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Datasets:
Liu-Hy
/
GenoTEX

Tasks:
Text Generation
Text2Text Generation
Table Question Answering
Languages:
English
Size:
100B<n<1T
ArXiv:
Tags:
ai4science
agent
llm
genomics
biology
License:
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  1. .gitattributes +13 -0
  2. p3/preprocess/Substance_Use_Disorder/gene_data/GSE103580.csv +3 -0
  3. p3/preprocess/Substance_Use_Disorder/gene_data/GSE138297.csv +3 -0
  4. p3/preprocess/Telomere_Length/GSE52237.csv +3 -0
  5. p3/preprocess/Telomere_Length/gene_data/GSE52237.csv +3 -0
  6. p3/preprocess/Testicular_Cancer/gene_data/GSE62523.csv +3 -0
  7. p3/preprocess/Thymoma/GSE42977.csv +3 -0
  8. p3/preprocess/Thymoma/gene_data/GSE131027.csv +3 -0
  9. p3/preprocess/Thymoma/gene_data/GSE42977.csv +3 -0
  10. p3/preprocess/Thymoma/gene_data/TCGA.csv +3 -0
  11. p3/preprocess/Thyroid_Cancer/GSE138198.csv +37 -0
  12. p3/preprocess/Thyroid_Cancer/GSE151179.csv +53 -0
  13. p3/preprocess/Thyroid_Cancer/GSE82208.csv +53 -0
  14. p3/preprocess/Thyroid_Cancer/TCGA.csv +573 -0
  15. p3/preprocess/Thyroid_Cancer/clinical_data/GSE107754.csv +3 -0
  16. p3/preprocess/Thyroid_Cancer/clinical_data/GSE138198.csv +3 -0
  17. p3/preprocess/Thyroid_Cancer/clinical_data/GSE151179.csv +2 -0
  18. p3/preprocess/Thyroid_Cancer/clinical_data/GSE151181.csv +2 -0
  19. p3/preprocess/Thyroid_Cancer/clinical_data/GSE58689.csv +4 -0
  20. p3/preprocess/Thyroid_Cancer/clinical_data/GSE76039.csv +3 -0
  21. p3/preprocess/Thyroid_Cancer/clinical_data/GSE80022.csv +2 -0
  22. p3/preprocess/Thyroid_Cancer/clinical_data/GSE82208.csv +4 -0
  23. p3/preprocess/Thyroid_Cancer/code/GSE104005.py +189 -0
  24. p3/preprocess/Thyroid_Cancer/code/GSE104006.py +125 -0
  25. p3/preprocess/Thyroid_Cancer/code/GSE107754.py +154 -0
  26. p3/preprocess/Thyroid_Cancer/code/GSE138198.py +170 -0
  27. p3/preprocess/Thyroid_Cancer/code/GSE151179.py +180 -0
  28. p3/preprocess/Thyroid_Cancer/code/GSE151181.py +150 -0
  29. p3/preprocess/Thyroid_Cancer/code/GSE58689.py +178 -0
  30. p3/preprocess/Thyroid_Cancer/code/GSE76039.py +182 -0
  31. p3/preprocess/Thyroid_Cancer/code/GSE80022.py +165 -0
  32. p3/preprocess/Thyroid_Cancer/code/GSE82208.py +183 -0
  33. p3/preprocess/Thyroid_Cancer/code/TCGA.py +116 -0
  34. p3/preprocess/Thyroid_Cancer/gene_data/GSE104005.csv +8 -0
  35. p3/preprocess/Thyroid_Cancer/gene_data/GSE104006.csv +0 -0
  36. p3/preprocess/Thyroid_Cancer/gene_data/GSE107754.csv +4 -0
  37. p3/preprocess/Thyroid_Cancer/gene_data/GSE138198.csv +18 -0
  38. p3/preprocess/Thyroid_Cancer/gene_data/GSE151179.csv +10 -0
  39. p3/preprocess/Thyroid_Cancer/gene_data/GSE151181.csv +3 -0
  40. p3/preprocess/Thyroid_Cancer/gene_data/GSE58689.csv +2 -0
  41. p3/preprocess/Thyroid_Cancer/gene_data/GSE76039.csv +6 -0
  42. p3/preprocess/Thyroid_Cancer/gene_data/GSE80022.csv +9 -0
  43. p3/preprocess/Thyroid_Cancer/gene_data/GSE82208.csv +6 -0
  44. p3/preprocess/Thyroid_Cancer/gene_data/TCGA.csv +9 -0
  45. p3/preprocess/Type_1_Diabetes/GSE156035.csv +3 -0
  46. p3/preprocess/Type_1_Diabetes/GSE193273.csv +3 -0
  47. p3/preprocess/Type_1_Diabetes/GSE232310.csv +3 -0
  48. p3/preprocess/Type_1_Diabetes/GSE75062.csv +0 -0
  49. p3/preprocess/Type_1_Diabetes/clinical_data/GSE123086.csv +4 -0
  50. p3/preprocess/Type_1_Diabetes/clinical_data/GSE123088.csv +4 -0
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p3/preprocess/Thyroid_Cancer/GSE151179.csv ADDED
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p3/preprocess/Thyroid_Cancer/GSE82208.csv ADDED
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p3/preprocess/Thyroid_Cancer/TCGA.csv ADDED
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1
+ ,Thyroid_Cancer,Age,Gender,LOC728323,OR4F17,OR4F21,OR4F29,OR4F4,OR4F5,PCMTD2,SEPT14
2
+ TCGA-4C-A93U-01,1,74,0,0.298006671869,-0.00698045259462,-0.0232130862271,-0.00123825204838,-0.0240291221225,-0.0353370815451,-0.106359703472,-0.380474880999
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+ TCGA-BJ-A0Z0-01,1,55,1,0.851606671869,-0.00698045259462,-0.0232130862271,-0.0897382520484,-0.0240291221225,-0.0353370815451,0.445140296528,0.0996251190012
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@@ -0,0 +1,3 @@
 
 
 
 
1
+ ,GSM4101749,GSM4101750,GSM4101751,GSM4101752,GSM4101753,GSM4101754,GSM4101755,GSM4101756,GSM4101757,GSM4101758,GSM4101759,GSM4101760,GSM4101761,GSM4101762,GSM4101763,GSM4101764,GSM4101765,GSM4101766,GSM4101767,GSM4101768,GSM4101769,GSM4101770,GSM4101771,GSM4101772,GSM4101773,GSM4101774,GSM4101775,GSM4101776,GSM4101777,GSM4101778,GSM4101779,GSM4101780,GSM4101781,GSM4101782,GSM4101783,GSM4101784
2
+ Thyroid_Cancer,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0
3
+ Gender,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,,,
p3/preprocess/Thyroid_Cancer/clinical_data/GSE151179.csv ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ ,GSM4567912,GSM4567913,GSM4567914,GSM4567915,GSM4567916,GSM4567917,GSM4567918,GSM4567919,GSM4567920,GSM4567921,GSM4567922,GSM4567923,GSM4567924,GSM4567925,GSM4567926,GSM4567927,GSM4567928,GSM4567929,GSM4567930,GSM4567931,GSM4567932,GSM4567933,GSM4567934,GSM4567935,GSM4567936,GSM4567937,GSM4567938,GSM4567939,GSM4567940,GSM4567941,GSM4567942,GSM4567943,GSM4567944,GSM4567945,GSM4567946,GSM4567947,GSM4567948,GSM4567949,GSM4567950,GSM4567951,GSM4567952,GSM4567953,GSM4567954,GSM4567955,GSM4567956,GSM4567957,GSM4567958,GSM4567959,GSM4567960,GSM4567961,GSM4567962,GSM4567963
2
+ Thyroid_Cancer,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
p3/preprocess/Thyroid_Cancer/clinical_data/GSE151181.csv ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ ,GSM4567912,GSM4567913,GSM4567914,GSM4567915,GSM4567916,GSM4567917,GSM4567918,GSM4567919,GSM4567920,GSM4567921,GSM4567922,GSM4567923,GSM4567924,GSM4567925,GSM4567926,GSM4567927,GSM4567928,GSM4567929,GSM4567930,GSM4567931,GSM4567932,GSM4567933,GSM4567934,GSM4567935,GSM4567936,GSM4567937,GSM4567938,GSM4567939,GSM4567940,GSM4567941,GSM4567942,GSM4567943,GSM4567944,GSM4567945,GSM4567946,GSM4567947,GSM4567948,GSM4567949,GSM4567950,GSM4567951,GSM4567952,GSM4567953,GSM4567954,GSM4567955,GSM4567956,GSM4567957,GSM4567958,GSM4567959,GSM4567960,GSM4567961,GSM4567962,GSM4567963
2
+ Thyroid_Cancer,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
p3/preprocess/Thyroid_Cancer/clinical_data/GSE58689.csv ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ ,GSM1413225,GSM1413226,GSM1413227,GSM1413228,GSM1413229,GSM1413230,GSM1413231,GSM1413232,GSM1413233,GSM1413234,GSM1413235,GSM1413236,GSM1413237,GSM1413238,GSM1413239,GSM1413240,GSM1413241,GSM1413242,GSM1413243,GSM1413244,GSM1413245,GSM1413246,GSM1413247,GSM1413248,GSM1413249,GSM1413250,GSM1413251,GSM1413252,GSM1413253,GSM1413254,GSM1413255,GSM1413256,GSM1413257,GSM1413258,GSM1413259,GSM1413260,GSM1413261,GSM1413262,GSM1413263,GSM1413264,GSM1413265,GSM1413266,GSM1413267,GSM1413268,GSM1413269
2
+ Thyroid_Cancer,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
3
+ Age,71.0,18.0,,47.0,,37.0,,67.0,24.0,32.0,35.0,,,,61.0,28.0,,16.0,12.0,,42.0,,69.0,6.0,,11.0,,41.0,,19.0,,,,,,,,,,,,,,,
4
+ Gender,0.0,0.0,,0.0,,0.0,,1.0,0.0,0.0,0.0,,,,1.0,0.0,,0.0,1.0,,0.0,,0.0,0.0,,1.0,,0.0,,0.0,,,,,,,,,,,,,,,
p3/preprocess/Thyroid_Cancer/clinical_data/GSE76039.csv ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ ,GSM2024824,GSM2024825,GSM2024826,GSM2024827,GSM2024828,GSM2024829,GSM2024830,GSM2024831,GSM2024832,GSM2024833,GSM2024834,GSM2024835,GSM2024836,GSM2024837,GSM2024838,GSM2024839,GSM2024840,GSM2024841,GSM2024842,GSM2024843,GSM2024844,GSM2024845,GSM2024846,GSM2024847,GSM2024848,GSM2024849,GSM2024850,GSM2024851,GSM2024852,GSM2024853,GSM2024854,GSM2024855,GSM2024856,GSM2024857,GSM2024858,GSM2024859,GSM2024860
2
+ Thyroid_Cancer,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
3
+ Gender,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0
p3/preprocess/Thyroid_Cancer/clinical_data/GSE80022.csv ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ ,GSM2111207,GSM2111208,GSM2111209,GSM2111210,GSM2111211,GSM2111212,GSM2111213,GSM2111214,GSM2111215,GSM2111216,GSM2111217,GSM2111218,GSM2111219,GSM2111220,GSM2111221,GSM2111222,GSM2111223,GSM2111224,GSM2111225,GSM2111226,GSM2111227,GSM2111228,GSM2111229,GSM2111230,GSM2111231,GSM2111232,GSM2111233,GSM2111234,GSM2111235,GSM2111236,GSM2111237,GSM2111238,GSM2111239,GSM2111240,GSM2111241,GSM2111242,GSM2111243,GSM2111244,GSM2111245,GSM2111246,GSM2111247,GSM2111248,GSM2111249,GSM2111250,GSM2111251,GSM2111252,GSM2111253,GSM2111254,GSM2111255,GSM2111256,GSM2111257,GSM2111258,GSM2111259,GSM2111260,GSM2111261,GSM2111262,GSM2111263,GSM2111264,GSM2111265,GSM2111266,GSM2111267,GSM2111268,GSM2111269,GSM2111270,GSM2111271,GSM2111272,GSM2111273,GSM2111274,GSM2111275,GSM2111276,GSM2111277,GSM2111278,GSM2111279
2
+ Thyroid_Cancer,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
p3/preprocess/Thyroid_Cancer/clinical_data/GSE82208.csv ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ ,GSM2186533,GSM2186534,GSM2186535,GSM2186536,GSM2186537,GSM2186538,GSM2186539,GSM2186540,GSM2186541,GSM2186542,GSM2186543,GSM2186544,GSM2186545,GSM2186546,GSM2186547,GSM2186548,GSM2186549,GSM2186550,GSM2186551,GSM2186552,GSM2186553,GSM2186554,GSM2186555,GSM2186556,GSM2186557,GSM2186558,GSM2186559,GSM2186560,GSM2186561,GSM2186562,GSM2186563,GSM2186564,GSM2186565,GSM2186566,GSM2186567,GSM2186568,GSM2186569,GSM2186570,GSM2186571,GSM2186572,GSM2186573,GSM2186574,GSM2186575,GSM2186576,GSM2186577,GSM2186578,GSM2186579,GSM2186580,GSM2186581,GSM2186582,GSM2186583,GSM2186584
2
+ Thyroid_Cancer,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
3
+ Age,74.0,80.0,66.0,78.0,74.0,50.0,61.0,72.0,80.0,61.0,38.0,66.0,47.0,69.0,44.0,72.0,66.0,34.0,39.0,23.0,14.0,42.0,29.0,43.0,31.0,58.0,52.0,60.0,69.0,60.0,49.0,67.0,68.0,72.0,66.0,61.0,,,76.0,53.0,61.0,61.0,60.0,71.0,36.0,71.0,55.0,71.0,33.0,33.0,25.0,29.0
4
+ Gender,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,,,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0
p3/preprocess/Thyroid_Cancer/code/GSE104005.py ADDED
@@ -0,0 +1,189 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Thyroid_Cancer"
6
+ cohort = "GSE104005"
7
+
8
+ # Input paths
9
+ in_trait_dir = "../DATA/GEO/Thyroid_Cancer"
10
+ in_cohort_dir = "../DATA/GEO/Thyroid_Cancer/GSE104005"
11
+
12
+ # Output paths
13
+ out_data_file = "./output/preprocess/3/Thyroid_Cancer/GSE104005.csv"
14
+ out_gene_data_file = "./output/preprocess/3/Thyroid_Cancer/gene_data/GSE104005.csv"
15
+ out_clinical_data_file = "./output/preprocess/3/Thyroid_Cancer/clinical_data/GSE104005.csv"
16
+ json_path = "./output/preprocess/3/Thyroid_Cancer/cohort_info.json"
17
+
18
+ # Get file paths for SOFT and matrix files
19
+ soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
20
+
21
