Add files using upload-large-folder tool

.gitattributes

@@ -1741,3 +1741,22 @@ p3/preprocess/Height/gene_data/GSE152073.csv filter=lfs diff=lfs merge=lfs -text
 p3/preprocess/Height/gene_data/GSE101710.csv filter=lfs diff=lfs merge=lfs -text
 p3/preprocess/Height/gene_data/GSE101709.csv filter=lfs diff=lfs merge=lfs -text
 p3/preprocess/Hemochromatosis/gene_data/GSE50579.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Height/gene_data/GSE97475.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Head_and_Neck_Cancer/gene_data/GSE156915.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/HIV_Resistance/GSE33580.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/HIV_Resistance/gene_data/GSE46599.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Huntingtons_Disease/GSE34201.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/HIV_Resistance/gene_data/GSE33580.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Huntingtons_Disease/GSE26927.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Huntingtons_Disease/gene_data/GSE34201.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Huntingtons_Disease/gene_data/GSE26927.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Huntingtons_Disease/GSE34721.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Huntingtons_Disease/GSE135589.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Huntingtons_Disease/gene_data/GSE95843.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Huntingtons_Disease/gene_data/GSE34721.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Huntingtons_Disease/gene_data/GSE135589.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Atherosclerosis/gene_data/GSE154851.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Hemochromatosis/TCGA.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Atherosclerosis/gene_data/GSE57691.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Head_and_Neck_Cancer/gene_data/TCGA.csv filter=lfs diff=lfs merge=lfs -text
+p3/preprocess/Hemochromatosis/gene_data/TCGA.csv filter=lfs diff=lfs merge=lfs -text

p3/preprocess/Atherosclerosis/clinical_data/GSE109048.csv

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p3/preprocess/Atherosclerosis/clinical_data/GSE123086.csv

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p3/preprocess/Atherosclerosis/clinical_data/GSE123088.csv

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p3/preprocess/Atherosclerosis/clinical_data/GSE125771.csv

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+Atherosclerosis,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+Age,73.0,60.0,81.0,85.0,60.0,84.0,76.0,57.0,71.0,69.0,79.0,78.0,79.0,54.0,72.0,73.0,64.0,67.0,63.0,75.0,62.0,64.0,73.0,81.0,79.0,72.0,71.0,75.0,74.0,76.0,69.0,65.0,83.0,85.0,61.0,72.0,64.0,69.0,61.0,71.0
+Gender,1.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0

p3/preprocess/Atherosclerosis/clinical_data/GSE154851.csv

@@ -0,0 +1,4 @@
+,GSM4681537,GSM4681538,GSM4681539,GSM4681540,GSM4681541,GSM4681542,GSM4681543,GSM4681544,GSM4681545,GSM4681546,GSM4681547,GSM4681548,GSM4681549,GSM4681550,GSM4681551,GSM4681552,GSM4681553,GSM4681554,GSM4681555,GSM4681556,GSM4681557,GSM4681558,GSM4681559,GSM4681560,GSM4681561,GSM4681562,GSM4681563,GSM4681564,GSM4681565,GSM4681566,GSM4681567,GSM4681568,GSM4681569,GSM4681570,GSM4681571,GSM4681572,GSM4681573,GSM4681574,GSM4681575,GSM4681576,GSM4681577,GSM4681578,GSM4681579,GSM4681580,GSM4681581,GSM4681582,GSM4681583,GSM4681584,GSM4681585,GSM4681586,GSM4681587,GSM4681588,GSM4681589,GSM4681590,GSM4681591,GSM4681592,GSM4681593,GSM4681594,GSM4681595,GSM4681596,GSM4681597,GSM4681598,GSM4681599,GSM4681600,GSM4681601,GSM4681602,GSM4681603,GSM4681604,GSM4681605,GSM4681606
+Atherosclerosis,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+Age,18.0,37.0,59.0,36.0,56.0,22.0,53.0,41.0,33.0,52.0,42.0,28.0,45.0,41.0,25.0,34.0,40.0,44.0,42.0,39.0,51.0,41.0,52.0,34.0,21.0,23.0,32.0,39.0,71.0,23.0,44.0,26.0,31.0,24.0,23.0,31.0,30.0,47.0,30.0,24.0,35.0,25.0,25.0,33.0,19.0,23.0,36.0,26.0,27.0,28.0,34.0,30.0,39.0,32.0,26.0,22.0,25.0,32.0,33.0,41.0,31.0,48.0,38.0,30.0,27.0,23.0,41.0,36.0,34.0,54.0
+Gender,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

p3/preprocess/Atherosclerosis/clinical_data/GSE57691.csv

@@ -0,0 +1,2 @@
+,!Sample_title,small AAA_Sample 1,small AAA_Sample 2,small AAA_Sample 3,small AAA_Sample 4,small AAA_Sample 5,small AAA_Sample 6,small AAA_Sample 7,small AAA_Sample 8,small AAA_Sample 9,small AAA_Sample 10,small AAA_Sample 11,small AAA_Sample 12,small AAA_Sample 13,small AAA_Sample 14,small AAA_Sample 15,small AAA_Sample 16,small AAA_Sample 17,small AAA_Sample 18,small AAA_Sample 19,small AAA_Sample 20,large AAA_Sample 21,large AAA_Sample 22,large AAA_Sample 23,large AAA_Sample 24,large AAA_Sample 25,large AAA_Sample 26,large AAA_Sample 27,large AAA_Sample 28,large AAA_Sample 29,large AAA_Sample 30,large AAA_Sample 31,large AAA_Sample 32,large AAA_Sample 33,large AAA_Sample 34,large AAA_Sample 35,large AAA_Sample 36,large AAA_Sample 37,large AAA_Sample 38,large AAA_Sample 39,large AAA_Sample 40,large AAA_Sample 41,large AAA_Sample 42,large AAA_Sample 43,large AAA_Sample 44,large AAA_Sample 45,large AAA_Sample 46,large AAA_Sample 47,large AAA_Sample 48,large AAA_Sample 49,AOD_Sample 50,AOD_Sample 51,AOD_Sample 52,AOD_Sample 53,AOD_Sample 54,AOD_Sample 55,AOD_Sample 56,AOD_Sample 57,AOD_Sample 58,Donor_Sample 59,Donor_Sample 60,Donor_Sample 61,Donor_Sample 62,Donor_Sample 63,Donor_Sample 64,Donor_Sample 65,Donor_Sample 66,Donor_Sample 67,Donor_Sample 68
+Atherosclerosis,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,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

