,1.213375,1.414045,1.397805,1.13786,1.209415,1.1038,1.18072,1.01403,1.06133,2.29645,1.504575,4.612045,1.574925,2.42099,1.319605,3.011985,1.14754,1.23199,1.738645,1.13736,2.34865,1.09164,1.577305,1.53316,1.04054,1.29438,1.290675,1.261975,1.2846,1.414045,1.121515,1.250625,1.019655,2.345975,1.001495,1.308125,1.15046,2.61509,1.30044,1.24856,2.832315,1.184245,1.187975,1.26879,1.10484,1.17268,1.30332,1.07604,1.545255,1.099615
47
+ GPX1,9.93875,10.9741,8.15548,11.6309,11.009,10.1798,11.0393,10.6407,9.68543,10.6104,11.1375,10.8902,10.7064,10.6725,9.67422,11.4357,11.1558,9.82802,10.4262,10.2463,10.3023,10.6402,10.1313,9.17688,10.188,11.2402,10.6523,10.7506,11.1738,9.72214,10.684,10.7652,9.48269,10.7501,10.6976,10.0145,10.5104,10.7871,10.2349,10.7983,10.7517,11.0391,10.8136,10.2624,9.75349,11.455,10.3438,10.4848,10.1952,11.0215,10.638,9.92139,10.3406,10.5072,9.6478
48
+ GUSBP4,11.662759999999999,13.68191,13.46317,9.68238,12.093589999999999,13.526309999999999,12.037410000000001,11.4566,13.69924,10.90771,12.453479999999999,11.13081,12.67928,11.82133,10.41186,12.5166,11.19493,11.93429,11.666360000000001,12.86857,11.78871,11.398119999999999,12.29803,12.95505,12.57362,12.59629,11.84416,12.705570000000002,12.57419,11.567499999999999,12.400210000000001,11.741900000000001,12.24408,10.280069999999998,11.891490000000001,12.12895,12.11131,12.15402,13.034569999999999,11.14381,12.24765,10.15828,12.21539,13.47315,12.15588,11.29675,13.029879999999999,12.40098,11.094529999999999,10.58318,12.71187,12.39097,12.143139999999999,12.76469,12.413730000000001
49
+ H4C14,4.24421,6.18943,4.2729,4.91225,2.65485,3.37685,4.44394,3.88298,4.77911,4.72454,6.13416,3.26923,4.02369,3.72551,4.9296,5.11541,4.4113,3.94571,5.48404,4.45824,4.69925,5.4369,4.83584,3.47925,5.18363,4.04572,4.20932,4.78969,4.66065,3.98593,4.98851,4.66335,3.28794,4.80654,4.90717,4.3306,5.35273,4.48535,3.13481,4.63225,5.27724,4.21735,3.81693,4.0826,4.70121,7.65827,4.47737,3.32192,4.56285,3.76181,3.95357,4.06318,4.36594,4.35403,3.31388
50
+ H4C15,4.24421,6.18943,4.2729,4.91225,2.65485,3.37685,4.44394,3.88298,4.77911,4.72454,6.13416,3.26923,4.02369,3.72551,4.9296,5.11541,4.4113,3.94571,5.48404,4.45824,4.69925,5.4369,4.83584,3.47925,5.18363,4.04572,4.20932,4.78969,4.66065,3.98593,4.98851,4.66335,3.28794,4.80654,4.90717,4.3306,5.35273,4.48535,3.13481,4.63225,5.27724,4.21735,3.81693,4.0826,4.70121,7.65827,4.47737,3.32192,4.56285,3.76181,3.95357,4.06318,4.36594,4.35403,3.31388
51
+ HHCM,3.82271,3.66842,4.01074,3.87445,3.8768,3.57863,3.74425,3.53342,3.50985,3.42918,3.34909,3.52213,3.41738,3.81325,3.62506,3.78829,3.75211,4.25452,3.83818,3.56016,3.93992,3.34697,3.62697,3.77929,3.22161,3.72545,3.61288,3.74479,3.47071,3.40696,3.81963,3.7397,3.68525,3.57596,3.82257,3.36592,3.28487,3.811,3.87674,3.69934,3.4581,3.6977,3.5381,3.53051,3.58533,3.10782,3.5729,3.52888,3.531,3.45538,3.95897,3.61457,3.38958,3.61144,4.10591
52
+ HMGA1,5.871354999999999,6.259774999999999,6.19632,5.911235,5.859285,5.56737,5.77655,5.80822,6.040495,6.19021,6.1384799999999995,5.7875700000000005,5.997085,5.7754449999999995,6.179025,5.740565,6.6073900000000005,6.31255,7.040755,6.1601,6.43511,6.0777350000000006,6.51839,5.46837,6.082065,6.352259999999999,5.866545,5.9800249999999995,5.633205,6.30463,5.83183,5.978315,6.03552,5.90709,5.992355,6.90946,5.699674999999999,5.861235,6.006265000000001,6.49311,6.39252,6.341315,6.33226,5.39578,6.396380000000001,5.78315,6.176935,6.37341,5.924575000000001,5.987875,5.41427,5.469405,5.846045,6.447735,5.93511
53
+ KCNMB3,12.365659999999998,13.16018,13.03941,13.06668,13.65718,12.777470000000001,14.31645,12.129280000000001,12.29045,11.948329999999999,12.41294,12.625129999999999,10.12246,11.17016,10.69928,13.206029999999998,12.80686,13.72369,12.78403,14.27771,12.825520000000001,11.99329,11.481060000000001,13.24384,14.50247,11.42433,12.4039,11.62171,12.794049999999999,11.382,12.684660000000001,11.69731,12.38993,14.30422,11.76774,10.815900000000001,13.0553,11.20995,11.70826,13.01768,13.71608,12.84192,13.14768,11.322230000000001,12.83655,12.49179,12.24833,11.75592,12.45088,12.45475,12.08156,12.14438,10.773150000000001,11.88635,11.916229999999999
54
+ KRTAP21-1,3.33848,3.30592,4.09827,3.90988,3.54255,3.22244,2.86288,3.37063,3.40426,3.65196,3.48202,3.21036,2.11904,2.88882,3.66415,4.27835,2.8199,3.07165,4.295,3.04586,3.11064,2.8199,3.09718,2.72179,3.7157,2.89434,3.22774,4.11505,2.6891,3.36251,2.34341,3.39774,2.986,3.28888,3.47908,3.01546,3.39208,3.19392,3.31659,2.35012,2.54244,4.31877,2.48663,3.8057,3.3648,3.63938,3.31659,3.52106,3.48831,2.8553,4.10153,3.00229,3.37066,3.2067,3.15585
55
+ KRTAP5-7,6.82663,6.75646,6.7752,6.87878,6.81741,6.54192,6.09008,6.3223,6.79005,6.33051,5.89449,6.27144,6.36281,6.03211,6.57772,6.60544,6.29952,6.87158,6.72578,6.28787,6.80041,5.97617,6.4761,6.51104,6.5615,6.28912,6.63854,6.91479,6.17251,7.04075,6.47867,6.51237,6.49881,6.39951,6.203,6.44693,6.5137,6.11105,6.30132,6.76355,6.44081,6.61704,6.14954,6.1688,6.56618,7.02912,6.81274,6.4289,6.38642,6.38261,6.28885,6.5298,6.06402,6.67852,6.88272
56
+ KRTAP5-8,5.95493,6.17379,6.2545,6.39737,6.1534,5.89652,5.73128,6.16758,6.05808,5.69617,5.48988,5.73708,5.91184,5.48246,5.9675,6.01349,5.96424,6.35431,6.08536,5.79486,6.42105,5.44814,6.25662,6.02184,6.45023,5.90767,5.9722,6.14418,5.68412,6.70425,5.97709,6.00302,5.48174,6.18755,5.95265,5.98499,5.86705,5.60485,5.74437,6.18065,6.13312,6.2001,5.70004,5.80366,6.19217,6.99362,6.2272,5.91736,5.80496,6.04967,5.90641,5.90483,5.48098,6.28866,6.42436
57
+ LILRA6,4.643795,4.311335,4.27933,4.6993849999999995,4.385365,4.20341,4.09865,4.221220000000001,4.661545,4.0580750000000005,4.125745,4.436485,4.34539,4.71889,4.13756,4.604795,4.125915,4.348485,4.814315000000001,4.58014,4.498365,4.202045,4.41113,4.808625,3.894655,4.15344,3.9893,4.482094999999999,4.335355,4.3694299999999995,4.420695,4.36236,4.098495,4.723515000000001,4.694295,4.003535,4.3276900000000005,4.172085,4.881315,4.4551549999999995,4.66354,4.284095000000001,4.277445,4.110125,4.443105,4.448225,3.9473900000000004,4.210955,4.145944999999999,4.328250000000001,4.716749999999999,4.434065,4.520595,4.5400849999999995,4.2650749999999995
58
+ LINC00652,3.01788,2.17977,2.69471,2.1298,3.3195,2.47708,2.79688,2.3296,3.02691,2.74794,3.03957,2.67983,2.38735,2.93886,2.53554,2.9512,2.14051,3.04148,2.68613,3.35644,2.40351,3.01876,2.70114,4.10075,2.65818,2.57284,2.56543,2.94542,2.5355,2.7552,2.82057,2.73521,2.42953,3.29885,2.69438,2.82632,2.50666,2.77923,3.17745,3.52395,3.07198,3.47085,2.7442,2.72631,2.36101,2.82929,2.41883,2.58583,2.35532,2.48546,3.19895,2.72778,2.7458,2.54608,2.55085
59
+ LINC01549,1.98323,1.30899,1.67958,2.01197,1.89848,1.88387,1.55302,1.52384,1.71505,1.79137,1.78903,2.09507,1.82251,1.78627,1.99752,2.31674,1.80952,1.66777,1.71593,1.96799,1.57797,1.63163,1.6723,2.19939,1.59221,1.37945,1.68171,1.57295,1.55341,1.54652,1.66625,1.69316,1.52576,1.71906,1.47967,1.71429,1.33299,2.16382,1.31695,1.71892,1.90246,1.68171,1.60115,1.61479,1.38416,1.52544,2.36593,1.68171,1.53792,1.6772,1.77019,1.71779,1.71906,1.6917,1.46563
60
+ METTL2B,11.01878,10.35535,11.08727,8.95081,9.351099999999999,11.91471,11.50297,11.93159,8.822659999999999,11.00297,10.89053,9.991389999999999,10.37738,10.09187,10.69263,8.46789,9.97334,11.61629,10.05462,10.28791,10.67446,11.079080000000001,13.41793,9.927050000000001,10.756219999999999,10.343250000000001,10.85908,10.735949999999999,11.975570000000001,11.67215,10.52609,9.14125,10.340910000000001,12.01941,10.66122,11.235050000000001,11.16643,12.325199999999999,9.88674,11.01681,10.743089999999999,10.0313,11.075289999999999,10.60528,8.11854,10.596910000000001,11.05137,11.681470000000001,11.35687,9.661249999999999,10.375440000000001,10.11242,9.86398,10.26022,10.73616
61
+ MPRIP,12.56934,13.12246,12.12871,14.564530000000001,12.43223,13.436060000000001,13.3503,13.83728,13.922429999999999,13.587990000000001,14.60422,13.87475,13.78621,12.967590000000001,12.88008,13.74373,14.71754,12.59172,12.513459999999998,13.422979999999999,13.51613,13.90193,14.27911,13.0064,13.530819999999999,13.3645,14.413219999999999,14.73243,13.9648,14.253160000000001,13.64169,12.82625,13.057870000000001,13.92429,14.647459999999999,14.280650000000001,12.745149999999999,13.450600000000001,11.71442,13.45777,13.90342,13.57503,13.8592,13.49399,13.68512,13.255099999999999,13.95835,13.85521,13.56361,14.61777,12.54584,13.27594,13.92976,12.83468,13.80501
62
+ MRGPRX3,0.835555,0.8313525,0.885315,0.8637875,1.03195,0.8221225,0.8342225,0.904215,0.842445,0.797125,0.85818,0.8915825,0.7423,0.8313525,0.894715,0.9325575,0.8786875,0.8633025,0.894715,0.82647,0.77447,0.8163625,0.9020775,0.8889675,0.8412375,0.8867175,0.7629475,0.8939725,0.7771925,0.7625375,0.759455,0.7903875,0.8573675,0.786135,0.93695,0.739215,0.7655675,0.790435,0.7701175,0.9125575,0.727325,0.82311,0.690785,0.747015,0.9123075,0.8010275,0.684925,0.8007025,0.8633025,0.82661,0.85984,0.85316,0.8313525,0.9341225,0.817065
63
+ MRPL36,4.343385,4.095675,4.37948,3.6961,4.39452,3.97606,4.34899,4.50977,4.174915,4.30167,4.22673,4.497825,4.420775,4.53185,4.06792,3.52504,4.199025,4.324375,3.921475,4.324375,4.06873,4.50679,4.0082,4.511935,4.282275,4.363225,4.671955,4.167455,4.14698,4.44912,3.31094,4.66464,3.9918,4.343285,4.30786,4.595785,4.20453,4.502845,4.317275,4.271965,4.973725,4.622945,4.45759,4.340585,4.313765,4.717545,4.36367,4.16514,4.258045,4.25331,4.47742,4.424605,4.395165,4.01333,3.652275
64
+ MTG1,9.925756666666667,9.432793333333333,9.621226666666667,9.463996666666667,9.620906666666666,9.49785,9.6738,9.983353333333334,9.279253333333333,9.803483333333332,10.117833333333333,10.237766666666667,9.777190000000001,9.739626666666666,9.81269,9.725933333333334,9.91506,9.815793333333332,9.99523,9.390943333333334,10.201266666666667,10.135133333333334,9.877613333333333,9.786023333333333,9.934660000000001,10.219966666666668,9.759689999999999,9.59639,9.532963333333333,9.978723333333333,9.74351,9.860473333333333,9.573193333333332,9.976099999999999,9.940873333333334,9.828233333333333,9.73548,9.900686666666667,9.55178,9.973743333333333,9.912886666666665,9.781846666666667,10.052546666666666,9.777786666666666,9.49371,9.759246666666668,9.75693,9.732776666666666,9.75967,9.9227,9.89989,10.081313333333334,9.84675,9.37607,9.670850000000002
65
+ MXRA7,24.78732,24.091920000000002,23.29421,24.659950000000002,26.55513,24.63986,24.311890000000002,24.36067,25.02689,23.687,25.10135,24.381990000000002,24.32877,24.604,25.50584,25.38965,24.16014,24.178800000000003,24.24482,25.430320000000002,24.85145,24.65932,25.06897,25.10663,23.761960000000002,25.4437,24.68799,25.10028,23.69171,23.54551,22.7762,25.67017,23.58243,24.93011,25.01295,24.33305,25.366319999999998,23.60006,25.278640000000003,24.96368,26.418779999999998,24.61365,24.32545,25.28887,25.20017,25.28165,24.26619,24.84554,24.80158,24.98085,24.2466,24.08459,24.871570000000002,24.58144,24.18368
66
+ NME2,18.841865,18.68745,18.349665,17.06017,19.161225,19.398285,20.45755,20.01054,19.84808,20.2461,19.821315,19.880445,20.004595000000002,19.88472,19.547105000000002,19.796585,19.06463,19.543075,20.0693,20.066905,20.289849999999998,19.852815,20.9393,19.84078,19.85015,20.20905,20.01522,20.39565,20.064735,19.573985,17.894935,20.2054,19.296509999999998,20.2578,20.07192,20.04164,20.2934,20.627200000000002,19.25694,19.95847,20.50415,19.682345,20.406399999999998,19.498725,19.922145,20.0836,19.958750000000002,20.50265,20.150100000000002,20.71195,20.438299999999998,20.84355,20.65275,16.6939,19.002485
67
+ NPM1,14.251655,14.133215,13.68862,13.719415000000001,14.137599999999999,14.285734999999999,14.103625000000001,14.801935,14.379439999999999,14.261555,14.7588,14.379435,14.244785,13.90757,14.818085,13.941939999999999,13.47837,14.136495,14.67354,12.738790000000002,14.54878,14.483135,14.377724999999998,14.185939999999999,14.654205000000001,14.331915,14.514125,13.818394999999999,14.09878,14.714245,14.097294999999999,14.661815,14.26021,14.216809999999999,13.540295,14.46821,14.366225,14.58157,14.13934,14.675865,14.796800000000001,14.029375,14.819009999999999,14.27214,14.18502,13.773354999999999,14.93561,14.999405,14.319345,14.23413,14.01944,14.703264999999998,14.322289999999999,14.74614,14.81688
68
+ NT5C3A,5.33465,5.5931999999999995,3.299525,4.3969249999999995,4.58444,4.918365,6.032085,5.56406,5.58385,5.539485,5.395205,6.01288,6.156875,4.806595,5.256915,4.76691,5.578245,4.5098199999999995,6.051935,4.7082049999999995,5.067445,5.378575,5.57587,5.63144,5.64006,4.741405,5.64478,4.6095299999999995,5.541275000000001,5.68923,4.827505,4.755755,5.215685000000001,5.245205,4.3876,6.061065,5.13186,5.11139,4.595515,4.913914999999999,5.9855149999999995,5.715555,5.2201450000000005,4.76166,5.952500000000001,4.8918099999999995,6.578049999999999,5.7416149999999995,5.147355,6.756425,6.16033,5.48836,4.766465,4.596025,5.222799999999999
69
+ NTM-AS1,3.91376,3.28804,4.0074,3.8193,3.80255,3.31977,3.46987,3.30752,3.26895,3.48801,3.47146,3.39389,3.58211,3.64834,3.32445,3.92353,3.6645,3.82965,3.95716,3.91371,3.0783,2.94752,3.54361,3.61456,3.15838,2.98271,3.15222,3.82142,3.14128,3.48496,3.51458,3.87041,3.1332,2.87281,3.46536,3.35443,2.99464,3.50488,4.15798,3.62281,3.62418,3.79747,3.03249,3.31237,3.49583,4.10729,3.33223,3.13163,3.3008,3.31974,3.51169,3.5138,3.51452,3.42451,3.94496
70
+ OBP2A,3.68216,3.6871,3.47313,3.82535,3.33858,3.44056,2.90091,3.27656,3.08625,2.99551,2.83236,3.20612,2.90111,3.3343,3.07147,3.62128,3.48397,3.10336,4.31493,3.24388,2.93913,3.43119,3.01279,3.66205,3.22932,3.09246,2.91768,3.5269,3.06265,3.16362,2.95641,2.75369,2.99684,2.90232,3.14367,3.51498,2.85845,3.03593,3.35309,3.40588,3.07053,3.22131,2.7477,3.06694,3.54584,3.17325,3.1122,3.05594,3.17728,2.82055,2.91856,3.35098,2.93436,3.29657,3.20156
71
+ OBP2B,4.67206,4.91092,4.61828,5.07643,4.64732,4.56121,4.18817,4.20602,4.29997,4.18316,3.97525,4.21423,4.00691,4.71191,4.04921,4.95951,4.58491,4.56539,5.68812,4.78218,4.05575,4.22118,4.32954,4.91997,4.36406,4.31726,3.9739,4.87039,4.22513,4.31414,3.96764,3.88779,4.14026,4.13445,4.4983,4.69281,3.81689,4.63004,4.75297,4.94703,4.25182,4.46429,3.9827,4.24582,4.74264,4.29007,4.36046,4.33508,4.24819,3.84576,4.07021,4.52395,4.16521,4.34302,4.47532
72
+ OR3A2,3.5307,3.64406,3.45876,4.11226,3.54634,3.40247,3.43885,3.17447,4.02028,3.26642,2.93175,3.40593,3.19135,3.59501,3.2546,3.76953,3.35312,3.58915,3.98354,3.57015,3.57557,3.06642,3.45876,4.05599,3.37636,3.45876,3.40601,4.01271,4.67846,3.42253,3.16392,3.14989,3.48174,3.30857,3.64062,3.00878,3.35048,3.49094,3.75,3.36995,3.29565,3.18568,2.96683,3.5826,3.8259,3.37878,3.73655,3.55261,3.39645,3.30393,3.60053,3.60574,3.1184,3.77253,3.25118
73
+ OR4N4,2.42284,2.25969,2.39866,2.65896,2.6167,2.31743,2.22196,2.06775,2.38809,2.28695,2.2307,2.27622,2.08782,2.12343,2.29433,2.67542,2.2323,2.98326,2.56785,2.65922,2.37064,2.2256,2.24257,2.80248,2.69438,2.2149,2.19452,2.88164,2.12019,2.16583,2.49703,2.30463,2.12853,1.92184,2.45431,2.10049,2.24842,2.24908,2.57637,2.18502,3.01941,2.10213,2.15362,2.13388,2.5688,2.42523,2.4391,2.42103,2.29617,1.96185,2.21134,2.48219,2.13769,2.14112,2.28644
74
+ OR8G1,2.59564,2.66077,2.09479,2.54377,3.12206,2.25707,2.45222,2.13515,2.78057,2.27522,2.23395,2.96027,1.88215,2.59592,2.28796,3.0822,2.43207,2.43528,3.05473,2.85091,2.67566,1.87909,2.62661,2.61671,2.33936,2.51128,1.87732,2.83707,2.15611,2.43152,2.48709,2.38472,2.17217,2.69069,2.24724,2.38929,2.54355,2.451,2.68191,2.06041,2.5222,2.28782,1.64087,2.07048,2.21899,2.36948,2.05152,2.63607,2.34909,2.63876,2.36421,2.64129,2.1838,1.9524,2.55265
75
+ PCDHA1,3.66224,4.40636,3.82023,4.80926,3.74819,4.30092,3.42377,4.2481,3.60701,3.52832,3.39039,4.39694,4.20239,3.49328,3.53313,5.0447,4.46877,3.94467,3.85387,3.45902,3.10401,2.91909,3.72765,4.02478,3.78736,3.89305,4.01112,3.28289,3.17159,4.21436,3.84182,3.9758,3.59287,3.15788,3.45561,3.29029,3.29864,3.77913,3.56839,4.57615,3.90034,4.18854,3.14508,4.33604,3.79435,4.28172,3.61266,3.70609,3.49771,3.3991,3.75712,3.96909,3.39807,3.50328,3.98465
76
+ PCDHA10,2.98755,4.13854,3.45578,4.1433,3.94253,4.13665,3.40375,3.64103,3.40746,4.22639,3.30065,3.51767,3.46307,3.15162,4.10456,5.32021,4.85619,3.54633,4.72249,3.66942,3.65677,2.90366,3.4939,3.83759,3.51465,3.50104,4.03801,3.39428,3.48162,3.70297,3.53776,3.84399,3.90042,3.60834,3.09899,3.14558,3.16894,3.59953,3.21734,4.04544,5.75176,3.68685,2.94177,3.55188,3.68933,4.08796,4.66971,2.99315,3.8241,3.58235,3.56043,3.71223,3.66308,3.37509,3.90194
77
+ PCDHA11,1.96583,2.92458,2.10938,2.65967,2.40492,2.41774,2.766,1.86033,2.19104,2.30119,1.93874,1.823,2.60671,1.68999,2.64707,2.60792,2.73126,2.46213,2.26775,2.26216,2.52567,1.59347,2.35449,2.73564,1.9227,2.12195,2.54927,2.11508,2.18747,2.72931,2.27338,2.10748,2.62056,2.22923,2.31036,2.03542,2.10357,2.16802,2.31036,2.63818,3.34715,2.2495,2.58188,2.88673,2.43585,2.31491,2.57041,1.826,2.74447,2.23595,2.21403,2.4089,2.26117,2.12161,2.31036
78
+ PCDHA12,3.45477,4.53356,3.61313,4.06089,4.09988,4.13556,3.66276,3.90177,3.5972,4.00956,3.54831,3.32676,3.78101,3.19296,3.99746,4.15222,4.04707,3.84784,4.39778,3.46735,4.02705,3.09153,3.54779,4.0428,4.13762,3.97964,4.19536,3.42435,3.84217,3.91318,4.34872,3.60886,3.81412,3.73061,3.47426,3.6639,3.37056,3.68579,3.8115,4.13956,4.89036,3.81412,3.66658,3.28067,3.81412,3.81605,4.73872,3.28632,4.21812,3.62959,3.5764,4.06532,3.5828,4.10056,4.5841
79
+ PCDHA13,4.5138,5.6017,4.81945,5.70228,4.64562,5.30714,4.45212,5.46301,5.23467,5.21703,4.30989,4.86605,5.10568,4.25016,5.19899,5.20138,5.1192,4.75619,5.66806,4.91081,4.53122,4.15365,4.91467,4.94335,5.02306,5.42705,4.97772,4.49249,4.57957,4.72865,5.21314,5.00581,4.75677,4.61124,4.87054,4.72191,5.07412,4.8493,4.87054,4.71769,5.88659,5.37265,4.31955,4.78367,5.0492,5.27147,5.25734,4.79339,5.20178,4.88103,4.77421,5.12174,4.71288,5.34463,5.1397
80
+ PCDHA2,5.41841,6.0062999999999995,5.00898,6.18679,5.39307,5.26801,4.81795,4.17723,5.651009999999999,5.0738,4.78642,5.28195,4.739269999999999,4.64326,5.19371,5.22712,5.69759,5.3986,6.85098,5.048159999999999,4.47996,4.18893,5.0479199999999995,5.93338,5.01992,5.18706,5.02374,4.968870000000001,4.584770000000001,4.84836,5.17202,5.68296,5.48292,5.24862,5.2003900000000005,4.68746,5.26203,5.22129,6.337009999999999,6.13782,5.470549999999999,6.0415,4.8899799999999995,5.26809,5.57302,5.90125,5.43818,4.85901,5.32286,4.96781,5.03778,5.680070000000001,4.88213,5.1366700000000005,5.24854
81
+ PCDHA3,4.97566,5.63485,5.37436,5.61961,5.38463,4.99523,4.57733,4.5021,4.94837,4.98118,4.37973,5.09785,4.63276,4.44471,5.51549,5.4058,5.42598,5.08035,6.00809,5.07686,4.98063,4.24506,5.08143,5.70586,5.22022,5.00228,5.32417,5.16332,4.83169,5.25198,5.09037,5.07546,5.35997,4.92395,4.94203,4.60723,4.62414,4.81741,5.44764,5.39551,5.67853,5.2845,4.51671,5.10135,5.19057,5.56771,5.9923,4.27129,5.0291,4.39785,5.03232,5.3189,4.9664,5.32511,5.10276
82
+ PCDHA4,3.56473,4.13854,3.6125,3.27996,4.5557,3.92547,3.46561,3.09379,2.89987,4.19605,3.44978,3.75044,3.35374,3.2937,3.84011,5.19876,4.79534,3.60337,4.59716,3.60857,3.50577,3.04965,3.67328,4.07173,3.60758,3.59397,3.90772,2.85721,3.0132,3.19507,4.19137,3.63814,4.03602,3.43834,3.23044,3.05993,3.71841,3.59953,4.3117,4.92845,4.96445,3.43089,3.52675,3.69042,3.92335,4.24231,3.7402,3.19624,4.02913,3.5377,3.62139,3.71223,3.43996,3.46802,4.09544
83
+ PCDHA5,4.4599,5.95434,5.18547,5.81031,4.44647,5.1484,4.91588,4.76996,4.96717,5.16585,4.40017,4.98269,4.51204,4.06518,5.33244,5.43856,5.54838,4.82776,5.24866,4.37943,4.64126,4.21381,4.73064,5.59433,5.16094,4.92174,5.27645,4.79488,5.12012,4.92747,5.46146,5.21486,5.30969,4.75794,4.65858,5.26939,4.49933,4.96485,5.13535,5.14074,5.8129,4.77212,4.34804,5.43293,4.86926,5.51203,4.93126,4.67681,5.33361,4.6312,4.84301,5.07981,4.51965,4.92719,5.39867
84
+ PCDHA6,3.5316,4.48003,3.71677,3.99701,3.90559,3.94888,3.32052,3.6407,3.60056,3.56621,3.322,3.73566,3.51449,3.38593,3.95759,5.0461,3.84224,3.90643,4.28157,3.71392,3.41481,3.21295,3.57906,3.85384,3.4243,3.76732,3.98203,3.33403,3.47057,4.08085,4.25645,3.879,3.71826,3.64485,3.38684,3.54143,3.30284,3.68044,3.69395,4.81701,4.86479,3.77332,3.35819,3.7808,3.67724,3.79377,3.82646,3.5922,4.43024,3.71123,3.49883,3.84545,3.92235,3.45995,4.27327
85
+ PCDHA7,4.06201,4.28267,3.53021,4.47562,4.13119,3.95309,3.43334,3.50869,3.72467,4.38321,3.28334,3.84399,3.41234,3.48093,3.98786,5.15202,4.09266,3.61428,4.57812,3.22512,2.89224,3.13848,3.85773,4.48216,3.66557,4.02123,4.10047,3.43449,3.60799,3.86784,3.55532,4.03122,4.12379,4.1503,3.02605,3.11793,3.27927,4.09366,4.68579,4.09951,4.48659,3.84399,3.6273,4.06082,3.84026,4.48118,3.62775,3.76684,3.51946,3.25461,3.71024,3.96858,4.081,3.52306,4.17357
86
+ PCDHA8,4.99922,5.35903,5.07861,5.05949,5.39415,5.27241,4.67819,5.09126,5.02653,4.67121,4.59383,4.86466,4.9158,4.90245,5.09126,5.14184,5.36036,5.20318,5.44746,4.60407,5.24141,4.26864,4.65883,5.09864,5.43862,5.42718,5.25795,4.71856,4.84514,4.98694,5.53857,5.00985,5.23845,4.93262,5.07396,5.49295,4.74253,5.39552,5.09126,5.81575,5.72374,5.05949,4.94372,5.00255,5.23939,5.78557,5.28615,4.98105,5.47836,5.15635,5.05588,5.14132,4.86935,5.35097,5.16923
87
+ PCDHGB4,6.67887,5.58097,5.72673,4.95316,5.56221,4.86781,4.88481,6.07543,5.24512,5.05465,4.82421,5.13548,5.15061,4.35999,6.63807,6.03695,4.36046,5.12678,5.40899,4.92572,5.1641,4.58253,5.17083,4.61466,4.78698,6.93383,4.72373,5.43521,4.43848,5.06194,4.05939,4.49465,4.80482,5.04821,4.69215,6.30936,4.2701,5.58692,5.60312,5.70551,6.30196,4.77627,4.76622,4.63199,5.12961,5.21072,5.46806,4.81087,5.801,4.55834,5.4954,5.24343,4.91305,4.40588,4.98486