+ # Get background info and clinical data from the matrix file
22
+ background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
23
+
24
+ # Create dictionary of unique values for each feature
25
+ unique_values_dict = get_unique_values_by_row(clinical_data)
26
+
27
+ # Print the information
28
+ print("Dataset Background Information:")
29
+ print(background_info)
30
+ print("\nSample Characteristics:")
31
+ for feature, values in unique_values_dict.items():
32
+ print(f"\n{feature}:")
33
+ print(values)
34
+ # 1. Gene Expression Data Availability
35
+ # Yes, dataset contains gene expression data (mentioned in background info)
36
+ is_gene_available = True
37
+
38
+ # 2. Variable Availability and Data Type Conversion
39
+ # 2.1 Data Availability
40
+ trait_row = 0 # From 'disease' field
41
+ age_row = 2 # From 'age' field
42
+ gender_row = 3 # From 'Sex' field
43
+
44
+ # 2.2 Data Type Conversion Functions
45
+ def convert_trait(value: str) -> int:
46
+ """Convert thyroid cancer status to binary"""
47
+ if not value or ':' not in value:
48
+ return None
49
+ value = value.split(':')[1].strip()
50
+ if 'Non-neoplastic' in value:
51
+ return 0
52
+ elif 'carcinoma' in value:
53
+ return 1
54
+ return None
55
+
56
+ def convert_age(value: str) -> float:
57
+ """Convert age to continuous value"""
58
+ if not value or ':' not in value:
59
+ return None
60
+ try:
61
+ return float(value.split(':')[1].strip())
62
+ except:
63
+ return None
64
+
65
+ def convert_gender(value: str) -> int:
66
+ """Convert gender to binary (0=F, 1=M)"""
67
+ if not value or ':' not in value:
68
+ return None
69
+ value = value.split(':')[1].strip()
70
+ if value == 'F':
71
+ return 0
72
+ elif value == 'M':
73
+ return 1
74
+ return None
75
+
76
+ # 3. Save Metadata
77
+ 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=trait_row is not None
83
+ )
84
+
85
+ # 4. Clinical Feature Extraction
86
+ if trait_row is not None:
87
+ clinical_features = 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
+
98
+ # Preview the extracted features
99
+ preview_result = preview_df(clinical_features)
100
+
101
+ # Save clinical features
102
+ clinical_features.to_csv(out_clinical_data_file)
103
+ # Extract genetic data matrix
104
+ genetic_data = get_genetic_data(matrix_file_path)
105
+
106
+ # Print first few rows with column names to examine data structure
107
+ print("Data preview:")
108
+ print("\nColumn names:")
109
+ print(list(genetic_data.columns)[:5])
110
+ print("\nFirst 5 rows:")
111
+ print(genetic_data.head())
112
+ print("\nShape:", genetic_data.shape)
113
+
114
+ # Verify this is gene expression data and check identifiers
115
+ is_gene_available = True
116
+
117
+ # Save updated metadata
118
+ validate_and_save_cohort_info(
119
+ is_final=False,
120
+ cohort=cohort,
121
+ info_path=json_path,
122
+ is_gene_available=is_gene_available,
123
+ is_trait_available=(trait_row is not None)
124
+ )
125
+
126
+ # Save gene expression data
127
+ genetic_data.to_csv(out_gene_data_file)
128
+ # These identifiers starting with 'ILMN_' are Illumina probe IDs, not standard human gene symbols
129
+ # They need to be mapped to HGNC gene symbols for consistent analysis
130
+ requires_gene_mapping = True
131
+ # Extract gene annotation data
132
+ gene_metadata = get_gene_annotation(soft_file_path)
133
+
134
+ # Preview column names and first few values
135
+ preview = preview_df(gene_metadata)
136
+ print("\nGene annotation columns and sample values:")
137
+ print(preview)
138
+ # 1. Identify columns storing gene identifiers and gene symbols
139
+ # 'ID' column matches the probe IDs in gene expression data
140
+ # 'Symbol' column stores gene symbols
141
+ prob_col = 'ID'
142
+ gene_col = 'Symbol'
143
+
144
+ # 2. Extract mapping information
145
+ mapping_data = get_gene_mapping(gene_metadata, prob_col, gene_col)
146
+
147
+ # 3. Convert probe-level measurements to gene expression data
148
+ gene_data = apply_gene_mapping(genetic_data, mapping_data)
149
+ gene_data = normalize_gene_symbols_in_index(gene_data)
150
+
151
+ # Preview results
152
+ print("\nMapped gene expression data preview:")
153
+ print("\nFirst 5 genes:")
154
+ print(gene_data.head())
155
+ print("\nShape:", gene_data.shape)
156
+
157
+ # Save mapped gene expression data
158
+ gene_data.to_csv(out_gene_data_file)
159
+ # 1. Normalize gene symbols and save gene data
160
+ normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
161
+ os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
162
+ normalized_gene_data.to_csv(out_gene_data_file)
163
+
164
+ # 2. Link clinical and genetic data
165
+ clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
166
+ linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)
167
+
168
+ # 3. Handle missing values
169
+ linked_data = handle_missing_values(linked_data, trait)
170
+
171
+ # 4. Judge bias in features and remove biased ones
172
+ trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
173
+
174
+ # 5. Final validation and save metadata
175
+ is_usable = validate_and_save_cohort_info(
176
+ is_final=True,
177
+ cohort=cohort,
178
+ info_path=json_path,
179
+ is_gene_available=is_gene_available,
180
+ is_trait_available=True,
181
+ is_biased=trait_biased,
182
+ df=linked_data,
183
+ note="Dataset contains gene expression data comparing 27 follicular thyroid cancers with 25 follicular thyroid adenomas."
184
+ )
185
+
186
+ # 6. Save linked data if usable
187
+ if is_usable:
188
+ os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
189
+ linked_data.to_csv(out_data_file)
p3/preprocess/Thyroid_Cancer/code/GSE104006.py ADDED
@@ -0,0 +1,125 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Thyroid_Cancer"
6
+ cohort = "GSE104006"
7
+
8
+ # Input paths
9
+ in_trait_dir = "../DATA/GEO/Thyroid_Cancer"
10
+ in_cohort_dir = "../DATA/GEO/Thyroid_Cancer/GSE104006"
11
+
12
+ # Output paths
13
+ out_data_file = "./output/preprocess/3/Thyroid_Cancer/GSE104006.csv"
14
+ out_gene_data_file = "./output/preprocess/3/Thyroid_Cancer/gene_data/GSE104006.csv"
15
+ out_clinical_data_file = "./output/preprocess/3/Thyroid_Cancer/clinical_data/GSE104006.csv"
16
+ json_path = "./output/preprocess/3/Thyroid_Cancer/cohort_info.json"
17
+
18
+ # Get file paths for SOFT and matrix files
19
+ soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
20
+
21
+ # Get background info and clinical data from the matrix file
22
+ background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
23
+
24
+ # Create dictionary of unique values for each feature
25
+ unique_values_dict = get_unique_values_by_row(clinical_data)
26
+
27
+ # Print the information
28
+ print("Dataset Background Information:")
29
+ print(background_info)
30
+ print("\nSample Characteristics:")
31
+ for feature, values in unique_values_dict.items():
32
+ print(f"\n{feature}:")
33
+ print(values)
34
+ # 1. Gene Expression Data Availability
35
+ is_gene_available = True # Based on Series title mentioning "gene expression profiling"
36
+
37
+ # 2.1 Data Availability
38
+ trait_row = 1 # Histology field contains tumor status
39
+ age_row = 2 # Age information is available
40
+ gender_row = 3 # Sex information is available
41
+
42
+ # 2.2 Data Type Conversion Functions
43
+ def convert_trait(value):
44
+ """Convert histology to binary: 1 for tumor types, 0 for non-neoplastic"""
45
+ if not isinstance(value, str):
46
+ return None
47
+ value = value.split(': ')[-1].strip()
48
+ if value == 'Non-neoplastic_thyroid':
49
+ return 0
50
+ elif value in ['PDTC', 'PTC', 'PDTC+PTC', 'PTC+PDTC', 'PTC_lymph_node_metastasis']:
51
+ return 1
52
+ return None
53
+
54
+ def convert_age(value):
55
+ """Convert age to continuous numeric value"""
56
+ if not isinstance(value, str):
57
+ return None
58
+ try:
59
+ return float(value.split(': ')[-1])
60
+ except:
61
+ return None
62
+
63
+ def convert_gender(value):
64
+ """Convert gender to binary: 0 for female, 1 for male"""
65
+ if not isinstance(value, str):
66
+ return None
67
+ value = value.split(': ')[-1].strip()
68
+ if value == 'F':
69
+ return 0
70
+ elif value == 'M':
71
+ return 1
72
+ return None
73
+
74
+ # 3. Save Metadata
75
+ validate_and_save_cohort_info(is_final=False,
76
+ cohort=cohort,
77
+ info_path=json_path,
78
+ is_gene_available=is_gene_available,
79
+ is_trait_available=(trait_row is not None))
80
+
81
+ # 4. Clinical Feature Extraction
82
+ # Since trait_row is not None, we extract clinical features
83
+ selected_clinical = geo_select_clinical_features(clinical_data,
84
+ trait=trait,
85
+ trait_row=trait_row,
86
+ convert_trait=convert_trait,
87
+ age_row=age_row,
88
+ convert_age=convert_age,
89
+ gender_row=gender_row,
90
+ convert_gender=convert_gender)
91
+
92
+ # Preview the extracted features
93
+ print(preview_df(selected_clinical))
94
+
95
+ # Save clinical data
96
+ selected_clinical.to_csv(out_clinical_data_file)
97
+ # Extract genetic data matrix
98
+ genetic_data = get_genetic_data(matrix_file_path)
99
+
100
+ # Print first 20 row IDs
101
+ print("\nFirst 20 row IDs:")
102
+ print(list(genetic_data.index)[:20])
103
+
104
+ # Print basic data info
105
+ print("\nData preview:")
106
+ print("\nColumn names:")
107
+ print(list(genetic_data.columns)[:5])
108
+ print("\nFirst 5 rows:")
109
+ print(genetic_data.head())
110
+ print("\nShape:", genetic_data.shape)
111
+
112
+ # Update is_gene_available since this is miRNA data
113
+ is_gene_available = False
114
+
115
+ # Save updated metadata
116
+ validate_and_save_cohort_info(
117
+ is_final=False,
118
+ cohort=cohort,
119
+ info_path=json_path,
120
+ is_gene_available=is_gene_available,
121
+ is_trait_available=(trait_row is not None)
122
+ )
123
+
124
+ # Save gene expression data
125
+ genetic_data.to_csv(out_gene_data_file)
p3/preprocess/Thyroid_Cancer/code/GSE107754.py ADDED
@@ -0,0 +1,154 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Thyroid_Cancer"
6
+ cohort = "GSE107754"
7
+
8
+ # Input paths
9
+ in_trait_dir = "../DATA/GEO/Thyroid_Cancer"
10
+ in_cohort_dir = "../DATA/GEO/Thyroid_Cancer/GSE107754"
11
+
12
+ # Output paths
13
+ out_data_file = "./output/preprocess/3/Thyroid_Cancer/GSE107754.csv"
14
+ out_gene_data_file = "./output/preprocess/3/Thyroid_Cancer/gene_data/GSE107754.csv"
15
+ out_clinical_data_file = "./output/preprocess/3/Thyroid_Cancer/clinical_data/GSE107754.csv"
16
+ json_path = "./output/preprocess/3/Thyroid_Cancer/cohort_info.json"
17
+
18
+ # Get file paths for SOFT and matrix files
19
+ soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
20
+
21
+ # Get background info and clinical data from the matrix file
22
+ background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
23
+
24
+ # Create dictionary of unique values for each feature
25
+ unique_values_dict = get_unique_values_by_row(clinical_data)