p3/preprocess/Atherosclerosis/clinical_data/GSE83500.csv

@@ -0,0 +1,2 @@
+,0
+Atherosclerosis,

p3/preprocess/Atherosclerosis/clinical_data/GSE87005.csv

@@ -0,0 +1,2 @@
+,!Sample_title,IS1,IS2,IS3,IS4,IS5,IS6,IS7,IS8,IS9,IS10,IR1,IR2,IR3,IR4,IR5,IR6,IR7,IR8,IR9,IR10,IS-BMI1,IS-BMI2,IS-BMI3,IS-BMI4,IS-BMI5,IS-BMI6,IS-BMI7,IS-BMI8,IS-BMI9,IS-BMI10,IR-BMI1,IR-BMI2,IR-BMI3,IR-BMI4,IR-BMI5,IR-BMI6,IR-BMI7,IR-BMI8,IR-BMI9,IR-BMI10
+Atherosclerosis,,,,,,,,,,,,,,,,,,,,,,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

p3/preprocess/Atherosclerosis/clinical_data/GSE90074.csv

@@ -0,0 +1,3 @@
+,!Sample_title,2S001_F_CAU_N_1_43,2S002_F_AA_Y_3_123,2S003_F_CAU_Y_4_135,2S004_M_AA_N_1_112,2S005_F_CAU_Y_2_114,2S006_F_CAU_Y_3_55,2S007_F_CAU_Y_4_39,2S008_M_CAU_Y_4_120,2S009_M_CAU_Y_4_59,2S010_M_CAU_Y_3_92,2S012_F_AA_Y_3_139,2S015_M_CAU_Y_4_37,2S023_F_AA_Y_3_101,2S025_F_CAU_N_1_100,2S026_F_CAU_Y_2_60,2S029_M_CAU_Y_2_91,2S031_M_CAU_Y_3_27,2S033_M_CAU_N_1_64,2S035_F_AA_N_1_106,2S037_F_CAU_Y_4_46,2S038_F_CAU_N_1_116,2S039_M_CAU_Y_3_72,2S040_M_CAU_N_0_13,2S042_F_CAU_N_1_131,2S044_F_AA_N_1_75,2S045_F_CAU_N_0_130,2S046_M_CAU_Y_4_77,2S047_M_AA_Y_2_126,2S048_F_AA_N_0_141,2S049_M_CAU_N_1_104,2S050_M_CAU_Y_4_103,2S051_M_CAU_N_0_109,2S052_F_AA_N_0_137,2S053_F_CAU_Y_3_21,2S054_F_CAU_N_1_33,2S056_F_CAU_Y_2_81,2S057_F_CAU_Y_2_89,2S058_F_CAU_Y_2_52,2S060_M_CAU_Y_4_93,2S061_M_CAU_N_1_99,2S062_F_CAU_Y_2_122,2S063_F_CAU_N_0_138,2S065_F_AA_Y_4_61,2S066_F_CAU_Y_4_56,2S067_F_AA_Y_4_142,2S069_M_CAU_N_1_84,2S070_F_CAU_N_0_47,2S071_M_CAU_N_0_67,2S072_M_CAU_N_0_54,2S073_M_CAU_N_1_45,2S074_F_CAU_N_0_83,2S075_F_AA_N_1_143,2S076_M_CAU_Y_3_7,2S077_F_CAU_Y_2_79,2S078_M_CAU_Y_3_132,2S079_M_CAU_Y_3_36,2S080_F_CAU_Y_2_128,2S082_F_AA_Y_4_19,2S083_M_CAU_Y_2_42,2S085_M_CAU_Y_3_29,2S087_M_CAU_Y_4_108,2S088_M_CAU_Y_2_69,2S089_M_CAU_Y_3_98,2S090_M_CAU_Y_4_53,2S092_F_AA_Y_4_85,2S093_M_AA_N_1_117,2S094_M_CAU_N_1_73,2S095_M_CAU_Y_4_51,2S097_M_CAU_Y_4_90,2S098_M_CAU_N_1_102,2S099_M_CAU_N_1_124,2S100_F_CAU_Y_2_118,2S101_F_CAU_N_1_110,2S102_F_CAU_N_0_125,2S103_M_CAU_Y_4_95,2S105_F_CAU_N_1_48,2S108_M_CAU_Y_4_86,2S109_M_CAU_Y_2_127,2S110_M_CAU_Y_4_57,2S111_M_CAU_Y_3_115,2S112_M_CAU_Y_3_40,2S113_M_CAU_Y_3_49,2S114_F_AA_Y_3_107,2S115_F_CAU_Y_2_121,2S116_M_AA_Y_3_134,2S118_M_CAU_N_1_119,2S119_M_CAU_N_1_136,2S120_M_CAU_Y_3_88,2S121_M_CAU_Y_3_140,2S124_M_CAU_Y_3_31,2S125_F_CAU_N_1_63,2S127_F_CAU_N_1_30,2S130_F_CAU_Y_2_24,2S132_F_CAU_N_1_113,2S133_F_AA_N_0_111,2S134_M_CAU_Y_2_22,2S137_M_CAU_Y_4_14,2S138_M_CAU_Y_2_4,2S139_F_CAU_Y_2_65,2S141_M_AA_Y_3_44,2S142_F_CAU_N_0_105,2S143_F_CAU_Y_4_16,2S144_M_CAU_Y_2_71,2S145_F_CAU_Y_2_3,2S146_F_AA_Y_3_82,2S147_M_CAU_N_0_6,2S148_F_CAU_Y_4_15,2S149_M_AA_Y_2_34,2S151_M_CAU_Y_4_5,2S152_F_CAU_Y_2_10,2S153_F_AA_Y_3_96,2S154_F_CAU_Y_4_9,2S156_F_CAU_Y_2_70,2S157_M_AA_Y_3_78,2S159_F_CAU_Y_2_32,2S160_M_CAU_Y_2_76,2S162_M_AA_Y_4_74,2S163_M_CAU_Y_2_26,2S165_M_AA_Y_4_23,2S167_F_CAU_N_1_87,2S168_F_AA_N_0_62,2S169_F_AA_N_0_38,2S171_M_CAU_Y_2_35,2S172_M_CAU_Y_4_68,2S173_M_AA_Y_4_11,2S175_F_AA_N_1_94,2S176_M_AA_N_1_97,2S177_M_CAU_Y_4_20,2S178_F_AA_N_1_50,2S179_M_CAU_Y_4_8,2S181_M_CAU_Y_4_41,2S182_F_CAU_N_0_129,2S183_F_CAU_N_1_133,2S184_M_AA_Y_3_18,2S186_M_CAU_Y_4_80,2S187_F_AA_Y_2_66,2S188_M_CAU_Y_4_2,2S189_F_CAU_N_1_12,2S191_M_CAU_Y_4_1,2S194_F_CAU_N_1_58,2S195_M_CAU_Y_2_25,2S199_M_CAU_Y_2_17,2S200_F_CAU_N_0_28
+Atherosclerosis,,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0
+Gender,,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0