88
+ PCSK4,2.30447,2.0362133333333334,2.0822333333333334,2.20925,2.0160066666666667,2.3184,2.3787,2.2737933333333333,2.23791,2.19255,2.2450566666666667,2.0967733333333336,2.5444066666666667,2.1346633333333336,2.369616666666667,2.24329,2.166376666666667,2.1503633333333334,1.99102,1.88688,2.2521066666666667,2.094,2.1875433333333336,2.0041366666666667,2.3871100000000003,2.1614733333333334,2.5408933333333334,1.9387400000000001,2.2215966666666667,2.1852066666666667,2.4047733333333334,2.214933333333333,2.4608433333333335,2.29094,2.1084033333333334,2.1832,1.98933,1.8515533333333334,2.1593633333333333,2.31816,2.6671866666666664,2.1940633333333333,2.1874966666666666,2.2655533333333335,2.0895966666666665,2.0264233333333332,2.4517666666666664,2.50258,2.197,2.2385533333333334,2.11936,2.39607,2.0738066666666666,2.5905366666666665,2.556083333333333
89
+ PDE4DIP,4.51372,4.66921,3.77557,4.22951,4.68536,4.91069,4.60756,4.93244,5.33367,5.77942,5.77408,4.94574,4.41938,4.45417,5.39893,5.05345,6.21507,4.83493,4.48755,4.82046,4.57312,5.03965,4.78097,4.75662,4.03614,4.71638,4.4032,4.61551,5.27497,5.37805,5.12833,4.81897,4.22216,5.01977,4.93863,4.77455,4.98296,4.87573,5.96183,4.65049,4.81653,4.65845,5.27124,4.50972,4.74758,5.6666,4.40218,5.00554,4.74378,4.64125,5.06675,5.09526,5.67351,5.19383,4.84826
90
+ PIGF,8.6521,8.58496,8.72198,7.20812,9.6502,9.65484,9.55024,8.43198,6.94502,10.7992,9.73738,8.55476,8.95952,9.64408,8.51804,8.23128,8.49808,8.65582,6.76188,9.94632,8.81676,9.78186,9.27976,9.55978,8.10812,9.56186,8.87122,7.57078,8.70686,7.71444,7.80884,9.87694,9.44622,11.94536,9.28384,8.26886,9.22656,10.13504,10.1822,8.90534,8.99324,11.41784,9.20842,9.51656,9.33284,8.55072,8.76422,9.0345,8.99948,9.35012,9.00248,8.61146,9.90948,8.20098,8.70734
91
+ PPIP5K1,4.89016,5.16014,5.71214,4.54351,6.07634,5.82475,6.28604,5.04876,4.83047,4.68799,5.91109,5.51671,5.74781,5.88616,5.51483,4.78516,5.20447,5.87572,4.23077,5.47808,4.45993,5.4115,4.958,5.41394,5.15584,5.14408,5.87988,5.84053,5.53063,5.11622,5.30638,5.38724,5.09669,5.50824,5.79691,5.20386,5.89179,5.8439,5.35459,4.85363,5.39174,5.60213,5.76131,5.25469,5.40804,6.17908,5.65805,5.43194,6.06714,5.49273,5.02858,3.84706,5.81359,5.22395,5.08989
92
+ PSIP1,2.30447,2.0362133333333334,2.0822333333333334,2.20925,2.0160066666666667,2.3184,2.3787,2.2737933333333333,2.23791,2.19255,2.2450566666666667,2.0967733333333336,2.5444066666666667,2.1346633333333336,2.369616666666667,2.24329,2.166376666666667,2.1503633333333334,1.99102,1.88688,2.2521066666666667,2.094,2.1875433333333336,2.0041366666666667,2.3871100000000003,2.1614733333333334,2.5408933333333334,1.9387400000000001,2.2215966666666667,2.1852066666666667,2.4047733333333334,2.214933333333333,2.4608433333333335,2.29094,2.1084033333333334,2.1832,1.98933,1.8515533333333334,2.1593633333333333,2.31816,2.6671866666666664,2.1940633333333333,2.1874966666666666,2.2655533333333335,2.0895966666666665,2.0264233333333332,2.4517666666666664,2.50258,2.197,2.2385533333333334,2.11936,2.39607,2.0738066666666666,2.5905366666666665,2.556083333333333
93
+ RAB7B,2.04024,2.027785,1.96921,2.210275,2.20528,1.973,1.85312,1.86012,2.032885,1.87896,1.84876,1.863535,1.849055,1.87784,1.99822,2.21857,1.96225,2.047,2.051095,2.05161,2.012885,1.88194,1.914385,2.080025,1.87061,1.935495,1.883775,2.08194,1.85804,1.905935,1.956435,1.990175,1.943185,1.829395,1.98601,1.887475,1.973,1.87938,2.13906,1.994815,1.9353,1.930065,1.911645,1.909865,1.95611,1.97774,2.03916,2.03992,2.028865,2.109395,1.883575,2.23802,1.91501,2.04232,2.10249
94
+ RAC1,9.925756666666667,9.432793333333333,9.621226666666667,9.463996666666667,9.620906666666666,9.49785,9.6738,9.983353333333334,9.279253333333333,9.803483333333332,10.117833333333333,10.237766666666667,9.777190000000001,9.739626666666666,9.81269,9.725933333333334,9.91506,9.815793333333332,9.99523,9.390943333333334,10.201266666666667,10.135133333333334,9.877613333333333,9.786023333333333,9.934660000000001,10.219966666666668,9.759689999999999,9.59639,9.532963333333333,9.978723333333333,9.74351,9.860473333333333,9.573193333333332,9.976099999999999,9.940873333333334,9.828233333333333,9.73548,9.900686666666667,9.55178,9.973743333333333,9.912886666666665,9.781846666666667,10.052546666666666,9.777786666666666,9.49371,9.759246666666668,9.75693,9.732776666666666,9.75967,9.9227,9.89989,10.081313333333334,9.84675,9.37607,9.670850000000002
95
+ RALGAPA1,12.59374,14.37766,13.67262,12.53508,12.37294,13.88194,14.58606,15.12004,14.63458,14.0103,14.90218,13.39258,13.92064,14.1392,13.94342,12.54058,13.82946,13.87194,12.49946,11.27736,13.09392,14.20006,13.85076,14.30168,14.22772,13.6507,14.5546,13.9524,14.90752,14.24888,14.8592,14.37102,14.33052,14.53252,14.46164,14.2294,14.74846,14.14086,11.56376,10.83946,12.58794,13.87288,13.63416,14.2671,13.57816,12.26236,13.64772,13.9591,13.36114,13.95762,14.0103,14.37718,14.17158,14.99604,14.1045
96
+ RBM8A,7.89615,6.23723,6.52344,5.49577,6.46439,7.24858,7.01634,7.41175,6.57185,6.82535,6.71809,7.09115,6.48776,6.85413,7.52164,7.41041,6.8783,6.67893,7.15833,6.54249,7.55083,7.1832,7.69688,6.60486,6.56081,6.91364,7.09465,6.26646,6.88198,6.97722,6.46301,7.64205,7.07372,7.59258,6.42319,6.67041,7.32295,6.97642,6.45244,7.11634,7.56073,6.84242,7.23552,7.09115,7.62739,6.50944,6.86157,6.5576,7.20736,7.36785,7.14601,7.50515,7.212,6.55883,7.11216
97
+ RFPL2,4.00459,3.9304,3.40998,3.24955,4.59711,3.86052,3.21496,3.58348,3.4023,4.52888,3.45708,3.50876,2.96619,3.37971,3.23628,3.46272,3.57334,3.08284,3.33354,3.82792,3.2679,3.30725,3.22353,3.48384,3.36796,3.06625,3.71386,3.618,3.47442,3.50876,3.13113,3.52796,3.80974,3.50631,3.65237,3.41474,3.95442,3.85681,3.43101,3.01785,3.03158,3.20815,3.50113,3.22799,3.54205,3.21813,3.24983,4.45108,3.93603,3.73798,4.00054,3.12893,3.32171,4.07233,3.35084
98
+ RFPL3,3.93219,3.71078,3.33758,3.54195,4.56051,3.86724,3.15088,3.63359,3.30025,4.78428,3.41568,3.53042,2.99145,3.10912,3.1375,3.52372,3.32175,3.23933,3.26114,3.4781,3.41144,3.33372,3.14802,3.57853,3.18885,3.0116,3.70314,3.53647,3.41144,3.34426,3.1042,3.54187,3.54788,3.46545,3.41144,3.41144,3.90916,3.92581,3.26444,3.22283,3.34444,3.06088,3.43391,3.0326,3.55565,3.08057,3.2561,4.37857,3.83725,3.6153,3.63315,3.08813,3.32058,3.87322,3.24468
99
+ RNF135,10.3731,9.88096,9.158895000000001,10.632549999999998,10.36085,10.701350000000001,10.4132,10.59815,10.7564,10.96025,10.25015,10.82065,11.0174,10.44665,11.5445,9.914985,10.3322,10.7512,10.7534,10.296050000000001,10.6087,10.358799999999999,11.0598,10.66325,10.960049999999999,10.7513,10.67845,10.00233,10.597349999999999,10.8205,9.38446,10.7117,10.51425,10.8468,10.75095,10.9086,10.76725,10.9082,10.25105,10.5307,10.54765,10.050464999999999,10.61635,10.39805,10.42465,10.17023,10.920300000000001,10.57245,10.626000000000001,10.801400000000001,10.693249999999999,10.78585,10.602049999999998,8.76033,10.3372
100
+ RPL13,10.3731,9.88096,9.158895000000001,10.632549999999998,10.36085,10.701350000000001,10.4132,10.59815,10.7564,10.96025,10.25015,10.82065,11.0174,10.44665,11.5445,9.914985,10.3322,10.7512,10.7534,10.296050000000001,10.6087,10.358799999999999,11.0598,10.66325,10.960049999999999,10.7513,10.67845,10.00233,10.597349999999999,10.8205,9.38446,10.7117,10.51425,10.8468,10.75095,10.9086,10.76725,10.9082,10.25105,10.5307,10.54765,10.050464999999999,10.61635,10.39805,10.42465,10.17023,10.920300000000001,10.57245,10.626000000000001,10.801400000000001,10.693249999999999,10.78585,10.602049999999998,8.76033,10.3372
101
+ RPL27A,11.0575,10.4232,10.3673,10.928,10.5848,11.0409,10.8436,11.1434,11.3297,11.6731,10.6617,11.2614,11.2995,11.0861,11.2296,10.2941,10.8457,11.0787,11.2631,11.1179,11.4497,11.1106,11.3238,11.0799,11.4101,11.2526,11.133,10.7707,11.1298,11.2047,9.41416,11.0926,11.0602,11.4135,11.1411,11.4221,11.3348,11.4447,10.7313,11.2355,10.2177,10.4458,11.1626,11.1745,11.0942,11.3597,11.3407,11.1999,11.0424,11.276,11.2848,11.6612,11.1812,9.67762,10.7134
102
+ RPL31,5.7526,5.70275,5.808,5.60355,5.70135,5.82085,5.77545,5.88775,5.841,5.89945,5.79925,5.92355,5.9119,5.82395,5.8741,5.62105,5.7117,5.6947,5.8503,5.6977,5.83745,5.84425,5.9585,5.8203,5.94525,5.85195,5.9048,5.66335,5.7886,5.9064,5.64835,5.89455,5.904,5.96115,5.80125,5.91665,5.8822,5.94445,5.7447,5.8394,5.80965,5.80855,5.86675,5.8197,5.8077,5.75505,5.9226,5.8912,5.85505,5.9264,5.8847,6.03,5.8208,5.5979,5.80965
103
+ RPL36,4.343385,4.095675,4.37948,3.6961,4.39452,3.97606,4.34899,4.50977,4.174915,4.30167,4.22673,4.497825,4.420775,4.53185,4.06792,3.52504,4.199025,4.324375,3.921475,4.324375,4.06873,4.50679,4.0082,4.511935,4.282275,4.363225,4.671955,4.167455,4.14698,4.44912,3.31094,4.66464,3.9918,4.343285,4.30786,4.595785,4.20453,4.502845,4.317275,4.271965,4.973725,4.622945,4.45759,4.340585,4.313765,4.717545,4.36367,4.16514,4.258045,4.25331,4.47742,4.424605,4.395165,4.01333,3.652275
104
+ RPL4,5.11045,5.18755,4.69091,5.001,4.99508,5.0911,5.0503,5.3236,5.24665,5.33625,5.219,5.2215,5.34085,5.08165,5.21725,4.99053,5.09215,5.2299,5.2265,4.997715,5.2024,5.1528,5.34895,5.20815,5.3595,5.3039,5.3362,5.24355,5.2628,5.31275,5.05565,5.22895,5.2939,5.3105,5.23335,5.3501,5.3057,5.3358,4.985075,5.16155,5.193,5.05175,5.1855,5.18815,5.17265,5.0167,5.3747,5.26635,5.1931,5.2887,5.2407,5.25485,5.1598,5.1524,5.2644
105
+ RPL7L1,3.3626,4.267415,3.600135,3.59501,3.86277,3.399635,3.62156,3.933595,3.789915,3.83155,3.976355,3.740055,3.758805,3.332965,3.581775,4.14149,3.718515,2.42802,4.473975,3.373045,4.176125,3.786235,3.615445,2.984035,4.058665,3.53923,3.818055,3.208315,4.372175,3.7396,3.945535,4.271425,4.12536,3.641065,3.18627,4.07969,3.845995,3.414325,3.7396,4.17492,3.99154,3.57806,3.43678,4.09328,3.56319,3.28656,3.515195,4.23529,3.939155,3.864355,3.723635,3.34621,3.370325,4.017815,3.756685
106
+ RPLP1,5.91165,5.92605,5.7631,5.90595,5.81135,5.9799,5.9725,6.10825,6.02815,6.09555,6.006,6.1496,6.0652,6.08715,5.9023,5.6933,5.9528,6.0292,6.0418,6.03205,6.02205,5.98705,6.08215,6.01645,6.1174,6.13775,6.0901,6.00135,6.0092,6.0143,5.84635,6.0358,6.00335,6.14995,6.07665,6.10235,6.1503,6.1531,5.908,6.03745,5.96925,5.9806,6.07675,6.06235,5.9906,6.0461,6.0946,6.07305,5.9819,6.07645,6.01495,6.06695,6.00555,5.72915,5.89335
107
+ RPS9,4.95138,4.74731,4.306995,4.843645,4.90163,4.72592,4.958505,5.12935,5.209,5.2476,4.95675,5.44425,5.25205,5.10585,5.2355,4.497155,5.21415,5.264,5.2449,5.27565,5.2894,5.1968,5.30275,5.274,5.3276,5.3535,5.208,5.09575,5.1083,5.35425,4.623685,5.224,5.29835,5.2259,5.3156,5.2557,5.39045,5.46605,5.089,5.02305,4.79638,5.3424,5.2484,5.2478,5.23645,5.2046,5.41455,5.17815,5.18035,5.24485,5.3532,5.26365,5.22615,4.20751,4.839755
108
+ SAA2,1.26943,4.03923,1.362305,1.30044,1.05539,1.213375,1.414045,1.397805,1.13786,1.209415,1.1038,1.18072,1.01403,1.06133,2.29645,1.504575,4.612045,1.574925,2.42099,1.319605,3.011985,1.14754,1.23199,1.738645,1.13736,2.34865,1.09164,1.577305,1.53316,1.04054,1.29438,1.290675,1.261975,1.2846,1.414045,1.121515,1.250625,1.019655,2.345975,1.001495,1.308125,1.15046,2.61509,1.30044,1.24856,2.832315,1.184245,1.187975,1.26879,1.10484,1.17268,1.30332,1.07604,1.545255,1.099615
109
+ SINHCAF,4.232175,5.68051,5.081305,4.346535,4.26169,5.80755,5.590765,5.9872700000000005,5.523804999999999,5.684875,6.05518,5.928,5.6623149999999995,4.893095,6.498305,4.669795,5.94356,5.373865,6.0947499999999994,5.742592500000001,6.00509,6.00643,6.639085,4.80042,5.9602699999999995,5.573465,5.8095300000000005,4.6669,5.38195,6.297185000000001,4.766855,5.728680000000001,6.034775,5.973255,5.125145,5.375245,5.64821,5.814775,4.6738325,5.40385,5.52823,5.398535000000001,5.47123,5.879425,5.90967,4.862220000000001,5.912649999999999,5.3705099999999995,5.1260200000000005,5.837265,5.8032,4.960695,5.443155,5.781015,5.83945
110
+ SKP1,4.49086,4.709805,4.88252,4.762,4.919795,4.904865,5.0223,4.899825,4.7415,4.79659,4.90249,4.833295,4.554515,4.90417,4.94867,4.858025,4.683775,4.53937,4.684635,4.661635,4.888125,4.980225,5.00985,5.10075,4.79462,4.86458,4.77924,4.492775,4.686455,4.60004,4.594,4.949985,4.779845,4.98912,4.72287,4.78483,4.943575,4.893665,5.06865,4.908145,4.976245,4.96128,5.00665,5.0097,5.0348,4.932615,4.914345,4.936445,4.929925,4.96462,4.8948,5.0142,4.996955,4.55505,4.60146
111
+ SLC25A51,6.31253,5.32326,3.88704,3.68946,3.52349,4.09669,5.09436,3.93779,4.39991,3.70709,4.36295,3.741,4.35019,3.30167,4.0392,4.05959,4.09041,3.90138,4.68326,3.82953,3.59062,4.30242,3.50904,3.57146,4.08912,3.7179,4.62631,3.65955,3.79699,4.70751,4.5012,3.96161,4.18008,5.14704,4.29392,4.6531,4.18008,4.99708,4.23237,3.8169,3.60781,4.62683,4.39644,3.59258,3.81517,4.13898,4.24194,5.36555,4.6409,5.28465,4.06207,4.72864,4.31048,3.75823,4.20457
112
+ SLC35E2B,2.467295,2.449055,2.80296,2.771465,2.58749,2.3436,2.41159,2.29665,2.63156,2.794,2.6211,2.417855,2.34347,2.7036,2.01067,2.38172,2.85014,2.666595,2.351765,2.97382,2.22876,2.68371,2.247085,2.40928,2.28538,2.335595,2.45074,2.310235,2.66226,2.539695,2.74329,2.37125,2.50662,2.575215,2.08489,2.93086,2.6609,2.457725,2.8161,2.407795,2.829735,2.14678,2.44353,2.651115,2.47597,2.98612,2.866915,2.4176,2.523205,2.28506,2.209595,2.422,2.40729,2.413535,2.503685
113
+ SLC4A1,2.330445,3.274275,3.48838,2.83536,3.22695,3.381965,3.35236,3.485915,2.976695,3.118135,3.443815,3.274275,3.274625,3.056295,2.60251,2.76576,3.42575,3.2619,3.282845,3.70403,3.22392,3.440005,3.33965,2.63525,3.67183,3.245635,2.91995,3.520135,3.27364,3.03944,3.415785,3.300075,3.35167,3.6158,3.177045,3.108855,3.325185,3.364005,2.863295,2.953975,3.53733,3.323875,3.19193,3.104835,3.470165,3.55104,3.255565,3.34841,3.51408,3.373255,3.514265,2.882855,2.91704,3.030965,3.14935
114
+ SLX9,5.00247,5.31064,4.92955,5.34474,5.53377,4.67356,4.89447,5.0822,4.61258,5.85494,4.65396,5.70248,5.20727,5.46626,4.31866,5.42419,5.31019,5.72026,5.18361,5.62692,5.06827,5.6048,4.88101,6.29705,5.63828,5.76537,5.87699,5.12628,4.89568,4.95886,4.77205,5.59169,4.15136,4.94762,5.21184,5.22283,5.30485,5.46996,5.61732,5.16507,4.9994,5.97793,5.35164,6.16108,5.22509,5.5386,5.23935,4.87264,5.45185,5.9599,5.20329,5.38818,5.38309,5.19719,4.41578
115
+ SNRPA1,6.9736,5.82176,6.92597,4.36901,6.22731,6.78861,7.96727,6.76471,4.96198,6.68141,6.09818,6.3659,6.41496,5.94514,6.72823,4.47663,6.38742,6.36111,6.41496,4.16323,6.73808,6.66627,6.56827,7.49676,6.35596,6.34648,6.32201,5.74372,7.02074,6.22691,6.14616,5.76484,7.30367,6.89666,6.38988,5.63618,7.08795,6.93432,6.51746,5.31406,6.68953,5.76866,6.47781,6.02798,5.87633,6.16902,6.18282,6.8153,6.32457,6.87754,6.7456,6.78648,7.50086,7.58077,5.79798
116
+ SPRR2F,5.89552,6.37677,6.03414,6.30837,5.90631,6.00833,5.92593,6.13312,6.05492,6.02845,5.78317,5.80831,5.87146,5.77329,6.2311,5.92021,5.96373,6.31077,6.19888,5.89008,5.79268,5.67173,6.03451,5.95253,6.09366,6.0637,5.91554,6.46613,6.22987,6.12794,6.33242,5.92787,6.23283,6.08509,6.00503,6.40484,6.21125,5.97877,5.83988,6.05987,5.8839,6.21812,5.86556,5.9386,6.20825,6.21808,6.10204,5.90099,5.90659,5.89388,5.98635,6.05544,5.6419,6.54199,6.49212
117
+ SPRR2G,5.09336,5.52355,5.36592,5.65946,5.39267,5.06495,5.27246,5.06006,5.29127,5.38619,4.85124,5.40743,5.01428,5.52446,5.45313,5.81848,5.03923,5.77193,5.77042,5.51538,5.36549,4.49676,5.40731,5.55652,5.22596,5.258,4.77188,5.34846,5.38361,5.42329,5.48991,5.56751,5.40687,4.99714,5.5941,5.29507,5.49772,4.85977,5.63712,5.59872,4.96808,5.53061,5.12025,5.22774,5.62003,5.11958,5.17088,5.28707,5.20912,5.39758,4.72188,5.14145,4.54679,5.42523,5.27903
118
+ SRSF1,2.30447,2.0362133333333334,2.0822333333333334,2.20925,2.0160066666666667,2.3184,2.3787,2.2737933333333333,2.23791,2.19255,2.2450566666666667,2.0967733333333336,2.5444066666666667,2.1346633333333336,2.369616666666667,2.24329,2.166376666666667,2.1503633333333334,1.99102,1.88688,2.2521066666666667,2.094,2.1875433333333336,2.0041366666666667,2.3871100000000003,2.1614733333333334,2.5408933333333334,1.9387400000000001,2.2215966666666667,2.1852066666666667,2.4047733333333334,2.214933333333333,2.4608433333333335,2.29094,2.1084033333333334,2.1832,1.98933,1.8515533333333334,2.1593633333333333,2.31816,2.6671866666666664,2.1940633333333333,2.1874966666666666,2.2655533333333335,2.0895966666666665,2.0264233333333332,2.4517666666666664,2.50258,2.197,2.2385533333333334,2.11936,2.39607,2.0738066666666666,2.5905366666666665,2.556083333333333
119
+ ST6GALNAC1,1.18474,1.1624700000000001,1.2232233333333333,1.3156033333333335,1.3699700000000001,1.1961,1.2933833333333333,1.0767666666666666,1.2755966666666667,1.17769,1.05916,1.1807733333333335,1.1203266666666667,1.2034533333333333,1.09664,1.3225633333333333,1.06122,1.2707766666666667,1.2346233333333334,1.31595,1.21861,1.0974533333333334,1.2651566666666667,1.3490233333333332,1.2557033333333334,1.21787,1.0928266666666666,1.38052,1.1441433333333333,1.0835633333333334,0.9998933333333334,1.2524733333333333,1.20528,1.13642,1.2871466666666667,1.1281166666666667,1.30018,1.0922533333333333,1.2589033333333333,1.31115,1.1833,1.19466,1.07898,1.0873033333333333,1.15039,1.2094533333333333,1.23346,1.1945333333333334,1.25326,1.0667633333333333,1.2116633333333333,1.1370833333333332,1.1426833333333333,1.28279,1.18344
120
+ ST6GALNAC2,1.4819633333333335,1.32479,1.4220366666666668,1.4630766666666668,1.3404233333333335,1.3525033333333332,1.4601,1.2081033333333333,1.3968366666666665,1.3220433333333335,1.4348666666666665,1.4875966666666667,1.21229,1.0077433333333332,1.3127133333333334,1.5080533333333335,2.079963333333333,1.4119400000000002,1.3105133333333334,1.326,1.31694,1.1791466666666668,1.1916133333333334,1.3010233333333334,1.2700366666666667,1.24536,1.3035933333333334,1.3574833333333334,1.3396066666666666,1.42675,1.2385066666666666,1.4669666666666668,1.2951300000000001,1.3648533333333335,1.3474766666666669,1.3163500000000001,1.3385466666666668,1.2083733333333333,1.53324,1.3876799999999998,1.3238999999999999,1.1903,1.29697,1.3257433333333333,1.3542100000000001,1.4192333333333333,1.3651200000000001,1.3154066666666666,1.3524266666666669,1.2252633333333334,1.2997733333333332,1.3967366666666667,1.1055599999999999,1.2523433333333334,1.4021633333333332
121
+ ST6GALNAC3,0.9650433333333334,1.17324,1.10717,1.21877,1.06067,1.0465266666666666,1.1842733333333333,0.9702833333333333,0.91379,0.9885366666666666,0.8499366666666667,1.0708900000000001,0.9786766666666668,1.0470666666666666,1.51422,1.1453499999999999,0.9559066666666666,1.1278733333333333,1.35203,1.0307733333333333,0.9858266666666666,0.8358166666666667,0.9147933333333333,1.11917,0.8194266666666666,1.05179,1.17249,1.03037,1.0581633333333333,1.07714,1.0623166666666666,1.3484133333333332,1.0904866666666666,1.03187,1.0999700000000001,0.9522866666666667,1.0406466666666667,0.8838266666666666,1.04185,1.05649,0.8774500000000001,1.0379,0.9689333333333333,0.9223733333333333,1.0877999999999999,1.1276,0.9491066666666667,1.0899033333333332,0.9466800000000001,1.02576,1.0682866666666666,0.9779033333333333,1.5164266666666668,0.8582000000000001,1.0341799999999999
122
+ ST6GALNAC4,3.6687933333333334,3.685336666666667,3.8537266666666663,4.067596666666667,3.446013333333333,3.579276666666667,3.39078,3.67125,3.3620066666666664,3.5278866666666664,3.79374,3.3553966666666666,3.2462166666666663,3.562663333333333,3.575406666666667,3.598606666666667,3.3207000000000004,4.047483333333333,3.769663333333333,4.105526666666666,3.4376166666666665,3.3467233333333333,3.63262,3.5977533333333334,3.4503866666666667,3.9769366666666666,3.894326666666667,3.82876,3.4250333333333334,3.7468866666666667,3.799336666666667,3.502183333333333,3.205796666666667,3.6286566666666666,3.6492266666666664,4.06694,3.5563166666666666,3.65002,3.3034366666666664,3.4853066666666663,3.77325,3.6493633333333335,3.464646666666667,3.418733333333334,3.4176666666666664,3.6795066666666667,3.6556966666666666,3.8810366666666667,3.9115366666666667,3.7535266666666667,3.5670333333333337,3.401206666666667,3.2274666666666665,3.637666666666667,3.8281599999999996
123
+ ST6GALNAC5,1.9990266666666667,1.98622,2.210643333333333,1.9164833333333335,1.9970299999999999,2.17612,1.7320233333333332,2.306503333333333,2.009113333333333,1.7609666666666666,2.01646,2.00841,2.3378633333333334,2.023233333333333,2.22503,1.9517499999999999,1.9108233333333333,2.41989,2.25081,1.8981866666666667,1.7705166666666667,2.15252,2.006206666666667,1.8337033333333332,2.0736399999999997,2.0481966666666667,2.2131333333333334,2.2149666666666668,1.9113333333333333,2.1510166666666666,2.0908866666666666,2.28728,2.2680466666666668,1.8333366666666666,2.1336666666666666,2.32434,2.0626333333333333,2.370983333333333,1.9195533333333332,2.1722333333333332,1.9236466666666667,2.13436,2.3309633333333335,1.7727933333333334,2.0609466666666667,1.7411233333333334,2.4691966666666665,2.125783333333333,2.2310166666666666,1.9678466666666665,1.8687066666666665,1.9384033333333335,1.9157433333333334,2.212216666666667,1.8635666666666666
124
+ ST6GALNAC6,1.77431,1.64631,1.6675133333333332,1.9737799999999999,1.7843233333333333,1.6299166666666667,1.8295966666666665,1.7126599999999998,1.6757833333333334,1.6983233333333334,1.78692,1.7977733333333334,1.67642,1.7638466666666668,1.7543300000000002,1.7181566666666666,1.50962,1.7362633333333333,1.7068033333333332,1.8265500000000001,1.8616366666666666,1.8190566666666665,1.79,1.7756533333333333,1.7235633333333331,1.78426,1.7031933333333333,2.1194433333333333,1.8020766666666665,1.8036199999999998,1.51909,1.61852,1.6665466666666668,1.6235233333333332,1.75755,1.94993,1.82624,1.9743666666666666,1.7844300000000002,2.04417,1.81977,1.7194633333333333,1.72884,1.80235,1.9796533333333333,1.8840333333333332,1.7635366666666668,1.7271433333333333,1.9027633333333334,1.6840966666666668,1.9417566666666666,1.7645433333333334,1.82883,1.7927433333333334,1.6886400000000001