26
+
27
+ # Print the information
28
+ print("Dataset Background Information:")
29
+ print(background_info)
30
+ print("\nSample Characteristics:")
31
+ for feature, values in unique_values_dict.items():
32
+ print(f"\n{feature}:")
33
+ print(values)
34
+ # 1. Gene Expression Data Availability
35
+ # The background shows this is human genome gene expression microarray data
36
+ is_gene_available = True
37
+
38
+ # 2.1 Data Availability
39
+ # Trait (thyroid cancer vs other cancers) from tissue field
40
+ trait_row = 2
41
+ # Age not available
42
+ age_row = None
43
+ # Gender available
44
+ gender_row = 0
45
+
46
+ # 2.2 Data Type Conversion Functions
47
+ def convert_trait(value):
48
+ if not value:
49
+ return None
50
+ value = value.lower()
51
+ if 'tissue:' in value:
52
+ value = value.split('tissue:')[1].strip()
53
+ elif ':' in value:
54
+ value = value.split(':')[1].strip()
55
+ return 1 if value == 'thyroid cancer' else 0
56
+
57
+ def convert_gender(value):
58
+ if not value or ':' not in value:
59
+ return None
60
+ value = value.split(': ')[1].lower()
61
+ return 1 if value == 'male' else 0
62
+
63
+ convert_age = None
64
+
65
+ # 3. Save Metadata
66
+ is_trait_available = trait_row is not None
67
+ validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,
68
+ is_gene_available=is_gene_available,
69
+ is_trait_available=is_trait_available)
70
+
71
+ # 4. Clinical Feature Extraction
72
+ if trait_row is not None:
73
+ clinical_df = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait,
74
+ age_row=age_row, convert_age=convert_age,
75
+ gender_row=gender_row, convert_gender=convert_gender)
76
+
77
+ print("Preview of clinical data:")
78
+ print(preview_df(clinical_df))
79
+
80
+ # Save clinical data
81
+ os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
82
+ clinical_df.to_csv(out_clinical_data_file)
83
+ # Extract genetic data matrix
84
+ genetic_data = get_genetic_data(matrix_file_path)
85
+
86
+ # Print first few rows with column names to examine data structure
87
+ print("Data preview:")
88
+ print("\nColumn names:")
89
+ print(list(genetic_data.columns)[:5])
90
+ print("\nFirst 5 rows:")
91
+ print(genetic_data.head())
92
+ print("\nShape:", genetic_data.shape)
93
+
94
+ # Verify this is gene expression data and check identifiers
95
+ is_gene_available = True
96
+
97
+ # Save updated metadata
98
+ validate_and_save_cohort_info(
99
+ is_final=False,
100
+ cohort=cohort,
101
+ info_path=json_path,
102
+ is_gene_available=is_gene_available,
103
+ is_trait_available=(trait_row is not None)
104
+ )
105
+
106
+ # Save gene expression data
107
+ genetic_data.to_csv(out_gene_data_file)
108
+ # Observing the gene identifiers starting with "A_23_P" which are Agilent probe IDs
109
+ # These are not human gene symbols and need to be mapped
110
+ requires_gene_mapping = True
111
+ # Extract gene annotation data
112
+ gene_metadata = get_gene_annotation(soft_file_path)
113
+
114
+ # Preview column names and first few values
115
+ preview = preview_df(gene_metadata)
116
+ print("\nGene annotation columns and sample values:")
117
+ print(preview)
118
+ # Based on previous output, 'ID' in gene annotation matches probe IDs in expression data
119
+ # and 'GENE_SYMBOL' contains the corresponding gene symbols
120
+ mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL')
121
+
122
+ # Map probe IDs to gene symbols and aggregate the expression data
123
+ gene_data = apply_gene_mapping(genetic_data, mapping_df)
124
+ # 1. Normalize gene symbols and save gene data
125
+ normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
126
+ os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
127
+ normalized_gene_data.to_csv(out_gene_data_file)
128
+
129
+ # 2. Link clinical and genetic data
130
+ clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
131
+ linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)
132
+
133
+ # 3. Handle missing values
134
+ linked_data = handle_missing_values(linked_data, trait)
135
+
136
+ # 4. Judge bias in features and remove biased ones
137
+ trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
138
+
139
+ # 5. Final validation and save metadata
140
+ is_usable = validate_and_save_cohort_info(
141
+ is_final=True,
142
+ cohort=cohort,
143
+ info_path=json_path,
144
+ is_gene_available=is_gene_available,
145
+ is_trait_available=True,
146
+ is_biased=trait_biased,
147
+ df=linked_data,
148
+ note="Dataset contains gene expression data comparing 27 follicular thyroid cancers with 25 follicular thyroid adenomas."
149
+ )
150
+
151
+ # 6. Save linked data if usable
152
+ if is_usable:
153
+ os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
154
+ linked_data.to_csv(out_data_file)
p3/preprocess/Thyroid_Cancer/code/GSE138198.py ADDED
@@ -0,0 +1,170 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Thyroid_Cancer"
6
+ cohort = "GSE138198"
7
+
8
+ # Input paths
9
+ in_trait_dir = "../DATA/GEO/Thyroid_Cancer"
10
+ in_cohort_dir = "../DATA/GEO/Thyroid_Cancer/GSE138198"
11
+
12
+ # Output paths
13
+ out_data_file = "./output/preprocess/3/Thyroid_Cancer/GSE138198.csv"
14
+ out_gene_data_file = "./output/preprocess/3/Thyroid_Cancer/gene_data/GSE138198.csv"
15
+ out_clinical_data_file = "./output/preprocess/3/Thyroid_Cancer/clinical_data/GSE138198.csv"
16
+ json_path = "./output/preprocess/3/Thyroid_Cancer/cohort_info.json"
17
+
18
+ # Get file paths for SOFT and matrix files
19
+ soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
20
+
21
+ # Get background info and clinical data from the matrix file
22
+ background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
23
+
24
+ # Create dictionary of unique values for each feature
25
+ unique_values_dict = get_unique_values_by_row(clinical_data)
26
+
27
+ # Print the information
28
+ print("Dataset Background Information:")
29
+ print(background_info)
30
+ print("\nSample Characteristics:")
31
+ for feature, values in unique_values_dict.items():
32
+ print(f"\n{feature}:")
33
+ print(values)
34
+ # 1. Gene Expression Data Availability
35
+ # Dataset uses Affymetrix Human Gene 1.0 ST arrays, which measures gene expression
36
+ is_gene_available = True
37
+
38
+ # 2.1 Data Availability and Row Identification
39
+ trait_row = 1 # 'sample type' contains cancer vs normal info
40
+ gender_row = 0 # Gender is available
41
+ age_row = None # Age is not available
42
+
43
+ # 2.2 Data Type Conversion Functions
44
+ def convert_trait(value):
45
+ if not value or ":" not in value:
46
+ return None
47
+ value = value.split(": ")[1].lower()
48
+ # Convert to binary: 1 for cancer (PTC), 0 for normal
49
+ if "normal" in value:
50
+ return 0
51
+ elif "ptc" in value or "papillary thyroid carcinoma" in value:
52
+ return 1
53
+ return None
54
+
55
+ def convert_gender(value):
56
+ if not value or ":" not in value:
57
+ return None
58
+ value = value.split(": ")[1].lower()
59
+ if value == "f":
60
+ return 0
61
+ elif value == "m":
62
+ return 1
63
+ return None
64
+
65
+ # 3. Save Metadata - Initial Filtering
66
+ validate_and_save_cohort_info(
67
+ is_final=False,
68
+ cohort=cohort,
69
+ info_path=json_path,
70
+ is_gene_available=is_gene_available,
71
+ is_trait_available=trait_row is not None
72
+ )
73
+
74
+ # 4. Clinical Feature Extraction
75
+ if trait_row is not None:
76
+ selected_clinical = geo_select_clinical_features(
77
+ clinical_df=clinical_data,
78
+ trait=trait,
79
+ trait_row=trait_row,
80
+ convert_trait=convert_trait,
81
+ gender_row=gender_row,
82
+ convert_gender=convert_gender
83
+ )
84
+
85
+ # Preview the processed clinical data
86
+ print("Preview of processed clinical data:")
87
+ print(preview_df(selected_clinical))
88
+
89
+ # Save clinical data
90
+ selected_clinical.to_csv(out_clinical_data_file)
91
+ # Extract genetic data matrix
92
+ genetic_data = get_genetic_data(matrix_file_path)
93
+
94
+ # Print first few rows with column names to examine data structure
95
+ print("Data preview:")
96
+ print("\nColumn names:")
97
+ print(list(genetic_data.columns)[:5])
98
+ print("\nFirst 5 rows:")
99
+ print(genetic_data.head())
100
+ print("\nShape:", genetic_data.shape)
101
+
102
+ # Verify this is gene expression data and check identifiers
103
+ is_gene_available = True
104
+
105
+ # Save updated metadata
106
+ validate_and_save_cohort_info(
107
+ is_final=False,
108
+ cohort=cohort,
109
+ info_path=json_path,
110
+ is_gene_available=is_gene_available,
111
+ is_trait_available=(trait_row is not None)
112
+ )
113
+
114
+ # Save gene expression data
115
+ genetic_data.to_csv(out_gene_data_file)
116
+ # The identifiers in the index appear to be numeric IDs (e.g. 7892501, 7892502)
117
+ # These are not standard human gene symbols (which are usually alphanumeric like BRCA1, TP53)
118
+ # Therefore, a mapping from probe IDs to gene symbols will be required
119
+ requires_gene_mapping = True
120
+ # Extract gene annotation data
121
+ gene_metadata = get_gene_annotation(soft_file_path)
122
+
123
+ # Preview column names and first few values
124
+ preview = preview_df(gene_metadata)
125
+ print("\nGene annotation columns and sample values:")
126
+ print(preview)
127
+ # Extract probe-to-gene mapping from annotation data
128
+ # The 'ID' column matches probe IDs in expression data
129
+ # 'gene_assignment' contains gene symbols in a specific format
130
+ mapping_df = get_gene_mapping(gene_metadata, 'ID', 'gene_assignment')
131
+
132
+ # Convert probe-level data to gene-level data using the mapping
133
+ gene_data = apply_gene_mapping(genetic_data, mapping_df)
134
+
135
+ # Preview gene expression data
136
+ print("\nGene expression data preview:")
137
+ print("Shape:", gene_data.shape)
138
+ print("\nFirst few rows:")
139
+ print(gene_data.head())
140
+ # 1. Normalize gene symbols and save gene data
141
+ normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
142
+ os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
143
+ normalized_gene_data.to_csv(out_gene_data_file)
144
+
145
+ # 2. Link clinical and genetic data
146
+ clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
147
+ linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)
148
+
149
+ # 3. Handle missing values
150
+ linked_data = handle_missing_values(linked_data, trait)
151
+
152
+ # 4. Judge bias in features and remove biased ones
153
+ trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
154
+
155
+ # 5. Final validation and save metadata
156
+ is_usable = validate_and_save_cohort_info(
157
+ is_final=True,
158
+ cohort=cohort,
159
+ info_path=json_path,
160
+ is_gene_available=is_gene_available,
161
+ is_trait_available=True,
162
+ is_biased=trait_biased,
163
+ df=linked_data,
164
+ note="Dataset contains gene expression data comparing 27 follicular thyroid cancers with 25 follicular thyroid adenomas."