p3/preprocess/Atherosclerosis/code/GSE109048.py

@@ -0,0 +1,188 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+cohort = "GSE109048"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atherosclerosis"
+in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE109048"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Atherosclerosis/GSE109048.csv"
+out_gene_data_file = "./output/preprocess/3/Atherosclerosis/gene_data/GSE109048.csv"
+out_clinical_data_file = "./output/preprocess/3/Atherosclerosis/clinical_data/GSE109048.csv"
+json_path = "./output/preprocess/3/Atherosclerosis/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data using specified prefixes
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
+    prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1']
+)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics 
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# From background info, this is platelet gene expression data
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+# Trait (Atherosclerosis) can be inferred from diagnosis in row 1
+# sCAD = stable coronary artery disease = Atherosclerosis
+trait_row = 1
+
+# Age and gender not available
+age_row = None  
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    # Extract value after colon
+    value = value.split(': ')[1].lower().strip() 
+    # sCAD indicates presence of atherosclerosis
+    if 'scad' in value:
+        return 1
+    # STEMI indicates severe atherosclerosis 
+    elif 'stemi' in value:
+        return 1
+    # Healthy indicates no atherosclerosis
+    elif 'healthy' in value:
+        return 0
+    return None
+
+def convert_age(value):
+    # Age not available
+    return None
+
+def convert_gender(value):
+    # Gender not available
+    return None
+
+# 3. Save Metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# 4. Clinical Feature Extraction
+# Since trait_row is not None, extract clinical features
+selected_clinical = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age, 
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+print("Preview of selected clinical features:")
+print(preview_df(selected_clinical))
+
+# Save clinical data
+selected_clinical.to_csv(out_clinical_data_file)
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug 
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Looking at the identifiers (e.g. "2824546_st", "2824549_st"), 
+# these appear to be Affymetrix probe IDs rather than gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file)
+
+# Preview the annotation data 
+print("Column names:", gene_metadata.columns.tolist())
+print("\nFirst few rows preview:")
+print(preview_df(gene_metadata))
+# Get mapping data
+# First rename the columns to match expected format
+mapping_df = gene_metadata[['probeset_id', 'gene_assignment']].copy()
+mapping_df = mapping_df.rename(columns={'probeset_id': 'ID', 'gene_assignment': 'Gene'})
+mapping_df = mapping_df.dropna()
+
+# Apply the gene mapping to convert probe-level data to gene-level data 
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Preview the mapped data
+print("\nPreview of gene expression data after mapping:")
+print(gene_data.head())
+print("\nShape of gene expression data after mapping:", gene_data.shape)
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
+
+print("Preview of linked data after linking:")
+print(linked_data.head())
+print("\nShape after linking:", linked_data.shape)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+print("Preview of linked data after missing value handling:")
+print(linked_data.head())
+print("\nShape after handling missing values:", linked_data.shape)
+
+# 4. Check for biased features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased, 
+    df=linked_data,
+    note="Contains gene expression data and trait labels for atherosclerosis."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Atherosclerosis/code/GSE123086.py

@@ -0,0 +1,232 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+cohort = "GSE123086"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atherosclerosis"
+in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE123086"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Atherosclerosis/GSE123086.csv"
+out_gene_data_file = "./output/preprocess/3/Atherosclerosis/gene_data/GSE123086.csv"
+out_clinical_data_file = "./output/preprocess/3/Atherosclerosis/clinical_data/GSE123086.csv"
+json_path = "./output/preprocess/3/Atherosclerosis/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data using specified prefixes
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
+    prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1']
+)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics 
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# According to Series_overall_design, RNA was extracted and microarrays were used
+is_gene_available = True
+
+# 2.1 Data Availability
+# Primary diagnosis in row 1 contains trait information
+trait_row = 1  
+
+# Sex is recorded in rows 2 and 3
+gender_row = 2
+
+# Age is recorded in rows 3 and 4 
+age_row = 3
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    if not isinstance(value, str) or 'primary diagnosis:' not in value:
+        return None
+    # Extract value after colon and strip whitespace
+    value = value.split(':', 1)[1].strip()
+    # Convert to binary based on trait name
+    return 1 if value == 'ATHEROSCLEROSIS' else 0
+
+def convert_age(value):
+    if not isinstance(value, str) or 'age:' not in value:
+        return None
+    try:
+        # Extract numeric age value after colon
+        age = int(value.split(':', 1)[1].strip())
+        return age
+    except:
+        return None
+
+def convert_gender(value):
+    if not isinstance(value, str) or 'Sex:' not in value:
+        return None
+    # Extract gender value after colon
+    gender = value.split(':', 1)[1].strip()
+    # Convert to binary (female=0, male=1)
+    if gender.upper() == 'FEMALE':
+        return 0
+    elif gender.upper() == 'MALE':
+        return 1
+    return None
+
+# 3. Save Metadata
+validate_and_save_cohort_info(is_final=False, 
+                             cohort=cohort,
+                             info_path=json_path,
+                             is_gene_available=is_gene_available,
+                             is_trait_available=trait_row is not None)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(clinical_data, trait, 
+                                                   trait_row=trait_row,
+                                                   convert_trait=convert_trait,
+                                                   age_row=age_row, 
+                                                   convert_age=convert_age,
+                                                   gender_row=gender_row,
+                                                   convert_gender=convert_gender)
+    
+    # Preview the processed clinical data
+    preview = preview_df(clinical_features)
+    print("Preview of processed clinical data:")
+    print(preview)
+    
+    # Save clinical features to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug 
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+requires_gene_mapping = True
+# Extract gene annotation data, excluding metadata lines
+gene_metadata = get_gene_annotation(soft_file, prefixes=['!Platform_table_begin', '^SPOT_ID', '^ENTREZ_GENE_ID', '^GENE_SYMBOL'])
+
+# Preview the annotation data 
+print("Column names:", gene_metadata.columns.tolist())
+print("\nFirst few rows preview:")
+print(preview_df(gene_metadata))
+# Extract gene annotation data by excluding lines with special prefixes
+gene_metadata = get_gene_annotation(soft_file, prefixes=['^', '!', '#'])
+
+# Print full info about the data to debug
+print("Shape:", gene_metadata.shape)
+print("\nAll columns:", gene_metadata.columns.tolist())
+print("\nFirst few rows:")
+print(gene_metadata.head())
+
+# Try importing the annotation file differently since the first attempt failed
+import gzip
+import pandas as pd
+
+with gzip.open(soft_file, 'rt') as f:
+    for line in f:
+        if '!platform_table_begin' in line.lower():
+            # Read the table that follows
+            gene_metadata = pd.read_csv(f, sep='\t', comment='!', on_bad_lines='skip')
+            break
+
+print("\nAfter direct reading:")
+print("Shape:", gene_metadata.shape)
+print("\nAll columns:", gene_metadata.columns.tolist())
+print("\nFirst few rows:")
+print(gene_metadata.head())
+
+# Get mapping between IDs and gene symbols
+mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')
+
+# Apply the mapping to convert probe-level data to gene-level data  
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Save the gene expression data
+gene_data.to_csv(out_gene_data_file)
+# Extract gene annotation data from the platform portion of the SOFT file
+with gzip.open(soft_file, 'rt') as f:
+    platform_found = False
+    table_found = False
+    lines = []
+    for line in f:
+        if '!Platform' in line:
+            platform_found = True
+        if platform_found and '!platform_table_begin' in line.lower():
+            table_found = True
+            continue
+        if table_found and not line.startswith('!'):
+            lines.append(line)
+            
+# Parse the platform table into a dataframe            
+annotation_text = ''.join(lines)
+gene_metadata = pd.read_csv(io.StringIO(annotation_text), sep='\t')
+
+# Create mapping between probe IDs and gene identifiers
+mapping_data = pd.DataFrame({
+    'ID': gene_metadata['ID'].astype(str),
+    'Gene': gene_metadata['ENTREZ_GENE_ID'].astype(str)  # Use ENTREZ_GENE_ID as gene identifier
+})
+
+# Apply mapping to convert probe data to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Save the processed gene expression data
+gene_data.to_csv(out_gene_data_file)
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, 'Atherosclerosis')
+
+# 4. Check for bias
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, 'Atherosclerosis')
+
+# 5. Final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Dataset contains both clinical features and gene expression data."
+)
+
+# 6. Save data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Atherosclerosis/code/GSE123088.py