125
+ SUMO1,2.366395,2.613885,3.05589,2.2989,2.89624,2.66982,2.878075,2.72667,2.49661,2.67417,2.70458,2.73199,2.64099,2.88904,3.099415,2.559155,2.555815,2.288035,2.197825,2.41244,2.772625,2.78071,2.73276,2.92266,2.682625,2.90001,2.714905,2.57633,2.561175,2.676185,2.166275,2.96835,3.09603,3.191095,2.53057,2.945555,2.824395,2.68661,2.048575,2.400985,3.16088,3.078,2.57614,2.89326,2.702625,2.633905,2.640495,2.476485,3.00498,2.932885,2.94825,2.757955,2.85512,2.452825,2.603375
126
+ TARP,3.03387,1.48662,1.7381,1.90066,2.05869,2.02651,1.62886,2.12997,1.96457,1.93818,2.01658,1.9854,1.96225,1.83387,1.50654,1.90875,3.68245,2.32448,1.87487,2.28596,1.98376,1.9866,2.80199,2.92619,1.74651,1.60345,1.69405,1.9155,1.72266,1.51772,1.75618,2.07746,2.04435,1.71232,1.9681,1.25688,1.90875,1.90875,2.08341,2.14209,1.71669,1.70056,2.60972,2.10668,1.8526,2.67628,1.90875,1.73639,2.01241,1.73026,1.75994,1.95541,1.69975,1.55133,1.49882
127
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128
+ TATDN1,5.6314,6.93326,7.11841,5.17213,7.2662,7.36597,7.55385,7.04316,6.70721,7.66902,6.89252,7.21804,5.6376,6.35418,6.99633,5.34051,6.82953,6.56487,7.06869,7.18519,7.63522,7.55194,7.42347,8.81604,7.49251,7.36381,7.3323,8.17662,6.58134,7.00829,6.46304,8.50127,8.05424,7.92731,6.54055,7.48831,6.86707,6.96381,7.78789,6.92223,6.38439,7.16798,7.40073,8.27988,7.35214,6.32893,7.43989,7.22841,7.51748,6.90519,7.07309,7.7851,7.31701,7.81295,7.11679
129
+ TBCA,9.48489,8.39609,9.41443,7.62056,9.53162,9.10516,9.35482,9.21473,9.10675,9.46159,8.82472,9.21832,8.97941,8.87066,9.11997,8.70024,8.51474,8.45347,9.11466,7.57059,9.35694,9.46799,9.32009,9.57436,9.00357,9.43748,9.1505,8.85023,8.60877,8.94886,8.29477,9.42978,9.14555,9.60777,9.18577,8.8259,9.48019,9.36161,9.35595,9.2925,9.5995,8.93161,9.44323,8.99282,9.22,8.48997,9.29161,9.55677,9.40911,9.39656,9.28994,9.9508,9.41127,8.01041,7.85051
130
+ TGIF1,6.932383333333333,9.831526666666667,5.97394,9.917456666666666,5.991716666666667,11.248263333333334,9.666806666666666,8.882873333333333,7.662763333333333,7.706286666666666,10.94613,9.3659,9.66306,7.702813333333333,9.851376666666667,4.5585466666666665,10.44638,9.01197,10.301223333333333,8.434646666666666,9.475433333333333,8.473636666666668,11.085223333333333,9.488796666666667,10.144246666666668,10.70706,9.543096666666667,9.700903333333333,10.703326666666667,11.62357,9.898183333333334,8.694253333333332,12.224689999999999,10.620153333333334,10.186863333333333,10.014896666666667,8.845056666666666,9.795166666666667,7.9321866666666665,6.025696666666667,11.975383333333333,11.892113333333333,9.199496666666667,11.358360000000001,9.65154,10.763033333333333,9.143726666666666,9.109603333333332,9.004996666666667,10.799976666666666,11.46777,9.534083333333333,9.509196666666668,10.17685,10.176496666666667
131
+ THAP5,3.001885,3.19089,3.10359,3.608355,3.4181,3.6545,4.03131,3.89468,2.99708,3.411205,3.59888,3.71583,3.352265,3.74704,3.92197,3.44497,3.0566,3.161335,2.921265,2.592145,3.534895,3.635255,3.65538,3.706415,3.408245,3.913685,3.58269,3.07869,3.615335,3.441935,3.15783,3.743835,3.66561,4.234275,3.173595,3.51122,3.8098,3.654345,2.55408,3.874305,4.13363,3.39637,3.789395,3.885825,3.17281,2.97933,3.59702,3.737935,4.043955,3.972145,3.772945,3.99958,3.66153,3.405285,3.14807
132
+ THBD,4.643795,4.311335,4.27933,4.6993849999999995,4.385365,4.20341,4.09865,4.221220000000001,4.661545,4.0580750000000005,4.125745,4.436485,4.34539,4.71889,4.13756,4.604795,4.125915,4.348485,4.814315000000001,4.58014,4.498365,4.202045,4.41113,4.808625,3.894655,4.15344,3.9893,4.482094999999999,4.335355,4.3694299999999995,4.420695,4.36236,4.098495,4.723515000000001,4.694295,4.003535,4.3276900000000005,4.172085,4.881315,4.4551549999999995,4.66354,4.284095000000001,4.277445,4.110125,4.443105,4.448225,3.9473900000000004,4.210955,4.145944999999999,4.328250000000001,4.716749999999999,4.434065,4.520595,4.5400849999999995,4.2650749999999995
133
+ TIMM8B,9.37212,7.7036,9.68181,8.3345,10.1364,9.54801,10.0608,9.96507,9.33353,9.85392,9.05333,10.1642,9.43275,9.64829,9.6645,9.75969,9.65794,9.3251,8.62352,10.0751,9.69964,9.74842,9.45959,9.8123,9.55712,9.30041,9.67185,9.0955,9.90775,9.1761,8.26468,10.1608,8.92014,10.1136,10.641,9.29596,9.92845,10.0489,10.1317,9.00015,9.50527,9.64586,9.78721,10.2135,9.62507,9.59827,9.77308,8.92476,10.3085,10.1189,9.56841,9.92924,10.0197,7.91529,8.6372
134
+ TPI1,8.69832,8.15088,9.0225,9.48528,8.55245,8.06727,8.25209,8.57086,8.52336,8.65686,9.13656,9.13454,8.3959,8.71132,8.6897,8.99337,9.03039,8.8815,8.50838,9.08579,8.31744,9.15881,8.73172,8.83653,8.72494,8.44718,9.77057,8.67874,8.64043,8.19788,8.22175,8.40875,7.9504,9.35894,8.93364,8.5341,8.74478,9.16571,7.61398,8.02326,8.26416,9.65049,8.83638,8.97482,8.43069,9.54778,8.58313,8.63369,8.64853,8.75007,8.80455,8.7344,8.55573,7.69508,7.89093
135
+ TPM4,9.13008,8.26574,7.41658,8.72162,8.54979,9.26135,8.8386,7.52914,8.85145,8.31315,8.38225,8.73134,8.66058,7.93124,9.51205,9.03069,8.35246,8.38478,8.40035,7.80522,9.30094,8.40981,9.04167,7.85518,8.39021,7.99129,8.56167,7.11542,8.37419,9.31069,7.68732,8.59279,9.20899,8.92503,7.58412,8.07625,8.47222,8.32874,8.66686,9.09676,7.88571,6.95163,8.49618,8.63214,8.09377,8.51431,8.35573,7.77547,8.55398,8.93863,8.98028,9.56954,8.72672,7.65715,8.88997
136
+ TPTE2,2.21757,1.84659,1.59148,1.95619,1.73225,2.12849,1.91794,1.70433,1.79706,1.65698,2.05927,1.69398,1.50179,2.07152,1.86968,1.65217,1.69997,1.55125,1.63283,1.9393,1.76414,1.82196,1.78862,1.94983,1.91672,1.71765,1.52505,1.49501,1.48558,1.69709,1.81348,1.60851,1.61711,1.65486,1.54942,1.85129,1.82656,1.7333,1.6604,1.60923,1.57088,1.80248,1.74416,1.72441,1.88404,2.05868,1.58687,1.83517,1.5159,1.61373,1.84086,1.98423,1.76515,2.17834,1.74726
137
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138
+ TRGC1,2.643035,1.90305,1.720845,2.40886,2.6956,2.496125,2.35898,2.14994,2.639025,2.390345,2.690365,2.08485,2.286345,1.71097,2.449515,1.889835,3.443135,1.55817,2.288405,2.45671,3.268885,2.369585,2.845685,2.11081,2.423075,1.74085,2.271945,1.76453,2.00136,2.078245,1.868205,2.001955,2.239195,2.366785,1.86909,2.26226,2.249455,2.32646,1.914715,2.528375,1.874005,1.64005,2.068245,2.774145,2.14915,2.10078,2.358065,2.69777,2.083615,1.928025,1.879935,2.79925,2.05766,2.13601,2.136445
139
+ TWIST2,6.19523,5.90318,5.88289,6.38389,6.06512,5.40436,5.36692,5.65825,5.74392,5.23184,5.45024,5.32222,5.52227,5.70314,5.37463,5.92766,5.79938,5.64616,5.78233,5.50709,6.27814,5.12699,5.99214,6.14606,5.12403,5.18707,5.31528,5.51453,5.62734,5.2774,5.04192,5.40077,5.5805,5.42382,5.4403,5.7525,5.31799,5.22733,6.35015,6.30019,5.69885,5.43032,5.83404,5.80745,5.72164,5.69554,5.75191,5.92322,5.55341,5.80955,5.95139,5.61739,5.16265,5.40213,6.28543
140
+ UBE2D3,25.6138,26.065466666666666,25.756866666666667,24.418200000000002,24.916800000000002,26.09313333333333,26.118266666666667,26.002666666666666,25.448866666666667,25.774233333333335,26.813933333333335,25.479033333333334,25.576900000000002,25.815333333333335,26.303866666666668,25.91616666666667,25.145333333333333,26.341933333333333,25.21933333333333,25.824066666666667,25.6335,26.7459,25.59476666666667,26.0433,25.957133333333335,26.197766666666666,25.858733333333333,26.0437,25.786033333333332,26.191333333333333,25.5811,25.8352,25.397766666666666,26.039733333333334,26.130499999999998,25.08106666666667,26.044733333333333,26.136966666666666,25.728033333333332,24.430033333333334,25.964199999999998,26.1277,26.089799999999997,25.628733333333333,25.944266666666667,25.370566666666665,25.72286666666667,25.760033333333332,25.8632,26.140766666666668,26.028566666666666,26.4018,25.9592,25.8479,25.102666666666668
141
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142
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143
+ UPF3A,6.56714,7.0048200000000005,7.29079,6.743105,7.180535,7.418889999999999,7.14605,7.611935,7.392585,6.90892,6.995464999999999,6.92753,6.541115,7.007905,7.15832,6.768385,6.58274,6.845235000000001,6.84677,6.411955,7.416365,7.344284999999999,6.918975,6.5868400000000005,6.639385,6.73108,7.019615,7.01614,6.855985,7.09636,7.268325,6.285080000000001,7.1932849999999995,6.479425,6.001605,6.751805,7.13088,5.960215,7.493045,7.1039449999999995,7.55373,6.66405,7.03795,7.42842,7.075685,6.90624,7.144494999999999,7.115325,6.981925,7.310565,6.53937,7.2477599999999995,6.941955,8.02102,7.348145
144
+ USP12,5.19959,6.05672,5.40948,4.73202,4.31246,4.70371,5.54811,5.48488,5.16773,5.71493,6.03943,6.75423,5.19772,4.22603,5.54444,5.0848,7.3755,5.04313,5.73818,5.80798,5.4317,6.19981,4.98109,5.21987,5.56921,5.46465,5.72163,5.47585,5.84746,5.80801,5.83432,5.55758,6.31872,5.17683,6.00243,6.042,5.37063,4.88496,4.03752,5.67475,5.35252,6.31189,5.72925,4.72967,6.25644,5.17049,5.49688,5.21341,5.56418,4.67577,6.3948,6.2542,5.84397,4.95319,6.10379
145
+ VN1R4,2.62713,2.82317,2.52105,3.45539,2.74743,3.01439,2.80886,2.69689,2.80828,2.70218,2.52996,2.46406,2.82613,2.82809,2.46747,2.84151,2.73878,2.7495,2.87946,3.07438,2.72446,2.42491,2.76197,2.7544,2.85071,2.7495,2.64267,3.20054,2.71682,2.46888,2.79274,2.69821,2.61215,2.37011,2.73069,2.52134,2.67799,2.76818,2.81125,2.62854,2.7495,2.74085,2.21154,2.36538,3.02869,2.93144,2.90558,2.68981,2.7495,2.71287,2.51803,2.57862,2.68724,3.18102,3.14834
146
+ WDR82,4.0887,4.93513,4.87756,2.43583,3.86266,5.52179,5.00842,5.83463,4.60768,5.35782,6.48919,6.03447,6.58333,5.01153,6.1241,3.39479,5.28158,4.97425,3.07641,3.9002,5.39394,5.31463,6.19354,2.90652,5.68553,6.33887,5.95575,3.80776,5.16766,6.0971,3.5056,6.58657,6.09992,6.76912,5.5345,6.37921,5.90664,6.6622,3.51166,5.52216,5.22659,6.60896,6.1967,5.66848,6.17582,5.34527,5.43531,5.66624,5.81455,5.57854,5.4795,3.38848,6.44679,4.54662,6.05622
147
+ XCL1,2.55026,2.71222,2.42375,2.79817,2.84524,2.10502,2.79766,2.49901,3.50779,2.96991,2.35566,2.40261,3.62031,3.15923,2.01587,2.77829,2.09714,2.50655,2.69365,2.52573,2.22367,2.38934,2.86385,3.00322,2.67619,2.74579,2.1378,2.25145,2.43574,2.25415,2.27156,2.48041,4.11804,2.64076,2.69944,2.34332,3.17104,4.72454,3.13186,2.58188,2.65362,2.48168,3.09994,2.55192,2.52042,2.52739,2.0943,2.36651,2.42884,2.25454,2.54681,4.04339,2.53256,4.41834,2.40485
148
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149
+ ZC3H11A,3.69886,3.813035,3.588135,3.54168,3.556825,3.680645,3.99425,4.05093,3.624175,3.725365,3.98284,3.818035,3.966015,3.94682,3.907785,3.68691,3.68815,3.79458,3.636435,3.56714,3.78525,3.906005,3.77568,3.68276,3.89586,3.818375,4.034425,3.80222,3.99724,3.944165,3.74643,3.55986,4.05737,4.055835,3.592105,3.67268,3.68011,3.85812,3.816835,3.69929,3.98209,3.674835,3.632465,3.873705,3.89939,3.587045,4.01934,4.051435,3.97587,3.9249,3.69192,3.887385,3.73584,3.889625,3.951815
150
+ ZNF208,11.31713,12.61486,12.639610000000001,11.62939,12.130030000000001,11.99482,11.333390000000001,11.881879999999999,11.38486,12.04242,12.030850000000001,11.245239999999999,12.0741,11.22008,12.25178,11.926870000000001,12.0399,11.47964,11.93849,11.46982,12.2424,11.27793,12.31905,11.83444,11.52406,11.98917,12.01929,11.06546,11.69388,12.34538,12.602170000000001,12.476659999999999,12.84152,12.329450000000001,11.47001,12.472819999999999,11.615079999999999,11.00408,11.59187,12.28959,12.12589,11.40308,11.72062,12.36317,11.93122,11.57348,12.18772,12.24507,11.77495,12.16084,12.28227,12.0278,11.66982,13.33849,12.789639999999999
151
+ ZNF257,7.8634,8.86257,8.54648,8.21254,8.76124,8.38275,8.12646,8.50774,8.19934,8.61253,8.76069,8.16905,9.03537,8.20842,8.73789,8.45066,8.42931,8.27697,8.32307,8.18577,8.6314,8.01295,8.2606,8.37341,7.83534,8.43337,8.52967,7.9132,8.41658,8.38768,8.90331,8.52428,8.6144,8.61923,8.41528,8.6479,8.38663,7.96441,8.31477,8.39993,8.28752,8.31875,8.44622,8.36143,8.43798,7.9994,8.63026,8.73181,8.39317,8.58165,9.0616,8.52469,8.25433,9.07147,8.87718
152
+ ZNF718,9.07726,9.95358,9.57934,8.16358,7.01226,8.68674,10.92252,9.91936,9.5601,8.89184,10.1637,10.0588,10.85924,10.10816,11.05396,10.47548,9.56526,8.62134,8.01262,11.82384,8.89944,10.84082,10.90308,8.96252,9.16026,11.47218,9.45436,7.57982,11.54944,9.3874,11.20564,9.10964,8.68546,12.0336,9.5163,10.0056,8.57712,9.60434,7.92478,10.1461,10.11048,9.30294,9.08188,10.75998,10.5508,7.72592,11.351,9.92562,12.6033,9.9608,10.33844,12.22418,10.52594,12.27392,10.2858
153
+ ZNF93,3.8279,4.23886,3.84081,3.925425,3.852845,3.71582,3.34136,4.068415,3.92643,3.608915,4.318495,4.11831,4.11238,3.940415,3.98234,4.084925,3.9318,3.67093,3.56359,3.96285,4.21312,4.29556,3.91889,3.146905,3.4985,3.851265,3.83712,3.811685,3.834145,3.63081,3.606155,3.51153,3.972605,3.615215,4.00982,4.03485,3.80511,3.568635,4.111635,3.71013,3.63306,3.855165,3.856945,3.796885,3.26788,4.006875,3.947765,4.07914,3.463225,3.737025,4.484935,4.00503,3.53968,4.156835,3.842525
p1/preprocess/Polycystic_Kidney_Disease/GSE74451.csv ADDED
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p1/preprocess/Polycystic_Kidney_Disease/clinical_data/GSE74451.csv ADDED
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p1/preprocess/Polycystic_Kidney_Disease/code/GSE74451.py ADDED
@@ -0,0 +1,236 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Polycystic_Kidney_Disease"
6
+ cohort = "GSE74451"
7
+
8
+ # Input paths
9
+ in_trait_dir = "../DATA/GEO/Polycystic_Kidney_Disease"
10
+ in_cohort_dir = "../DATA/GEO/Polycystic_Kidney_Disease/GSE74451"
11
+
12
+ # Output paths
13
+ out_data_file = "./output/preprocess/1/Polycystic_Kidney_Disease/GSE74451.csv"
14
+ out_gene_data_file = "./output/preprocess/1/Polycystic_Kidney_Disease/gene_data/GSE74451.csv"
15
+ out_clinical_data_file = "./output/preprocess/1/Polycystic_Kidney_Disease/clinical_data/GSE74451.csv"
16
+ json_path = "./output/preprocess/1/Polycystic_Kidney_Disease/cohort_info.json"
17
+
18
+ # STEP1
19
+ from tools.preprocess import *
20
+ # 1. Identify the paths to the SOFT file and the matrix file
21
+ soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
22
+
23
+ # 2. Read the matrix file to obtain background information and sample characteristics data
24
+ background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
25
+ clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
26
+ background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
27
+
28
+ # 3. Obtain the sample characteristics dictionary from the clinical dataframe
29
+ sample_characteristics_dict = get_unique_values_by_row(clinical_data)
30
+
31
+ # 4. Explicitly print out all the background information and the sample characteristics dictionary
32
+ print("Background Information:")
33
+ print(background_info)
34
+ print("Sample Characteristics Dictionary:")
35
+ print(sample_characteristics_dict)
36
+ # 1. Determine if gene expression data is available
37
+ is_gene_available = True # Based on the description that this dataset measures gene expression profiles
38
+
39
+ # 2. Identify row indices and define conversion functions
40
+
41
+ # From the sample characteristics dictionary:
42
+ # row=1 indicates "subject status" with distinct values: healthy control or ADPKD patient
43
+ trait_row = 1
44
+
45
+ # No age information is found in the dictionary
46
+ age_row = None
47
+ convert_age = None # Not needed if age is not available
48
+
49
+ # For gender, row=2 shows "gender: Male" or "gender: Female"
50
+ gender_row = 2
51
+
52
+ def convert_trait(value: str):
53
+ """
54
+ Convert the trait value to binary (0,1).
55
+ Healthy/control = 0, ADPKD = 1.
56
+ """
57
+ # Split on colon and strip
58
+ parts = value.split(':')
59
+ val = parts[-1].strip() if len(parts) > 1 else value.strip()
60
+ val_lower = val.lower()
61
+ if "healthy" in val_lower:
62
+ return 0
63
+ elif "autosomal dominant polycystic kidney disease" in val_lower or "adpkd" in val_lower:
64
+ return 1
65
+ else:
66
+ return None
67
+
68
+ def convert_gender(value: str):
69
+ """
70
+ Convert gender to binary (0,1).
71
+ Female = 0, Male = 1.
72
+ """
73
+ parts = value.split(':')
74
+ val = parts[-1].strip() if len(parts) > 1 else value.strip()
75
+ val_lower = val.lower()
76
+ if "female" in val_lower:
77
+ return 0
78
+ elif "male" in val_lower:
79
+ return 1
80
+ else:
81
+ return None
82
+
83
+ # 3. Save metadata (initial filtering)
84
+ is_trait_available = (trait_row is not None)
85
+ is_usable = validate_and_save_cohort_info(
86
+ is_final=False,
87
+ cohort=cohort,
88
+ info_path=json_path,
89
+ is_gene_available=is_gene_available,
90
+ is_trait_available=is_trait_available
91
+ )
92
+
93
+ # 4. If clinical trait data is available, extract and save clinical features
94
+ if trait_row is not None:
95
+ selected_clinical_df = geo_select_clinical_features(
96
+ clinical_data,
97
+ trait=trait,
98
+ trait_row=trait_row,
99
+ convert_trait=convert_trait,
100
+ age_row=age_row,
101
+ convert_age=convert_age,
102
+ gender_row=gender_row,
103
+ convert_gender=convert_gender
104
+ )
105
+ print("Preview of clinical features:")
106
+ print(preview_df(selected_clinical_df))
107
+ selected_clinical_df.to_csv(out_clinical_data_file, index=False)
108
+ # STEP3
109
+ import gzip
110
+ import pandas as pd
111
+
112
+ try:
113
+ # 1. Attempt to extract gene expression data using the library function
114
+ gene_data = get_genetic_data(matrix_file)
115
+ except KeyError:
116
+ # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
117
+ # and rename the first column to "ID".
118
+ marker = "!series_matrix_table_begin"
119
+ skip_rows = None
120
+
121
+ # Determine how many rows to skip before the matrix data begins
122
+ with gzip.open(matrix_file, 'rt') as f:
123
+ for i, line in enumerate(f):
124
+ if marker in line:
125
+ skip_rows = i + 1
126
+ break
127
+ else:
128
+ raise ValueError(f"Marker '{marker}' not found in the file.")
129
+
130
+ # Read the data from the determined position
131
+ gene_data = pd.read_csv(
132
+ matrix_file,
133
+ compression='gzip',
134
+ skiprows=skip_rows,
135
+ comment='!',
136
+ delimiter='\t',
137
+ on_bad_lines='skip'
138
+ )
139
+
140
+ # If a different column name is used instead of 'ID_REF', rename appropriately
141
+ if 'ID_REF' in gene_data.columns:
142
+ gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
143
+ else:
144
+ first_col = gene_data.columns[0]
145
+ gene_data.rename(columns={first_col: 'ID'}, inplace=True)
146
+
147
+ gene_data['ID'] = gene_data['ID'].astype(str)
148
+ gene_data.set_index('ID', inplace=True)
149
+
150
+ # 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
151
+ print(gene_data.index[:20])
152
+ print("requires_gene_mapping = True")
153
+ # STEP5
154
+ # 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
155
+ gene_annotation = get_gene_annotation(soft_file)
156
+
157
+ # 2. Use the 'preview_df' function from the library to preview the data and print out the results.
158
+ print("Gene annotation preview:")
159
+ print(preview_df(gene_annotation))
160
+ # STEP: Gene Identifier Mapping
161
+ # 1. We have determined that the 'ID' column in gene_annotation corresponds to
162
+ # the probe identifiers, and the 'GENE_SYMBOL' column stores the gene symbols.
163
+ mapping_df = get_gene_mapping(
164
+ annotation=gene_annotation,
165
+ prob_col="ID",
166
+ gene_col="GENE_SYMBOL"
167
+ )
168
+
169
+ # 2. Convert probe-level expression into gene-level expression
170
+ gene_data = apply_gene_mapping(
171
+ expression_df=gene_data,
172
+ mapping_df=mapping_df
173
+ )
174
+
175
+ # Optional: Print shape or a small preview of the resulting gene_data
176
+ print("Mapped gene_data shape:", gene_data.shape)
177
+ print("Preview of mapped gene_data:")
178
+ print(gene_data.head(5))
179
+ import os
180
+ import pandas as pd
181
+
182
+ # STEP 7: Data Normalization and Linking
183
+
184
+ # First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
185
+ if not os.path.exists(out_clinical_data_file):
186
+ # No trait data file => dataset is not usable for trait analysis
187
+ df_null = pd.DataFrame()
188
+ is_biased = True # Arbitrary boolean to satisfy function requirement
189
+ validate_and_save_cohort_info(
190
+ is_final=True,
191
+ cohort=cohort,
192
+ info_path=json_path,
193
+ is_gene_available=True,
194
+ is_trait_available=False,
195
+ is_biased=is_biased,
196
+ df=df_null,
197
+ note="No trait data file found; dataset not usable for trait analysis."
198
+ )
199
+
200
+ else:
201
+ # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
202
+ normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
203
+ normalized_gene_data.to_csv(out_gene_data_file)
204
+
205
+ # 2. Load the previously extracted clinical CSV.
206
+ selected_clinical_df = pd.read_csv(out_clinical_data_file)
207
+ # If we had a single-row trait, rename row 0 to the trait name (example usage).
208
+ selected_clinical_df = selected_clinical_df.rename(index={0: trait})
209
+
210
+ # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
211
+ combined_clinical_df = selected_clinical_df
212
+
213
+ # Link the clinical and genetic data by matching sample IDs in columns.