165
+ )
166
+
167
+ # 6. Save linked data if usable
168
+ if is_usable:
169
+ os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
170
+ linked_data.to_csv(out_data_file)
p3/preprocess/Thyroid_Cancer/code/GSE151179.py ADDED
@@ -0,0 +1,180 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Thyroid_Cancer"
6
+ cohort = "GSE151179"
7
+
8
+ # Input paths
9
+ in_trait_dir = "../DATA/GEO/Thyroid_Cancer"
10
+ in_cohort_dir = "../DATA/GEO/Thyroid_Cancer/GSE151179"
11
+
12
+ # Output paths
13
+ out_data_file = "./output/preprocess/3/Thyroid_Cancer/GSE151179.csv"
14
+ out_gene_data_file = "./output/preprocess/3/Thyroid_Cancer/gene_data/GSE151179.csv"
15
+ out_clinical_data_file = "./output/preprocess/3/Thyroid_Cancer/clinical_data/GSE151179.csv"
16
+ json_path = "./output/preprocess/3/Thyroid_Cancer/cohort_info.json"
17
+
18
+ # Get file paths for SOFT and matrix files
19
+ soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
20
+
21
+ # Get background info and clinical data from the matrix file
22
+ background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
23
+
24
+ # Create dictionary of unique values for each feature
25
+ unique_values_dict = get_unique_values_by_row(clinical_data)
26
+
27
+ # Print the information
28
+ print("Dataset Background Information:")
29
+ print(background_info)
30
+ print("\nSample Characteristics:")
31
+ for feature, values in unique_values_dict.items():
32
+ print(f"\n{feature}:")
33
+ print(values)
34
+ # 1. Gene Expression Data Availability
35
+ # From background info, this is gene expression data
36
+ is_gene_available = True
37
+
38
+ # 2. Clinical Feature Variables
39
+ # 2.1 Data Availability
40
+
41
+ # Trait (cancer status) can be inferred from tissue type (row 1)
42
+ # Non-neoplastic thyroid = control, others = cancer case
43
+ trait_row = 1
44
+
45
+ # Age and gender not available in sample characteristics
46
+ age_row = None
47
+ gender_row = None
48
+
49
+ # 2.2 Data Type Conversion Functions
50
+ def convert_trait(value: str) -> int:
51
+ """Convert tissue type to binary trait (0=control, 1=cancer)"""
52
+ if not isinstance(value, str):
53
+ return None
54
+ value = value.split(': ')[1].lower() if ': ' in value else value.lower()
55
+ if 'non-neoplastic thyroid' in value:
56
+ return 0
57
+ elif any(x in value for x in ['tumor', 'metastasis']):
58
+ return 1
59
+ return None
60
+
61
+ def convert_age(value: str) -> float:
62
+ return None
63
+
64
+ def convert_gender(value: str) -> int:
65
+ return None
66
+
67
+ # 3. Save Metadata
68
+ is_trait_available = (trait_row is not None)
69
+ validate_and_save_cohort_info(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
+ # 4. Clinical Feature Extraction
76
+ if trait_row is not None:
77
+ clinical_features = geo_select_clinical_features(
78
+ clinical_df=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 the extracted features
89
+ preview = preview_df(clinical_features)
90
+ print("Preview of clinical features:")
91
+ print(preview)
92
+
93
+ # Save to CSV
94
+ clinical_features.to_csv(out_clinical_data_file)
95
+ # Extract genetic data matrix
96
+ genetic_data = get_genetic_data(matrix_file_path)
97
+
98
+ # Print first few rows with column names to examine data structure
99
+ print("Data preview:")
100
+ print("\nColumn names:")
101
+ print(list(genetic_data.columns)[:5])
102
+ print("\nFirst 5 rows:")
103
+ print(genetic_data.head())
104
+ print("\nShape:", genetic_data.shape)
105
+
106
+ # Verify this is gene expression data and check identifiers
107
+ is_gene_available = True
108
+
109
+ # Save updated metadata
110
+ validate_and_save_cohort_info(
111
+ is_final=False,
112
+ cohort=cohort,
113
+ info_path=json_path,
114
+ is_gene_available=is_gene_available,
115
+ is_trait_available=(trait_row is not None)
116
+ )
117
+
118
+ # Save gene expression data
119
+ genetic_data.to_csv(out_gene_data_file)
120
+ # Review the gene identifiers
121
+ requires_gene_mapping = True
122
+
123
+ # Explanation: The gene identifiers appear to be probe/sequence IDs (e.g. 23064070)
124
+ # rather than standard human gene symbols (which would look like BRCA1, TP53 etc).
125
+ # Therefore mapping to gene symbols will be required.
126
+ # Extract gene annotation data
127
+ gene_metadata = get_gene_annotation(soft_file_path)
128
+
129
+ # Preview column names and first few values
130
+ preview = preview_df(gene_metadata)
131
+ print("\nGene annotation columns and sample values:")
132
+ print(preview)
133
+ # Extract gene id and name mapping from annotation
134
+ mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='SPOT_ID.1')
135
+
136
+ # Apply gene mapping to convert probe-level data to gene expression data
137
+ gene_data = apply_gene_mapping(genetic_data, mapping_df)
138
+
139
+ # Normalize gene symbols in index
140
+ gene_data = normalize_gene_symbols_in_index(gene_data)
141
+
142
+ # Preview the mapped data
143
+ print("\nGene expression data after mapping:")
144
+ print(gene_data.shape)
145
+ print("\nFirst few gene names:")
146
+ print(list(gene_data.index)[:5])
147
+
148
+ # Save the gene expression data
149
+ gene_data.to_csv(out_gene_data_file)
150
+ # 1. Normalize gene symbols and save gene data
151
+ normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
152
+ os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
153
+ normalized_gene_data.to_csv(out_gene_data_file)
154
+
155
+ # 2. Link clinical and genetic data
156
+ clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
157
+ linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)
158
+
159
+ # 3. Handle missing values
160
+ linked_data = handle_missing_values(linked_data, trait)
161
+
162
+ # 4. Judge bias in features and remove biased ones
163
+ trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
164
+
165
+ # 5. Final validation and save metadata
166
+ is_usable = validate_and_save_cohort_info(
167
+ is_final=True,
168
+ cohort=cohort,
169
+ info_path=json_path,
170
+ is_gene_available=is_gene_available,
171
+ is_trait_available=True,
172
+ is_biased=trait_biased,
173
+ df=linked_data,
174
+ note="Dataset contains gene expression data comparing 27 follicular thyroid cancers with 25 follicular thyroid adenomas."
175
+ )
176
+
177
+ # 6. Save linked data if usable
178
+ if is_usable:
179
+ os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
180
+ linked_data.to_csv(out_data_file)
p3/preprocess/Thyroid_Cancer/code/GSE151181.py ADDED
@@ -0,0 +1,150 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Thyroid_Cancer"
6
+ cohort = "GSE151181"
7
+
8
+ # Input paths
9
+ in_trait_dir = "../DATA/GEO/Thyroid_Cancer"
10
+ in_cohort_dir = "../DATA/GEO/Thyroid_Cancer/GSE151181"
11
+
12
+ # Output paths
13
+ out_data_file = "./output/preprocess/3/Thyroid_Cancer/GSE151181.csv"
14
+ out_gene_data_file = "./output/preprocess/3/Thyroid_Cancer/gene_data/GSE151181.csv"
15
+ out_clinical_data_file = "./output/preprocess/3/Thyroid_Cancer/clinical_data/GSE151181.csv"
16
+ json_path = "./output/preprocess/3/Thyroid_Cancer/cohort_info.json"
17
+
18
+ # Get file paths for SOFT and matrix files
19
+ soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
20
+
21
+ # Get background info and clinical data from the matrix file
22
+ background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
23
+
24
+ # Create dictionary of unique values for each feature
25
+ unique_values_dict = get_unique_values_by_row(clinical_data)
26
+
27
+ # Print the information
28
+ print("Dataset Background Information:")
29
+ print(background_info)
30
+ print("\nSample Characteristics:")
31
+ for feature, values in unique_values_dict.items():
32
+ print(f"\n{feature}:")
33
+ print(values)
34
+ # 1. Gene Expression Data Availability
35
+ # Yes, this dataset contains gene expression data as indicated by the dataset title
36
+ is_gene_available = True
37
+
38
+ # 2.1 Data Availability
39
+ # tissue type (row 1) indicates tumor vs normal tissue
40
+ trait_row = 1
41
+ # Age and gender not available in sample characteristics
42
+ age_row = None
43
+ gender_row = None
44
+
45
+ # 2.2 Data Type Conversion Functions
46
+ def convert_trait(value: str) -> int:
47
+ """Convert tissue type to binary (0=normal, 1=tumor)"""
48
+ if not isinstance(value, str):
49
+ return None
50
+ value = value.split(": ")[-1].lower()
51
+ if "non-neoplastic" in value:
52
+ return 0
53
+ elif any(x in value for x in ["tumor", "metastasis"]):
54
+ return 1
55
+ return None
56
+
57
+ def convert_age(value: str) -> float:
58
+ """Convert age to float"""
59
+ return None
60
+
61
+ def convert_gender(value: str) -> int:
62
+ """Convert gender to binary"""
63
+ return None
64
+
65
+ # 3. Save metadata about dataset availability
66
+ validate_and_save_cohort_info(is_final=False,
67
+ cohort=cohort,
68
+ info_path=json_path,
69
+ is_gene_available=is_gene_available,
70
+ is_trait_available=(trait_row is not None))
71
+
72
+ # 4. Extract clinical features since trait_row is not None
73
+ clinical_features_df = geo_select_clinical_features(clinical_df=clinical_data,
74
+ trait=trait,
75
+ trait_row=trait_row,
76
+ convert_trait=convert_trait,
77
+ age_row=age_row,
78
+ convert_age=convert_age,
79
+ gender_row=gender_row,
80
+ convert_gender=convert_gender)
81
+
82
+ # Preview the extracted features
83
+ print(preview_df(clinical_features_df))
84
+
85
+ # Save clinical data
86
+ clinical_features_df.to_csv(out_clinical_data_file)
87
+ # Extract genetic data matrix
88
+ genetic_data = get_genetic_data(matrix_file_path)
89
+
90
+ # Print first few rows with column names to examine data structure
91
+ print("Data preview:")
92
+ print("\nColumn names:")
93
+ print(list(genetic_data.columns)[:5])
94
+ print("\nFirst 5 rows:")
95
+ print(genetic_data.head())
96
+ print("\nShape:", genetic_data.shape)
97
+
98
+ # Verify this is gene expression data and check identifiers
99
+ is_gene_available = True
100
+
101
+ # Save updated metadata
102
+ validate_and_save_cohort_info(
103
+ is_final=False,
104
+ cohort=cohort,
105
+ info_path=json_path,
106
+ is_gene_available=is_gene_available,
107
+ is_trait_available=(trait_row is not None)
108
+ )
109
+
110
+ # Save gene expression data
111
+ genetic_data.to_csv(out_gene_data_file)
112
+ # The IDs in the row index appear to be numeric identifiers (e.g. 23064070)
113
+ # rather than standard human gene symbols (e.g. BRCA1, TP53)
114
+ # These numeric IDs likely need to be mapped to gene symbols
115
+ requires_gene_mapping = True
116
+ # Extract gene annotation data with modified prefix filtering
117
+ gene_metadata = get_gene_annotation(soft_file_path, prefixes=['!Platform_table_begin'])
118
+
119
+ # Clean up column names by removing leading/trailing whitespace
120
+ gene_metadata = gene_metadata.rename(columns=lambda x: x.strip())
121
+
122
+ # Preview column names and first few values
123
+ print("\nGene annotation columns preview:")
124
+ print(gene_metadata.columns.tolist())
125
+
126
+ print("\nFirst few rows:")
127
+ print(gene_metadata.head())
128
+ # Extract gene annotation data
129
+ gene_metadata = get_gene_annotation(soft_file_path, prefixes=['^', '!', '#'])
130
+
131
+ # Clean up any whitespace in column names
132
+ gene_metadata.columns = gene_metadata.columns.str.strip()
133
+
134
+ # Preview column names and first few values
135
+ preview = preview_df(gene_metadata, n=5)
136
+ print("\nGene annotation preview:")
137
+ for col, values in preview.items():
138
+ print(f"\n{col}:")
139
+ print(values)
140
+ # Update status since we determined this is a miRNA dataset without gene mapping
141
+ is_gene_available = False
142
+
143
+ # Save updated metadata indicating gene expression data is not available
144
+ validate_and_save_cohort_info(
145
+ is_final=False,
146
+ cohort=cohort,
147
+ info_path=json_path,
148
+ is_gene_available=is_gene_available,
149
+ is_trait_available=(trait_row is not None)
150
+ )
p3/preprocess/Thyroid_Cancer/code/GSE58689.py ADDED
@@ -0,0 +1,178 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Thyroid_Cancer"
6
+ cohort = "GSE58689"
7
+
8