@@ -0,0 +1,353 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+cohort = "GSE123088"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atherosclerosis"
+in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE123088"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Atherosclerosis/GSE123088.csv"
+out_gene_data_file = "./output/preprocess/3/Atherosclerosis/gene_data/GSE123088.csv"
+out_clinical_data_file = "./output/preprocess/3/Atherosclerosis/clinical_data/GSE123088.csv"
+json_path = "./output/preprocess/3/Atherosclerosis/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data using specified prefixes
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
+    prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1']
+)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics 
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# This dataset contains mixed disease data and focuses on CD4+ T cells, likely containing gene expression data
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+# trait data in Feature 1 under 'primary diagnosis'
+trait_row = 1 
+# age data appears in Features 3 and 4
+age_row = 3
+# gender data in Features 2 and 3 
+gender_row = 2
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    if not isinstance(x, str):
+        return None
+    diagnosis = x.split(': ')[1] if ': ' in x else x
+    # Convert to binary based on diagnosis
+    if diagnosis in ['ATHEROSCLEROSIS', 'diagnosis2: ATHEROSCLEROSIS']:
+        return 1
+    elif diagnosis in ['HEALTHY_CONTROL', 'Control']:
+        return 0
+    return None
+
+def convert_age(x):
+    if not isinstance(x, str):
+        return None
+    try:
+        # Extract age value after colon
+        age_str = x.split(': ')[1] if ': ' in x else x
+        return float(age_str)
+    except:
+        return None
+
+def convert_gender(x):
+    if not isinstance(x, str):
+        return None
+    if 'Sex:' not in x:
+        return None
+    gender = x.split(': ')[1]
+    # Convert gender to binary (0 for female, 1 for male)
+    if gender.lower() == 'female':
+        return 0
+    elif gender.lower() == 'male':
+        return 1
+    return None
+
+# 3. Save Metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,  
+    is_trait_available=trait_row is not None
+)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the processed data
+    print("Preview of processed clinical features:")
+    print(preview_df(clinical_features))
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug 
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+requires_gene_mapping = True
+# First locate platform and annotation information
+with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
+    print("Searching for platform/annotation info:")
+    content = f.read(10000)
+    relevant = [line for line in content.split('\n') 
+               if 'platform' in line.lower() or 'annotation' in line.lower()]
+    for line in relevant:
+        print(line)
+
+print("\n" + "="*80 + "\n")
+
+# Extract gene annotation, filtering platform/ID information
+gene_metadata = filter_content_by_prefix(
+    soft_file,
+    prefixes_a=['!Platform_table_begin'],
+    unselect=True,
+    source_type='file',
+    return_df_a=True
+)[0]
+
+# Preview the annotation data
+print("Column names:", gene_metadata.columns.tolist())
+print("\nFirst few rows preview:")
+print(preview_df(gene_metadata))
+# Try to extract data from SOFT file with more focused extraction
+from io import StringIO
+import gzip
+
+# First read a portion of the file to find where the platform table starts
+platform_data = ""
+in_table = False
+with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
+    for line in f:
+        if '!Platform_table_begin' in line:
+            in_table = True
+            # Skip the header row
+            next(f)
+            continue
+        if in_table and '!Platform_table_end' in line:
+            break
+        if in_table:
+            platform_data += line
+
+# Create DataFrame from the extracted platform data
+gene_metadata = pd.read_csv(StringIO(platform_data), sep='\t')
+
+# Print column names to identify correct ones for mapping
+print("Available columns in gene metadata:")
+print(gene_metadata.columns.tolist())
+print("\nFirst few rows:")
+print(gene_metadata.head())
+
+# Create mapping between probe IDs and gene symbols
+# Adjust column names based on actual output
+mapping_data = get_gene_mapping(gene_metadata, prob_col='ProbeName', gene_col='GeneName')
+
+# Convert probe-level measurements to gene-level measurements
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Preview the mapped data
+print("\nShape of gene expression data after mapping:", gene_data.shape)
+print("\nFirst few rows of mapped data:")
+print(gene_data.head())
+# Extract gene annotation using the provided function
+gene_metadata = get_gene_annotation(soft_file)
+
+# Preview the annotation data
+print("Column names:", gene_metadata.columns.tolist())
+print("\nFirst few rows preview:") 
+print(preview_df(gene_metadata))
+# 1. Extract gene mapping from platform table in SOFT file
+platform_data = ""
+in_table = False
+with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
+    for line in f:
+        if '!Platform_table_begin' in line:
+            in_table = True
+            continue
+        if in_table and '!Platform_table_end' in line:
+            break
+        if in_table:
+            platform_data += line
+
+# Parse platform data into DataFrame
+gene_metadata = pd.read_csv(StringIO(platform_data), sep='\t')
+
+# Map probe IDs to gene symbols
+mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GeneName')
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Normalize gene symbols and save gene data 
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# Check for biased features
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Final validation and info saving
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note="Dataset contains gene expression data and clinical information, but some demographic features were removed due to bias."
+)
+
+# Save linked data if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)
+# Print the first few lines of platform data to check format
+platform_data = []
+in_table = False
+header_found = False
+with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
+    for line in f:
+        if '!Platform_table_begin' in line:
+            in_table = True
+            continue
+        if in_table and not header_found:
+            header = line.strip().split('\t')
+            header_found = True
+            continue
+        if in_table and '!Platform_table_end' in line:
+            break
+        if in_table and header_found:
+            platform_data.append(line.strip().split('\t'))
+
+# Create DataFrame with proper columns
+gene_metadata = pd.DataFrame(platform_data, columns=header)
+print("Available columns:", gene_metadata.columns.tolist())
+print("\nFirst few rows:")
+print(gene_metadata.head())
+
+# Create mapping using ENTREZ_GENE_ID as these match the numeric IDs
+mapping_data = pd.DataFrame({
+    'ID': gene_metadata['ID'].astype(str),
+    'Gene': gene_metadata['ENTREZ_GENE_ID'].astype(str)
+})
+
+# Convert probe-level measurements to gene-level measurements
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+print("\nShape after mapping:", gene_data.shape)
+print("\nMapped data preview:")
+print(gene_data.head())
+# We can use the gene_metadata from previous step which has numeric IDs matching gene expression data
+# We'll query NCBI's gene database to get gene symbols from Entrez IDs
+import gzip
+import requests
+import time
+from typing import Dict
+
+def get_gene_symbols_from_entrez(entrez_ids: list) -> Dict[str, str]:
+    """Get gene symbols from Entrez Gene IDs using NCBI E-utilities"""
+    base_url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi"
+    symbols = {}
+    
+    # Process in batches of 100 to avoid overloading the server
+    batch_size = 100
+    for i in range(0, len(entrez_ids), batch_size):
+        batch = entrez_ids[i:i + batch_size]
+        params = {
+            'db': 'gene',
+            'id': ','.join(batch),
+            'retmode': 'json'
+        }
+        
+        try:
+            response = requests.get(base_url, params=params)
+            data = response.json()
+            
+            for gene_id in data['result']:
+                if gene_id != 'uids':  # Skip the uids key
+                    try:
+                        symbol = data['result'][gene_id]['name']
+                        symbols[gene_id] = symbol
+                    except KeyError:
+                        continue
+                        
+        except Exception as e:
+            print(f"Error fetching symbols for batch {i}: {e}")
+            
+        # Be nice to NCBI's servers
+        time.sleep(0.1)
+        
+    return symbols
+
+# Create mapping DataFrame
+mapping_data = pd.DataFrame({
+    'ID': gene_metadata['ID'],
+    'Gene': gene_metadata['ENTREZ_GENE_ID']
+})
+
+# Convert gene_data probe IDs to string type to match mapping_data
+gene_data.index = gene_data.index.astype(str)
+
+# Apply mapping to convert probe-level measurements to gene-level measurements
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+print("\nShape of gene expression data after mapping:", gene_data.shape)
+print("\nFirst few rows of mapped data:")
+print(gene_data.head())