214
+ linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
215
+
216
+ # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
217
+ processed_data = handle_missing_values(linked_data, trait)
218
+
219
+ # 4. Check trait bias and remove any biased demographic features (if any).
220
+ trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
221
+
222
+ # 5. Final validation and metadata saving.
223
+ is_usable = validate_and_save_cohort_info(
224
+ is_final=True,
225
+ cohort=cohort,
226
+ info_path=json_path,
227
+ is_gene_available=True,
228
+ is_trait_available=True,
229
+ is_biased=trait_biased,
230
+ df=processed_data,
231
+ note="Completed trait-based preprocessing."
232
+ )
233
+
234
+ # 6. If final dataset is usable, save. Otherwise, skip.
235
+ if is_usable:
236
+ processed_data.to_csv(out_data_file)
p1/preprocess/Polycystic_Kidney_Disease/code/GSE74453.py ADDED
@@ -0,0 +1,228 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Polycystic_Kidney_Disease"
6
+ cohort = "GSE74453"
7
+
8
+ # Input paths
9
+ in_trait_dir = "../DATA/GEO/Polycystic_Kidney_Disease"
10
+ in_cohort_dir = "../DATA/GEO/Polycystic_Kidney_Disease/GSE74453"
11
+
12
+ # Output paths
13
+ out_data_file = "./output/preprocess/1/Polycystic_Kidney_Disease/GSE74453.csv"
14
+ out_gene_data_file = "./output/preprocess/1/Polycystic_Kidney_Disease/gene_data/GSE74453.csv"
15
+ out_clinical_data_file = "./output/preprocess/1/Polycystic_Kidney_Disease/clinical_data/GSE74453.csv"
16
+ json_path = "./output/preprocess/1/Polycystic_Kidney_Disease/cohort_info.json"
17
+
18
+ # STEP1
19
+ from tools.preprocess import *
20
+ # 1. Identify the paths to the SOFT file and the matrix file
21
+ soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
22
+
23
+ # 2. Read the matrix file to obtain background information and sample characteristics data
24
+ background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
25
+ clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
26
+ background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
27
+
28
+ # 3. Obtain the sample characteristics dictionary from the clinical dataframe
29
+ sample_characteristics_dict = get_unique_values_by_row(clinical_data)
30
+
31
+ # 4. Explicitly print out all the background information and the sample characteristics dictionary
32
+ print("Background Information:")
33
+ print(background_info)
34
+ print("Sample Characteristics Dictionary:")
35
+ print(sample_characteristics_dict)
36
+ # 1. Gene Expression Data Availability
37
+ is_gene_available = True # Based on background, we assume gene expression is present
38
+
39
+ # 2. Variable Availability and Data Type Conversion
40
+
41
+ # From the sample characteristics dictionary, row 1 is a single value ("ADPKD patient") for all samples → constant.
42
+ # Hence, for our associative study, we do not treat it as available. So trait_row = None.
43
+ trait_row = None
44
+
45
+ # There is no row indicating age data → age_row = None.
46
+ age_row = None
47
+
48
+ # Row 2 contains "gender: Female" and "gender: Male", which is a valid binary variable.
49
+ gender_row = 2
50
+
51
+ # Define data conversion functions.
52
+
53
+ def convert_trait(value: str):
54
+ """Convert trait values to binary (1 for ADPKD, else None)."""
55
+ parts = value.split(':', 1)
56
+ label = parts[1].strip().lower() if len(parts) > 1 else value.strip().lower()
57
+ if "adpkd" in label:
58
+ return 1
59
+ return None
60
+
61
+ def convert_age(value: str):
62
+ """Convert age values to float. Unknown or non-numeric → None."""
63
+ parts = value.split(':', 1)
64
+ label = parts[1].strip() if len(parts) > 1 else value.strip()
65
+ try:
66
+ return float(label)
67
+ except ValueError:
68
+ return None
69
+
70
+ def convert_gender(value: str):
71
+ """Convert gender: Female->0, Male->1, else None."""
72
+ parts = value.split(':', 1)
73
+ label = parts[1].strip().lower() if len(parts) > 1 else value.strip().lower()
74
+ if label.startswith("female"):
75
+ return 0
76
+ elif label.startswith("male"):
77
+ return 1
78
+ return None
79
+
80
+ # 3. Save Metadata (initial filtering)
81
+ # Trait data availability is determined by whether trait_row is None.
82
+ is_trait_available = (trait_row is not None)
83
+
84
+ is_usable = validate_and_save_cohort_info(
85
+ is_final=False,
86
+ cohort=cohort,
87
+ info_path=json_path,
88
+ is_gene_available=is_gene_available,
89
+ is_trait_available=is_trait_available
90
+ )
91
+
92
+ # 4. Clinical Feature Extraction
93
+ # Skip this step if trait_row is None (i.e., clinical trait data not available).
94
+ if trait_row is not None:
95
+ selected_clinical_df = geo_select_clinical_features(
96
+ clinical_data,
97
+ trait="Polycystic_Kidney_Disease",
98
+ trait_row=trait_row,
99
+ convert_trait=convert_trait,
100
+ age_row=age_row,
101
+ convert_age=convert_age,
102
+ gender_row=gender_row,
103
+ convert_gender=convert_gender
104
+ )
105
+ print("Preview of clinical dataframe:", preview_df(selected_clinical_df))
106
+ selected_clinical_df.to_csv(out_clinical_data_file, index=False)
107
+ # STEP3
108
+ import gzip
109
+ import pandas as pd
110
+
111
+ try:
112
+ # 1. Attempt to extract gene expression data using the library function
113
+ gene_data = get_genetic_data(matrix_file)
114
+ except KeyError:
115
+ # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
116
+ # and rename the first column to "ID".
117
+ marker = "!series_matrix_table_begin"
118
+ skip_rows = None
119
+
120
+ # Determine how many rows to skip before the matrix data begins
121
+ with gzip.open(matrix_file, 'rt') as f:
122
+ for i, line in enumerate(f):
123
+ if marker in line:
124
+ skip_rows = i + 1
125
+ break
126
+ else:
127
+ raise ValueError(f"Marker '{marker}' not found in the file.")
128
+
129
+ # Read the data from the determined position
130
+ gene_data = pd.read_csv(
131
+ matrix_file,
132
+ compression='gzip',
133
+ skiprows=skip_rows,
134
+ comment='!',
135
+ delimiter='\t',
136
+ on_bad_lines='skip'
137
+ )
138
+
139
+ # If a different column name is used instead of 'ID_REF', rename appropriately
140
+ if 'ID_REF' in gene_data.columns:
141
+ gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
142
+ else:
143
+ first_col = gene_data.columns[0]
144
+ gene_data.rename(columns={first_col: 'ID'}, inplace=True)
145
+
146
+ gene_data['ID'] = gene_data['ID'].astype(str)
147
+ gene_data.set_index('ID', inplace=True)
148
+
149
+ # 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
150
+ print(gene_data.index[:20])
151
+ print("These identifiers appear to be numeric probes rather than human gene symbols."
152
+ "\nrequires_gene_mapping = True")
153
+ # STEP5
154
+ # 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
155
+ gene_annotation = get_gene_annotation(soft_file)
156
+
157
+ # 2. Use the 'preview_df' function from the library to preview the data and print out the results.
158
+ print("Gene annotation preview:")
159
+ print(preview_df(gene_annotation))
160
+ # STEP: Gene Identifier Mapping
161
+ # 1. Identify the columns in the annotation dataframe that correspond to probe ID and gene symbols
162
+ prob_col = "ID" # Matches the gene expression 'ID'
163
+ gene_col = "gene_assignment" # Contains gene symbol-like strings
164
+
165
+ # 2. Get the gene mapping dataframe
166
+ mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_col)
167
+
168
+ # 3. Convert the probe-level data to gene-level data
169
+ gene_data = apply_gene_mapping(gene_data, mapping_df)
170
+ print("Gene-level expression data shape:", gene_data.shape)
171
+ import os
172
+ import pandas as pd
173
+
174
+ # STEP 7: Data Normalization and Linking
175
+
176
+ # First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
177
+ if not os.path.exists(out_clinical_data_file):
178
+ # No trait data file => dataset is not usable for trait analysis
179
+ df_null = pd.DataFrame()
180
+ is_biased = True # Arbitrary boolean to satisfy function requirement
181
+ validate_and_save_cohort_info(
182
+ is_final=True,
183
+ cohort=cohort,
184
+ info_path=json_path,
185
+ is_gene_available=True,
186
+ is_trait_available=False,
187
+ is_biased=is_biased,
188
+ df=df_null,
189
+ note="No trait data file found; dataset not usable for trait analysis."
190
+ )
191
+
192
+ else:
193
+ # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
194
+ normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
195
+ normalized_gene_data.to_csv(out_gene_data_file)
196
+
197
+ # 2. Load the previously extracted clinical CSV.
198
+ selected_clinical_df = pd.read_csv(out_clinical_data_file)
199
+ # If we had a single-row trait, rename row 0 to the trait name (example usage).
200
+ selected_clinical_df = selected_clinical_df.rename(index={0: trait})
201
+
202
+ # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
203
+ combined_clinical_df = selected_clinical_df
204
+
205
+ # Link the clinical and genetic data by matching sample IDs in columns.
206
+ linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
207
+
208
+ # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
209
+ processed_data = handle_missing_values(linked_data, trait)
210
+
211
+ # 4. Check trait bias and remove any biased demographic features (if any).
212
+ trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
213
+
214
+ # 5. Final validation and metadata saving.
215
+ is_usable = validate_and_save_cohort_info(
216
+ is_final=True,
217
+ cohort=cohort,
218
+ info_path=json_path,
219
+ is_gene_available=True,
220
+ is_trait_available=True,
221
+ is_biased=trait_biased,
222
+ df=processed_data,
223
+ note="Completed trait-based preprocessing."
224
+ )
225
+
226
+ # 6. If final dataset is usable, save. Otherwise, skip.
227
+ if is_usable:
228
+ processed_data.to_csv(out_data_file)
p1/preprocess/Polycystic_Kidney_Disease/code/TCGA.py ADDED
@@ -0,0 +1,74 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Polycystic_Kidney_Disease"
6
+
7
+ # Input paths
8
+ tcga_root_dir = "../DATA/TCGA"
9
+
10
+ # Output paths
11
+ out_data_file = "./output/preprocess/1/Polycystic_Kidney_Disease/TCGA.csv"
12
+ out_gene_data_file = "./output/preprocess/1/Polycystic_Kidney_Disease/gene_data/TCGA.csv"
13
+ out_clinical_data_file = "./output/preprocess/1/Polycystic_Kidney_Disease/clinical_data/TCGA.csv"
14
+ json_path = "./output/preprocess/1/Polycystic_Kidney_Disease/cohort_info.json"
15
+
16
+ import os
17
+ import pandas as pd
18
+
19
+ # List of subdirectories provided in the instructions:
20
+ subdirectories = [
21
+ 'CrawlData.ipynb', '.DS_Store', 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)',
22
+ 'TCGA_Uterine_Carcinosarcoma_(UCS)', 'TCGA_Thyroid_Cancer_(THCA)', 'TCGA_Thymoma_(THYM)',
23
+ 'TCGA_Testicular_Cancer_(TGCT)', 'TCGA_Stomach_Cancer_(STAD)', 'TCGA_Sarcoma_(SARC)',
24
+ 'TCGA_Rectal_Cancer_(READ)', 'TCGA_Prostate_Cancer_(PRAD)', 'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)',
25
+ 'TCGA_Pancreatic_Cancer_(PAAD)', 'TCGA_Ovarian_Cancer_(OV)', 'TCGA_Ocular_melanomas_(UVM)',
26
+ 'TCGA_Mesothelioma_(MESO)', 'TCGA_Melanoma_(SKCM)', 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)',
27
+ 'TCGA_Lung_Cancer_(LUNG)', 'TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lower_Grade_Glioma_(LGG)',
28
+ 'TCGA_Liver_Cancer_(LIHC)', 'TCGA_Large_Bcell_Lymphoma_(DLBC)', 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)',
29
+ 'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)', 'TCGA_Kidney_Chromophobe_(KICH)', 'TCGA_Head_and_Neck_Cancer_(HNSC)',
30
+ 'TCGA_Glioblastoma_(GBM)', 'TCGA_Esophageal_Cancer_(ESCA)', 'TCGA_Endometrioid_Cancer_(UCEC)',
31
+ 'TCGA_Colon_and_Rectal_Cancer_(COADREAD)', 'TCGA_Colon_Cancer_(COAD)', 'TCGA_Cervical_Cancer_(CESC)',
32
+ 'TCGA_Breast_Cancer_(BRCA)', 'TCGA_Bladder_Cancer_(BLCA)', 'TCGA_Bile_Duct_Cancer_(CHOL)',
33
+ 'TCGA_Adrenocortical_Cancer_(ACC)', 'TCGA_Acute_Myeloid_Leukemia_(LAML)'
34
+ ]
35
+
36
+ # Synonyms or terms possibly related to our target trait "Polycystic_Kidney_Disease"
37
+ trait_synonyms = [
38
+ "polycystic", "pkd", "kidney cyst", "kidneycyst", "polycystic_kidney"
39
+ ]
40
+
41
+ selected_subdirectory = None
42
+ for subdir in subdirectories:
43
+ if subdir.lower() in ['crawldata.ipynb', '.ds_store']:
44
+ continue
45
+ subdir_lower = subdir.lower()
46
+ if any(syn in subdir_lower for syn in trait_synonyms):
47
+ selected_subdirectory = subdir
48
+ break
49
+
50
+ if not selected_subdirectory:
51
+ # If no matching directory is found, mark dataset as unavailable
52
+ is_final = False
53
+ is_gene_available = False
54
+ is_trait_available = False
55
+ _ = validate_and_save_cohort_info(
56
+ is_final=is_final,
57
+ cohort="TCGA",
58
+ info_path=json_path,
59
+ is_gene_available=is_gene_available,
60
+ is_trait_available=is_trait_available
61
+ )
62
+ print(f"No suitable directory found for '{trait}'. Skipped this trait.")
63
+ else:
64
+ # Step 2: Identify clinicalMatrix file and PANCAN file
65
+ cohort_dir = os.path.join(tcga_root_dir, selected_subdirectory)
66
+ clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
67
+
68
+ # Step 3: Load both files as dataframes
69
+ clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
70
+ genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')
71
+
72
+ # Step 4: Print the column names of the clinical data
73
+ print("Clinical data columns:")
74
+ print(list(clinical_df.columns))
p1/preprocess/Polycystic_Kidney_Disease/cohort_info.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"GSE74453": {"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 file found; dataset not usable for trait analysis."}, "GSE74451": {"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": 31, "note": "Completed trait-based preprocessing."}, "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/Polycystic_Kidney_Disease/gene_data/GSE74451.csv ADDED
The diff for this file is too large to render. See raw diff
 
p1/preprocess/Polycystic_Ovary_Syndrome/GSE43322.csv ADDED
The diff for this file is too large to render. See raw diff
 
p1/preprocess/Polycystic_Ovary_Syndrome/GSE87435.csv ADDED
The diff for this file is too large to render. See raw diff
 
p1/preprocess/Polycystic_Ovary_Syndrome/clinical_data/GSE43322.csv ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ GSM1059640,GSM1059641,GSM1059642,GSM1059643,GSM1059644,GSM1059645,GSM1059646,GSM1059647,GSM1059648,GSM1059649,GSM1059650,GSM1059651,GSM1059652,GSM1059653,GSM1059654,GSM1059686,GSM1059687,GSM1059688,GSM1059689,GSM1059690,GSM1059691,GSM1059692,GSM1059693,GSM1059694,GSM1059695,GSM1059696,GSM1059697,GSM1059698,GSM1059699,GSM1059700,GSM1059701
2
+ 1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
3
+ 39.0,32.0,22.0,25.0,26.0,28.0,27.0,28.0,36.0,37.0,39.0,34.0,30.0,22.0,40.0,39.0,39.0,32.0,32.0,22.0,22.0,25.0,25.0,26.0,26.0,28.0,28.0,27.0,27.0,28.0,28.0
p1/preprocess/Polycystic_Ovary_Syndrome/clinical_data/GSE87435.csv ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ GSM2331293,GSM2331295,GSM2331297,GSM2331299,GSM2331301,GSM2331303,GSM2331305,GSM2331307,GSM2331309,GSM2331311,GSM2331313,GSM2331315,GSM2331317,GSM2331319,GSM2331321,GSM2331323,GSM2331325,GSM2331327
2
+ 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
3
+ 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
p1/preprocess/Polycystic_Ovary_Syndrome/code/GSE151158.py ADDED
@@ -0,0 +1,104 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Polycystic_Ovary_Syndrome"
6
+ cohort = "GSE151158"
7
+
8
+ # Input paths
9
+ in_trait_dir = "../DATA/GEO/Polycystic_Ovary_Syndrome"
10
+ in_cohort_dir = "../DATA/GEO/Polycystic_Ovary_Syndrome/GSE151158"
11
+
12
+ # Output paths
13
+ out_data_file = "./output/preprocess/1/Polycystic_Ovary_Syndrome/GSE151158.csv"
14
+ out_gene_data_file = "./output/preprocess/1/Polycystic_Ovary_Syndrome/gene_data/GSE151158.csv"
15
+ out_clinical_data_file = "./output/preprocess/1/Polycystic_Ovary_Syndrome/clinical_data/GSE151158.csv"
16
+ json_path = "./output/preprocess/1/Polycystic_Ovary_Syndrome/cohort_info.json"
17
+
18
+ # STEP1
19
+ from tools.preprocess import *
20
+ # 1. Identify the paths to the SOFT file and the matrix file
21
+ soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
22
+
23
+ # 2. Read the matrix file to obtain background information and sample characteristics data
24
+ background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
25
+ clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
26
+ background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
27
+
28
+ # 3. Obtain the sample characteristics dictionary from the clinical dataframe
29
+ sample_characteristics_dict = get_unique_values_by_row(clinical_data)
30
+
31
+ # 4. Explicitly print out all the background information and the sample characteristics dictionary
32
+ print("Background Information:")
33
+ print(background_info)
34
+ print("Sample Characteristics Dictionary:")
35
+ print(sample_characteristics_dict)
36
+ # 1. Decide whether the dataset likely contains gene expression data
37
+ is_gene_available = True # From the series description, it measures RNA expression of multiple genes.
38
+
39
+ # 2.1 Determine data availability for trait, age, and gender
40
+ # Our trait is "Polycystic_Ovary_Syndrome". Looking at row 11 "pcos: N" or NaN only,
41
+ # there is effectively no variation (all "N" or missing), so trait data is not available for our analysis.
42
+ trait_row = None
43
+
44
+ # Age data is found in row 1: multiple values like "age: 53", "age: 40", etc. => available
45
+ age_row = 1
46
+
47
+ # Gender data is found in row 2: values like "Sex: F", "Sex: M", NaN => available
48
+ gender_row = 2
49
+
50
+ # 2.2 Define data type conversion functions
51
+
52
+ def convert_trait(x: str) -> int:
53
+ """
54
+ Since trait_row is None for this dataset, this function won't be used.
55
+ But we define it here as required.
56
+ """
57
+ return None # No meaningful data
58
+
59
+ def convert_age(x: str) -> float:
60
+ """
61
+ Convert the string of the form 'age: 53' to a float 53. If parsing fails, return None.
62
+ """
63
+ if not x or not isinstance(x, str):
64
+ return None
65
+ parts = x.split(':')
66
+ if len(parts) < 2:
67
+ return None
68
+ val_str = parts[1].strip()
69
+ try:
70
+ return float(val_str)
71
+ except ValueError:
72
+ return None
73
+
74
+ def convert_gender(x: str) -> int:
75
+ """
76
+ Convert 'Sex: F' to 0 and 'Sex: M' to 1.
77
+ If unknown or parsing fails, return None.
78
+ """
79
+ if not x or not isinstance(x, str):
80
+ return None
81
+ parts = x.split(':')
82
+ if len(parts) < 2:
83
+ return None
84
+ val_str = parts[1].strip().upper()
85
+ if val_str == 'F':
86
+ return 0
87
+ elif val_str == 'M':
88
+ return 1
89
+ return None
90
+
91
+ # 3. Save initial metadata (is_final=False).
92
+ # Trait data is not available => is_trait_available = False
93
+ is_trait_available = False
94
+
95
+ is_usable = validate_and_save_cohort_info(
96
+ is_final=False,
97
+ cohort=cohort,
98
+ info_path=json_path,
99
+ is_gene_available=is_gene_available,
100
+ is_trait_available=is_trait_available
101
+ )
102
+
103
+ # 4. If trait_row is not None => extract clinical features.
104
+ # However, trait_row is None, so we skip the clinical feature extraction step.
p1/preprocess/Polycystic_Ovary_Syndrome/code/GSE43322.py ADDED
@@ -0,0 +1,221 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Polycystic_Ovary_Syndrome"
6
+ cohort = "GSE43322"
7
+
8
+ # Input paths
9
+ in_trait_dir = "../DATA/GEO/Polycystic_Ovary_Syndrome"
10
+ in_cohort_dir = "../DATA/GEO/Polycystic_Ovary_Syndrome/GSE43322"
11
+
12
+ # Output paths
13
+ out_data_file = "./output/preprocess/1/Polycystic_Ovary_Syndrome/GSE43322.csv"
14
+ out_gene_data_file = "./output/preprocess/1/Polycystic_Ovary_Syndrome/gene_data/GSE43322.csv"
15
+ out_clinical_data_file = "./output/preprocess/1/Polycystic_Ovary_Syndrome/clinical_data/GSE43322.csv"
16
+ json_path = "./output/preprocess/1/Polycystic_Ovary_Syndrome/cohort_info.json"
17
+
18
+ # STEP1
19
+ from tools.preprocess import *
20
+ # 1. Identify the paths to the SOFT file and the matrix file
21
+ soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
22
+
23
+ # 2. Read the matrix file to obtain background information and sample characteristics data
24
+ background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
25
+ clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
26
+ background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
27
+
28
+ # 3. Obtain the sample characteristics dictionary from the clinical dataframe
29
+ sample_characteristics_dict = get_unique_values_by_row(clinical_data)
30
+
31
+ # 4. Explicitly print out all the background information and the sample characteristics dictionary
32
+ print("Background Information:")
33
+ print(background_info)
34
+ print("Sample Characteristics Dictionary:")
35
+ print(sample_characteristics_dict)
36
+ # Step 1: Determine if gene expression data is available
37
+ # Based on the series title indicating "gene expression", we conclude:
38
+ is_gene_available = True
39
+
40
+ # Step 2: Identify data availability and define conversion functions
41
+
42
+ # 2.1 - trait data: found in row 3 with two unique values => suitable for binary.
43
+ trait_row = 3
44
+ def convert_trait(value: str):
45
+ val = value.split(":", 1)[-1].strip().lower()
46
+ if "pcos" in val or "polycystic" in val:
47
+ return 1
48
+ elif "control" in val:
49
+ return 0
50
+ else:
51
+ return None
52
+
53
+ # 2.1 - age data: found in row 1 with multiple distinct numeric values => continuous.
54
+ age_row = 1
55
+ def convert_age(value: str):
56
+ val = value.split(":", 1)[-1].strip()
57
+ try:
58
+ return float(val)
59
+ except:
60
+ return None
61
+
62
+ # 2.1 - gender data: row 0 has only "Female" => no variation => treat as not available.
63
+ gender_row = None
64
+ def convert_gender(value: str):
65
+ val = value.split(":", 1)[-1].strip().lower()
66
+ if val in ["female", "f"]:
67
+ return 0
68
+ elif val in ["male", "m"]:
69
+ return 1
70
+ else:
71
+ return None
72
+
73
+ # Check trait availability
74
+ is_trait_available = (trait_row is not None)
75
+
76
+ # Step 3: Initial filtering and metadata saving
77
+ is_usable = validate_and_save_cohort_info(
78
+ is_final=False,
79
+ cohort=cohort,
80
+ info_path=json_path,
81
+ is_gene_available=is_gene_available,
82
+ is_trait_available=is_trait_available
83
+ )
84
+
85
+ # Step 4: Clinical feature extraction if trait data is available
86
+ if trait_row is not None:
87
+ selected_clinical_df = geo_select_clinical_features(
88
+ clinical_df=clinical_data,
89
+ trait=trait,
90
+ trait_row=trait_row,
91
+ convert_trait=convert_trait,
92
+ age_row=age_row,
93
+ convert_age=convert_age,
94
+ gender_row=gender_row,
95
+ convert_gender=convert_gender
96
+ )
97
+ preview_result = preview_df(selected_clinical_df)
98
+ print(preview_result)
99
+ selected_clinical_df.to_csv(out_clinical_data_file, index=False)
100
+ # STEP3
101
+ import gzip
102
+ import pandas as pd
103
+
104
+ try:
105
+ # 1. Attempt to extract gene expression data using the library function
106
+ gene_data = get_genetic_data(matrix_file)
107
+ except KeyError:
108
+ # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
109
+ # and rename the first column to "ID".
110
+ marker = "!series_matrix_table_begin"
111
+ skip_rows = None
112
+
113
+ # Determine how many rows to skip before the matrix data begins
114
+ with gzip.open(matrix_file, 'rt') as f:
115
+ for i, line in enumerate(f):
116
+ if marker in line:
117
+ skip_rows = i + 1
118
+ break
119
+ else:
120
+ raise ValueError(f"Marker '{marker}' not found in the file.")
121
+
122
+ # Read the data from the determined position
123
+ gene_data = pd.read_csv(
124
+ matrix_file,
125
+ compression='gzip',
126
+ skiprows=skip_rows,
127
+ comment='!',
128
+ delimiter='\t',
129
+ on_bad_lines='skip'
130
+ )
131
+
132
+ # If a different column name is used instead of 'ID_REF', rename appropriately
133
+ if 'ID_REF' in gene_data.columns:
134
+ gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
135
+ else:
136
+ first_col = gene_data.columns[0]
137
+ gene_data.rename(columns={first_col: 'ID'}, inplace=True)
138
+
139
+ gene_data['ID'] = gene_data['ID'].astype(str)
140
+ gene_data.set_index('ID', inplace=True)
141
+
142
+ # 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
143
+ print(gene_data.index[:20])
144
+ # Based on the format of identifiers (e.g., "10001_at"), these are probe IDs rather than direct human gene symbols.
145
+ # Therefore, mapping to gene symbols is needed.
146
+
147
+ print("requires_gene_mapping = True")
148
+ # STEP5
149
+ # 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
150
+ gene_annotation = get_gene_annotation(soft_file)
151
+
152
+ # 2. Use the 'preview_df' function from the library to preview the data and print out the results.
153
+ print("Gene annotation preview:")
154
+ print(preview_df(gene_annotation))
155
+ # STEP: Gene Identifier Mapping
156
+
157
+ # 1. From the annotation dataframe preview, the 'ID' column holds the same type of probe identifiers
158
+ # as in our gene expression data, and the 'ORF' column holds the gene symbols.
159
+ # 2. Get a gene mapping dataframe by extracting these two columns.
160
+ mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="ORF")
161
+
162
+ # 3. Convert probe-level measurements to gene expression data using the mapping.
163
+ gene_data = apply_gene_mapping(gene_data, mapping_df)
164
+ import os
165
+ import pandas as pd
166
+
167
+ # STEP 7: Data Normalization and Linking
168
+
169
+ # First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
170
+ if not os.path.exists(out_clinical_data_file):
171
+ # No trait data file => dataset is not usable for trait analysis
172
+ df_null = pd.DataFrame()
173
+ is_biased = True # Arbitrary boolean to satisfy function requirement
174
+ validate_and_save_cohort_info(
175
+ is_final=True,
176
+ cohort=cohort,
177
+ info_path=json_path,
178
+ is_gene_available=True,
179
+ is_trait_available=False,
180
+ is_biased=is_biased,
181
+ df=df_null,
182
+ note="No trait data file found; dataset not usable for trait analysis."
183
+ )
184
+
185
+ else:
186
+ # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
187
+ normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
188
+ normalized_gene_data.to_csv(out_gene_data_file)
189
+
190
+ # 2. Load the previously extracted clinical CSV.
191
+ selected_clinical_df = pd.read_csv(out_clinical_data_file)
192
+ # If we had a single-row trait, rename row 0 to the trait name (example usage).
193
+ selected_clinical_df = selected_clinical_df.rename(index={0: trait})
194
+
195
+ # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
196
+ combined_clinical_df = selected_clinical_df
197
+
198
+ # Link the clinical and genetic data by matching sample IDs in columns.