+ # Input paths
9
+ in_trait_dir = "../DATA/GEO/Thyroid_Cancer"
10
+ in_cohort_dir = "../DATA/GEO/Thyroid_Cancer/GSE58689"
11
+
12
+ # Output paths
13
+ out_data_file = "./output/preprocess/3/Thyroid_Cancer/GSE58689.csv"
14
+ out_gene_data_file = "./output/preprocess/3/Thyroid_Cancer/gene_data/GSE58689.csv"
15
+ out_clinical_data_file = "./output/preprocess/3/Thyroid_Cancer/clinical_data/GSE58689.csv"
16
+ json_path = "./output/preprocess/3/Thyroid_Cancer/cohort_info.json"
17
+
18
+ # Get file paths for SOFT and matrix files
19
+ soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
20
+
21
+ # Get background info and clinical data from the matrix file
22
+ background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
23
+
24
+ # Create dictionary of unique values for each feature
25
+ unique_values_dict = get_unique_values_by_row(clinical_data)
26
+
27
+ # Print the information
28
+ print("Dataset Background Information:")
29
+ print(background_info)
30
+ print("\nSample Characteristics:")
31
+ for feature, values in unique_values_dict.items():
32
+ print(f"\n{feature}:")
33
+ print(values)
34
+ # 1. Gene expression data availability check
35
+ # From the background info, this is gene expression data related to thyroid cancer
36
+ is_gene_available = True
37
+
38
+ # 2.1 Data availability check
39
+ # Trait data available in row 0 (normal vs PTC)
40
+ trait_row = 0
41
+
42
+ # Gender data available in row 1 (under "Sex:")
43
+ gender_row = 1
44
+
45
+ # Age data available in row 2
46
+ age_row = 2
47
+
48
+ # 2.2 Data type conversion functions
49
+ def convert_trait(value: str) -> Optional[int]:
50
+ if pd.isna(value):
51
+ return None
52
+ value = value.split(': ')[1].lower()
53
+ if 'normal' in value:
54
+ return 0
55
+ elif 'papillary thyroid carcinoma' in value:
56
+ return 1
57
+ return None
58
+
59
+ def convert_age(value: str) -> Optional[float]:
60
+ if pd.isna(value):
61
+ return None
62
+ try:
63
+ return float(value.split(': ')[1])
64
+ except:
65
+ return None
66
+
67
+ def convert_gender(value: str) -> Optional[int]:
68
+ if pd.isna(value):
69
+ return None
70
+ value = value.split(': ')[1].lower()
71
+ if 'female' in value:
72
+ return 0
73
+ elif 'male' in value:
74
+ return 1
75
+ return None
76
+
77
+ # 3. Save metadata using initial filtering
78
+ is_trait_available = trait_row is not None
79
+ validate_and_save_cohort_info(is_final=False,
80
+ cohort=cohort,
81
+ info_path=json_path,
82
+ is_gene_available=is_gene_available,
83
+ is_trait_available=is_trait_available)
84
+
85
+ # 4. Extract clinical features since trait_row is not None
86
+ clinical_df = geo_select_clinical_features(clinical_data, trait,
87
+ trait_row=trait_row,
88
+ convert_trait=convert_trait,
89
+ age_row=age_row,
90
+ convert_age=convert_age,
91
+ gender_row=gender_row,
92
+ convert_gender=convert_gender)
93
+
94
+ # Preview the extracted clinical features
95
+ preview_result = preview_df(clinical_df)
96
+ print("Preview of clinical features:")
97
+ print(preview_result)
98
+
99
+ # Save clinical data
100
+ clinical_df.to_csv(out_clinical_data_file)
101
+ # Extract genetic data matrix
102
+ genetic_data = get_genetic_data(matrix_file_path)
103
+
104
+ # Print first few rows with column names to examine data structure
105
+ print("Data preview:")
106
+ print("\nColumn names:")
107
+ print(list(genetic_data.columns)[:5])
108
+ print("\nFirst 5 rows:")
109
+ print(genetic_data.head())
110
+ print("\nShape:", genetic_data.shape)
111
+
112
+ # Verify this is gene expression data and check identifiers
113
+ is_gene_available = True
114
+
115
+ # Save updated metadata
116
+ validate_and_save_cohort_info(
117
+ is_final=False,
118
+ cohort=cohort,
119
+ info_path=json_path,
120
+ is_gene_available=is_gene_available,
121
+ is_trait_available=(trait_row is not None)
122
+ )
123
+
124
+ # Save gene expression data
125
+ genetic_data.to_csv(out_gene_data_file)
126
+ # These appear to be probe IDs (like "1007_s_at") from an Affymetrix microarray,
127
+ # not standard human gene symbols, so they will need to be mapped
128
+ requires_gene_mapping = True
129
+ # Extract gene annotation data
130
+ gene_metadata = get_gene_annotation(soft_file_path)
131
+
132
+ # Preview column names and first few values
133
+ preview = preview_df(gene_metadata)
134
+ print("\nGene annotation columns and sample values:")
135
+ print(preview)
136
+ # Extract gene mapping information
137
+ mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')
138
+
139
+ # Apply gene mapping to get gene-level expression data
140
+ gene_data = apply_gene_mapping(genetic_data, mapping_df)
141
+
142
+ # Preview results
143
+ print("\nGene mapping preview:")
144
+ print(mapping_df.head())
145
+ print("\nGene expression data preview:")
146
+ print(gene_data.head())
147
+ print("\nShape:", gene_data.shape)
148
+ # 1. Normalize gene symbols and save gene data
149
+ normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
150
+ os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
151
+ normalized_gene_data.to_csv(out_gene_data_file)
152
+
153
+ # 2. Link clinical and genetic data
154
+ clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
155
+ linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)
156
+
157
+ # 3. Handle missing values
158
+ linked_data = handle_missing_values(linked_data, trait)
159
+
160
+ # 4. Judge bias in features and remove biased ones
161
+ trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
162
+
163
+ # 5. Final validation and save metadata
164
+ is_usable = validate_and_save_cohort_info(
165
+ is_final=True,
166
+ cohort=cohort,
167
+ info_path=json_path,
168
+ is_gene_available=is_gene_available,
169
+ is_trait_available=is_trait_available,
170
+ is_biased=trait_biased,
171
+ df=linked_data,
172
+ note="Dataset contains gene expression data comparing normal thyroid tissue (18 samples) with papillary thyroid carcinoma (27 samples)"
173
+ )
174
+
175
+ # 6. Save linked data if usable
176
+ if is_usable:
177
+ os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
178
+ linked_data.to_csv(out_data_file)
p3/preprocess/Thyroid_Cancer/code/GSE76039.py ADDED
@@ -0,0 +1,182 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Thyroid_Cancer"
6
+ cohort = "GSE76039"
7
+
8
+ # Input paths
9
+ in_trait_dir = "../DATA/GEO/Thyroid_Cancer"
10
+ in_cohort_dir = "../DATA/GEO/Thyroid_Cancer/GSE76039"
11
+
12
+ # Output paths
13
+ out_data_file = "./output/preprocess/3/Thyroid_Cancer/GSE76039.csv"
14
+ out_gene_data_file = "./output/preprocess/3/Thyroid_Cancer/gene_data/GSE76039.csv"
15
+ out_clinical_data_file = "./output/preprocess/3/Thyroid_Cancer/clinical_data/GSE76039.csv"
16
+ json_path = "./output/preprocess/3/Thyroid_Cancer/cohort_info.json"
17
+
18
+ # Get file paths for SOFT and matrix files
19
+ soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
20
+
21
+ # Get background info and clinical data from the matrix file
22
+ background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
23
+
24
+ # Create dictionary of unique values for each feature
25
+ unique_values_dict = get_unique_values_by_row(clinical_data)
26
+
27
+ # Print the information
28
+ print("Dataset Background Information:")
29
+ print(background_info)
30
+ print("\nSample Characteristics:")
31
+ for feature, values in unique_values_dict.items():
32
+ print(f"\n{feature}:")
33
+ print(values)
34
+ # 1. Gene Expression Data Availability
35
+ # Series used Affymetrix array for expression profiling, so gene data is available
36
+ is_gene_available = True
37
+
38
+ # 2.1 Data Availability
39
+ # For trait: Can be inferred from tumor type field
40
+ trait_row = 2
41
+ # Age not available
42
+ age_row = None
43
+ # Gender available in row 0
44
+ gender_row = 0
45
+
46
+ # 2.2 Data Type Conversion Functions
47
+ def convert_trait(value):
48
+ # Binary: 0 for non-ATC, 1 for ATC
49
+ if not isinstance(value, str):
50
+ return None
51
+ try:
52
+ value = value.split(": ")[1].lower()
53
+ if 'anaplastic' in value:
54
+ return 1
55
+ elif value: # Any other tumor type is non-ATC
56
+ return 0
57
+ return None
58
+ except:
59
+ return None
60
+
61
+ def convert_gender(value):
62
+ if not isinstance(value, str):
63
+ return None
64
+ try:
65
+ value = value.split(": ")[1].lower()
66
+ if value == 'female':
67
+ return 0
68
+ elif value == 'male':
69
+ return 1
70
+ return None
71
+ except:
72
+ return None
73
+
74
+ convert_age = None # Not needed since age data unavailable
75
+
76
+ # 3. Save initial filtering metadata
77
+ is_trait_available = trait_row is not None
78
+ validate_and_save_cohort_info(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
+ # 4. Clinical Feature Extraction
85
+ if trait_row is not None:
86
+ clinical_df = geo_select_clinical_features(clinical_data,
87
+ trait=trait,
88
+ trait_row=trait_row,
89
+ convert_trait=convert_trait,
90
+ age_row=age_row,
91
+ convert_age=convert_age,
92
+ gender_row=gender_row,
93
+ convert_gender=convert_gender)
94
+
95
+ # Preview the extracted features
96
+ preview = preview_df(clinical_df)
97
+ print("Clinical feature preview:", preview)
98
+
99
+ # Save clinical data
100
+ clinical_df.to_csv(out_clinical_data_file)
101
+ # Extract genetic data matrix
102
+ genetic_data = get_genetic_data(matrix_file_path)
103
+
104
+ # Print first few rows with column names to examine data structure
105
+ print("Data preview:")
106
+ print("\nColumn names:")
107
+ print(list(genetic_data.columns)[:5])
108
+ print("\nFirst 5 rows:")
109
+ print(genetic_data.head())
110
+ print("\nShape:", genetic_data.shape)
111
+
112
+ # Verify this is gene expression data and check identifiers
113
+ is_gene_available = True
114
+
115
+ # Save updated metadata
116
+ validate_and_save_cohort_info(
117
+ is_final=False,
118
+ cohort=cohort,
119
+ info_path=json_path,
120
+ is_gene_available=is_gene_available,
121
+ is_trait_available=(trait_row is not None)
122
+ )
123
+
124
+ # Save gene expression data
125
+ genetic_data.to_csv(out_gene_data_file)
126
+ # The gene identifiers appear to be Affymetrix probe IDs (e.g. 1007_s_at, 1053_at)
127
+ # which need to be mapped to official gene symbols
128
+ requires_gene_mapping = True
129
+ # Extract gene annotation data
130
+ gene_metadata = get_gene_annotation(soft_file_path)
131
+
132
+ # Preview column names and first few values
133
+ preview = preview_df(gene_metadata)
134
+ print("\nGene annotation columns and sample values:")
135
+ print(preview)
136
+ # Get mapping between probe IDs and gene symbols
137
+ mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')
138
+
139
+ # Apply the mapping to convert probe data to gene expression data
140
+ gene_data = apply_gene_mapping(genetic_data, mapping_df)
141
+
142
+ # Preview transformed data
143
+ print("\nGene expression data preview:")
144
+ print(f"Shape: {gene_data.shape}")
145
+ print("\nFirst 5 genes:")
146
+ print(gene_data.head())
147
+
148
+ # Save gene expression data
149
+ gene_data.to_csv(out_gene_data_file)
150
+ # 1. Normalize gene symbols and save gene data
151
+ normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
152
+ os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
153
+ normalized_gene_data.to_csv(out_gene_data_file)
154
+
155
+ # 2. Link clinical and genetic data
156
+ clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
157
+ linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)
158
+
159
+ # 3. Handle missing values
160
+ linked_data = handle_missing_values(linked_data, trait)
161
+
162
+ # 4. Judge bias in features and remove biased ones
163
+ trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
164
+
165
+ # 5. Final validation and save metadata
166
+ is_usable = validate_and_save_cohort_info(
167
+ is_final=True,
168
+ cohort=cohort,
169
+ info_path=json_path,
170
+ is_gene_available=is_gene_available,
171
+ is_trait_available=True,
172
+ is_biased=trait_biased,
173
+ df=linked_data,
174
+ note="Dataset contains gene expression data comparing follicular thyroid cancers with follicular thyroid adenomas."