p3/preprocess/Atherosclerosis/code/GSE125771.py

@@ -0,0 +1,148 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+cohort = "GSE125771"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atherosclerosis"
+in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE125771"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Atherosclerosis/GSE125771.csv"
+out_gene_data_file = "./output/preprocess/3/Atherosclerosis/gene_data/GSE125771.csv"
+out_clinical_data_file = "./output/preprocess/3/Atherosclerosis/clinical_data/GSE125771.csv"
+json_path = "./output/preprocess/3/Atherosclerosis/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data using specified prefixes
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
+    prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1']
+)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics 
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Determine gene expression data availability
+# Based on the Series_title and Series_overall_design, this is RNA expression data
+is_gene_available = True
+
+# 2.1 Data availability
+# Trait: everyone has atherosclerotic plaque (constant feature)
+trait_row = None
+
+# Age: available in Feature 3
+age_row = 3
+
+# Gender: available in Feature 2 
+gender_row = 2
+
+# 2.2 Data type conversion functions
+def convert_trait(x):
+    return None # Not used since trait_row is None
+
+def convert_age(x):
+    try:
+        # Extract value after colon and convert to float
+        age = float(x.split(': ')[1])
+        return age
+    except:
+        return None
+
+def convert_gender(x):
+    try:
+        # Extract value after colon and convert to binary (Female=0, Male=1)
+        gender = x.split(': ')[1]
+        if gender.lower() == 'female':
+            return 0
+        elif gender.lower() == 'male':
+            return 1
+        return None
+    except:
+        return None
+        
+# 3. Save metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# 4. Skip clinical feature extraction since trait_row is None
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug 
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# The gene identifiers appear to be probe IDs from a custom Affymetrix array
+# They have the format "TC########.hg.1" which indicates they are transcript cluster IDs
+# These are not standard human gene symbols and will need to be mapped
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file)
+
+# Preview the annotation data 
+print("Column names:", gene_metadata.columns.tolist())
+print("\nFirst few rows preview:")
+print(preview_df(gene_metadata))
+# Get probe-to-gene mapping
+mapping_data = get_gene_mapping(gene_metadata, 'ID', 'gene_assignment')
+
+# Apply mapping to convert probe measurements to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Save processed gene data to CSV
+gene_data.to_csv(out_gene_data_file)
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# Clinical data is not available (trait_row was None), so skip remaining steps
+# Record that this dataset cannot be used for association studies
+validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=False,
+    df=gene_data,
+    note="Contains gene expression data but lacks clinical information needed for trait association studies."
+)

p3/preprocess/Atherosclerosis/code/GSE133601.py

@@ -0,0 +1,155 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+cohort = "GSE133601"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atherosclerosis"
+in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE133601"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Atherosclerosis/GSE133601.csv"
+out_gene_data_file = "./output/preprocess/3/Atherosclerosis/gene_data/GSE133601.csv"
+out_clinical_data_file = "./output/preprocess/3/Atherosclerosis/clinical_data/GSE133601.csv"
+json_path = "./output/preprocess/3/Atherosclerosis/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data using specified prefixes
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
+    prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1']
+)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics 
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene expression data availability check
+# Based on the background information, this dataset contains gene expression data from peripheral blood mononuclear cells
+is_gene_available = True
+
+# 2. Variable availability and data type conversion
+# 2.1 Data rows
+# trait (Atherosclerosis): Not directly available in the data
+trait_row = None
+
+# Age: Not available in sample characteristics
+age_row = None 
+
+# Gender: Not available in sample characteristics
+gender_row = None
+
+# 2.2 Conversion functions
+def convert_trait(x):
+    return None
+
+def convert_age(x):
+    return None
+
+def convert_gender(x):
+    return None
+
+# 3. Save metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+
+# 4. Clinical feature extraction is skipped since trait_row is None
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug 
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Looking at the gene identifiers, they appear to be Affymetrix probe IDs (ending in "_at")
+# These are not standard human gene symbols and will need to be mapped
+requires_gene_mapping = True
+# Let's explore raw SOFT file content to find the correct section
+import gzip
+print("Preview of raw SOFT file content (searching for platform data):")
+with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
+    in_platform = False
+    for i, line in enumerate(f):
+        if '!Platform_table_begin' in line:
+            in_platform = True
+            print("\nFound platform table. Next 10 lines:")
+        if in_platform and i < 10:
+            print(line.strip())
+        elif i > 100:  # Limit preview length
+            break
+
+# Extract gene annotation data, skipping header lines and keeping table rows
+gene_metadata = get_gene_annotation(soft_file, prefixes=['^', '!', '#'])
+
+# Preview the annotation data, showing more rows to better understand structure
+print("\nColumn names:", gene_metadata.columns.tolist())
+print("\nFirst 10 rows preview:")
+preview_size = min(10, len(gene_metadata))
+print(preview_df(gene_metadata, n=preview_size))
+
+# Print shape to understand size of annotation data
+print("\nShape of annotation data:", gene_metadata.shape)
+# Extract gene mapping from annotation data
+# 'ID' column in annotation matches probe IDs in expression data (ending in '_at')
+# 'Description' column contains gene names/descriptions
+mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Description')
+
+# Convert probe-level measurements to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Print shape and preview mapped data 
+print("Shape after mapping to genes:", gene_data.shape)
+print("\nFirst few rows of gene expression data:")
+print(gene_data.head())
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# Clinical data is not available (trait_row was None), so skip remaining steps
+# Record that this dataset cannot be used for association studies
+validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=False,
+    df=gene_data,
+    note="Contains gene expression data but lacks clinical information needed for trait association studies."
+)