199
+ linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
200
+
201
+ # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
202
+ processed_data = handle_missing_values(linked_data, trait)
203
+
204
+ # 4. Check trait bias and remove any biased demographic features (if any).
205
+ trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
206
+
207
+ # 5. Final validation and metadata saving.
208
+ is_usable = validate_and_save_cohort_info(
209
+ is_final=True,
210
+ cohort=cohort,
211
+ info_path=json_path,
212
+ is_gene_available=True,
213
+ is_trait_available=True,
214
+ is_biased=trait_biased,
215
+ df=processed_data,
216
+ note="Completed trait-based preprocessing."
217
+ )
218
+
219
+ # 6. If final dataset is usable, save. Otherwise, skip.
220
+ if is_usable:
221
+ processed_data.to_csv(out_data_file)
p1/preprocess/Polycystic_Ovary_Syndrome/code/GSE87435.py ADDED
@@ -0,0 +1,269 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Polycystic_Ovary_Syndrome"
6
+ cohort = "GSE87435"
7
+
8
+ # Input paths
9
+ in_trait_dir = "../DATA/GEO/Polycystic_Ovary_Syndrome"
10
+ in_cohort_dir = "../DATA/GEO/Polycystic_Ovary_Syndrome/GSE87435"
11
+
12
+ # Output paths
13
+ out_data_file = "./output/preprocess/1/Polycystic_Ovary_Syndrome/GSE87435.csv"
14
+ out_gene_data_file = "./output/preprocess/1/Polycystic_Ovary_Syndrome/gene_data/GSE87435.csv"
15
+ out_clinical_data_file = "./output/preprocess/1/Polycystic_Ovary_Syndrome/clinical_data/GSE87435.csv"
16
+ json_path = "./output/preprocess/1/Polycystic_Ovary_Syndrome/cohort_info.json"
17
+
18
+ # STEP1
19
+ from tools.preprocess import *
20
+ # 1. Identify the paths to the SOFT file and the matrix file
21
+ soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
22
+
23
+ # 2. Read the matrix file to obtain background information and sample characteristics data
24
+ background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
25
+ clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
26
+ background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
27
+
28
+ # 3. Obtain the sample characteristics dictionary from the clinical dataframe
29
+ sample_characteristics_dict = get_unique_values_by_row(clinical_data)
30
+
31
+ # 4. Explicitly print out all the background information and the sample characteristics dictionary
32
+ print("Background Information:")
33
+ print(background_info)
34
+ print("Sample Characteristics Dictionary:")
35
+ print(sample_characteristics_dict)
36
+ # Step 2: Dataset Analysis and Clinical Feature Extraction
37
+
38
+ # 1. Determine if the dataset likely contains gene expression data.
39
+ # Based on the background info indicating "Microarray expression data",
40
+ # we conclude that gene expression data is available.
41
+ is_gene_available = True
42
+
43
+ # 2. Identify row keys for trait, age, gender, and define data conversion functions.
44
+
45
+ # Observing the sample characteristics:
46
+ # Row 0: ['gender: female', 'gender history: female to male transsexual']
47
+ # Row 1: ['disease state: PCOS', 'tissue: ovary']
48
+ # Row 2: ['tissue: ovary', nan]
49
+ #
50
+ # Trait:
51
+ # "disease state: PCOS" indicates the specified trait is present here.
52
+ # So we set trait_row = 1.
53
+ #
54
+ # Age:
55
+ # No mention of age in any row, so age_row = None.
56
+ #
57
+ # Gender:
58
+ # The first row (0) has different "gender" values, so we set gender_row = 0.
59
+
60
+ trait_row = 1
61
+ age_row = None
62
+ gender_row = 0
63
+
64
+ # Define data type conversion functions.
65
+ def convert_trait(value):
66
+ """
67
+ Convert trait values to a binary indicator:
68
+ 1 for PCOS, 0 if not PCOS, None if unknown/unparseable.
69
+ """
70
+ if not value or pd.isna(value):
71
+ return None
72
+ parts = value.split(":")
73
+ if len(parts) < 2:
74
+ return None
75
+ val = parts[1].strip().lower()
76
+ if "pcos" in val:
77
+ return 1
78
+ # If it's not identified as PCOS or is something else, interpret as 0
79
+ return 0
80
+
81
+ def convert_age(value):
82
+ """
83
+ Age data is unavailable, so return None.
84
+ """
85
+ return None
86
+
87
+ def convert_gender(value):
88
+ """
89
+ Convert gender values to binary:
90
+ 0 -> female, 1 -> male,
91
+ If uncertain or unparseable, return None.
92
+ Here 'female to male transsexual' is interpreted as male = 1.
93
+ """
94
+ if not value or pd.isna(value):
95
+ return None
96
+ parts = value.split(":")
97
+ if len(parts) < 2:
98
+ return None
99
+ val = parts[1].strip().lower()
100
+ if "female" in val and "to male" not in val:
101
+ return 0
102
+ elif "male" in val:
103
+ return 1
104
+ return None
105
+
106
+ # 3. Save metadata & perform initial filtering using validate_and_save_cohort_info.
107
+ is_trait_available = (trait_row is not None)
108
+ passed_filtering = validate_and_save_cohort_info(
109
+ is_final=False,
110
+ cohort=cohort,
111
+ info_path=json_path,
112
+ is_gene_available=is_gene_available,
113
+ is_trait_available=is_trait_available
114
+ )
115
+
116
+ # If the dataset passes the initial filtering (i.e., is_gene_available and is_trait_available),
117
+ # validate_and_save_cohort_info returns False, but we continue with further preprocessing.
118
+ # Otherwise, if it fails (one of them is False), we do not proceed.
119
+
120
+ if not passed_filtering and is_gene_available and is_trait_available:
121
+ # 4. Clinical Feature Extraction (only if trait_row is not None).
122
+ selected_clinical_df = geo_select_clinical_features(
123
+ clinical_data,
124
+ trait=trait,
125
+ trait_row=trait_row,
126
+ convert_trait=convert_trait,
127
+ age_row=age_row,
128
+ convert_age=convert_age,
129
+ gender_row=gender_row,
130
+ convert_gender=convert_gender
131
+ )
132
+
133
+ # Preview the selected clinical DataFrame
134
+ preview = preview_df(selected_clinical_df, n=5, max_items=200)
135
+ print("Preview of Selected Clinical Features:", preview)
136
+
137
+ # Save the clinical features to CSV
138
+ selected_clinical_df.to_csv(out_clinical_data_file, index=False)
139
+ # STEP3
140
+ import gzip
141
+ import pandas as pd
142
+
143
+ try:
144
+ # 1. Attempt to extract gene expression data using the library function
145
+ gene_data = get_genetic_data(matrix_file)
146
+ except KeyError:
147
+ # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
148
+ # and rename the first column to "ID".
149
+ marker = "!series_matrix_table_begin"
150
+ skip_rows = None
151
+
152
+ # Determine how many rows to skip before the matrix data begins
153
+ with gzip.open(matrix_file, 'rt') as f:
154
+ for i, line in enumerate(f):
155
+ if marker in line:
156
+ skip_rows = i + 1
157
+ break
158
+ else:
159
+ raise ValueError(f"Marker '{marker}' not found in the file.")
160
+
161
+ # Read the data from the determined position
162
+ gene_data = pd.read_csv(
163
+ matrix_file,
164
+ compression='gzip',
165
+ skiprows=skip_rows,
166
+ comment='!',
167
+ delimiter='\t',
168
+ on_bad_lines='skip'
169
+ )
170
+
171
+ # If a different column name is used instead of 'ID_REF', rename appropriately
172
+ if 'ID_REF' in gene_data.columns:
173
+ gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
174
+ else:
175
+ first_col = gene_data.columns[0]
176
+ gene_data.rename(columns={first_col: 'ID'}, inplace=True)
177
+
178
+ gene_data['ID'] = gene_data['ID'].astype(str)
179
+ gene_data.set_index('ID', inplace=True)
180
+
181
+ # 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
182
+ print(gene_data.index[:20])
183
+ # After reviewing the provided gene identifiers (Affymetrix probe IDs), they are not standard official gene symbols.
184
+ # Therefore, they do require mapping to gene symbols.
185
+
186
+ requires_gene_mapping = True
187
+ # STEP5
188
+ # 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
189
+ gene_annotation = get_gene_annotation(soft_file)
190
+
191
+ # 2. Use the 'preview_df' function from the library to preview the data and print out the results.
192
+ print("Gene annotation preview:")
193
+ print(preview_df(gene_annotation))
194
+ # STEP: Gene Identifier Mapping
195
+
196
+ # 1. Identify columns in the annotation dataframe that match the probe IDs (gene_data.index) and the gene symbols.
197
+ # From the preview, the annotation column "ID" aligns with the probe IDs (e.g., "1007_s_at", "1053_at", etc.).
198
+ # The column "Gene Symbol" contains the corresponding gene symbols.
199
+
200
+ probe_col = "ID"
201
+ symbol_col = "Gene Symbol"
202
+
203
+ # 2. Create a mapping dataframe from the annotation dataframe using the designated columns.
204
+ mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=symbol_col)
205
+
206
+ # 3. Apply this mapping to convert from probe-level data to gene-level data.
207
+ gene_data = apply_gene_mapping(gene_data, mapping_df)
208
+
209
+ # For verification, print the shape of the resulting dataframe and some gene names (index).
210
+ print("Mapped gene_data shape:", gene_data.shape)
211
+ print("First 20 gene symbols in the mapped dataframe index:", list(gene_data.index[:20]))
212
+ import os
213
+ import pandas as pd
214
+
215
+ # STEP 7: Data Normalization and Linking
216
+
217
+ # First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
218
+ if not os.path.exists(out_clinical_data_file):
219
+ # No trait data file => dataset is not usable for trait analysis
220
+ df_null = pd.DataFrame()
221
+ is_biased = True # Arbitrary boolean to satisfy function requirement
222
+ validate_and_save_cohort_info(
223
+ is_final=True,
224
+ cohort=cohort,
225
+ info_path=json_path,
226
+ is_gene_available=True,
227
+ is_trait_available=False,
228
+ is_biased=is_biased,
229
+ df=df_null,
230
+ note="No trait data file found; dataset not usable for trait analysis."
231
+ )
232
+
233
+ else:
234
+ # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
235
+ normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
236
+ normalized_gene_data.to_csv(out_gene_data_file)
237
+
238
+ # 2. Load the previously extracted clinical CSV.
239
+ selected_clinical_df = pd.read_csv(out_clinical_data_file)
240
+ # If we had a single-row trait, rename row 0 to the trait name (example usage).
241
+ selected_clinical_df = selected_clinical_df.rename(index={0: trait})
242
+
243
+ # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
244
+ combined_clinical_df = selected_clinical_df
245
+
246
+ # Link the clinical and genetic data by matching sample IDs in columns.
247
+ linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
248
+
249
+ # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
250
+ processed_data = handle_missing_values(linked_data, trait)
251
+
252
+ # 4. Check trait bias and remove any biased demographic features (if any).
253
+ trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
254
+
255
+ # 5. Final validation and metadata saving.
256
+ is_usable = validate_and_save_cohort_info(
257
+ is_final=True,
258
+ cohort=cohort,
259
+ info_path=json_path,
260
+ is_gene_available=True,
261
+ is_trait_available=True,
262
+ is_biased=trait_biased,
263
+ df=processed_data,
264
+ note="Completed trait-based preprocessing."
265
+ )
266
+
267
+ # 6. If final dataset is usable, save. Otherwise, skip.
268
+ if is_usable:
269
+ processed_data.to_csv(out_data_file)
p1/preprocess/Polycystic_Ovary_Syndrome/code/TCGA.py ADDED
@@ -0,0 +1,135 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Polycystic_Ovary_Syndrome"
6
+
7
+ # Input paths
8
+ tcga_root_dir = "../DATA/TCGA"
9
+
10
+ # Output paths
11
+ out_data_file = "./output/preprocess/1/Polycystic_Ovary_Syndrome/TCGA.csv"
12
+ out_gene_data_file = "./output/preprocess/1/Polycystic_Ovary_Syndrome/gene_data/TCGA.csv"
13
+ out_clinical_data_file = "./output/preprocess/1/Polycystic_Ovary_Syndrome/clinical_data/TCGA.csv"
14
+ json_path = "./output/preprocess/1/Polycystic_Ovary_Syndrome/cohort_info.json"
15
+
16
+ import os
17
+ import pandas as pd
18
+
19
+ # List of subdirectories provided in the instructions:
20
+ subdirectories = [
21
+ 'CrawlData.ipynb', '.DS_Store', 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)',
22
+ 'TCGA_Uterine_Carcinosarcoma_(UCS)', 'TCGA_Thyroid_Cancer_(THCA)', 'TCGA_Thymoma_(THYM)',
23
+ 'TCGA_Testicular_Cancer_(TGCT)', 'TCGA_Stomach_Cancer_(STAD)', 'TCGA_Sarcoma_(SARC)',
24
+ 'TCGA_Rectal_Cancer_(READ)', 'TCGA_Prostate_Cancer_(PRAD)', 'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)',
25
+ 'TCGA_Pancreatic_Cancer_(PAAD)', 'TCGA_Ovarian_Cancer_(OV)', 'TCGA_Ocular_melanomas_(UVM)',
26
+ 'TCGA_Mesothelioma_(MESO)', 'TCGA_Melanoma_(SKCM)', 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)',
27
+ 'TCGA_Lung_Cancer_(LUNG)', 'TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lower_Grade_Glioma_(LGG)',
28
+ 'TCGA_Liver_Cancer_(LIHC)', 'TCGA_Large_Bcell_Lymphoma_(DLBC)', 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)',
29
+ 'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)', 'TCGA_Kidney_Chromophobe_(KICH)', 'TCGA_Head_and_Neck_Cancer_(HNSC)',
30
+ 'TCGA_Glioblastoma_(GBM)', 'TCGA_Esophageal_Cancer_(ESCA)', 'TCGA_Endometrioid_Cancer_(UCEC)',
31
+ 'TCGA_Colon_and_Rectal_Cancer_(COADREAD)', 'TCGA_Colon_Cancer_(COAD)', 'TCGA_Cervical_Cancer_(CESC)',
32
+ 'TCGA_Breast_Cancer_(BRCA)', 'TCGA_Bladder_Cancer_(BLCA)', 'TCGA_Bile_Duct_Cancer_(CHOL)',
33
+ 'TCGA_Adrenocortical_Cancer_(ACC)', 'TCGA_Acute_Myeloid_Leukemia_(LAML)'
34
+ ]
35
+
36
+ # Synonyms or terms possibly related to our target trait "Polycystic_Ovary_Syndrome"
37
+ trait_synonyms = [
38
+ "polycystic_ovary", "pcos", "ovarian", "pos"
39
+ ]
40
+
41
+ selected_subdirectory = None
42
+ for subdir in subdirectories:
43
+ if subdir.lower() in ['crawldata.ipynb', '.ds_store']:
44
+ continue
45
+ subdir_lower = subdir.lower()
46
+ if any(syn in subdir_lower for syn in trait_synonyms):
47
+ selected_subdirectory = subdir
48
+ break
49
+
50
+ if not selected_subdirectory:
51
+ # If no matching directory is found, mark dataset as unavailable
52
+ is_final = False
53
+ is_gene_available = False
54
+ is_trait_available = False
55
+ _ = validate_and_save_cohort_info(
56
+ is_final=is_final,
57
+ cohort="TCGA",
58
+ info_path=json_path,
59
+ is_gene_available=is_gene_available,
60
+ is_trait_available=is_trait_available
61
+ )
62
+ print(f"No suitable directory found for '{trait}'. Skipped this trait.")
63
+ else:
64
+ # Step 2: Identify clinicalMatrix file and PANCAN file
65
+ cohort_dir = os.path.join(tcga_root_dir, selected_subdirectory)
66
+ clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
67
+
68
+ # Step 3: Load both files as dataframes
69
+ clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
70
+ genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')
71
+
72
+ # Step 4: Print the column names of the clinical data
73
+ print("Clinical data columns:")
74
+ print(list(clinical_df.columns))
75
+ # Identify columns likely containing age or gender data
76
+ candidate_age_cols = ["age_at_initial_pathologic_diagnosis", "days_to_birth"]
77
+ candidate_gender_cols = ["gender"]
78
+
79
+ print("candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']")
80
+ print("candidate_gender_cols = ['gender']")
81
+
82
+ # Extract and preview the candidate columns
83
+ if candidate_age_cols:
84
+ extracted_age = clinical_df[candidate_age_cols]
85
+ print(preview_df(extracted_age, n=5))
86
+
87
+ if candidate_gender_cols:
88
+ extracted_gender = clinical_df[candidate_gender_cols]
89
+ print(preview_df(extracted_gender, n=5))
90
+ age_col = None
91
+ gender_col = None
92
+
93
+ print("Chosen age_col:", age_col)
94
+ print("Chosen gender_col:", gender_col)
95
+ # 1. Extract and standardize the clinical features
96
+ selected_clinical_df = tcga_select_clinical_features(
97
+ clinical_df=clinical_df,
98
+ trait=trait,
99
+ age_col=age_col,
100
+ gender_col=gender_col
101
+ )
102
+
103
+ # 2. Normalize gene symbols in the expression data and save
104
+ gene_df = normalize_gene_symbols_in_index(genetic_df)
105
+ gene_df.to_csv(out_gene_data_file)
106
+
107
+ # 3. Link clinical and genetic data
108
+ # The genetic data has samples as columns, so transpose before joining
109
+ linked_data = selected_clinical_df.join(gene_df.T, how='inner')
110
+
111
+ # 4. Handle missing values
112
+ processed_data = handle_missing_values(linked_data, trait_col=trait)
113
+
114
+ # 5. Determine and remove biased features
115
+ biased_trait, processed_data = judge_and_remove_biased_features(processed_data, trait)
116
+
117
+ # 6. Final quality validation
118
+ gene_cols = [col for col in processed_data.columns if col not in [trait, "Age", "Gender"]]
119
+ is_gene_available = len(gene_cols) > 0
120
+ is_trait_available = trait in processed_data.columns
121
+
122
+ is_usable = validate_and_save_cohort_info(
123
+ is_final=True,
124
+ cohort="TCGA",
125
+ info_path=json_path,
126
+ is_gene_available=is_gene_available,
127
+ is_trait_available=is_trait_available,
128
+ is_biased=biased_trait,
129
+ df=processed_data,
130
+ note="Final data processing for Kidney_Chromophobe"
131
+ )
132
+
133
+ # 7. Save linked data if usable
134
+ if is_usable:
135
+ processed_data.to_csv(out_data_file)
p1/preprocess/Polycystic_Ovary_Syndrome/cohort_info.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"GSE87435": {"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": 18, "note": "Completed trait-based preprocessing."}, "GSE43322": {"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": 31, "note": "Completed trait-based preprocessing."}, "GSE151158": {"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}, "TCGA": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": false, "has_gender": false, "sample_size": 308, "note": "Final data processing for Kidney_Chromophobe"}}
p1/preprocess/Polycystic_Ovary_Syndrome/gene_data/GSE43322.csv ADDED
The diff for this file is too large to render. See raw diff
 
p1/preprocess/Polycystic_Ovary_Syndrome/gene_data/GSE87435.csv ADDED
The diff for this file is too large to render. See raw diff
 
p1/preprocess/Post-Traumatic_Stress_Disorder/GSE199841.csv ADDED
The diff for this file is too large to render. See raw diff
 
p1/preprocess/Post-Traumatic_Stress_Disorder/code/GSE52875.py ADDED
@@ -0,0 +1,216 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Post-Traumatic_Stress_Disorder"
6
+ cohort = "GSE52875"
7
+
8
+ # Input paths
9
+ in_trait_dir = "../DATA/GEO/Post-Traumatic_Stress_Disorder"
10
+ in_cohort_dir = "../DATA/GEO/Post-Traumatic_Stress_Disorder/GSE52875"
11
+
12
+ # Output paths
13
+ out_data_file = "./output/preprocess/1/Post-Traumatic_Stress_Disorder/GSE52875.csv"
14
+ out_gene_data_file = "./output/preprocess/1/Post-Traumatic_Stress_Disorder/gene_data/GSE52875.csv"
15
+ out_clinical_data_file = "./output/preprocess/1/Post-Traumatic_Stress_Disorder/clinical_data/GSE52875.csv"
16
+ json_path = "./output/preprocess/1/Post-Traumatic_Stress_Disorder/cohort_info.json"
17
+
18
+ # STEP1
19
+ from tools.preprocess import *
20
+ # 1. Identify the paths to the SOFT file and the matrix file
21
+ soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
22
+
23
+ # 2. Read the matrix file to obtain background information and sample characteristics data
24
+ background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
25
+ clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
26
+ background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
27
+
28
+ # 3. Obtain the sample characteristics dictionary from the clinical dataframe
29
+ sample_characteristics_dict = get_unique_values_by_row(clinical_data)
30
+
31
+ # 4. Explicitly print out all the background information and the sample characteristics dictionary
32
+ print("Background Information:")
33
+ print(background_info)
34
+ print("Sample Characteristics Dictionary:")
35
+ print(sample_characteristics_dict)
36
+ # Step 2: Dataset Analysis and Clinical Feature Extraction
37
+
38
+ # 1. Gene Expression Data Availability
39
+ # Based on the background info "Expression signatures in heart tissues...," we assume it's gene expression data.
40
+ # Therefore, we set is_gene_available to True.
41
+ is_gene_available = True
42
+
43
+ # 2. Variable Availability and Data Type Conversion
44
+
45
+ # From the sample characteristics, we see only two keys:
46
+ # 0 -> ['strain: C57BL/6']
47
+ # 1 -> ['tissue: heart tissue']
48
+ # There's no mention of human trait ("PTSD" or control), age, or gender.
49
+ # Hence, none of these variables are available for our associative study:
50
+ trait_row = None
51
+ age_row = None
52
+ gender_row = None
53
+
54
+ # We define conversion functions as required, even though the variables are not available:
55
+
56
+ def convert_trait(value: str):
57
+ """
58
+ Convert the trait value to a binary variable (0 or 1).
59
+ Since we have no actual trait values in the data, we will return None.
60
+ """
61
+ return None
62
+
63
+ def convert_age(value: str):
64
+ """
65
+ Convert the age value to a continuous variable.
66
+ Since we have no age information in the data, we will return None.
67
+ """
68
+ return None
69
+
70
+ def convert_gender(value: str):
71
+ """
72
+ Convert the gender value to a binary variable, female=0, male=1.
73
+ Since we have no gender information in the data, we will return None.
74
+ """
75
+ return None
76
+
77
+ # 3. Save Metadata using initial filtering
78
+ # If the trait row is not available, then our is_trait_available = False.
79
+ is_trait_available = (trait_row is not None)
80
+
81
+ is_usable = validate_and_save_cohort_info(
82
+ is_final=False,
83
+ cohort=cohort,
84
+ info_path=json_path,
85
+ is_gene_available=is_gene_available,
86
+ is_trait_available=is_trait_available
87
+ )
88
+
89
+ # 4. Clinical Feature Extraction
90
+ # We only extract clinical features if trait_row is not None. Here, trait_row is None, so we skip this step.
91
+ # STEP3
92
+ import gzip
93
+ import pandas as pd
94
+
95
+ try:
96
+ # 1. Attempt to extract gene expression data using the library function
97
+ gene_data = get_genetic_data(matrix_file)
98
+ except KeyError:
99
+ # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
100
+ # and rename the first column to "ID".
101
+ marker = "!series_matrix_table_begin"
102
+ skip_rows = None
103
+
104
+ # Determine how many rows to skip before the matrix data begins
105
+ with gzip.open(matrix_file, 'rt') as f:
106
+ for i, line in enumerate(f):
107
+ if marker in line:
108
+ skip_rows = i + 1
109
+ break
110
+ else:
111
+ raise ValueError(f"Marker '{marker}' not found in the file.")
112
+
113
+ # Read the data from the determined position
114
+ gene_data = pd.read_csv(
115
+ matrix_file,
116
+ compression='gzip',
117
+ skiprows=skip_rows,
118
+ comment='!',
119
+ delimiter='\t',
120
+ on_bad_lines='skip'
121
+ )
122
+
123
+ # If a different column name is used instead of 'ID_REF', rename appropriately
124
+ if 'ID_REF' in gene_data.columns:
125
+ gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
126
+ else:
127
+ first_col = gene_data.columns[0]
128
+ gene_data.rename(columns={first_col: 'ID'}, inplace=True)
129
+
130
+ gene_data['ID'] = gene_data['ID'].astype(str)
131
+ gene_data.set_index('ID', inplace=True)
132
+
133
+ # 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
134
+ print(gene_data.index[:20])
135
+ # Based on the numeric IDs (e.g., '1', '2', '3', ...), they do not appear to be standard human gene symbols.
136
+ # They likely need to be mapped to gene symbols.
137
+
138
+ print("requires_gene_mapping = True")
139
+ # STEP5
140
+ # 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
141
+ gene_annotation = get_gene_annotation(soft_file)
142
+
143
+ # 2. Use the 'preview_df' function from the library to preview the data and print out the results.
144
+ print("Gene annotation preview:")
145
+ print(preview_df(gene_annotation))
146
+ # STEP: Gene Identifier Mapping
147
+
148
+ # 1. Decide which columns in the annotation correspond to probe IDs and which correspond to gene symbols.
149
+ # From the preview, 'ID' matches the numeric identifiers seen in 'gene_data', and 'name' holds the gene (miRNA) names.
150
+ probe_col = "ID"
151
+ gene_col = "name"
152
+
153
+ # 2. Get a gene mapping dataframe, which links the probe identifiers to gene symbols.
154
+ mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_col)
155
+
156
+ # 3. Convert probe-level measurements to gene-level expression by applying the mapping rules.
157
+ # This handles probes that map to multiple genes by splitting expression, and sums across probes for each gene.
158
+ gene_data = apply_gene_mapping(gene_data, mapping_df)
159
+ import os
160
+ import pandas as pd
161
+
162
+ # STEP 7: Data Normalization and Linking
163
+
164
+ # First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
165
+ if not os.path.exists(out_clinical_data_file):
166
+ # No trait data file => dataset is not usable for trait analysis
167
+ df_null = pd.DataFrame()
168
+ is_biased = True # Arbitrary boolean to satisfy function requirement
169
+ validate_and_save_cohort_info(
170
+ is_final=True,
171
+ cohort=cohort,
172
+ info_path=json_path,
173
+ is_gene_available=True,
174
+ is_trait_available=False,
175
+ is_biased=is_biased,
176
+ df=df_null,
177
+ note="No trait data file found; dataset not usable for trait analysis."
178
+ )
179
+
180
+ else:
181
+ # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
182
+ normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
183
+ normalized_gene_data.to_csv(out_gene_data_file)
184
+
185
+ # 2. Load the previously extracted clinical CSV.
186
+ selected_clinical_df = pd.read_csv(out_clinical_data_file)
187
+ # If we had a single-row trait, rename row 0 to the trait name (example usage).
188
+ selected_clinical_df = selected_clinical_df.rename(index={0: trait})
189
+
190
+ # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
191
+ combined_clinical_df = selected_clinical_df
192
+
193
+ # Link the clinical and genetic data by matching sample IDs in columns.
194
+ linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
195
+
196
+ # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
197
+ processed_data = handle_missing_values(linked_data, trait)
198
+
199
+ # 4. Check trait bias and remove any biased demographic features (if any).
200
+ trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
201
+
202
+ # 5. Final validation and metadata saving.
203
+ is_usable = validate_and_save_cohort_info(
204
+ is_final=True,
205
+ cohort=cohort,
206
+ info_path=json_path,
207
+ is_gene_available=True,
208
+ is_trait_available=True,
209
+ is_biased=trait_biased,
210
+ df=processed_data,
211
+ note="Completed trait-based preprocessing."
212
+ )
213
+
214
+ # 6. If final dataset is usable, save. Otherwise, skip.