175
+ )
176
+
177
+ # 6. Save linked data if usable
178
+ if is_usable:
179
+ os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
180
+ linked_data.to_csv(out_data_file)
181
+ else:
182
+ print("Dataset marked as unusable due to biased trait distribution.")
p3/preprocess/Thyroid_Cancer/code/GSE80022.py ADDED
@@ -0,0 +1,165 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Thyroid_Cancer"
6
+ cohort = "GSE80022"
7
+
8
+ # Input paths
9
+ in_trait_dir = "../DATA/GEO/Thyroid_Cancer"
10
+ in_cohort_dir = "../DATA/GEO/Thyroid_Cancer/GSE80022"
11
+
12
+ # Output paths
13
+ out_data_file = "./output/preprocess/3/Thyroid_Cancer/GSE80022.csv"
14
+ out_gene_data_file = "./output/preprocess/3/Thyroid_Cancer/gene_data/GSE80022.csv"
15
+ out_clinical_data_file = "./output/preprocess/3/Thyroid_Cancer/clinical_data/GSE80022.csv"
16
+ json_path = "./output/preprocess/3/Thyroid_Cancer/cohort_info.json"
17
+
18
+ # Get file paths for SOFT and matrix files
19
+ soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
20
+
21
+ # Get background info and clinical data from the matrix file
22
+ background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
23
+
24
+ # Create dictionary of unique values for each feature
25
+ unique_values_dict = get_unique_values_by_row(clinical_data)
26
+
27
+ # Print the information
28
+ print("Dataset Background Information:")
29
+ print(background_info)
30
+ print("\nSample Characteristics:")
31
+ for feature, values in unique_values_dict.items():
32
+ print(f"\n{feature}:")
33
+ print(values)
34
+ # 1. Check gene expression data availability
35
+ is_gene_available = True # This is transcriptomic/gene expression data
36
+
37
+ # 2.1 Data row identification
38
+ trait_row = 1 # Can use xenograft tissue type (GOT1 vs GOT2)
39
+ age_row = None # No age data available
40
+ gender_row = None # No gender data available, all mice are from same strain
41
+
42
+ # 2.2 Data type conversion functions
43
+ def convert_trait(value: str) -> Optional[int]:
44
+ # Extract value after colon and strip whitespace
45
+ if ':' in value:
46
+ value = value.split(':')[1].strip()
47
+ # Convert GOT1 (normal) to 0, GOT2 (thyroid cancer) to 1
48
+ if 'GOT1' in value:
49
+ return 0
50
+ elif 'GOT2' in value:
51
+ return 1
52
+ return None
53
+
54
+ def convert_age(value: str) -> Optional[float]:
55
+ return None # Not used
56
+
57
+ def convert_gender(value: str) -> Optional[int]:
58
+ return None # Not used
59
+
60
+ # 3. Save metadata about data availability
61
+ is_usable = validate_and_save_cohort_info(
62
+ is_final=False,
63
+ cohort=cohort,
64
+ info_path=json_path,
65
+ is_gene_available=is_gene_available,
66
+ is_trait_available=trait_row is not None
67
+ )
68
+
69
+ # 4. Extract clinical features if trait data is available
70
+ if trait_row is not None:
71
+ clinical_df = geo_select_clinical_features(
72
+ clinical_df=clinical_data,
73
+ trait=trait,
74
+ trait_row=trait_row,
75
+ convert_trait=convert_trait,
76
+ age_row=age_row,
77
+ convert_age=convert_age,
78
+ gender_row=gender_row,
79
+ convert_gender=convert_gender
80
+ )
81
+
82
+ # Preview the data
83
+ preview = preview_df(clinical_df)
84
+ print("Clinical data preview:")
85
+ print(preview)
86
+
87
+ # Save to CSV
88
+ clinical_df.to_csv(out_clinical_data_file)
89
+ # Extract genetic data matrix
90
+ genetic_data = get_genetic_data(matrix_file_path)
91
+
92
+ # Print first few rows with column names to examine data structure
93
+ print("Data preview:")
94
+ print("\nColumn names:")
95
+ print(list(genetic_data.columns)[:5])
96
+ print("\nFirst 5 rows:")
97
+ print(genetic_data.head())
98
+ print("\nShape:", genetic_data.shape)
99
+
100
+ # Verify this is gene expression data and check identifiers
101
+ is_gene_available = True
102
+
103
+ # Save updated metadata
104
+ validate_and_save_cohort_info(
105
+ is_final=False,
106
+ cohort=cohort,
107
+ info_path=json_path,
108
+ is_gene_available=is_gene_available,
109
+ is_trait_available=(trait_row is not None)
110
+ )
111
+
112
+ # Save gene expression data
113
+ genetic_data.to_csv(out_gene_data_file)
114
+ # The row names start with ILMN which indicates these are Illumina probe IDs
115
+ # These need to be mapped to human gene symbols for biological interpretation
116
+ requires_gene_mapping = True
117
+ # Extract gene annotation data
118
+ gene_metadata = get_gene_annotation(soft_file_path)
119
+
120
+ # Preview column names and first few values
121
+ preview = preview_df(gene_metadata)
122
+ print("\nGene annotation columns and sample values:")
123
+ print(preview)
124
+ # Get gene mapping from annotation data
125
+ # 'ID' contains Illumina probe IDs (ILMN_*), 'Symbol' contains gene symbols
126
+ mapping_data = get_gene_mapping(gene_metadata, 'ID', 'Symbol')
127
+
128
+ # Map probes to genes and aggregate expression values
129
+ gene_data = apply_gene_mapping(genetic_data, mapping_data)
130
+
131
+ # Preview transformed data
132
+ print("\nFirst few rows of gene expression data:")
133
+ print(gene_data.head())
134
+ print("\nShape of gene expression data:", gene_data.shape)
135
+ # 1. Normalize gene symbols and save gene data
136
+ normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
137
+ os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
138
+ normalized_gene_data.to_csv(out_gene_data_file)
139
+
140
+ # 2. Link clinical and genetic data
141
+ clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
142
+ linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)
143
+
144
+ # 3. Handle missing values
145
+ linked_data = handle_missing_values(linked_data, trait)
146
+
147
+ # 4. Judge bias in features and remove biased ones
148
+ trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
149
+
150
+ # 5. Final validation and save metadata
151
+ is_usable = validate_and_save_cohort_info(
152
+ is_final=True,
153
+ cohort=cohort,
154
+ info_path=json_path,
155
+ is_gene_available=is_gene_available,
156
+ is_trait_available=True,
157
+ is_biased=trait_biased,
158
+ df=linked_data,
159
+ note="Dataset contains gene expression data comparing 27 follicular thyroid cancers with 25 follicular thyroid adenomas."
160
+ )
161
+
162
+ # 6. Save linked data if usable
163
+ if is_usable:
164
+ os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
165
+ linked_data.to_csv(out_data_file)
p3/preprocess/Thyroid_Cancer/code/GSE82208.py ADDED
@@ -0,0 +1,183 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Thyroid_Cancer"
6
+ cohort = "GSE82208"
7
+
8
+ # Input paths
9
+ in_trait_dir = "../DATA/GEO/Thyroid_Cancer"
10
+ in_cohort_dir = "../DATA/GEO/Thyroid_Cancer/GSE82208"
11
+
12
+ # Output paths
13
+ out_data_file = "./output/preprocess/3/Thyroid_Cancer/GSE82208.csv"
14
+ out_gene_data_file = "./output/preprocess/3/Thyroid_Cancer/gene_data/GSE82208.csv"
15
+ out_clinical_data_file = "./output/preprocess/3/Thyroid_Cancer/clinical_data/GSE82208.csv"
16
+ json_path = "./output/preprocess/3/Thyroid_Cancer/cohort_info.json"
17
+
18
+ # Get file paths for SOFT and matrix files
19
+ soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
20
+
21
+ # Get background info and clinical data from the matrix file
22
+ background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
23
+
24
+ # Create dictionary of unique values for each feature
25
+ unique_values_dict = get_unique_values_by_row(clinical_data)
26
+
27
+ # Print the information
28
+ print("Dataset Background Information:")
29
+ print(background_info)
30
+ print("\nSample Characteristics:")
31
+ for feature, values in unique_values_dict.items():
32
+ print(f"\n{feature}:")
33
+ print(values)
34
+ # 1. Gene Expression Data Availability
35
+ # Series title and summary indicate gene expression data
36
+ is_gene_available = True
37
+
38
+ # 2. Variable Availability and Data Type Conversion
39
+ # 2.1 Row Identification
40
+ trait_row = 2 # 'class' indicates FTC vs FTA
41
+ age_row = 1 # age in years available
42
+ gender_row = 0 # sex information available
43
+
44
+ # 2.2 Data Type Conversion Functions
45
+ def convert_trait(x: str) -> int:
46
+ """Convert FTC/FTA to binary (FTC=1, FTA=0)"""
47
+ if not isinstance(x, str):
48
+ return None
49
+ x = x.split(': ')[-1]
50
+ if x == 'FTC':
51
+ return 1
52
+ elif x == 'FTA':
53
+ return 0
54
+ return None
55
+
56
+ def convert_age(x: str) -> float:
57
+ """Convert age string to float"""
58
+ if not isinstance(x, str):
59
+ return None
60
+ x = x.split(': ')[-1]
61
+ try:
62
+ return float(x)
63
+ except:
64
+ return None
65
+
66
+ def convert_gender(x: str) -> int:
67
+ """Convert gender to binary (Female=0, Male=1)"""
68
+ if not isinstance(x, str):
69
+ return None
70
+ x = x.split(': ')[-1]
71
+ if x == 'Female':
72
+ return 0
73
+ elif x == 'Male':
74
+ return 1
75
+ return None
76
+
77
+ # 3. Save Metadata
78
+ validate_and_save_cohort_info(
79
+ is_final=False,
80
+ cohort=cohort,
81
+ info_path=json_path,
82
+ is_gene_available=is_gene_available,
83
+ is_trait_available=trait_row is not None
84
+ )
85
+
86
+ # 4. Clinical Feature Extraction
87
+ if trait_row is not None:
88
+ clinical_features = geo_select_clinical_features(
89
+ clinical_df=clinical_data,
90
+ trait=trait,
91
+ trait_row=trait_row,
92
+ convert_trait=convert_trait,
93
+ age_row=age_row,
94
+ convert_age=convert_age,
95
+ gender_row=gender_row,
96
+ convert_gender=convert_gender
97
+ )
98
+
99
+ # Preview the extracted features
100
+ preview = preview_df(clinical_features)
101
+ print("Clinical features preview:", preview)
102
+
103
+ # Save to CSV
104
+ clinical_features.to_csv(out_clinical_data_file)
105
+ # Extract genetic data matrix
106
+ genetic_data = get_genetic_data(matrix_file_path)
107
+
108
+ # Print first few rows with column names to examine data structure
109
+ print("Data preview:")
110
+ print("\nColumn names:")
111
+ print(list(genetic_data.columns)[:5])
112
+ print("\nFirst 5 rows:")
113
+ print(genetic_data.head())
114
+ print("\nShape:", genetic_data.shape)
115
+
116
+ # Verify this is gene expression data and check identifiers
117
+ is_gene_available = True
118
+
119
+ # Save updated metadata
120
+ validate_and_save_cohort_info(
121
+ is_final=False,
122
+ cohort=cohort,
123
+ info_path=json_path,
124
+ is_gene_available=is_gene_available,
125
+ is_trait_available=(trait_row is not None)
126
+ )
127
+
128
+ # Save gene expression data
129
+ genetic_data.to_csv(out_gene_data_file)
130
+ requires_gene_mapping = True
131
+ # These appear to be Affymetrix probe IDs like "1007_s_at" rather than gene symbols,
132
+ # so they will need to be mapped to human gene symbols for analysis
133
+ # Extract gene annotation data
134
+ gene_metadata = get_gene_annotation(soft_file_path)
135
+
136
+ # Preview column names and first few values
137
+ preview = preview_df(gene_metadata)
138
+ print("\nGene annotation columns and sample values:")
139
+ print(preview)
140
+ # Extract gene mapping from annotation data
141
+ prob_col = 'ID' # Column containing probe IDs (matches gene expression data indices)
142
+ gene_col = 'Gene Symbol' # Column containing gene symbols
143
+ mapping_data = get_gene_mapping(gene_metadata, prob_col, gene_col)
144
+
145
+ # Convert probe expressions to gene expressions by applying the mapping
146
+ gene_data = apply_gene_mapping(genetic_data, mapping_data)
147
+
148
+ # Preview the mapped data
149
+ print("Gene expression data preview:")
150
+ print("\nShape:", gene_data.shape)
151
+ print("\nFirst few genes and their expression values:")
152
+ print(gene_data.head())
153
+ # 1. Normalize gene symbols and save gene data
154
+ normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
155
+ os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
156
+ normalized_gene_data.to_csv(out_gene_data_file)
157
+
158
+ # 2. Link clinical and genetic data
159
+ clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
160
+ linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)
161
+
162
+ # 3. Handle missing values
163
+ linked_data = handle_missing_values(linked_data, trait)
164
+
165
+ # 4. Judge bias in features and remove biased ones
166
+ trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
167
+
168
+ # 5. Final validation and save metadata
169
+ is_usable = validate_and_save_cohort_info(
170
+ is_final=True,
171
+ cohort=cohort,
172
+ info_path=json_path,
173
+ is_gene_available=is_gene_available,
174
+ is_trait_available=True,
175
+ is_biased=trait_biased,
176
+ df=linked_data,
177
+ note="Dataset contains gene expression data comparing 27 follicular thyroid cancers with 25 follicular thyroid adenomas."