p3/preprocess/Atherosclerosis/code/GSE154851.py

@@ -0,0 +1,153 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+cohort = "GSE154851"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atherosclerosis"
+in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE154851"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Atherosclerosis/GSE154851.csv"
+out_gene_data_file = "./output/preprocess/3/Atherosclerosis/gene_data/GSE154851.csv"
+out_clinical_data_file = "./output/preprocess/3/Atherosclerosis/clinical_data/GSE154851.csv"
+json_path = "./output/preprocess/3/Atherosclerosis/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data using specified prefixes
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
+    prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1']
+)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics 
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Based on background info - using Sureprint G3 Human Gene Expression microarray
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# Trait: Cannot reliably determine from available characteristics data
+trait_row = None 
+
+# Age is in row 2
+age_row = 2 
+
+# Gender is in row 1
+gender_row = 1
+
+def convert_trait(x):
+    return None
+
+def convert_age(x):
+    if not isinstance(x, str):
+        return None
+    try:
+        # Extract age value before 'y'
+        age = int(x.split(': ')[1].replace('y',''))
+        return age
+    except:
+        return None
+
+def convert_gender(x):
+    if not isinstance(x, str):
+        return None
+    try:
+        gender = x.split(': ')[1].lower()
+        if gender == 'female':
+            return 0
+        elif gender == 'male':
+            return 1
+        return None
+    except:
+        return None
+
+# 3. Save metadata
+is_trait_available = False if trait_row is None else True
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Skip clinical feature extraction since trait_row is None
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug 
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Based on the data preview, I observe that:
+# 1. The gene identifiers are numeric indices (1,2,3...)
+# 2. These are not standard gene symbols which are typically alphanumeric (e.g. BRCA1, TP53)
+# 3. These appear to be microarray probe IDs that need to be mapped to gene symbols
+
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file)
+
+# Preview the annotation data 
+print("Column names:", gene_metadata.columns.tolist())
+print("\nFirst few rows preview:")
+print(preview_df(gene_metadata))
+# Extract mapping between probe IDs and gene symbols from annotation data
+mapping_data = get_gene_mapping(gene_metadata, 'ID', 'GENE_SYMBOL')
+
+# Apply the mapping to convert probe measurements to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Save the processed gene expression data
+gene_data.to_csv(out_gene_data_file)
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# Clinical data is not available (trait_row was None), so skip remaining steps
+# Record that this dataset cannot be used for association studies
+validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=False,
+    df=gene_data,
+    note="Contains gene expression data but lacks clinical information needed for trait association studies."
+)

p3/preprocess/Atherosclerosis/code/GSE57691.py

@@ -0,0 +1,305 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+cohort = "GSE57691"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atherosclerosis"
+in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE57691"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Atherosclerosis/GSE57691.csv"
+out_gene_data_file = "./output/preprocess/3/Atherosclerosis/gene_data/GSE57691.csv"
+out_clinical_data_file = "./output/preprocess/3/Atherosclerosis/clinical_data/GSE57691.csv"
+json_path = "./output/preprocess/3/Atherosclerosis/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data using broader sample prefixes
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
+    prefixes_b=['!Sample_', '!Sample_disease_state', '!Sample_description'] 
+)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics 
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Based on background info mentioning "Genome-wide expression analysis" and platform GPL10558
+is_gene_available = True
+
+# 2.1 Data Availability
+# Trait can be inferred from disease state in Feature 8
+trait_row = 8  
+
+# Age and gender are not available in the characteristics
+age_row = None
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert trait value to binary: 1 for presence of atherosclerosis, 0 for control"""
+    if value is None or ':' not in value:
+        return None
+    value = value.split(':')[1].strip().lower()
+    if 'aod' in value:  # AOD is atherosclerotic occlusive disease
+        return 1
+    elif 'control' in value:
+        return 0
+    else:  # AAA samples are not relevant for atherosclerosis
+        return None
+
+def convert_age(value: str) -> Optional[float]:
+    """Placeholder function as age data is not available"""
+    return None
+
+def convert_gender(value: str) -> Optional[int]:
+    """Placeholder function as gender data is not available"""
+    return None
+
+# 3. Save Metadata
+# is_trait_available is True since trait_row is not None
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=True)
+
+# 4. Clinical Feature Extraction
+# Since trait_row is not None, we need to extract clinical features
+selected_clinical_df = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview the extracted features
+preview_result = preview_df(selected_clinical_df)
+print("Preview of clinical features:")
+print(preview_result)
+
+# Save clinical data
+os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+selected_clinical_df.to_csv(out_clinical_data_file)
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug 
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Looking at the gene identifiers (e.g. ILMN_1343291), these are Illumina probe IDs 
+# that need to be mapped to standard human gene symbols
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file)
+
+# Preview the annotation data 
+print("Column names:", gene_metadata.columns.tolist())
+print("\nFirst few rows preview:")
+print(preview_df(gene_metadata))
+# Create gene mapping dataframe from gene annotation data
+# ID column in gene_metadata contains probe IDs (e.g. ILMN_1343291)
+# Symbol column contains gene symbols
+mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Symbol')
+
+# Apply gene mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Save gene expression data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+# Need to transpose selected_clinical_df to match sample orientation
+selected_clinical_df = selected_clinical_df.T 
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias  
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Gene expression study comparing AOD (atherosclerotic occlusive disease) vs normal control aortic tissue."
+)
+
+# 6. Save if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)
+# Cannot perform analysis without output from previous step showing dataset information
+# Request previous output showing:
+# - Dataset background information 
+# - Sample characteristics dictionary
+# - Matrix file information
+print("Error: Missing required output from previous step showing dataset characteristics")
+raise ValueError("Previous step output required to analyze dataset and determine data availability")
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data using broader sample prefixes
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
+    prefixes_b=['!Sample_', '!Sample_disease_state', '!Sample_description'] 
+)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics 
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# The dataset measures gene expression in aortic tissue using Illumina arrays
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+
+# Trait (Atherosclerosis) - Feature 8 contains disease state info
+# Both AAA and AOD represent atherosclerotic disease
+trait_row = 8  
+
+# Age not available - no age info in characteristics 
+age_row = None
+
+# Gender not available - no gender info in characteristics
+gender_row = None
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> Optional[int]:
+    """Convert disease state to binary trait value"""
+    if not isinstance(value, str):
+        return None
+    # Extract value after colon if present
+    if ':' in value:
+        value = value.split(':')[1].strip()
+    # Both AAA and AOD represent atherosclerotic disease
+    if 'AAA' in value or 'AOD' in value:
+        return 1
+    elif 'control' in value:
+        return 0
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    """Convert age value to float"""
+    return None  # Not used since age data not available
+
+def convert_gender(value: str) -> Optional[int]:  
+    """Convert gender to binary"""
+    return None  # Not used since gender data not available
+
+# 3. Save Metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=trait_row is not None
+)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the extracted features
+    preview = preview_df(clinical_features)
+    print("Preview of clinical features:")
+    print(preview)
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data 
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias  
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Study examining expression profiles in endometriotic cyst stromal cells versus normal endometrial stromal cells."
+)
+
+# 6. Save if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Atherosclerosis/code/GSE83500.py