215
+ if is_usable:
216
+ processed_data.to_csv(out_data_file)
p1/preprocess/Post-Traumatic_Stress_Disorder/code/GSE63878.py ADDED
@@ -0,0 +1,216 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Post-Traumatic_Stress_Disorder"
6
+ cohort = "GSE63878"
7
+
8
+ # Input paths
9
+ in_trait_dir = "../DATA/GEO/Post-Traumatic_Stress_Disorder"
10
+ in_cohort_dir = "../DATA/GEO/Post-Traumatic_Stress_Disorder/GSE63878"
11
+
12
+ # Output paths
13
+ out_data_file = "./output/preprocess/1/Post-Traumatic_Stress_Disorder/GSE63878.csv"
14
+ out_gene_data_file = "./output/preprocess/1/Post-Traumatic_Stress_Disorder/gene_data/GSE63878.csv"
15
+ out_clinical_data_file = "./output/preprocess/1/Post-Traumatic_Stress_Disorder/clinical_data/GSE63878.csv"
16
+ json_path = "./output/preprocess/1/Post-Traumatic_Stress_Disorder/cohort_info.json"
17
+
18
+ # STEP1
19
+ from tools.preprocess import *
20
+ # 1. Identify the paths to the SOFT file and the matrix file
21
+ soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
22
+
23
+ # 2. Read the matrix file to obtain background information and sample characteristics data
24
+ background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
25
+ clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
26
+ background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
27
+
28
+ # 3. Obtain the sample characteristics dictionary from the clinical dataframe
29
+ sample_characteristics_dict = get_unique_values_by_row(clinical_data)
30
+
31
+ # 4. Explicitly print out all the background information and the sample characteristics dictionary
32
+ print("Background Information:")
33
+ print(background_info)
34
+ print("Sample Characteristics Dictionary:")
35
+ print(sample_characteristics_dict)
36
+ # 1. Determine if gene expression data is available
37
+ is_gene_available = True # microarray data suggests gene expression is likely available
38
+
39
+ # 2. Identify rows for trait, age, and gender in the sample characteristics dictionary
40
+ trait_row = 1 # "condition: case (PTSD risk/PTSD), control"
41
+ age_row = None # no mention of "age" in sample characteristics
42
+ gender_row = None # no mention of "gender" in sample characteristics
43
+
44
+ # 2.2 Define the conversion functions
45
+ def convert_trait(value: str):
46
+ raw = value.split(':')[-1].strip().lower()
47
+ if 'case' in raw:
48
+ return 1
49
+ elif 'control' in raw:
50
+ return 0
51
+ return None # unknown or unexpected
52
+
53
+ def convert_age(value: str):
54
+ # No age data is available here
55
+ return None
56
+
57
+ def convert_gender(value: str):
58
+ # No gender data is available here
59
+ return None
60
+
61
+ # 3. Initial dataset usability validation and metadata saving
62
+ is_trait_available = (trait_row is not None)
63
+ is_usable = validate_and_save_cohort_info(
64
+ is_final=False,
65
+ cohort=cohort,
66
+ info_path=json_path,
67
+ is_gene_available=is_gene_available,
68
+ is_trait_available=is_trait_available
69
+ )
70
+
71
+ # 4. Extract clinical features if trait data is available
72
+ if trait_row is not None:
73
+ clinical_features_df = geo_select_clinical_features(
74
+ clinical_df=clinical_data,
75
+ trait=trait,
76
+ trait_row=trait_row,
77
+ convert_trait=convert_trait,
78
+ age_row=age_row,
79
+ convert_age=convert_age,
80
+ gender_row=gender_row,
81
+ convert_gender=convert_gender
82
+ )
83
+ # Preview the extracted clinical data
84
+ preview_result = preview_df(clinical_features_df, n=5)
85
+ print(preview_result)
86
+
87
+ # Save the clinical data to CSV
88
+ clinical_features_df.to_csv(out_clinical_data_file, index=False)
89
+ # STEP3
90
+ import gzip
91
+ import pandas as pd
92
+
93
+ try:
94
+ # 1. Attempt to extract gene expression data using the library function
95
+ gene_data = get_genetic_data(matrix_file)
96
+ except KeyError:
97
+ # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
98
+ # and rename the first column to "ID".
99
+ marker = "!series_matrix_table_begin"
100
+ skip_rows = None
101
+
102
+ # Determine how many rows to skip before the matrix data begins
103
+ with gzip.open(matrix_file, 'rt') as f:
104
+ for i, line in enumerate(f):
105
+ if marker in line:
106
+ skip_rows = i + 1
107
+ break
108
+ else:
109
+ raise ValueError(f"Marker '{marker}' not found in the file.")
110
+
111
+ # Read the data from the determined position
112
+ gene_data = pd.read_csv(
113
+ matrix_file,
114
+ compression='gzip',
115
+ skiprows=skip_rows,
116
+ comment='!',
117
+ delimiter='\t',
118
+ on_bad_lines='skip'
119
+ )
120
+
121
+ # If a different column name is used instead of 'ID_REF', rename appropriately
122
+ if 'ID_REF' in gene_data.columns:
123
+ gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
124
+ else:
125
+ first_col = gene_data.columns[0]
126
+ gene_data.rename(columns={first_col: 'ID'}, inplace=True)
127
+
128
+ gene_data['ID'] = gene_data['ID'].astype(str)
129
+ gene_data.set_index('ID', inplace=True)
130
+
131
+ # 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
132
+ print(gene_data.index[:20])
133
+ # Based on the observed numeric probe-like identifiers, these are not conventional human gene symbols.
134
+ # Hence, we determine that gene mapping is required.
135
+ print("requires_gene_mapping = True")
136
+ # STEP5
137
+ # 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
138
+ gene_annotation = get_gene_annotation(soft_file)
139
+
140
+ # 2. Use the 'preview_df' function from the library to preview the data and print out the results.
141
+ print("Gene annotation preview:")
142
+ print(preview_df(gene_annotation))
143
+ # STEP: Gene Identifier Mapping
144
+
145
+ # 1. Determine the proper columns for probe IDs and gene symbols.
146
+ # From the previous preview, "ID" matches our probe identifiers, and "gene_assignment" appears to hold gene symbol info.
147
+ probe_col = "ID"
148
+ symbol_col = "gene_assignment"
149
+
150
+ # 2. Create the gene mapping dataframe using the library function.
151
+ mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=symbol_col)
152
+
153
+ # 3. Convert probe-level measurements to gene-level expression by applying the gene mapping.
154
+ gene_data = apply_gene_mapping(gene_data, mapping_df)
155
+
156
+ # For verification, show the resulting gene_data dimensions and first few gene symbols.
157
+ print("Mapped gene expression data dimensions:", gene_data.shape)
158
+ print("First 10 gene symbols in the index:", list(gene_data.index[:10]))
159
+ import os
160
+ import pandas as pd
161
+
162
+ # STEP 7: Data Normalization and Linking
163
+
164
+ # First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
165
+ if not os.path.exists(out_clinical_data_file):
166
+ # No trait data file => dataset is not usable for trait analysis
167
+ df_null = pd.DataFrame()
168
+ is_biased = True # Arbitrary boolean to satisfy function requirement
169
+ validate_and_save_cohort_info(
170
+ is_final=True,
171
+ cohort=cohort,
172
+ info_path=json_path,
173
+ is_gene_available=True,
174
+ is_trait_available=False,
175
+ is_biased=is_biased,
176
+ df=df_null,
177
+ note="No trait data file found; dataset not usable for trait analysis."
178
+ )
179
+
180
+ else:
181
+ # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
182
+ normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
183
+ normalized_gene_data.to_csv(out_gene_data_file)
184
+
185
+ # 2. Load the previously extracted clinical CSV.
186
+ selected_clinical_df = pd.read_csv(out_clinical_data_file)
187
+ # If we had a single-row trait, rename row 0 to the trait name (example usage).
188
+ selected_clinical_df = selected_clinical_df.rename(index={0: trait})
189
+
190
+ # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
191
+ combined_clinical_df = selected_clinical_df
192
+
193
+ # Link the clinical and genetic data by matching sample IDs in columns.
194
+ linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
195
+
196
+ # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
197
+ processed_data = handle_missing_values(linked_data, trait)
198
+
199
+ # 4. Check trait bias and remove any biased demographic features (if any).
200
+ trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
201
+
202
+ # 5. Final validation and metadata saving.
203
+ is_usable = validate_and_save_cohort_info(
204
+ is_final=True,
205
+ cohort=cohort,
206
+ info_path=json_path,
207
+ is_gene_available=True,
208
+ is_trait_available=True,
209
+ is_biased=trait_biased,
210
+ df=processed_data,
211
+ note="Completed trait-based preprocessing."
212
+ )
213
+
214
+ # 6. If final dataset is usable, save. Otherwise, skip.
215
+ if is_usable:
216
+ processed_data.to_csv(out_data_file)
p1/preprocess/Post-Traumatic_Stress_Disorder/code/GSE64814.py ADDED
@@ -0,0 +1,222 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Post-Traumatic_Stress_Disorder"
6
+ cohort = "GSE64814"
7
+
8
+ # Input paths
9
+ in_trait_dir = "../DATA/GEO/Post-Traumatic_Stress_Disorder"
10
+ in_cohort_dir = "../DATA/GEO/Post-Traumatic_Stress_Disorder/GSE64814"
11
+
12
+ # Output paths
13
+ out_data_file = "./output/preprocess/1/Post-Traumatic_Stress_Disorder/GSE64814.csv"
14
+ out_gene_data_file = "./output/preprocess/1/Post-Traumatic_Stress_Disorder/gene_data/GSE64814.csv"
15
+ out_clinical_data_file = "./output/preprocess/1/Post-Traumatic_Stress_Disorder/clinical_data/GSE64814.csv"
16
+ json_path = "./output/preprocess/1/Post-Traumatic_Stress_Disorder/cohort_info.json"
17
+
18
+ # STEP1
19
+ from tools.preprocess import *
20
+ # 1. Identify the paths to the SOFT file and the matrix file
21
+ soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
22
+
23
+ # 2. Read the matrix file to obtain background information and sample characteristics data
24
+ background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
25
+ clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
26
+ background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
27
+
28
+ # 3. Obtain the sample characteristics dictionary from the clinical dataframe
29
+ sample_characteristics_dict = get_unique_values_by_row(clinical_data)
30
+
31
+ # 4. Explicitly print out all the background information and the sample characteristics dictionary
32
+ print("Background Information:")
33
+ print(background_info)
34
+ print("Sample Characteristics Dictionary:")
35
+ print(sample_characteristics_dict)
36
+ # 1. Determine gene expression data availability
37
+ is_gene_available = True # Based on the dataset background mentioning "Gene Networks...", we consider it gene expression.
38
+
39
+ # 2. Identify rows and define type conversion functions
40
+ trait_row = 1 # row 1 has "condition: case (PTSD risk)", "condition: case (PTSD)", "condition: control"
41
+ age_row = None # No mention of age in the dictionary
42
+ gender_row = None # No mention of gender in the dictionary
43
+
44
+ def convert_trait(x: str) -> Optional[int]:
45
+ # Extract the substring after "condition:" if present
46
+ parts = x.split(":", 1)
47
+ if len(parts) < 2:
48
+ return None
49
+ raw_value = parts[1].strip().lower()
50
+ # Map "case (ptsd risk)" or "case (ptsd)" to 1, "control" to 0
51
+ if "case" in raw_value:
52
+ return 1
53
+ elif "control" in raw_value:
54
+ return 0
55
+ return None
56
+
57
+ def convert_age(x: str) -> Optional[float]:
58
+ # Not applicable here; no age data found
59
+ return None
60
+
61
+ def convert_gender(x: str) -> Optional[int]:
62
+ # Not applicable here; no gender data found
63
+ return None
64
+
65
+ # 3. Conduct initial filtering and save metadata
66
+ is_trait_available = (trait_row is not None)
67
+ is_usable = validate_and_save_cohort_info(
68
+ is_final=False,
69
+ cohort=cohort,
70
+ info_path=json_path,
71
+ is_gene_available=is_gene_available,
72
+ is_trait_available=is_trait_available
73
+ )
74
+
75
+ # 4. Clinical feature extraction (only if trait_row is not None)
76
+ if trait_row is not None:
77
+ selected_clinical_df = geo_select_clinical_features(
78
+ clinical_data,
79
+ trait=trait,
80
+ trait_row=trait_row,
81
+ convert_trait=convert_trait,
82
+ age_row=age_row,
83
+ convert_age=convert_age,
84
+ gender_row=gender_row,
85
+ convert_gender=convert_gender
86
+ )
87
+
88
+ # Preview extracted clinical features
89
+ preview = preview_df(selected_clinical_df)
90
+ print("Clinical Data Preview:", preview)
91
+
92
+ # Save clinical data
93
+ selected_clinical_df.to_csv(out_clinical_data_file, index=False)
94
+ # STEP3
95
+ import gzip
96
+ import pandas as pd
97
+
98
+ try:
99
+ # 1. Attempt to extract gene expression data using the library function
100
+ gene_data = get_genetic_data(matrix_file)
101
+ except KeyError:
102
+ # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
103
+ # and rename the first column to "ID".
104
+ marker = "!series_matrix_table_begin"
105
+ skip_rows = None
106
+
107
+ # Determine how many rows to skip before the matrix data begins
108
+ with gzip.open(matrix_file, 'rt') as f:
109
+ for i, line in enumerate(f):
110
+ if marker in line:
111
+ skip_rows = i + 1
112
+ break
113
+ else:
114
+ raise ValueError(f"Marker '{marker}' not found in the file.")
115
+
116
+ # Read the data from the determined position
117
+ gene_data = pd.read_csv(
118
+ matrix_file,
119
+ compression='gzip',
120
+ skiprows=skip_rows,
121
+ comment='!',
122
+ delimiter='\t',
123
+ on_bad_lines='skip'
124
+ )
125
+
126
+ # If a different column name is used instead of 'ID_REF', rename appropriately
127
+ if 'ID_REF' in gene_data.columns:
128
+ gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
129
+ else:
130
+ first_col = gene_data.columns[0]
131
+ gene_data.rename(columns={first_col: 'ID'}, inplace=True)
132
+
133
+ gene_data['ID'] = gene_data['ID'].astype(str)
134
+ gene_data.set_index('ID', inplace=True)
135
+
136
+ # 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
137
+ print(gene_data.index[:20])
138
+ # Gene Identifier Review
139
+ # Based on these numeric probe-like IDs, they do not appear to be recognized human gene symbols.
140
+ print("These are likely probe identifiers, not common human gene symbols.\nrequires_gene_mapping = True")
141
+ # STEP5
142
+ # 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
143
+ gene_annotation = get_gene_annotation(soft_file)
144
+
145
+ # 2. Use the 'preview_df' function from the library to preview the data and print out the results.
146
+ print("Gene annotation preview:")
147
+ print(preview_df(gene_annotation))
148
+ # STEP: Gene Identifier Mapping
149
+
150
+ # 1. Identify the columns in gene_annotation that correspond to the probe IDs in the gene expression data ("ID")
151
+ # and the gene symbol information (stored in "gene_assignment").
152
+ prob_col_name = "ID"
153
+ gene_col_name = "gene_assignment"
154
+
155
+ # 2. Create a mapping dataframe from the annotation.
156
+ mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col_name, gene_col=gene_col_name)
157
+
158
+ # 3. Convert probe-level expression data to gene-level data.
159
+ gene_data = apply_gene_mapping(gene_data, mapping_df)
160
+
161
+ # Print a summary of the resulting gene-level expression data.
162
+ print("Mapped gene expression dataframe shape:", gene_data.shape)
163
+ print("Gene expression dataframe preview:")
164
+ print(preview_df(gene_data))
165
+ import os
166
+ import pandas as pd
167
+
168
+ # STEP 7: Data Normalization and Linking
169
+
170
+ # First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
171
+ if not os.path.exists(out_clinical_data_file):
172
+ # No trait data file => dataset is not usable for trait analysis
173
+ df_null = pd.DataFrame()
174
+ is_biased = True # Arbitrary boolean to satisfy function requirement
175
+ validate_and_save_cohort_info(
176
+ is_final=True,
177
+ cohort=cohort,
178
+ info_path=json_path,
179
+ is_gene_available=True,
180
+ is_trait_available=False,
181
+ is_biased=is_biased,
182
+ df=df_null,
183
+ note="No trait data file found; dataset not usable for trait analysis."
184
+ )
185
+
186
+ else:
187
+ # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
188
+ normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
189
+ normalized_gene_data.to_csv(out_gene_data_file)
190
+
191
+ # 2. Load the previously extracted clinical CSV.
192
+ selected_clinical_df = pd.read_csv(out_clinical_data_file)
193
+ # If we had a single-row trait, rename row 0 to the trait name (example usage).
194
+ selected_clinical_df = selected_clinical_df.rename(index={0: trait})
195
+
196
+ # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
197
+ combined_clinical_df = selected_clinical_df
198
+
199
+ # Link the clinical and genetic data by matching sample IDs in columns.
200
+ linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
201
+
202
+ # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
203
+ processed_data = handle_missing_values(linked_data, trait)
204
+
205
+ # 4. Check trait bias and remove any biased demographic features (if any).
206
+ trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
207
+
208
+ # 5. Final validation and metadata saving.
209
+ is_usable = validate_and_save_cohort_info(
210
+ is_final=True,
211
+ cohort=cohort,
212
+ info_path=json_path,
213
+ is_gene_available=True,
214
+ is_trait_available=True,
215
+ is_biased=trait_biased,
216
+ df=processed_data,
217
+ note="Completed trait-based preprocessing."
218
+ )
219
+
220
+ # 6. If final dataset is usable, save. Otherwise, skip.
221
+ if is_usable:
222
+ processed_data.to_csv(out_data_file)
p1/preprocess/Post-Traumatic_Stress_Disorder/code/GSE67663.py ADDED
@@ -0,0 +1,253 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Post-Traumatic_Stress_Disorder"
6
+ cohort = "GSE67663"
7
+
8
+ # Input paths
9
+ in_trait_dir = "../DATA/GEO/Post-Traumatic_Stress_Disorder"
10
+ in_cohort_dir = "../DATA/GEO/Post-Traumatic_Stress_Disorder/GSE67663"
11
+
12
+ # Output paths
13
+ out_data_file = "./output/preprocess/1/Post-Traumatic_Stress_Disorder/GSE67663.csv"
14
+ out_gene_data_file = "./output/preprocess/1/Post-Traumatic_Stress_Disorder/gene_data/GSE67663.csv"
15
+ out_clinical_data_file = "./output/preprocess/1/Post-Traumatic_Stress_Disorder/clinical_data/GSE67663.csv"
16
+ json_path = "./output/preprocess/1/Post-Traumatic_Stress_Disorder/cohort_info.json"
17
+
18
+ # STEP1
19
+ from tools.preprocess import *
20
+ # 1. Identify the paths to the SOFT file and the matrix file
21
+ soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
22
+
23
+ # 2. Read the matrix file to obtain background information and sample characteristics data
24
+ background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
25
+ clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
26
+ background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
27
+
28
+ # 3. Obtain the sample characteristics dictionary from the clinical dataframe
29
+ sample_characteristics_dict = get_unique_values_by_row(clinical_data)
30
+
31
+ # 4. Explicitly print out all the background information and the sample characteristics dictionary
32
+ print("Background Information:")
33
+ print(background_info)
34
+ print("Sample Characteristics Dictionary:")
35
+ print(sample_characteristics_dict)
36
+ # 1. Gene Expression Data Availability
37
+ is_gene_available = True # Based on the background info that it's a genome-wide gene expression study.
38
+
39
+ # 2. Variable Availability and Data Type Conversion
40
+
41
+ # From the sample characteristics dictionary, we observe:
42
+ # Row 0 => "Sex: female/male"
43
+ # Row 1 => "age: N"
44
+ # Row 2 => "comorbid ptsd and depression status: 0/1"
45
+ #
46
+ # None of these rows appear constant, so all three are available.
47
+
48
+ trait_row = 2 # "comorbid ptsd and depression status" (binary)
49
+ age_row = 1 # "age" (continuous)
50
+ gender_row = 0 # "Sex" (binary)
51
+
52
+ def convert_trait(value: str) -> int:
53
+ """
54
+ Convert the trait string (e.g., 'comorbid ptsd and depression status: 1')
55
+ to a binary integer 0 or 1. Unknown values are converted to None.
56
+ """
57
+ parts = value.split(':')
58
+ if len(parts) < 2:
59
+ return None
60
+ val_str = parts[1].strip()
61
+ if val_str in ['0', '1']:
62
+ return int(val_str)
63
+ return None
64
+
65
+ def convert_age(value: str) -> float:
66
+ """
67
+ Convert the age string (e.g., 'age: 44') to a float.
68
+ Unknown or invalid values are converted to None.
69
+ """
70
+ parts = value.split(':')
71
+ if len(parts) < 2:
72
+ return None
73
+ val_str = parts[1].strip()
74
+ try:
75
+ return float(val_str)
76
+ except ValueError:
77
+ return None
78
+
79
+ def convert_gender(value: str) -> int:
80
+ """
81
+ Convert the gender string (e.g., 'Sex: male') to a binary integer.
82
+ female -> 0
83
+ male -> 1
84
+ Unknown or invalid values are converted to None.
85
+ """
86
+ parts = value.split(':')
87
+ if len(parts) < 2:
88
+ return None
89
+ val_str = parts[1].strip().lower()
90
+ if val_str == 'female':
91
+ return 0
92
+ elif val_str == 'male':
93
+ return 1
94
+ return None
95
+
96
+ # 3. Initial metadata saving
97
+ # Determine if trait data is available
98
+ is_trait_available = (trait_row is not None)
99
+
100
+ # Perform initial filtering and save
101
+ is_usable = validate_and_save_cohort_info(
102
+ is_final=False,
103
+ cohort=cohort,
104
+ info_path=json_path,
105
+ is_gene_available=is_gene_available,
106
+ is_trait_available=is_trait_available
107
+ )
108
+
109
+ # 4. Clinical Feature Extraction (only if trait_row is not None)
110
+ if trait_row is not None:
111
+ # Assume 'clinical_data' is the DataFrame with sample characteristics
112
+ # from the previous parsing step.
113
+ selected_clinical_df = geo_select_clinical_features(
114
+ clinical_df=clinical_data, # replaced with the variable from previous step
115
+ trait=trait,
116
+ trait_row=trait_row,
117
+ convert_trait=convert_trait,
118
+ age_row=age_row,
119
+ convert_age=convert_age,
120
+ gender_row=gender_row,
121
+ convert_gender=convert_gender
122
+ )
123
+
124
+ # Preview the resulting clinical features
125
+ preview_result = preview_df(selected_clinical_df)
126
+ print("Clinical feature preview:", preview_result)
127
+
128
+ # Save the clinical features
129
+ selected_clinical_df.to_csv(out_clinical_data_file, index=False)
130
+ # STEP3
131
+ import gzip
132
+ import pandas as pd
133
+
134
+ try:
135
+ # 1. Attempt to extract gene expression data using the library function
136
+ gene_data = get_genetic_data(matrix_file)
137
+ except KeyError:
138
+ # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
139
+ # and rename the first column to "ID".
140
+ marker = "!series_matrix_table_begin"
141
+ skip_rows = None
142
+
143
+ # Determine how many rows to skip before the matrix data begins
144
+ with gzip.open(matrix_file, 'rt') as f:
145
+ for i, line in enumerate(f):
146
+ if marker in line:
147
+ skip_rows = i + 1
148
+ break
149
+ else:
150
+ raise ValueError(f"Marker '{marker}' not found in the file.")
151
+
152
+ # Read the data from the determined position
153
+ gene_data = pd.read_csv(
154
+ matrix_file,
155
+ compression='gzip',
156
+ skiprows=skip_rows,
157
+ comment='!',
158
+ delimiter='\t',
159
+ on_bad_lines='skip'
160
+ )
161
+
162
+ # If a different column name is used instead of 'ID_REF', rename appropriately
163
+ if 'ID_REF' in gene_data.columns:
164
+ gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
165
+ else:
166
+ first_col = gene_data.columns[0]
167
+ gene_data.rename(columns={first_col: 'ID'}, inplace=True)
168
+
169
+ gene_data['ID'] = gene_data['ID'].astype(str)
170
+ gene_data.set_index('ID', inplace=True)
171
+
172
+ # 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
173
+ print(gene_data.index[:20])
174
+ # The gene identifiers are Illumina probe IDs and not standard gene symbols, hence they need to be mapped.
175
+ print("requires_gene_mapping = True")
176
+ # STEP5
177
+ # 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
178
+ gene_annotation = get_gene_annotation(soft_file)
179
+
180
+ # 2. Use the 'preview_df' function from the library to preview the data and print out the results.
181
+ print("Gene annotation preview:")
182
+ print(preview_df(gene_annotation))
183
+ # STEP: Gene Identifier Mapping
184
+
185
+ # 1. Decide which columns to map:
186
+ # - "ID" in gene_annotation stores the Illumina probe identifiers.
187
+ # - "Symbol" in gene_annotation stores the gene symbols.
188
+ gene_id_column = "ID"
189
+ gene_symbol_column = "Symbol"
190
+
191
+ # 2. Get the gene mapping dataframe from the gene annotation
192
+ mapping_df = get_gene_mapping(gene_annotation, prob_col=gene_id_column, gene_col=gene_symbol_column)
193
+
194
+ # 3. Convert probe-level measurements to gene-level expression data
195
+ gene_data = apply_gene_mapping(gene_data, mapping_df)
196
+ import os
197
+ import pandas as pd
198
+
199
+ # STEP 7: Data Normalization and Linking
200
+
201
+ # First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
202
+ if not os.path.exists(out_clinical_data_file):
203
+ # No trait data file => dataset is not usable for trait analysis
204
+ df_null = pd.DataFrame()
205
+ is_biased = True # Arbitrary boolean to satisfy function requirement
206
+ validate_and_save_cohort_info(
207
+ is_final=True,
208
+ cohort=cohort,
209
+ info_path=json_path,
210
+ is_gene_available=True,
211
+ is_trait_available=False,
212
+ is_biased=is_biased,
213
+ df=df_null,
214
+ note="No trait data file found; dataset not usable for trait analysis."
215
+ )
216
+
217
+ else:
218
+ # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
219
+ normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
220
+ normalized_gene_data.to_csv(out_gene_data_file)
221
+
222
+ # 2. Load the previously extracted clinical CSV.
223
+ selected_clinical_df = pd.read_csv(out_clinical_data_file)
224
+ # If we had a single-row trait, rename row 0 to the trait name (example usage).
225
+ selected_clinical_df = selected_clinical_df.rename(index={0: trait})
226
+
227
+ # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
228
+ combined_clinical_df = selected_clinical_df
229
+
230
+ # Link the clinical and genetic data by matching sample IDs in columns.
231
+ linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
232
+
233
+ # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
234
+ processed_data = handle_missing_values(linked_data, trait)
235
+
236
+ # 4. Check trait bias and remove any biased demographic features (if any).
237
+ trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
238
+
239
+ # 5. Final validation and metadata saving.
240
+ is_usable = validate_and_save_cohort_info(
241
+ is_final=True,
242
+ cohort=cohort,
243
+ info_path=json_path,
244
+ is_gene_available=True,
245
+ is_trait_available=True,
246
+ is_biased=trait_biased,
247
+ df=processed_data,
248
+ note="Completed trait-based preprocessing."
249
+ )
250
+
251
+ # 6. If final dataset is usable, save. Otherwise, skip.
252
+ if is_usable:
253
+ processed_data.to_csv(out_data_file)
p1/preprocess/Post-Traumatic_Stress_Disorder/code/GSE77164.py ADDED
@@ -0,0 +1,210 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Post-Traumatic_Stress_Disorder"
6
+ cohort = "GSE77164"
7
+
8
+ # Input paths
9
+ in_trait_dir = "../DATA/GEO/Post-Traumatic_Stress_Disorder"
10
+ in_cohort_dir = "../DATA/GEO/Post-Traumatic_Stress_Disorder/GSE77164"
11
+
12
+ # Output paths
13
+ out_data_file = "./output/preprocess/1/Post-Traumatic_Stress_Disorder/GSE77164.csv"
14
+ out_gene_data_file = "./output/preprocess/1/Post-Traumatic_Stress_Disorder/gene_data/GSE77164.csv"
15
+ out_clinical_data_file = "./output/preprocess/1/Post-Traumatic_Stress_Disorder/clinical_data/GSE77164.csv"
16
+ json_path = "./output/preprocess/1/Post-Traumatic_Stress_Disorder/cohort_info.json"
17
+
18
+ # STEP1
19
+ from tools.preprocess import *
20
+ # 1. Identify the paths to the SOFT file and the matrix file
21
+ soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
22
+
23
+ # 2. Read the matrix file to obtain background information and sample characteristics data
24
+ background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
25
+ clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
26
+ background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
27
+
28
+ # 3. Obtain the sample characteristics dictionary from the clinical dataframe
29
+ sample_characteristics_dict = get_unique_values_by_row(clinical_data)
30
+
31
+ # 4. Explicitly print out all the background information and the sample characteristics dictionary
32
+ print("Background Information:")
33
+ print(background_info)
34
+ print("Sample Characteristics Dictionary:")
35
+ print(sample_characteristics_dict)
36
+ # 1) Gene Expression Data Availability
37
+ is_gene_available = True
38
+
39
+ # 2) Variable Availability and Data Type Conversion
40
+ trait_row = 6 # "pts: 0 or pts: 1" matches PTSD symptom presence/absence
41
+ age_row = 2 # "age: {number}" is available and variable
42
+ gender_row = 1 # "female: 1 or female: 0"; we will invert to male=1, female=0
43
+
44
+ def convert_trait(value: str):
45
+ """
46
+ Convert PTSD symptom data ("pts: 0/1") to binary (0 or 1).