178
+ )
179
+
180
+ # 6. Save linked data if usable
181
+ if is_usable:
182
+ os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
183
+ linked_data.to_csv(out_data_file)
p3/preprocess/Thyroid_Cancer/code/TCGA.py ADDED
@@ -0,0 +1,116 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Path Configuration
2
+ from tools.preprocess import *
3
+
4
+ # Processing context
5
+ trait = "Thyroid_Cancer"
6
+
7
+ # Input paths
8
+ tcga_root_dir = "../DATA/TCGA"
9
+
10
+ # Output paths
11
+ out_data_file = "./output/preprocess/3/Thyroid_Cancer/TCGA.csv"
12
+ out_gene_data_file = "./output/preprocess/3/Thyroid_Cancer/gene_data/TCGA.csv"
13
+ out_clinical_data_file = "./output/preprocess/3/Thyroid_Cancer/clinical_data/TCGA.csv"
14
+ json_path = "./output/preprocess/3/Thyroid_Cancer/cohort_info.json"
15
+
16
+ # Find thyroid cancer data directory
17
+ cohort_dir = os.path.join(tcga_root_dir, "TCGA_Thyroid_Cancer_(THCA)")
18
+
19
+ # Get paths to clinical and genetic data files
20
+ clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir)
21
+
22
+ # Load the data files
23
+ clinical_df = pd.read_csv(clinical_path, index_col=0, sep='\t')
24
+ genetic_df = pd.read_csv(genetic_path, index_col=0, sep='\t')
25
+
26
+ # Print clinical columns
27
+ print("Clinical data columns:")
28
+ print(clinical_df.columns.tolist())
29
+
30
+ # Mark data as available
31
+ is_gene_available = True
32
+ is_trait_available = True
33
+ validate_and_save_cohort_info(
34
+ is_final=False,
35
+ cohort="TCGA",
36
+ info_path=json_path,
37
+ is_gene_available=is_gene_available,
38
+ is_trait_available=is_trait_available
39
+ )
40
+ # 1. Identify candidate columns
41
+ candidate_age_cols = ["age_at_initial_pathologic_diagnosis", "days_to_birth"]
42
+ candidate_gender_cols = ["gender"]
43
+
44
+ # 2. Preview the candidate columns
45
+ age_df = clinical_df[candidate_age_cols]
46
+ gender_df = clinical_df[candidate_gender_cols]
47
+
48
+ print("Age columns preview:")
49
+ print(preview_df(age_df))
50
+
51
+ print("\nGender columns preview:")
52
+ print(preview_df(gender_df))
53
+ # Analyze age columns
54
+ # Both columns contain valid age information, but age_at_initial_pathologic_diagnosis is more intuitive
55
+ age_col = "age_at_initial_pathologic_diagnosis"
56
+
57
+ # Analyze gender columns
58
+ # gender column contains valid gender information
59
+ gender_col = "gender"
60
+
61
+ # Print chosen columns
62
+ print(f"Selected age column: {age_col}")
63
+ print(f"Selected gender column: {gender_col}")
64
+ # Get paths
65
+ cohort_dir = os.path.join(tcga_root_dir, "TCGA_Thyroid_Cancer_(THCA)")
66
+ clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir)
67
+
68
+ # Load data
69
+ clinical_df = pd.read_csv(clinical_path, index_col=0, sep='\t')
70
+ genetic_df = pd.read_csv(genetic_path, index_col=0, sep='\t')
71
+
72
+ # Extract clinical features
73
+ selected_clinical_df = tcga_select_clinical_features(
74
+ clinical_df=clinical_df,
75
+ trait=trait,
76
+ age_col=age_col,
77
+ gender_col=gender_col
78
+ )
79
+
80
+ # Normalize gene symbols
81
+ normalized_gene_df = normalize_gene_symbols_in_index(genetic_df)
82
+
83
+ # Save normalized gene data
84
+ os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
85
+ normalized_gene_df.to_csv(out_gene_data_file)
86
+
87
+ # Link clinical and genetic data
88
+ linked_data = pd.merge(
89
+ selected_clinical_df,
90
+ normalized_gene_df.T,
91
+ left_index=True,
92
+ right_index=True
93
+ )
94
+
95
+ # Handle missing values
96
+ linked_data = handle_missing_values(linked_data, trait)
97
+
98
+ # Check for bias and remove biased demographic features
99
+ is_biased, cleaned_data = judge_and_remove_biased_features(linked_data, trait)
100
+
101
+ # Final validation and save metadata
102
+ is_usable = validate_and_save_cohort_info(
103
+ is_final=True,
104
+ cohort="TCGA",
105
+ info_path=json_path,
106
+ is_gene_available=True,
107
+ is_trait_available=True,
108
+ is_biased=is_biased,
109
+ df=cleaned_data,
110
+ note="This dataset contains TCGA thyroid cancer data. However, it only contains positive cases (all samples are cancer cases), making it unsuitable for phenotype prediction."
111
+ )
112
+
113
+ # Save processed data if usable
114
+ if is_usable:
115
+ os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
116
+ cleaned_data.to_csv(out_data_file)
p3/preprocess/Thyroid_Cancer/gene_data/GSE104005.csv ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ Gene,GSM2787612,GSM2787613,GSM2787614,GSM2787615,GSM2787616,GSM2787617,GSM2787618,GSM2787619,GSM2787620,GSM2787621,GSM2787622,GSM2787623,GSM2787624,GSM2787625,GSM2787626,GSM2787627,GSM2787628,GSM2787629,GSM2787630,GSM2787631,GSM2787632,GSM2787633,GSM2787634,GSM2787635,GSM2787636,GSM2787637,GSM2787638,GSM2787639,GSM2787640,GSM2787641,GSM2787642,GSM2787643,GSM2787644,GSM2787645
2
+ OR4F17,5.181299572,4.519390543,4.98444393,4.653434743,4.7415875,4.973208931,4.98897484,4.907562419,4.851338573,4.444230481,4.555883724,4.64295786,5.307384778,4.84498755,4.315523071,5.061409867,4.708178071,5.084980217,6.374146265,8.575917361,6.543255213,7.655642737,6.907065609,8.461427115,8.22805201,8.627109412,5.972727024,9.055829918,7.438219732,7.246453363,7.744829477,7.68949715,6.835868415,7.572287921
3
+ OR4F21,7.948194378,7.983533185,7.885292142,7.775461081,7.645245105,7.096310313,7.543321283,7.251046903,7.029819422,8.405378861,6.510438521,7.15880716,7.231047695,7.097163988,7.340786001,6.948924899,7.241692004,7.503674302,7.484163365,8.277984747,7.002152113,7.526702768,7.047873031,9.053658515,8.89544259,8.994951999,6.84517517,8.903974151,7.965917202,7.894099945,8.195721367,8.097305119,7.583283437,8.072630132
4
+ OR4F29,7.54973451,7.10443217,7.232030025,7.215096951,7.239600182,6.763498208,7.319093552,6.930718237,6.940209159,7.798784309,6.260930148,6.946698832,7.005844288,6.791106867,7.105079517,6.536171422,6.971177869,7.202114342,7.183496398,8.397888508,6.757942959,7.474346914,6.755065382,8.737165121,8.752134085,8.841536692,6.69535619,9.105375149,7.882754723,7.57992094,8.11310161,7.426039153,7.521330678,7.850324572
5
+ OR4F3,8.101679319,7.801173962,7.80247119,7.864611708,7.857704098,7.102234483,7.599374243,7.163572352,7.1007228,8.959024821,6.695647631,7.164707935,7.394585511,7.219476202,7.384907965,7.01390838,7.426003401,7.557190361,7.793481618,8.616548844,7.144281264,7.425670618,7.227083629,8.863679566,8.910304052,9.134135933,6.993669467,9.261615805,8.041661328,8.303116804,8.285573253,8.637619087,7.764894044,8.286916792
6
+ OR4F4,4.692175756,4.831047791,4.945269816,4.750005905,4.710949467,4.8745294,5.371801014,5.372228796,4.689458915,4.956143749,4.652074847,4.876944574,4.662457351,4.978981651,4.70143193,5.250039165,5.490042207,5.004333966,5.680050757,7.813139851,6.039928343,7.143714515,6.200125582,8.202032574,8.13393412,8.136157295,4.816129202,8.570292346,7.07692169,6.647749115,7.667860831,5.943304983,6.652780084,7.297038369
7
+ OR4F5,5.628190209,5.11613061,4.98444393,4.553020518,4.679917025,4.422059591,4.900403139,4.531134673,4.547733862,4.786312508,4.588379438,4.608826064,5.164042892,5.400465986,4.785653823,4.652967818,4.375211654,4.5672527,6.295414993,7.502235115,5.878805168,7.184412058,5.647588935,8.331427398,8.20134189,8.168812943,5.080024268,8.384297868,7.149006492,6.278972091,7.459374316,5.399010036,6.786358748,7.383424683
8
+ PCMTD2,12.50675417,13.0973691,11.19857244,12.43180712,11.95450511,11.99386234,12.30348436,12.26081926,12.22398959,12.66258453,12.52587097,12.39975515,12.74812622,12.26743796,12.49856108,11.84208062,11.69177699,12.02424896,10.86863132,11.05236427,12.25061592,11.94895269,11.79850295,12.11098219,12.20100516,12.72991704,12.27680492,11.138021,12.34760812,12.49535026,12.30979267,12.24124558,12.34190284,12.39926072
p3/preprocess/Thyroid_Cancer/gene_data/GSE104006.csv ADDED
The diff for this file is too large to render. See raw diff
 
p3/preprocess/Thyroid_Cancer/gene_data/GSE107754.csv ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ Gene,GSM2878070,GSM2878071,GSM2878072,GSM2878073,GSM2878074,GSM2878075,GSM2878076,GSM2878077,GSM2878078,GSM2878079,GSM2878080,GSM2878081,GSM2878082,GSM2891194,GSM2891195,GSM2891196,GSM2891197,GSM2891198,GSM2891199,GSM2891200,GSM2891201,GSM2891202,GSM2891203,GSM2891204,GSM2891205,GSM2891206,GSM2891207,GSM2891208,GSM2891209,GSM2891210,GSM2891211,GSM2891212,GSM2891213,GSM2891214,GSM2891215,GSM2891216,GSM2891217,GSM2891218,GSM2891219,GSM2891220,GSM2891221,GSM2891222,GSM2891223,GSM2891224,GSM2891225,GSM2891226,GSM2891227,GSM2891228,GSM2891229,GSM2891230,GSM2891231,GSM2891232,GSM2891233,GSM2891234,GSM2891235,GSM2891236,GSM2891237,GSM2891238,GSM2891239,GSM2891240,GSM2891241,GSM2891242,GSM2891243,GSM2891244,GSM2891245,GSM2891246,GSM2891247,GSM2891248,GSM2891249,GSM2891250,GSM2891251,GSM2891252,GSM2891253,GSM2891254,GSM2891255,GSM2891256,GSM2891257,GSM2891258,GSM2891259,GSM2891260,GSM2891261,GSM2891262,GSM2891263,GSM2891264
2
+ OR4F4,7.51471204,7.610735337,7.740376624,7.58503848,7.542011567,7.55041018,7.590904176,7.551360436,7.577624147,7.509467944,9.664437266,7.577884575,7.521334345,7.529524624,7.590310631,7.584678085,7.57775565,7.58536239,7.559309921,7.590777038,7.624100615,7.585709837,7.672938701,7.555008547,7.592843306,7.614236183,7.542313638,7.596226327,7.541280889,7.567793692,7.619625124,7.595456545,7.522463569,7.582526577,7.569428875,7.652437033,7.613504862,7.556807566,7.582558738,7.796666182,7.570375709,7.567128469,7.544628039,7.508682587,7.559379399,7.595878139,7.591016832,7.555688312,7.531277311,7.578860829,7.582079751,7.657252748,7.570922056,7.585696092,7.594557211,7.574905154,7.558204119,7.591629626,7.951748741,7.635740028,7.564245261,7.581910059,7.608635201,7.557164788,7.613655116,7.613748154,7.609468118,7.598876253,7.579392336,7.591583455,7.5476539,7.584865841,7.610042714,7.635998002,7.570717635,7.582134433,7.586022158,7.875926085,7.571833184,7.579174541,7.606394566,7.558699075,7.587826708,7.577199548
3
+ PCMTD2,26.772304962,26.133108436,28.09089806,27.746912549,28.207736288,29.362909871,27.029309814,27.544501554,27.814833909999997,25.495575977,27.278215461000002,26.168137082,28.789207062000003,27.966357582,29.364034476999997,26.593093064999998,27.435257932,31.108761008,28.051762823,25.751861662,31.152191003,26.197674935000002,26.084354476,25.985274557,27.619313097000003,26.907706368,27.575922443000003,26.950454493000002,26.778770648,26.531142755,25.656687601,26.275393023,27.075938671,26.881598512,27.269520209,27.390808378,26.109470635,28.781509865,27.836386745,28.34472268,30.30671367,28.271575415,26.097245203,27.643107164,29.026729574999997,27.977927934,27.572297251,28.099232229000002,26.240728308,27.553068398,26.78809866,27.981587034,26.333004502,28.006678759,28.444344708,29.804593643,27.266204157,26.366422781,27.820419251,26.654767172,28.180596526,27.882455261,27.584099894,26.793613649,27.474358995,27.743413511,27.363405112,26.327684381,25.491052851,31.09780984,25.680469151,26.809121997,26.317434138,28.499374359,27.568897646,26.987310690999998,25.962481199,26.599348002,26.96621862,30.966616154,27.639992540999998,27.70129915,26.691884983,26.759888500000002
4
+ SEPT14,8.035776906,8.107821381,8.36300959,8.210895452,8.353820295,8.720205708,7.978304166,8.168304176,8.298666082,7.589765536,8.069810765,7.839932896,8.191097776,8.160970128,8.77533563,7.894307935,8.173704184,8.503961727,8.123745468,7.88003618,9.152509524,7.724597524,7.587422727,7.89875933,8.032318143,7.787443389,8.140500304,7.94250986,7.953979515,7.990364124,7.621687226,7.926862143,8.143626109,8.072029006,8.026816706,8.006877826,7.851435537,8.456629328,8.16736722,8.070916591,8.863964998,8.232720887,7.809078453,8.313767473,8.527219749,8.154906465,7.964374241,8.155814699,7.89367483,8.013342185,8.044920049,8.259122232,7.676603313,8.119747249,8.396003378,8.246209858,8.166860847,8.033532816,8.333722513,7.922320125,7.979323415,8.070453382,8.365098533,8.261323704,8.151296956,8.048271885,8.085846571,8.143438514,7.820814341,9.471194029,8.036132345,8.116173276,7.882832468,8.64581977,8.725901524,8.202068128,8.014856673,8.407283229,8.311545426,8.818215558,8.021354421,8.045183894,8.330207428,8.246814941