@@ -0,0 +1,442 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+cohort = "GSE83500"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atherosclerosis"
+in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE83500"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Atherosclerosis/GSE83500.csv"
+out_gene_data_file = "./output/preprocess/3/Atherosclerosis/gene_data/GSE83500.csv"
+out_clinical_data_file = "./output/preprocess/3/Atherosclerosis/clinical_data/GSE83500.csv"
+json_path = "./output/preprocess/3/Atherosclerosis/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data using broader sample prefixes
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
+    prefixes_b=['!Sample_', '!Sample_disease_state', '!Sample_description'] 
+)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics 
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Based on platform (GPL13667 - Affymetrix Human Genome U219) and series title/design, 
+# this is a gene expression microarray dataset
+is_gene_available = True
+
+# 2.1 Data Availability
+# Feature 8 indicates MI vs non-MI status (atherosclerosis trait)
+trait_row = 8
+
+# Feature 9 has age information
+age_row = 9  
+
+# Feature 10 has gender information
+gender_row = 10
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value):
+    # Convert MI status to binary (1=MI, 0=non-MI)
+    if not value or ':' not in value:
+        return None
+    value = value.split(': ')[1].lower()
+    if 'mi patient' in value:
+        return 1
+    elif 'non-mi patient' in value:
+        return 0
+    return None
+
+def convert_age(value):
+    # Convert age to continuous numeric value
+    if not value or ':' not in value:
+        return None
+    try:
+        return int(value.split(': ')[1])
+    except:
+        return None
+
+def convert_gender(value):
+    # Convert gender to binary (0=Female, 1=Male)
+    if not value or ':' not in value:
+        return None
+    value = value.split(': ')[1].lower()
+    if value == 'female':
+        return 0
+    elif value == 'male':
+        return 1
+    return None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age, 
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the extracted features
+    print("Preview of clinical features:")
+    print(preview_df(clinical_features))
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug 
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# Based on the identifier format (e.g., "11715100_at"), these appear to be Affymetrix probe IDs
+# rather than gene symbols. They need to be mapped to official gene symbols.
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file)
+
+# Preview the annotation data 
+print("Column names:", gene_metadata.columns.tolist())
+print("\nFirst few rows preview:")
+print(preview_df(gene_metadata))
+# Get gene mapping from annotation data
+# The 'ID' column contains probe IDs that match gene expression data identifiers
+# The 'Gene Symbol' column contains the human gene symbols
+mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')
+
+# Apply gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Save genetic data to CSV
+gene_data.to_csv(out_gene_data_file)
+
+# Print preview to verify correct mapping
+print("Shape of mapped gene expression data:", gene_data.shape)
+print("\nFirst few rows of mapped data:")
+print(gene_data.head())
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data 
+# Transpose and extract feature data rows from clinical features
+trait_data = clinical_features.loc[trait, :]
+age_data = clinical_features.loc['Age', :]  
+gender_data = clinical_features.loc['Gender', :]
+
+clinical_data = pd.concat([trait_data, age_data, gender_data], axis=0).T
+clinical_data = clinical_data.rename({trait: 'Atherosclerosis'}, axis=1)
+
+linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, 'Atherosclerosis')
+
+# 4. Check for bias  
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, 'Atherosclerosis')
+
+# 5. Validate and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Study examining gene expression in aortic wall samples between MI and non-MI patients, relevant to atherosclerosis."
+)
+
+# 6. Save if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+# Restructure clinical features first 
+clinical_data = clinical_features.T  # Transpose to get features as columns
+
+# Link the data
+linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias  
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path, 
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Study examining gene expression in aortic wall samples between MI and non-MI patients, relevant to atherosclerosis."
+)
+
+# 6. Save if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Dataset has gene expression data
+
+# 2.1 Data Availability
+trait_row = 0  # Group/Status contains disease/control info
+age_row = 1  # Age
+gender_row = None  # Gender not available
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(value: str) -> int:
+    """Convert disease/control status to binary (0=control, 1=disease)"""
+    if value is None:
+        return None
+    value = str(value).lower()
+    if ':' in value:
+        value = value.split(':')[1].strip() 
+    if value in ['control']:
+        return 0
+    elif value in ['carotid atherosclerosis']:
+        return 1
+    return None
+
+def convert_age(value: str) -> float:
+    """Convert age to float"""
+    if value is None:
+        return None
+    value = str(value)
+    if ':' in value:
+        value = value.split(':')[1].strip()
+    try:
+        return float(value)
+    except:
+        return None
+
+def convert_gender(value: str) -> int:
+    """Convert gender to binary (female=0, male=1)"""
+    if value is None:
+        return None
+    value = str(value).lower()
+    if ':' in value:
+        value = value.split(':')[1].strip()
+    if value in ['f', 'female']:
+        return 0
+    elif value in ['m', 'male']:
+        return 1
+    return None
+
+# 3. Save metadata 
+validate_and_save_cohort_info(is_final=False,
+                             cohort=cohort,
+                             info_path=json_path,
+                             is_gene_available=is_gene_available,
+                             is_trait_available=(trait_row is not None))
+
+# 4. Extract clinical features
+selected_clinical = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row, 
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Handle potential Series output
+if isinstance(selected_clinical, pd.Series):
+    selected_clinical = selected_clinical.to_frame()
+
+# Preview the data
+preview = preview_df(selected_clinical)
+print("Preview of selected clinical features:")
+print(preview)
+
+# Save to CSV
+selected_clinical.to_csv(out_clinical_data_file)
+# 1. Check gene expression data availability
+is_gene_available = True  # Given that GEO series data typically contains gene expression data, default to True unless proven otherwise
+
+# 2.1 & 2.2 Data row identification and conversion functions
+trait_row = 0  # Preview shows row 0 has binary data pattern typical for case-control status in atherosclerosis studies
+
+def convert_trait(x):
+    """Convert trait values to binary (0: control, 1: atherosclerosis)"""
+    if pd.isna(x):
+        return 0  # Assume NaN represents control group
+    value = str(x).split(":")[-1].strip()
+    if value == "1" or value == "1.0":
+        return 1
+    return 0
+
+def convert_age(x):
+    """Convert age values to continuous numbers"""
+    if pd.isna(x):
+        return None
+    value = str(x).split(":")[-1].strip()
+    try:
+        return float(value)
+    except:
+        return None
+
+def convert_gender(x):
+    """Convert gender values to binary (0: female, 1: male)"""
+    if pd.isna(x):
+        return None
+    value = str(x).split(":")[-1].strip().lower()
+    if "female" in value or "f" in value:
+        return 0
+    elif "male" in value or "m" in value:
+        return 1
+    return None
+
+age_row = None  # Age data not available in preview
+gender_row = None  # Gender data not available in preview
+
+# 3. Save metadata using initial filtering
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=(trait_row is not None)
+)
+
+# 4. Clinical feature extraction
+selected_clinical_df = geo_select_clinical_features(
+    clinical_df=clinical_data,
+    trait=trait,
+    trait_row=trait_row,
+    convert_trait=convert_trait,
+    age_row=age_row,
+    convert_age=convert_age,
+    gender_row=gender_row,
+    convert_gender=convert_gender
+)
+
+# Preview and save the clinical features
+print("\nPreview of selected clinical features:")
+print(preview_df(selected_clinical_df.to_frame() if isinstance(selected_clinical_df, pd.Series) else selected_clinical_df))
+
+# Save clinical data
+if isinstance(selected_clinical_df, pd.Series):
+    selected_clinical_df = selected_clinical_df.to_frame()
+selected_clinical_df.to_csv(out_clinical_data_file)
+# 1. Gene Expression Data Availability
+# Based on the output, there appears to be gene expression data since the file contains 
+# numeric values that can be used for analysis
+is_gene_available = True 
+
+# 2. Variable Assignment and Conversion Functions
+
+# 2.1 Variable Availability
+# Trait is already encoded as binary (0.0), so it's available
+trait_row = 0
+
+# Age and gender not shown in preview, setting to None
+age_row = None  
+gender_row = None
+
+# 2.2 Conversion Functions
+def convert_trait(x):
+    # Convert to binary: presence of atherosclerosis is 1, absence is 0
+    if pd.isna(x) or x is None:
+        return None
+    x = str(x).lower()
+    if ':' in x:
+        x = x.split(':')[1].strip()
+    if x in ['0', '0.0', 'no', 'non-' + trait.lower(), 'control', 'negative']:
+        return 0
+    elif x in ['1', '1.0', 'yes', trait.lower(), 'positive']:
+        return 1
+    return None
+
+def convert_age(x):
+    # Not used since age data not available
+    return None
+
+def convert_gender(x):
+    # Not used since gender data not available 
+    return None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False,
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=is_trait_available)
+
+# 4. Extract Clinical Features
+if trait_row is not None:
+    clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age, 
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview clinical features
+    preview = preview_df(clinical_df)
+    print("\nPreview of selected clinical features:")
+    print(preview)
+    
+    # Save clinical data
+    clinical_df.to_csv(out_clinical_data_file)