47
+ Unknown or invalid values => None.
48
+ """
49
+ try:
50
+ val_str = value.split(':', 1)[1].strip()
51
+ if val_str in ['0', '1']:
52
+ return int(val_str)
53
+ return None
54
+ except:
55
+ return None
56
+
57
+ def convert_age(value: str):
58
+ """
59
+ Convert age ("age: 19", etc.) to continuous (float).
60
+ Unknown or invalid values => None.
61
+ """
62
+ try:
63
+ val_str = value.split(':', 1)[1].strip()
64
+ return float(val_str)
65
+ except:
66
+ return None
67
+
68
+ def convert_gender(value: str):
69
+ """
70
+ Convert "female: 1" => 0 (female), "female: 0" => 1 (male).
71
+ Unknown or invalid values => None.
72
+ """
73
+ try:
74
+ val_str = value.split(':', 1)[1].strip()
75
+ if val_str == '1':
76
+ return 0 # female
77
+ elif val_str == '0':
78
+ return 1 # male
79
+ return None
80
+ except:
81
+ return None
82
+
83
+ # 3) Save Metadata (initial filtering)
84
+ is_trait_available = (trait_row is not None)
85
+ is_usable = validate_and_save_cohort_info(
86
+ is_final=False,
87
+ cohort=cohort,
88
+ info_path=json_path,
89
+ is_gene_available=is_gene_available,
90
+ is_trait_available=is_trait_available
91
+ )
92
+
93
+ # 4) Clinical Feature Extraction (only if trait data is available)
94
+ if trait_row is not None:
95
+ selected_clinical_df = geo_select_clinical_features(
96
+ clinical_df=clinical_data,
97
+ trait=trait, # "Post-Traumatic_Stress_Disorder"
98
+ trait_row=trait_row,
99
+ convert_trait=convert_trait,
100
+ age_row=age_row,
101
+ convert_age=convert_age,
102
+ gender_row=gender_row,
103
+ convert_gender=convert_gender
104
+ )
105
+ preview_result = preview_df(selected_clinical_df)
106
+ print("Preview of selected clinical features:", preview_result)
107
+ selected_clinical_df.to_csv(out_clinical_data_file, index=False)
108
+ # STEP3
109
+ import gzip
110
+ import pandas as pd
111
+
112
+ try:
113
+ # 1. Attempt to extract gene expression data using the library function
114
+ gene_data = get_genetic_data(matrix_file)
115
+ except KeyError:
116
+ # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
117
+ # and rename the first column to "ID".
118
+ marker = "!series_matrix_table_begin"
119
+ skip_rows = None
120
+
121
+ # Determine how many rows to skip before the matrix data begins
122
+ with gzip.open(matrix_file, 'rt') as f:
123
+ for i, line in enumerate(f):
124
+ if marker in line:
125
+ skip_rows = i + 1
126
+ break
127
+ else:
128
+ raise ValueError(f"Marker '{marker}' not found in the file.")
129
+
130
+ # Read the data from the determined position
131
+ gene_data = pd.read_csv(
132
+ matrix_file,
133
+ compression='gzip',
134
+ skiprows=skip_rows,
135
+ comment='!',
136
+ delimiter='\t',
137
+ on_bad_lines='skip'
138
+ )
139
+
140
+ # If a different column name is used instead of 'ID_REF', rename appropriately
141
+ if 'ID_REF' in gene_data.columns:
142
+ gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
143
+ else:
144
+ first_col = gene_data.columns[0]
145
+ gene_data.rename(columns={first_col: 'ID'}, inplace=True)
146
+
147
+ gene_data['ID'] = gene_data['ID'].astype(str)
148
+ gene_data.set_index('ID', inplace=True)
149
+
150
+ # 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
151
+ print(gene_data.index[:20])
152
+ print("\nrequires_gene_mapping = False")
153
+ import os
154
+ import pandas as pd
155
+
156
+ # STEP 7: Data Normalization and Linking
157
+
158
+ # First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
159
+ if not os.path.exists(out_clinical_data_file):
160
+ # No trait data file => dataset is not usable for trait analysis
161
+ df_null = pd.DataFrame()
162
+ is_biased = True # Arbitrary boolean to satisfy function requirement
163
+ validate_and_save_cohort_info(
164
+ is_final=True,
165
+ cohort=cohort,
166
+ info_path=json_path,
167
+ is_gene_available=True,
168
+ is_trait_available=False,
169
+ is_biased=is_biased,
170
+ df=df_null,
171
+ note="No trait data file found; dataset not usable for trait analysis."
172
+ )
173
+
174
+ else:
175
+ # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
176
+ normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
177
+ normalized_gene_data.to_csv(out_gene_data_file)
178
+
179
+ # 2. Load the previously extracted clinical CSV.
180
+ selected_clinical_df = pd.read_csv(out_clinical_data_file)
181
+ # If we had a single-row trait, rename row 0 to the trait name (example usage).
182
+ selected_clinical_df = selected_clinical_df.rename(index={0: trait})
183
+
184
+ # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
185
+ combined_clinical_df = selected_clinical_df
186
+
187
+ # Link the clinical and genetic data by matching sample IDs in columns.
188
+ linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
189
+
190
+ # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
191
+ processed_data = handle_missing_values(linked_data, trait)
192
+
193
+ # 4. Check trait bias and remove any biased demographic features (if any).
194
+ trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
195
+
196
+ # 5. Final validation and metadata saving.
197
+ is_usable = validate_and_save_cohort_info(
198
+ is_final=True,
199
+ cohort=cohort,
200
+ info_path=json_path,
201
+ is_gene_available=True,
202
+ is_trait_available=True,
203
+ is_biased=trait_biased,
204
+ df=processed_data,
205
+ note="Completed trait-based preprocessing."
206
+ )
207
+
208
+ # 6. If final dataset is usable, save. Otherwise, skip.
209
+ if is_usable:
210
+ processed_data.to_csv(out_data_file)
p1/preprocess/Post-Traumatic_Stress_Disorder/code/GSE81761.py ADDED
@@ -0,0 +1,252 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Post-Traumatic_Stress_Disorder"
6
+ cohort = "GSE81761"
7
+
8
+ # Input paths
9
+ in_trait_dir = "../DATA/GEO/Post-Traumatic_Stress_Disorder"
10
+ in_cohort_dir = "../DATA/GEO/Post-Traumatic_Stress_Disorder/GSE81761"
11
+
12
+ # Output paths
13
+ out_data_file = "./output/preprocess/1/Post-Traumatic_Stress_Disorder/GSE81761.csv"
14
+ out_gene_data_file = "./output/preprocess/1/Post-Traumatic_Stress_Disorder/gene_data/GSE81761.csv"
15
+ out_clinical_data_file = "./output/preprocess/1/Post-Traumatic_Stress_Disorder/clinical_data/GSE81761.csv"
16
+ json_path = "./output/preprocess/1/Post-Traumatic_Stress_Disorder/cohort_info.json"
17
+
18
+ # STEP1
19
+ from tools.preprocess import *
20
+ # 1. Identify the paths to the SOFT file and the matrix file
21
+ soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
22
+
23
+ # 2. Read the matrix file to obtain background information and sample characteristics data
24
+ background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
25
+ clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
26
+ background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
27
+
28
+ # 3. Obtain the sample characteristics dictionary from the clinical dataframe
29
+ sample_characteristics_dict = get_unique_values_by_row(clinical_data)
30
+
31
+ # 4. Explicitly print out all the background information and the sample characteristics dictionary
32
+ print("Background Information:")
33
+ print(background_info)
34
+ print("Sample Characteristics Dictionary:")
35
+ print(sample_characteristics_dict)
36
+ # Step 1: Determine if gene expression data is available
37
+ # Based on the background info, this dataset measures gene expression on the Affymetrix chip.
38
+ is_gene_available = True
39
+
40
+ # Step 2: Determine availability and data type of trait, age, and gender; define row indices.
41
+
42
+ # From the sample characteristics dictionary, we see that:
43
+ # - Row 1 has case/control info for PTSD vs. No PTSD
44
+ # - Row 4 has "Sex: Male"/"Sex: Female"
45
+ # - Row 5 has "age: <number>"
46
+ # These rows vary meaningfully, so they are not constant and are available.
47
+
48
+ trait_row = 1 # case/control: PTSD vs. No PTSD
49
+ age_row = 5 # age: ...
50
+ gender_row = 4 # Sex: Male/Female
51
+
52
+ # Step 2.2: Define converters for each variable
53
+
54
+ def convert_trait(value: str):
55
+ # Example string: "case/control: PTSD"
56
+ # Extract the text after the colon
57
+ parts = value.split(":", 1)
58
+ if len(parts) < 2:
59
+ return None
60
+ val = parts[1].strip().lower() # e.g. "ptsd", "no pstd"
61
+ if val == "ptsd":
62
+ return 1
63
+ elif val == "no ptsd":
64
+ return 0
65
+ else:
66
+ return None
67
+
68
+ def convert_age(value: str):
69
+ # Example string: "age: 30"
70
+ parts = value.split(":", 1)
71
+ if len(parts) < 2:
72
+ return None
73
+ val_str = parts[1].strip()
74
+ try:
75
+ return float(val_str)
76
+ except ValueError:
77
+ return None
78
+
79
+ def convert_gender(value: str):
80
+ # Example string: "Sex: Male"
81
+ parts = value.split(":", 1)
82
+ if len(parts) < 2:
83
+ return None
84
+ val = parts[1].strip().lower()
85
+ if val == "male":
86
+ return 1
87
+ elif val == "female":
88
+ return 0
89
+ else:
90
+ return None
91
+
92
+ # Check trait availability based on trait_row
93
+ is_trait_available = (trait_row is not None)
94
+
95
+ # Step 3: Conduct initial filtering and save cohort info
96
+ is_usable = validate_and_save_cohort_info(
97
+ is_final=False,
98
+ cohort=cohort,
99
+ info_path=json_path,
100
+ is_gene_available=is_gene_available,
101
+ is_trait_available=is_trait_available
102
+ )
103
+
104
+ # Step 4: If trait_row is not None, extract clinical features
105
+ if trait_row is not None:
106
+ selected_clinical_df = geo_select_clinical_features(
107
+ clinical_data,
108
+ trait=trait,
109
+ trait_row=trait_row,
110
+ convert_trait=convert_trait,
111
+ age_row=age_row,
112
+ convert_age=convert_age,
113
+ gender_row=gender_row,
114
+ convert_gender=convert_gender
115
+ )
116
+ # Preview the extracted clinical features
117
+ clinical_preview = preview_df(selected_clinical_df)
118
+ print("Clinical Data Preview:", clinical_preview)
119
+
120
+ # Save the clinical data to CSV
121
+ selected_clinical_df.to_csv(out_clinical_data_file, index=False)
122
+ # STEP3
123
+ import gzip
124
+ import pandas as pd
125
+
126
+ try:
127
+ # 1. Attempt to extract gene expression data using the library function
128
+ gene_data = get_genetic_data(matrix_file)
129
+ except KeyError:
130
+ # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
131
+ # and rename the first column to "ID".
132
+ marker = "!series_matrix_table_begin"
133
+ skip_rows = None
134
+
135
+ # Determine how many rows to skip before the matrix data begins
136
+ with gzip.open(matrix_file, 'rt') as f:
137
+ for i, line in enumerate(f):
138
+ if marker in line:
139
+ skip_rows = i + 1
140
+ break
141
+ else:
142
+ raise ValueError(f"Marker '{marker}' not found in the file.")
143
+
144
+ # Read the data from the determined position
145
+ gene_data = pd.read_csv(
146
+ matrix_file,
147
+ compression='gzip',
148
+ skiprows=skip_rows,
149
+ comment='!',
150
+ delimiter='\t',
151
+ on_bad_lines='skip'
152
+ )
153
+
154
+ # If a different column name is used instead of 'ID_REF', rename appropriately
155
+ if 'ID_REF' in gene_data.columns:
156
+ gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
157
+ else:
158
+ first_col = gene_data.columns[0]
159
+ gene_data.rename(columns={first_col: 'ID'}, inplace=True)
160
+
161
+ gene_data['ID'] = gene_data['ID'].astype(str)
162
+ gene_data.set_index('ID', inplace=True)
163
+
164
+ # 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
165
+ print(gene_data.index[:20])
166
+ print("requires_gene_mapping = True")
167
+ # STEP5
168
+ # 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
169
+ gene_annotation = get_gene_annotation(soft_file)
170
+
171
+ # 2. Use the 'preview_df' function from the library to preview the data and print out the results.
172
+ print("Gene annotation preview:")
173
+ print(preview_df(gene_annotation))
174
+ # STEP: Gene Identifier Mapping
175
+
176
+ # 1. Identify the columns in the gene annotation that correspond to probe identifiers ("ID")
177
+ # and gene symbols ("Gene Symbol").
178
+ probe_id_col = "ID"
179
+ gene_symbol_col = "Gene Symbol"
180
+
181
+ # 2. Get the gene mapping dataframe.
182
+ mapping_df = get_gene_mapping(
183
+ gene_annotation,
184
+ prob_col=probe_id_col,
185
+ gene_col=gene_symbol_col
186
+ )
187
+
188
+ # 3. Convert probe-level measurements to gene-level expression.
189
+ gene_data = apply_gene_mapping(gene_data, mapping_df)
190
+
191
+ # (Optional) Check the resulting gene expression data structure.
192
+ print("Gene expression data dimensions:", gene_data.shape)
193
+ print("Gene expression data preview:")
194
+ print(gene_data.head())
195
+ import os
196
+ import pandas as pd
197
+
198
+ # STEP 7: Data Normalization and Linking
199
+
200
+ # First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
201
+ if not os.path.exists(out_clinical_data_file):
202
+ # No trait data file => dataset is not usable for trait analysis
203
+ df_null = pd.DataFrame()
204
+ is_biased = True # Arbitrary boolean to satisfy function requirement
205
+ validate_and_save_cohort_info(
206
+ is_final=True,
207
+ cohort=cohort,
208
+ info_path=json_path,
209
+ is_gene_available=True,
210
+ is_trait_available=False,
211
+ is_biased=is_biased,
212
+ df=df_null,
213
+ note="No trait data file found; dataset not usable for trait analysis."
214
+ )
215
+
216
+ else:
217
+ # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
218
+ normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
219
+ normalized_gene_data.to_csv(out_gene_data_file)
220
+
221
+ # 2. Load the previously extracted clinical CSV.
222
+ selected_clinical_df = pd.read_csv(out_clinical_data_file)
223
+ # If we had a single-row trait, rename row 0 to the trait name (example usage).
224
+ selected_clinical_df = selected_clinical_df.rename(index={0: trait})
225
+
226
+ # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
227
+ combined_clinical_df = selected_clinical_df
228
+
229
+ # Link the clinical and genetic data by matching sample IDs in columns.
230
+ linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
231
+
232
+ # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
233
+ processed_data = handle_missing_values(linked_data, trait)
234
+
235
+ # 4. Check trait bias and remove any biased demographic features (if any).
236
+ trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
237
+
238
+ # 5. Final validation and metadata saving.
239
+ is_usable = validate_and_save_cohort_info(
240
+ is_final=True,
241
+ cohort=cohort,
242
+ info_path=json_path,
243
+ is_gene_available=True,
244
+ is_trait_available=True,
245
+ is_biased=trait_biased,
246
+ df=processed_data,
247
+ note="Completed trait-based preprocessing."
248
+ )
249
+
250
+ # 6. If final dataset is usable, save. Otherwise, skip.
251
+ if is_usable:
252
+ processed_data.to_csv(out_data_file)
p1/preprocess/Post-Traumatic_Stress_Disorder/code/TCGA.py ADDED
@@ -0,0 +1,74 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Post-Traumatic_Stress_Disorder"
6
+
7
+ # Input paths
8
+ tcga_root_dir = "../DATA/TCGA"
9
+
10
+ # Output paths
11
+ out_data_file = "./output/preprocess/1/Post-Traumatic_Stress_Disorder/TCGA.csv"
12
+ out_gene_data_file = "./output/preprocess/1/Post-Traumatic_Stress_Disorder/gene_data/TCGA.csv"
13
+ out_clinical_data_file = "./output/preprocess/1/Post-Traumatic_Stress_Disorder/clinical_data/TCGA.csv"
14
+ json_path = "./output/preprocess/1/Post-Traumatic_Stress_Disorder/cohort_info.json"
15
+
16
+ import os
17
+ import pandas as pd
18
+
19
+ # List of subdirectories provided in the instructions:
20
+ subdirectories = [
21
+ 'CrawlData.ipynb', '.DS_Store', 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)',
22
+ 'TCGA_Uterine_Carcinosarcoma_(UCS)', 'TCGA_Thyroid_Cancer_(THCA)', 'TCGA_Thymoma_(THYM)',
23
+ 'TCGA_Testicular_Cancer_(TGCT)', 'TCGA_Stomach_Cancer_(STAD)', 'TCGA_Sarcoma_(SARC)',
24
+ 'TCGA_Rectal_Cancer_(READ)', 'TCGA_Prostate_Cancer_(PRAD)', 'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)',
25
+ 'TCGA_Pancreatic_Cancer_(PAAD)', 'TCGA_Ovarian_Cancer_(OV)', 'TCGA_Ocular_melanomas_(UVM)',
26
+ 'TCGA_Mesothelioma_(MESO)', 'TCGA_Melanoma_(SKCM)', 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)',
27
+ 'TCGA_Lung_Cancer_(LUNG)', 'TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lower_Grade_Glioma_(LGG)',
28
+ 'TCGA_Liver_Cancer_(LIHC)', 'TCGA_Large_Bcell_Lymphoma_(DLBC)', 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)',
29
+ 'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)', 'TCGA_Kidney_Chromophobe_(KICH)', 'TCGA_Head_and_Neck_Cancer_(HNSC)',
30
+ 'TCGA_Glioblastoma_(GBM)', 'TCGA_Esophageal_Cancer_(ESCA)', 'TCGA_Endometrioid_Cancer_(UCEC)',
31
+ 'TCGA_Colon_and_Rectal_Cancer_(COADREAD)', 'TCGA_Colon_Cancer_(COAD)', 'TCGA_Cervical_Cancer_(CESC)',
32
+ 'TCGA_Breast_Cancer_(BRCA)', 'TCGA_Bladder_Cancer_(BLCA)', 'TCGA_Bile_Duct_Cancer_(CHOL)',
33
+ 'TCGA_Adrenocortical_Cancer_(ACC)', 'TCGA_Acute_Myeloid_Leukemia_(LAML)'
34
+ ]
35
+
36
+ # Synonyms or terms possibly related to our target trait "Post-Traumatic_Stress_Disorder"
37
+ trait_synonyms = [
38
+ "post-traumatic_stress_disorder", "ptsd"
39
+ ]
40
+
41
+ selected_subdirectory = None
42
+ for subdir in subdirectories:
43
+ if subdir.lower() in ['crawldata.ipynb', '.ds_store']:
44
+ continue
45
+ subdir_lower = subdir.lower()
46
+ if any(syn in subdir_lower for syn in trait_synonyms):
47
+ selected_subdirectory = subdir
48
+ break
49
+
50
+ if not selected_subdirectory:
51
+ # If no matching directory is found, mark dataset as unavailable
52
+ is_final = False
53
+ is_gene_available = False
54
+ is_trait_available = False
55
+ _ = validate_and_save_cohort_info(
56
+ is_final=is_final,
57
+ cohort="TCGA",
58
+ info_path=json_path,
59
+ is_gene_available=is_gene_available,
60
+ is_trait_available=is_trait_available
61
+ )
62
+ print(f"No suitable directory found for '{trait}'. Skipped this trait.")
63
+ else:
64
+ # Step 2: Identify clinicalMatrix file and PANCAN file
65
+ cohort_dir = os.path.join(tcga_root_dir, selected_subdirectory)
66
+ clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
67
+
68
+ # Step 3: Load both files as dataframes
69
+ clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
70
+ genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')
71
+
72
+ # Step 4: Print the column names of the clinical data
73
+ print("Clinical data columns:")
74
+ print(list(clinical_df.columns))
p1/preprocess/Post-Traumatic_Stress_Disorder/cohort_info.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"GSE81761": {"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": 109, "note": "Completed trait-based preprocessing."}, "GSE77164": {"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": 254, "note": "Completed trait-based preprocessing."}, "GSE67663": {"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": 184, "note": "Completed trait-based preprocessing."}, "GSE64814": {"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": 96, "note": "Completed trait-based preprocessing."}, "GSE63878": {"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": 96, "note": "Completed trait-based preprocessing."}, "GSE52875": {"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 file found; dataset not usable for trait analysis."}, "GSE44456": {"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 file found; dataset not usable for trait analysis."}, "GSE199841": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 48, "note": "Completed trait-based preprocessing."}, "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/Prostate_Cancer/clinical_data/GSE192817.csv ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ GSM5766129,GSM5766130,GSM5766131,GSM5766132,GSM5766133,GSM5766134,GSM5766135,GSM5766136,GSM5766137,GSM5766138,GSM5766139,GSM5766140,GSM5766141,GSM5766142,GSM5766143,GSM5766144,GSM5766145,GSM5766146,GSM5766147,GSM5766148,GSM5766149,GSM5766150,GSM5766151,GSM5766152,GSM5766153,GSM5766154,GSM5766155,GSM5766156,GSM5766157,GSM5766158,GSM5766159,GSM5766160,GSM5766161,GSM5766162,GSM5766163,GSM5766164,GSM5766165,GSM5766166,GSM5766167,GSM5766168,GSM5766169,GSM5766170,GSM5766171,GSM5766172,GSM5766173,GSM5766174,GSM5766175,GSM5766176,GSM5766177,GSM5766178,GSM5766179,GSM5766180
2
+ 1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
p1/preprocess/Prostate_Cancer/clinical_data/GSE200879.csv ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ GSM6045848,GSM6045849,GSM6045850,GSM6045851,GSM6045852,GSM6045853,GSM6045854,GSM6045855,GSM6045856,GSM6045857,GSM6045858,GSM6045859,GSM6045860,GSM6045861,GSM6045862,GSM6045863,GSM6045864,GSM6045865,GSM6045866,GSM6045867,GSM6045868,GSM6045869,GSM6045870,GSM6045871,GSM6045872,GSM6045873,GSM6045874,GSM6045875,GSM6045876,GSM6045877,GSM6045878,GSM6045879,GSM6045880,GSM6045881,GSM6045882,GSM6045883,GSM6045884,GSM6045885,GSM6045886,GSM6045887,GSM6045888,GSM6045889,GSM6045890,GSM6045891,GSM6045892,GSM6045893,GSM6045894,GSM6045895,GSM6045896,GSM6045897,GSM6045898,GSM6045899,GSM6045900,GSM6045901,GSM6045902,GSM6045903,GSM6045904,GSM6045905,GSM6045906,GSM6045907,GSM6045908,GSM6045909,GSM6045910,GSM6045911,GSM6045912,GSM6045913,GSM6045914,GSM6045915,GSM6045916,GSM6045917,GSM6045918,GSM6045919,GSM6045920,GSM6045921,GSM6045922,GSM6045923,GSM6045924,GSM6045925,GSM6045926,GSM6045927,GSM6045928,GSM6045929,GSM6045930,GSM6045931,GSM6045932,GSM6045933,GSM6045934,GSM6045935,GSM6045936,GSM6045937,GSM6045938,GSM6045939,GSM6045940,GSM6045941,GSM6045942,GSM6045943,GSM6045944,GSM6045945,GSM6045946,GSM6045947,GSM6045948,GSM6045949,GSM6045950,GSM6045951,GSM6045952,GSM6045953,GSM6045954,GSM6045955,GSM6045956,GSM6045957,GSM6045958,GSM6045959,GSM6045960,GSM6045961,GSM6045962,GSM6045963,GSM6045964,GSM6045965,GSM6045966,GSM6045967,GSM6045968,GSM6045969,GSM6045970,GSM6045971,GSM6045972,GSM6045973,GSM6045974,GSM6045975,GSM6045976,GSM6045977,GSM6045978,GSM6045979,GSM6045980,GSM6045981,GSM6045982,GSM6045983,GSM6045984
2
+ 1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
p1/preprocess/Prostate_Cancer/clinical_data/GSE206793.csv ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ GSM6262785,GSM6262786,GSM6262787,GSM6262788,GSM6262789,GSM6262790,GSM6262791,GSM6262792,GSM6262793,GSM6262794,GSM6262795,GSM6262796,GSM6262797,GSM6262798,GSM6262799,GSM6262800,GSM6262801,GSM6262802,GSM6262803,GSM6262804,GSM6262805,GSM6262806,GSM6262807,GSM6262808,GSM6262809,GSM6262810,GSM6262811,GSM6262812,GSM6262813,GSM6262814,GSM6262815,GSM6262816,GSM6262817,GSM6262818,GSM6262819,GSM6262820,GSM6262821,GSM6262822,GSM6262823,GSM6262824,GSM6262825,GSM6262826,GSM6262827,GSM6262828,GSM6262829,GSM6262830,GSM6262831,GSM6262832,GSM6262833,GSM6262834,GSM6262835,GSM6262836,GSM6262837,GSM6262838,GSM6262839,GSM6262840,GSM6262841,GSM6262842,GSM6262843,GSM6262844,GSM6262845,GSM6262846,GSM6262847,GSM6262848,GSM6262849,GSM6262850,GSM6262851,GSM6262852,GSM6262853,GSM6262854,GSM6262855,GSM6262856,GSM6262857,GSM6262858,GSM6262859,GSM6262860,GSM6262861,GSM6262862,GSM6262863,GSM6262864,GSM6262865,GSM6262866,GSM6262867,GSM6262868,GSM6262869,GSM6262870,GSM6262871,GSM6262872,GSM6262873,GSM6262874,GSM6262875,GSM6262876,GSM6262877,GSM6262878,GSM6262879,GSM6262880,GSM6262881,GSM6262882,GSM6262883,GSM6262884,GSM6262885,GSM6262886,GSM6262887,GSM6262888,GSM6262889,GSM6262890,GSM6262891,GSM6262892,GSM6262893,GSM6262894,GSM6262895,GSM6262896,GSM6262897,GSM6262898,GSM6262899,GSM6262900,GSM6262901,GSM6262902,GSM6262903,GSM6262904,GSM6262905,GSM6262906,GSM6262907,GSM6262908,GSM6262909,GSM6262910,GSM6262911,GSM6262912,GSM6262913,GSM6262914,GSM6262915,GSM6262916,GSM6262917,GSM6262918,GSM6262919,GSM6262920,GSM6262921,GSM6262922,GSM6262923,GSM6262924,GSM6262925,GSM6262926,GSM6262927,GSM6262928,GSM6262929,GSM6262930,GSM6262931,GSM6262932,GSM6262933,GSM6262934,GSM6262935,GSM6262936
2
+ 1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0
3
+ 60.0,67.0,75.0,66.0,58.0,60.0,64.0,67.0,65.0,70.0,67.0,61.0,63.0,65.0,60.0,56.0,57.0,68.0,59.0,71.0,68.0,68.0,62.0,58.0,66.0,61.0,70.0,61.0,76.0,64.0,64.0,64.0,76.0,68.0,67.0,64.0,58.0,62.0,65.0,48.0,73.0,65.0,58.0,,59.0,72.0,48.0,68.0,71.0,68.0,66.0,62.0,55.0,66.0,63.0,79.0,80.0,67.0,62.0,63.0,78.0,61.0,64.0,64.0,80.0,67.0,56.0,67.0,64.0,62.0,55.0,82.0,62.0,69.0,60.0,68.0,73.0,62.0,73.0,64.0,66.0,71.0,80.0,,56.0,66.0,58.0,74.0,56.0,48.0,63.0,77.0,68.0,68.0,61.0,78.0,68.0,60.0,73.0,57.0,67.0,83.0,61.0,65.0,66.0,74.0,63.0,68.0,73.0,75.0,65.0,65.0,74.0,66.0,54.0,60.0,59.0,69.0,62.0,61.0,69.0,52.0,49.0,,66.0,67.0,55.0,69.0,59.0,68.0,45.0,59.0,58.0,60.0,68.0,49.0,72.0,58.0,65.0,54.0,68.0,51.0,60.0,57.0,64.0,47.0,66.0,32.0,30.0,29.0,34.0,33.0
p1/preprocess/Prostate_Cancer/clinical_data/GSE248619.csv ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ GSM7918062,GSM7918063,GSM7918064,GSM7918065,GSM7918066,GSM7918067,GSM7918068,GSM7918069,GSM7918070,GSM7918071,GSM7918072,GSM7918073,GSM7918074,GSM7918075,GSM7918076,GSM7918077,GSM7918078,GSM7918079,GSM7918080,GSM7918081,GSM7918082,GSM7918083,GSM7918084,GSM7918085,GSM7918086,GSM7918087,GSM7918088,GSM7918089,GSM7918090,GSM7918091,GSM7918092,GSM7918093,GSM7918094,GSM7918095,GSM7918096,GSM7918097,GSM7918098,GSM7918099,GSM7918100,GSM7918101,GSM7918102,GSM7918103,GSM7918104,GSM7918105,GSM7918106,GSM7918107,GSM7918108,GSM7918109,GSM7918110,GSM7918111,GSM7918112,GSM7918113,GSM7918114,GSM7918115,GSM7918116,GSM7918117,GSM7918118,GSM7918119,GSM7918120,GSM7918121,GSM7918122,GSM7918123,GSM7918124,GSM7918125,GSM7918126,GSM7918127,GSM7918128,GSM7918129,GSM7918130,GSM7918131,GSM7918132,GSM7918133,GSM7918134,GSM7918135,GSM7918136,GSM7918137,GSM7918138,GSM7918139,GSM7918140,GSM7918141,GSM7918142,GSM7918143,GSM7918144,GSM7918145,GSM7918146,GSM7918147,GSM7918148,GSM7918149,GSM7918150,GSM7918151,GSM7918152,GSM7918153,GSM7918154,GSM7918155,GSM7918156,GSM7918157,GSM7918158,GSM7918159,GSM7918160,GSM7918161
2
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p1/preprocess/Prostate_Cancer/clinical_data/TCGA.csv ADDED
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1
+ ,Prostate_Cancer,Age
2
+ TCGA-2A-A8VL-01,1,51
3
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4
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p1/preprocess/Prostate_Cancer/code/GSE125341.py ADDED
@@ -0,0 +1,150 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Prostate_Cancer"
6
+ cohort = "GSE125341"
7
+
8
+ # Input paths
9
+ in_trait_dir = "../DATA/GEO/Prostate_Cancer"
10
+ in_cohort_dir = "../DATA/GEO/Prostate_Cancer/GSE125341"
11
+
12
+ # Output paths
13
+ out_data_file = "./output/preprocess/1/Prostate_Cancer/GSE125341.csv"
14
+ out_gene_data_file = "./output/preprocess/1/Prostate_Cancer/gene_data/GSE125341.csv"
15
+ out_clinical_data_file = "./output/preprocess/1/Prostate_Cancer/clinical_data/GSE125341.csv"
16
+ json_path = "./output/preprocess/1/Prostate_Cancer/cohort_info.json"
17
+
18
+ # STEP1
19
+ from tools.preprocess import *
20
+ # 1. Identify the paths to the SOFT file and the matrix file
21
+ soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
22
+
23
+ # 2. Read the matrix file to obtain background information and sample characteristics data
24
+ background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
25
+ clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
26
+ background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
27
+
28
+ # 3. Obtain the sample characteristics dictionary from the clinical dataframe
29
+ sample_characteristics_dict = get_unique_values_by_row(clinical_data)
30
+
31
+ # 4. Explicitly print out all the background information and the sample characteristics dictionary
32
+ print("Background Information:")
33
+ print(background_info)
34
+ print("Sample Characteristics Dictionary:")
35
+ print(sample_characteristics_dict)
36
+ # 1. Gene Expression Data Availability
37
+ is_gene_available = True # The background information indicates a transcriptome-wide expression study.