p3/preprocess/Thyroid_Cancer/gene_data/GSE138198.csv ADDED
@@ -0,0 +1,18 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Gene,GSM4101749,GSM4101750,GSM4101751,GSM4101752,GSM4101753,GSM4101754,GSM4101755,GSM4101756,GSM4101757,GSM4101758,GSM4101759,GSM4101760,GSM4101761,GSM4101762,GSM4101763,GSM4101764,GSM4101765,GSM4101766,GSM4101767,GSM4101768,GSM4101769,GSM4101770,GSM4101771,GSM4101772,GSM4101773,GSM4101774,GSM4101775,GSM4101776,GSM4101777,GSM4101778,GSM4101779,GSM4101780,GSM4101781,GSM4101782,GSM4101783,GSM4101784
2
+ OR4F16,1.2105290909090909,1.5661209090909092,1.5950409090909092,1.792,2.047191818181818,1.5404863636363637,1.7627927272727273,1.6926709090909091,1.554149090909091,2.158764545454545,1.7152354545454545,2.077417272727273,1.9251281818181818,1.301870909090909,1.4374654545454544,1.61656,1.7996145454545456,1.8026718181818184,1.6357172727272726,1.8616672727272727,1.9618563636363635,1.6107663636363636,2.134390909090909,1.7285472727272728,1.7333736363636363,1.8601827272727274,2.0307136363636364,1.4752363636363637,1.5678400000000001,1.5751109090909092,1.8002799999999999,2.120352727272727,1.8816818181818182,1.2871127272727272,1.362950909090909,1.2955981818181819
3
+ OR4F17,1.1025428571428573,1.1957871428571427,1.1760042857142856,1.3324928571428571,1.3737857142857142,1.4116328571428571,1.375902857142857,1.3055914285714287,1.2425314285714286,1.587767142857143,1.23081,1.210654285714286,1.7244085714285715,1.1930914285714285,1.193412857142857,1.33947,1.1104757142857142,1.2065314285714286,1.2279642857142856,1.1929757142857143,1.5626100000000003,1.0797985714285714,1.5305142857142857,1.3935257142857143,1.2653571428571428,1.2487114285714287,1.4240914285714286,1.290574285714286,1.2281142857142857,1.4777657142857143,1.3032814285714285,1.3480885714285713,1.23507,1.2465857142857142,1.2375342857142857,1.365737142857143
4
+ OR4F1P,1.2105290909090909,1.5661209090909092,1.5950409090909092,1.792,2.047191818181818,1.5404863636363637,1.7627927272727273,1.6926709090909091,1.554149090909091,2.158764545454545,1.7152354545454545,2.077417272727273,1.9251281818181818,1.301870909090909,1.4374654545454544,1.61656,1.7996145454545456,1.8026718181818184,1.6357172727272726,1.8616672727272727,1.9618563636363635,1.6107663636363636,2.134390909090909,1.7285472727272728,1.7333736363636363,1.8601827272727274,2.0307136363636364,1.4752363636363637,1.5678400000000001,1.5751109090909092,1.8002799999999999,2.120352727272727,1.8816818181818182,1.2871127272727272,1.362950909090909,1.2955981818181819
5
+ OR4F21,1.2105290909090909,1.5661209090909092,1.5950409090909092,1.792,2.047191818181818,1.5404863636363637,1.7627927272727273,1.6926709090909091,1.554149090909091,2.158764545454545,1.7152354545454545,2.077417272727273,1.9251281818181818,1.301870909090909,1.4374654545454544,1.61656,1.7996145454545456,1.8026718181818184,1.6357172727272726,1.8616672727272727,1.9618563636363635,1.6107663636363636,2.134390909090909,1.7285472727272728,1.7333736363636363,1.8601827272727274,2.0307136363636364,1.4752363636363637,1.5678400000000001,1.5751109090909092,1.8002799999999999,2.120352727272727,1.8816818181818182,1.2871127272727272,1.362950909090909,1.2955981818181819
6
+ OR4F28P,2.909266590909091,3.073785909090909,3.195853409090909,3.367965,3.601859318181818,3.081456363636364,3.1130302272727275,3.448095909090909,3.0819265909090907,3.7275945454545454,3.1238104545454544,3.516932272727273,3.507703181818182,2.781080909090909,2.9846729545454544,3.1973175,3.2040620454545454,3.3822568181818182,3.1078922727272724,3.3754497727272725,3.5895988636363634,3.0956338636363636,3.660550909090909,3.2842747727272728,3.1867261363636366,3.559585227272727,3.5241686363636364,2.9222338636363636,3.1880550000000003,3.0386609090909094,3.3212924999999998,3.682462727272727,3.3000893181818185,2.8438577272727272,3.0468259090909093,2.8256931818181816
7
+ OR4F29,1.2105290909090909,1.5661209090909092,1.5950409090909092,1.792,2.047191818181818,1.5404863636363637,1.7627927272727273,1.6926709090909091,1.554149090909091,2.158764545454545,1.7152354545454545,2.077417272727273,1.9251281818181818,1.301870909090909,1.4374654545454544,1.61656,1.7996145454545456,1.8026718181818184,1.6357172727272726,1.8616672727272727,1.9618563636363635,1.6107663636363636,2.134390909090909,1.7285472727272728,1.7333736363636363,1.8601827272727274,2.0307136363636364,1.4752363636363637,1.5678400000000001,1.5751109090909092,1.8002799999999999,2.120352727272727,1.8816818181818182,1.2871127272727272,1.362950909090909,1.2955981818181819
8
+ OR4F2P,1.2105290909090909,1.5661209090909092,1.5950409090909092,1.792,2.047191818181818,1.5404863636363637,1.7627927272727273,1.6926709090909091,1.554149090909091,2.158764545454545,1.7152354545454545,2.077417272727273,1.9251281818181818,1.301870909090909,1.4374654545454544,1.61656,1.7996145454545456,1.8026718181818184,1.6357172727272726,1.8616672727272727,1.9618563636363635,1.6107663636363636,2.134390909090909,1.7285472727272728,1.7333736363636363,1.8601827272727274,2.0307136363636364,1.4752363636363637,1.5678400000000001,1.5751109090909092,1.8002799999999999,2.120352727272727,1.8816818181818182,1.2871127272727272,1.362950909090909,1.2955981818181819
9
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13
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14
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17
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18
+ SEPT14,6.6898307692307695,6.817035000000001,6.866875,6.981311153846154,6.8614011538461535,6.393868461538462,6.604908461538462,7.53084,7.845172307692309,6.918141923076924,6.463448461538462,7.180299230769232,6.678633846153845,6.125595384615384,6.450847307692308,6.502652692307692,6.929437307692308,6.22633,6.49314423076923,7.116677307692308,7.155825384615384,6.799666153846155,7.097932692307692,5.954733076923077,7.042211153846154,6.590938076923077,6.864115384615384,6.632488846153846,6.738447692307693,6.886945000000001,6.292143461538461,6.7276180769230765,7.013631153846154,6.156647692307692,5.7881800000000005,5.769045384615384
p3/preprocess/Thyroid_Cancer/gene_data/GSE151179.csv ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ Gene,GSM4567912,GSM4567913,GSM4567914,GSM4567915,GSM4567916,GSM4567917,GSM4567918,GSM4567919,GSM4567920,GSM4567921,GSM4567922,GSM4567923,GSM4567924,GSM4567925,GSM4567926,GSM4567927,GSM4567928,GSM4567929,GSM4567930,GSM4567931,GSM4567932,GSM4567933,GSM4567934,GSM4567935,GSM4567936,GSM4567937,GSM4567938,GSM4567939,GSM4567940,GSM4567941,GSM4567942,GSM4567943,GSM4567944,GSM4567945,GSM4567946,GSM4567947,GSM4567948,GSM4567949,GSM4567950,GSM4567951,GSM4567952,GSM4567953,GSM4567954,GSM4567955,GSM4567956,GSM4567957,GSM4567958,GSM4567959,GSM4567960,GSM4567961,GSM4567962,GSM4567963
2
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p3/preprocess/Thyroid_Cancer/gene_data/GSE151181.csv ADDED
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p3/preprocess/Thyroid_Cancer/gene_data/GSE58689.csv ADDED
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1
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p3/preprocess/Thyroid_Cancer/gene_data/GSE76039.csv ADDED
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1
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1
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+ OR4F21,0.56625070675,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56661486625,0.56436822825,0.56436822825,0.83305713125,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.59265495775,0.569935921,0.56436822825,0.5656104435,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.567072312,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.57431155175,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.573312487,0.56436822825,0.56436822825
4
+ OR4F29,0.56625070675,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56661486625,0.56436822825,0.56436822825,0.83305713125,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.59265495775,0.569935921,0.56436822825,0.5656104435,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.567072312,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.57431155175,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.573312487,0.56436822825,0.56436822825
5
+ OR4F3,0.56625070675,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56661486625,0.56436822825,0.56436822825,0.83305713125,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.59265495775,0.569935921,0.56436822825,0.5656104435,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.567072312,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.57431155175,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.56436822825,0.573312487,0.56436822825,0.56436822825
6
+ PCMTD2,13.340083793,11.696405838,12.674283164,12.134027695999999,11.7511866,11.967323545,12.644154974000001,11.433079322,11.439497604,12.341327494,11.918212836,12.76499734,12.892185727000001,11.982029146,11.992884867,11.864432357,11.582239492,11.946876533,11.680585538999999,12.161589961,12.27008558,12.022847056,11.850357563,12.224271863999999,11.669124342,11.561379617,11.313221948,11.508705157,12.27396976,11.78633447,12.195693576,15.287510292,10.15973545,12.952136995,12.343602082999999,10.340761082,11.743684362,11.811592713,11.003436892,11.846090768,12.567798336000001,12.691589792,11.454818623,12.458335203,15.967245672,11.257699696,12.732074233,12.346004101,14.663855000000002,12.744262900999999,13.016909309999999,12.139404054
p3/preprocess/Thyroid_Cancer/gene_data/TCGA.csv ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
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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,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
2
+ Type_1_Diabetes,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,,,,,,,,,,,,,,,,,,,0.0,0.0,,,0.0,,0.0,,,0.0,,0.0,0.0,,,0.0,,,,,,0.0,0.0,0.0,,,,,,0.0,,,,,0.0,0.0
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+ Age,56.0,,20.0,51.0,37.0,61.0,,31.0,56.0,41.0,61.0,,80.0,53.0,61.0,73.0,60.0,76.0,77.0,74.0,69.0,77.0,81.0,70.0,82.0,69.0,82.0,67.0,67.0,78.0,67.0,74.0,,51.0,72.0,66.0,80.0,36.0,67.0,31.0,31.0,45.0,56.0,65.0,53.0,48.0,50.0,76.0,,24.0,42.0,76.0,22.0,,23.0,34.0,43.0,47.0,24.0,55.0,48.0,58.0,30.0,28.0,41.0,63.0,55.0,55.0,67.0,47.0,46.0,49.0,23.0,68.0,39.0,24.0,36.0,58.0,38.0,27.0,67.0,61.0,69.0,63.0,60.0,17.0,10.0,9.0,13.0,10.0,13.0,15.0,12.0,13.0,81.0,94.0,51.0,40.0,,97.0,23.0,93.0,58.0,28.0,54.0,15.0,8.0,11.0,12.0,8.0,14.0,8.0,10.0,14.0,13.0,40.0,52.0,42.0,29.0,43.0,41.0,54.0,42.0,49.0,45.0,56.0,64.0,71.0,48.0,20.0,53.0,32.0,26.0,28.0,47.0,24.0,48.0,,19.0,41.0,38.0,,15.0,12.0,13.0,,11.0,,16.0,11.0,,35.0,26.0,39.0,46.0,42.0,20.0,69.0,69.0,47.0,47.0,56.0,54.0,53.0,50.0,22.0,62.0,74.0,57.0,47.0,70.0,50.0,52.0,43.0,57.0,53.0,70.0,41.0,61.0,39.0,58.0,55.0,63.0,60.0,43.0,68.0,67.0,50.0,67.0,51.0,59.0,44.0,35.0,83.0,78.0,88.0,41.0,60.0,72.0,53.0,73.0,56.0,38.0,53.0
4
+ Gender,1.0,,0.0,0.0,1.0,1.0,,1.0,0.0,0.0,0.0,,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,,0.0,0.0,1.0,1.0,,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,0.0,0.0,0.0,,0.0,1.0,0.0,,1.0,,1.0,1.0,,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0