38
+
39
+ # 2. Variable Availability and Data Type Conversion
40
+ # From the sample characteristics, all samples derive from a single prostate cancer
41
+ # cell line (LNCaP), and there is no mention of distinct age or gender data.
42
+ # Hence, all variables (trait, age, gender) effectively provide no variation.
43
+
44
+ trait_row = None
45
+ age_row = None
46
+ gender_row = None
47
+
48
+ # Define the conversion functions as requested, even though they will not be used
49
+ # because the corresponding *_row variables are None.
50
+
51
+ def convert_trait(value: str):
52
+ # Typically parse after the colon, but there's no actual varied data here.
53
+ # Return None to indicate no recognized value.
54
+ return None
55
+
56
+ def convert_age(value: str):
57
+ # Usually parse numeric age data here, but it's not available.
58
+ return None
59
+
60
+ def convert_gender(value: str):
61
+ # Usually parse "male"->1, "female"->0, but it's not available.
62
+ return None
63
+
64
+ # 3. Save Metadata with initial filtering.
65
+ # Trait data is considered available only if trait_row is not None.
66
+ is_trait_available = trait_row is not None
67
+
68
+ validate_and_save_cohort_info(
69
+ is_final=False,
70
+ cohort=cohort,
71
+ info_path=json_path,
72
+ is_gene_available=is_gene_available,
73
+ is_trait_available=is_trait_available
74
+ )
75
+
76
+ # 4. Clinical Feature Extraction
77
+ # Skip because trait_row is None (trait data not available).
78
+ # STEP3
79
+ # 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
80
+ gene_data = get_genetic_data(matrix_file)
81
+
82
+ # 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
83
+ print(gene_data.index[:20])
84
+ # Based on observation, these identifiers (e.g., A_14_P100100) are probe IDs, not human gene symbols.
85
+ # Therefore, they require mapping to gene symbols.
86
+ print("requires_gene_mapping = True")
87
+ # STEP5
88
+ # 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
89
+ gene_annotation = get_gene_annotation(soft_file)
90
+
91
+ # 2. Use the 'preview_df' function from the library to preview the data and print out the results.
92
+ print("Gene annotation preview:")
93
+ print(preview_df(gene_annotation))
94
+ # STEP: Gene Identifier Mapping
95
+
96
+ # 1. Identify the columns in the gene_annotation DataFrame that correspond to the probe IDs and gene symbols.
97
+ # From the preview, the probe IDs match the 'ID' or 'SPOT_ID' column, but since we see 'ID' column is exactly
98
+ # the probe identifier from our gene_data, we'll use 'ID' as the probe column. The gene symbol is in the 'symbol' column.
99
+
100
+ # 2. Extract the two columns (probe and symbol) from the gene_annotation DataFrame to get the mapping.
101
+ mapping_df = get_gene_mapping(annotation=gene_annotation, prob_col='ID', gene_col='symbol')
102
+
103
+ # 3. Apply the mapping to convert probe-level data to gene-level data.
104
+ gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
105
+ # STEP7
106
+
107
+ # We know from a previous step that trait_row was None, so the trait is not actually available.
108
+ # Therefore, we should skip all steps that rely on the trait column. We'll still normalize gene data
109
+ # and finalize metadata to reflect that no trait data is available.
110
+
111
+ # 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
112
+ normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
113
+ normalized_gene_data.to_csv(out_gene_data_file)
114
+
115
+ # 2. Since trait_row is None, we do NOT attempt to link clinical data or handle missing values for the trait.
116
+ # Instead, we directly finalize metadata indicating that trait data is not available.
117
+ validate_and_save_cohort_info(
118
+ is_final=True,
119
+ cohort=cohort,
120
+ info_path=json_path,
121
+ is_gene_available=True, # We do have gene expression data
122
+ is_trait_available=False, # Trait is not available
123
+ note="No trait or demographic data available for association analysis."
124
+ )
125
+
126
+ # 3. Because the dataset lacks a trait column, it is unusable for trait-based research. We do NOT save any linked data.
127
+ # STEP8
128
+
129
+ # 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function
130
+ normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
131
+ normalized_gene_data.to_csv(out_gene_data_file)
132
+
133
+ # Because trait data is not available (trait_row was None), we skip linking clinical data and trait-based analyses.
134
+
135
+ # 2. Perform final validation and save cohort info.
136
+ # Per the library requirements, we must provide 'df' and 'is_biased' even though trait is unavailable.
137
+ # Setting 'is_biased=False' does not indicate the trait is balanced; rather, we are forced to supply a boolean.
138
+ # The function will mark the dataset as unusable because is_trait_available=False.
139
+ validate_and_save_cohort_info(
140
+ is_final=True,
141
+ cohort=cohort,
142
+ info_path=json_path,
143
+ is_gene_available=True,
144
+ is_trait_available=False,
145
+ df=normalized_gene_data,
146
+ is_biased=False,
147
+ note="No trait or demographic data is available for association analysis."
148
+ )
149
+
150
+ # 3. Since the dataset is not usable for trait-based analysis, we do not save any final linked data.
p1/preprocess/Prostate_Cancer/code/GSE178631.py ADDED
@@ -0,0 +1,139 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Prostate_Cancer"
6
+ cohort = "GSE178631"
7
+
8
+ # Input paths
9
+ in_trait_dir = "../DATA/GEO/Prostate_Cancer"
10
+ in_cohort_dir = "../DATA/GEO/Prostate_Cancer/GSE178631"
11
+
12
+ # Output paths
13
+ out_data_file = "./output/preprocess/1/Prostate_Cancer/GSE178631.csv"
14
+ out_gene_data_file = "./output/preprocess/1/Prostate_Cancer/gene_data/GSE178631.csv"
15
+ out_clinical_data_file = "./output/preprocess/1/Prostate_Cancer/clinical_data/GSE178631.csv"
16
+ json_path = "./output/preprocess/1/Prostate_Cancer/cohort_info.json"
17
+
18
+ # STEP1
19
+ from tools.preprocess import *
20
+ # 1. Identify the paths to the SOFT file and the matrix file
21
+ soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
22
+
23
+ # 2. Read the matrix file to obtain background information and sample characteristics data
24
+ background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
25
+ clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
26
+ background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
27
+
28
+ # 3. Obtain the sample characteristics dictionary from the clinical dataframe
29
+ sample_characteristics_dict = get_unique_values_by_row(clinical_data)
30
+
31
+ # 4. Explicitly print out all the background information and the sample characteristics dictionary
32
+ print("Background Information:")
33
+ print(background_info)
34
+ print("Sample Characteristics Dictionary:")
35
+ print(sample_characteristics_dict)
36
+ # Step 1: Determine gene expression data availability
37
+ is_gene_available = True # Based on background info indicating a 32-gene signature
38
+
39
+ # Step 2.1: Identify data availability
40
+ # After reviewing the sample characteristics, we see:
41
+ # - The entire cohort is prostate cancer => no variability for trait (only "Adeno carcinoma" or all are "Prostate_Cancer")
42
+ # - No "age" or "gender" entries appear in the dictionary
43
+ # => mark them as not available
44
+ trait_row = None
45
+ age_row = None
46
+ gender_row = None
47
+
48
+ # Step 2.2: Define data type conversion functions
49
+ import pandas as pd
50
+
51
+ def convert_trait(x: str):
52
+ if pd.isna(x):
53
+ return None
54
+ # Typically split by ':', pick the latter part
55
+ parts = x.split(':', 1)
56
+ val = parts[-1].strip().lower() if len(parts) > 1 else x.strip().lower()
57
+ # Since trait data is not truly meaningful (dataset is all prostate cancer), return None
58
+ return None
59
+
60
+ def convert_age(x: str):
61
+ if pd.isna(x):
62
+ return None
63
+ parts = x.split(':', 1)
64
+ val = parts[-1].strip().lower() if len(parts) > 1 else x.strip().lower()
65
+ # No actual age data => return None
66
+ return None
67
+
68
+ def convert_gender(x: str):
69
+ if pd.isna(x):
70
+ return None
71
+ parts = x.split(':', 1)
72
+ val = parts[-1].strip().lower() if len(parts) > 1 else x.strip().lower()
73
+ # No actual gender data => return None
74
+ return None
75
+
76
+ # Step 3: Conduct initial filtering and save metadata
77
+ # trait data is considered unavailable if trait_row is None
78
+ is_trait_available = (trait_row is not None)
79
+
80
+ is_usable = validate_and_save_cohort_info(
81
+ is_final=False,
82
+ cohort=cohort,
83
+ info_path=json_path,
84
+ is_gene_available=is_gene_available,
85
+ is_trait_available=is_trait_available
86
+ )
87
+
88
+ # Step 4: Since trait_row is None, we skip clinical feature extraction
89
+ # STEP3
90
+ # 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
91
+ gene_data = get_genetic_data(matrix_file)
92
+
93
+ # 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
94
+ print(gene_data.index[:20])
95
+ # Based on the observed identifiers (e.g., ILMN_1343291), these are Illumina probe IDs rather than human gene symbols.
96
+ # They will need to be mapped to gene symbols for downstream analysis.
97
+ print("requires_gene_mapping = True")
98
+ # STEP5
99
+ # 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
100
+ gene_annotation = get_gene_annotation(soft_file)
101
+
102
+ # 2. Use the 'preview_df' function from the library to preview the data and print out the results.
103
+ print("Gene annotation preview:")
104
+ print(preview_df(gene_annotation))
105
+ # STEP6: Gene Identifier Mapping
106
+
107
+ # 1. Identify columns in 'gene_annotation' that match the probe ID in our expression data, and the column with gene symbols.
108
+ # From the preview, we see that 'ID' is the Illumina probe identifier (e.g., "ILMN_1343048"),
109
+ # and 'Symbol' holds the gene symbols (e.g., "thrB").
110
+ probe_col = 'ID'
111
+ symbol_col = 'Symbol'
112
+
113
+ # 2. Get the mapping dataframe with just these two columns.
114
+ mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=symbol_col)
115
+
116
+ # 3. Convert probe-level measurements in 'gene_data' to gene-level measurements using 'apply_gene_mapping'
117
+ gene_data = apply_gene_mapping(gene_data, mapping_df)
118
+
119
+ # (The resulting gene_data now contains expression values aggregated at the gene symbol level.)
120
+ # STEP7
121
+ # Since from previous steps we concluded that trait data is not available (trait_row=None),
122
+ # we cannot perform a final trait-based analysis. Instead, we'll do partial validation.
123
+
124
+ # 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
125
+ normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
126
+ normalized_gene_data.to_csv(out_gene_data_file)
127
+
128
+ # 2. Since trait data is unavailable, we skip linking clinical data and skip any final trait-based validation.
129
+
130
+ # 3. Conduct partial validation (is_final=False) and save metadata indicating that the dataset lacks trait data.
131
+ is_usable = validate_and_save_cohort_info(
132
+ is_final=False,
133
+ cohort=cohort,
134
+ info_path=json_path,
135
+ is_gene_available=True, # Gene data was found
136
+ is_trait_available=False # Trait data not available
137
+ )
138
+
139
+ # No final dataset can be produced without trait data, so we do not generate out_data_file.
p1/preprocess/Prostate_Cancer/code/GSE192817.py ADDED
@@ -0,0 +1,137 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Prostate_Cancer"
6
+ cohort = "GSE192817"
7
+
8
+ # Input paths
9
+ in_trait_dir = "../DATA/GEO/Prostate_Cancer"
10
+ in_cohort_dir = "../DATA/GEO/Prostate_Cancer/GSE192817"
11
+
12
+ # Output paths
13
+ out_data_file = "./output/preprocess/1/Prostate_Cancer/GSE192817.csv"
14
+ out_gene_data_file = "./output/preprocess/1/Prostate_Cancer/gene_data/GSE192817.csv"
15
+ out_clinical_data_file = "./output/preprocess/1/Prostate_Cancer/clinical_data/GSE192817.csv"
16
+ json_path = "./output/preprocess/1/Prostate_Cancer/cohort_info.json"
17
+
18
+ # STEP1
19
+ from tools.preprocess import *
20
+ # 1. Identify the paths to the SOFT file and the matrix file
21
+ soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
22
+
23
+ # 2. Read the matrix file to obtain background information and sample characteristics data
24
+ background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
25
+ clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
26
+ background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
27
+
28
+ # 3. Obtain the sample characteristics dictionary from the clinical dataframe
29
+ sample_characteristics_dict = get_unique_values_by_row(clinical_data)
30
+
31
+ # 4. Explicitly print out all the background information and the sample characteristics dictionary
32
+ print("Background Information:")
33
+ print(background_info)
34
+ print("Sample Characteristics Dictionary:")
35
+ print(sample_characteristics_dict)
36
+ # 1. Determine whether gene expression data is available
37
+ is_gene_available = True # Based on the background info, it is likely gene expression data (not just miRNA/methylation).
38
+
39
+ # 2. Identify rows for trait, age, and gender based on sample characteristics
40
+ # and define data conversion functions.
41
+
42
+ trait_row = 0 # Row 0 has multiple cell lines, some are prostate cancer cell lines (DU145, PC3)
43
+ age_row = None # No indication of age in the dictionary
44
+ gender_row = None # No indication of gender in the dictionary
45
+
46
+ def convert_trait(value: str) -> int:
47
+ """
48
+ Convert cell line info to binary: 1 if it's a prostate cancer line (DU145 or PC3), 0 otherwise.
49
+ """
50
+ parts = value.split(':')
51
+ if len(parts) < 2:
52
+ return None
53
+ name = parts[1].strip().lower()
54
+ if name in ['du145', 'pc3']:
55
+ return 1
56
+ else:
57
+ return 0
58
+
59
+ def convert_age(value: str):
60
+ return None # Not available or not applicable
61
+
62
+ def convert_gender(value: str):
63
+ return None # Not available or not applicable
64
+
65
+ # 3. Conduct initial filtering and save metadata
66
+ is_trait_available = (trait_row is not None)
67
+ validate_and_save_cohort_info(
68
+ is_final=False,
69
+ cohort=cohort,
70
+ info_path=json_path,
71
+ is_gene_available=is_gene_available,
72
+ is_trait_available=is_trait_available
73
+ )
74
+
75
+ # 4. If trait data is available, extract clinical features, preview, and save
76
+ if trait_row is not None:
77
+ clinical_features = geo_select_clinical_features(
78
+ clinical_data,
79
+ trait=trait,
80
+ trait_row=trait_row,
81
+ convert_trait=convert_trait,
82
+ age_row=age_row,
83
+ convert_age=convert_age,
84
+ gender_row=gender_row,
85
+ convert_gender=convert_gender
86
+ )
87
+ preview = preview_df(clinical_features, n=5)
88
+ print("Preview of extracted clinical features:", preview)
89
+ clinical_features.to_csv(out_clinical_data_file, index=False)
90
+ # STEP3
91
+ # 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
92
+ gene_data = get_genetic_data(matrix_file)
93
+
94
+ # 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
95
+ print(gene_data.index[:20])
96
+ # Based on the index provided, the identifiers are numeric and do not match standard human gene symbols.
97
+ # Therefore, gene mapping is required.
98
+ print("requires_gene_mapping = True")
99
+ # STEP5
100
+ # 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
101
+ gene_annotation = get_gene_annotation(soft_file)
102
+
103
+ # 2. Use the 'preview_df' function from the library to preview the data and print out the results.
104
+ print("Gene annotation preview:")
105
+ print(preview_df(gene_annotation))
106
+ # STEP: Gene Identifier Mapping
107
+
108
+ # 1. Decide which columns in the annotation store the same ID as in the gene expression data, and which store the gene symbols.
109
+ # From the preview, we see the "ID" column matches the expression data ID and "GENE_SYMBOL" corresponds to actual gene symbols.
110
+
111
+ # 2. Get a gene mapping dataframe by extracting the two columns from gene_annotation.
112
+ mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
113
+
114
+ # 3. Convert probe-level measurements to gene expression data by applying the mapping.
115
+ gene_data = apply_gene_mapping(gene_data, mapping_df)
116
+ print("Shape of the gene_data after mapping:", gene_data.shape)
117
+ print("First few genes in the mapped gene_data:\n", gene_data.head())
118
+ # STEP7
119
+ # 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
120
+ normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
121
+ normalized_gene_data.to_csv(out_gene_data_file)
122
+
123
+ # 2. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.
124
+ linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)
125
+
126
+ # 3. Handle missing values in the linked data
127
+ linked_data = handle_missing_values(linked_data, trait)
128
+
129
+ # 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.
130
+ is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
131
+
132
+ # 5. Conduct quality check and save the cohort information.
133
+ is_usable = validate_and_save_cohort_info(True, cohort, json_path, True, True, is_trait_biased, linked_data)
134
+
135
+ # 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.
136
+ if is_usable:
137
+ unbiased_linked_data.to_csv(out_data_file)
p1/preprocess/Prostate_Cancer/code/GSE200879.py ADDED
@@ -0,0 +1,157 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Prostate_Cancer"
6
+ cohort = "GSE200879"
7
+
8
+ # Input paths
9
+ in_trait_dir = "../DATA/GEO/Prostate_Cancer"
10
+ in_cohort_dir = "../DATA/GEO/Prostate_Cancer/GSE200879"
11
+
12
+ # Output paths
13
+ out_data_file = "./output/preprocess/1/Prostate_Cancer/GSE200879.csv"
14
+ out_gene_data_file = "./output/preprocess/1/Prostate_Cancer/gene_data/GSE200879.csv"
15
+ out_clinical_data_file = "./output/preprocess/1/Prostate_Cancer/clinical_data/GSE200879.csv"
16
+ json_path = "./output/preprocess/1/Prostate_Cancer/cohort_info.json"
17
+
18
+ # STEP1
19
+ from tools.preprocess import *
20
+ # 1. Identify the paths to the SOFT file and the matrix file
21
+ soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
22
+
23
+ # 2. Read the matrix file to obtain background information and sample characteristics data
24
+ background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
25
+ clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
26
+ background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
27
+
28
+ # 3. Obtain the sample characteristics dictionary from the clinical dataframe
29
+ sample_characteristics_dict = get_unique_values_by_row(clinical_data)
30
+
31
+ # 4. Explicitly print out all the background information and the sample characteristics dictionary
32
+ print("Background Information:")
33
+ print(background_info)
34
+ print("Sample Characteristics Dictionary:")
35
+ print(sample_characteristics_dict)
36
+ # 1. Decide if this dataset likely contains valid gene expression data
37
+ is_gene_available = True # Based on "Transcriptomics" mention in the series summary
38
+
39
+ # 2. Identify the row keys for trait, age, and gender
40
+ # and define data type conversion functions
41
+
42
+ # From the sample characteristics dictionary, row 0 has both "tissue: tumor" and "tissue: normal prostate".
43
+ # We interpret this as indicating the presence or absence of prostate cancer (trait).
44
+ trait_row = 0
45
+ age_row = None # No age information found
46
+ gender_row = None # No gender information found
47
+
48
+ # Define the conversion function for the trait as a binary variable: 1 for tumor, 0 for normal
49
+ def convert_trait(value: str):
50
+ # Extract the portion after the colon
51
+ parts = value.split(':')
52
+ if len(parts) < 2:
53
+ return None
54
+ v = parts[1].strip().lower()
55
+ if "tumor" in v:
56
+ return 1
57
+ elif "normal" in v:
58
+ return 0
59
+ else:
60
+ return None
61
+
62
+ # No data available for age or gender, so we won't provide conversion functions
63
+ convert_age = None
64
+ convert_gender = None
65
+
66
+ # 3. Conduct initial filtering on dataset usability and save metadata
67
+ is_trait_available = (trait_row is not None)
68
+ is_usable = validate_and_save_cohort_info(
69
+ is_final=False,
70
+ cohort=cohort,
71
+ info_path=json_path,
72
+ is_gene_available=is_gene_available,
73
+ is_trait_available=is_trait_available
74
+ )
75
+
76
+ # 4. If trait data is available, extract clinical features and preview/save them
77
+ if trait_row is not None:
78
+ # Assume 'clinical_data' is the DataFrame previously obtained from GEO
79
+ selected_clinical_df = geo_select_clinical_features(
80
+ clinical_data,
81
+ trait=trait,
82
+ trait_row=trait_row,
83
+ convert_trait=convert_trait,
84
+ age_row=age_row,
85
+ convert_age=convert_age,
86
+ gender_row=gender_row,
87
+ convert_gender=convert_gender
88
+ )
89
+
90
+ # Preview the extracted clinical features
91
+ preview = preview_df(selected_clinical_df, n=5)
92
+ print("Preview of Selected Clinical Features:", preview)
93
+
94
+ # Save the extracted clinical features
95
+ selected_clinical_df.to_csv(out_clinical_data_file, index=False)
96
+ # STEP3
97
+ # 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
98
+ gene_data = get_genetic_data(matrix_file)
99
+
100
+ # 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
101
+ print(gene_data.index[:20])
102
+ # Based on the observed IDs ("GSHG0000..."), these do not appear to be standard human gene symbols.
103
+ # Therefore, they likely require mapping to official gene symbols.
104
+
105
+ requires_gene_mapping = True
106
+ # STEP5
107
+ # 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
108
+ gene_annotation = get_gene_annotation(soft_file)
109
+
110
+ # 2. Use the 'preview_df' function from the library to preview the data and print out the results.
111
+ print("Gene annotation preview:")
112
+ print(preview_df(gene_annotation))
113
+ # STEP: Gene Identifier Mapping
114
+
115
+ # 1 & 2. Determine the matching identifier columns and create the mapping dataframe
116
+ mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
117
+
118
+ # 3. Convert probe-level data to gene-level data using the mapping
119
+ gene_data = apply_gene_mapping(gene_data, mapping_df)
120
+ import pandas as pd
121
+
122
+ # STEP7
123
+ # Reload the clinical data so that 'selected_clinical_data' is properly shaped:
124
+ # The CSV file currently has one row (with the trait label) and many columns (sample IDs).
125
+ # We transpose it so sample IDs become the row index and "Prostate_Cancer" becomes the column.
126
+ selected_clinical_data = pd.read_csv(out_clinical_data_file, header=0, index_col=0)
127
+ selected_clinical_data = selected_clinical_data.T
128
+ selected_clinical_data.columns = [trait] # rename the single column to "Prostate_Cancer"
129
+ selected_clinical_data.index.name = None # remove the name of the index if any
130
+
131
+ # 1. Normalize the obtained gene data
132
+ normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
133
+ normalized_gene_data.to_csv(out_gene_data_file)
134
+
135
+ # 2. Link the clinical and genetic data
136
+ linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
137
+
138
+ # 3. Handle missing values in the linked data
139
+ linked_data = handle_missing_values(linked_data, trait)
140
+
141
+ # 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.
142
+ is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
143
+
144
+ # 5. Conduct final quality validation and save the cohort information
145
+ is_usable = validate_and_save_cohort_info(
146
+ is_final=True,
147
+ cohort=cohort,
148
+ info_path=json_path,
149
+ is_gene_available=True,
150
+ is_trait_available=True,
151
+ is_biased=is_trait_biased,
152
+ df=linked_data
153
+ )
154
+
155
+ # 6. If the linked data is usable, save it
156
+ if is_usable:
157
+ unbiased_linked_data.to_csv(out_data_file)
p1/preprocess/Psoriasis/GSE182740.csv ADDED
@@ -0,0 +1,75 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ,Psoriasis,OR4F16,OR4F21,OR4F29,OR4F3,PCMTD2
2
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p1/preprocess/Psoriasis/GSE183134.csv ADDED
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p1/preprocess/Psoriasis/GSE226244.csv ADDED
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+ 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
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+ ,63.0,,,,,74.0,,,,,49.0,,,,,,,,,,,,,,,,,,,,,60.0,,,,,,,,,,,,,,,,49.0,,,,,49.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38.0,,,,49.0,,,,16.0,,12.0,,,27.0,,,,,,,,,,,,,,,
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+ 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/Psoriasis/clinical_data/GSE123088.csv ADDED
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1
+ 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
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+ ,63.0,,,,,74.0,,,,,49.0,,,,,,,,,,,,,,,,,,,,,60.0,,,,,,,,,,,,,,,,49.0,,,,,49.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,68.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,38.0,,,,49.0,,,,16.0,,12.0,,,27.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
4
+ 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/Psoriasis/clinical_data/GSE162998.csv ADDED
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1
+ GSM4969892,GSM4969893,GSM4969894,GSM4969895,GSM4969896,GSM4969897,GSM4969898,GSM4969899,GSM4969900,GSM4969901,GSM4969902,GSM4969903,GSM4969904,GSM4969905,GSM4969906,GSM4969907,GSM4969908,GSM4969909,GSM4969910,GSM4969911,GSM4969912,GSM4969913,GSM4969914,GSM4969915,GSM4969916,GSM4969917,GSM4969918,GSM4969919,GSM4969920,GSM4969921,GSM4969922,GSM4969923,GSM4969924,GSM4969925,GSM4969926,GSM4969927,GSM4969928,GSM4969929,GSM4969930,GSM4969931,GSM4969932,GSM4969933,GSM4969934,GSM4969935,GSM4969936,GSM4969937,GSM4969938,GSM4969939
2
+ 1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0