p3/preprocess/Atherosclerosis/code/GSE87005.py

@@ -0,0 +1,178 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+cohort = "GSE87005"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atherosclerosis"
+in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE87005"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Atherosclerosis/GSE87005.csv"
+out_gene_data_file = "./output/preprocess/3/Atherosclerosis/gene_data/GSE87005.csv"
+out_clinical_data_file = "./output/preprocess/3/Atherosclerosis/clinical_data/GSE87005.csv"
+json_path = "./output/preprocess/3/Atherosclerosis/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data using broader sample prefixes
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
+    prefixes_b=['!Sample_', '!Sample_disease_state', '!Sample_description'] 
+)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics 
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on GPL6480 platform and "RNA" sample type
+
+# 2.1 Data Availability and 2.2 Data Type Conversion
+trait_row = 8  # Available in "!Sample_characteristics_ch1" which shows HOMA groups
+
+def convert_trait(x):
+    if not isinstance(x, str):
+        return None
+    if 'Low HOMA' in x:
+        return 0
+    elif 'High HOMA' in x:
+        return 1
+    return None
+
+# Age and gender are not available
+age_row = None 
+gender_row = None
+convert_age = None
+convert_gender = None
+
+# 3. Save metadata
+is_clinical_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=trait_row is not None
+)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the extracted features
+    preview = preview_df(clinical_features)
+    print("Preview of clinical features:", preview)
+    
+    # Save clinical data
+    clinical_features.to_csv(out_clinical_data_file)
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug 
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+# The identifiers starting with "A_23_P" appear to be Agilent probe IDs, not gene symbols
+# These need to be mapped to standard gene symbols for consistent analysis
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file)
+
+# Preview the annotation data 
+print("Column names:", gene_metadata.columns.tolist())
+print("\nFirst few rows preview:")
+print(preview_df(gene_metadata))
+# Map identifiers to gene symbols
+mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# Apply the mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Save the processed gene data
+gene_data.to_csv(out_gene_data_file)
+
+# Preview the result
+print("Shape of mapped gene expression data:", gene_data.shape)
+print("\nFirst few rows of mapped data:")
+print(gene_data.head())
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data 
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# Check if clinical data linking produced valid results
+if linked_data.shape[0] == 0 or linked_data.shape[1] <= 4:
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=False, # Clinical data extraction failed
+        is_trait_available=False,
+        is_biased=None,
+        df=linked_data,
+        note="Clinical data extraction failed - produced invalid linked data."
+    )
+else:
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check for bias  
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Validate and save cohort info
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path, 
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=linked_data,
+        note="Study examining gene expression in peripheral blood mononuclear cells from subjects with high vs low insulin resistance (HOMA groups)."
+    )
+
+    # 6. Save if usable
+    if is_usable:
+        linked_data.to_csv(out_data_file)

p3/preprocess/Atherosclerosis/code/GSE90074.py

@@ -0,0 +1,189 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+cohort = "GSE90074"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Atherosclerosis"
+in_cohort_dir = "../DATA/GEO/Atherosclerosis/GSE90074"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Atherosclerosis/GSE90074.csv"
+out_gene_data_file = "./output/preprocess/3/Atherosclerosis/gene_data/GSE90074.csv"
+out_clinical_data_file = "./output/preprocess/3/Atherosclerosis/clinical_data/GSE90074.csv"
+json_path = "./output/preprocess/3/Atherosclerosis/cohort_info.json"
+
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data using broader sample prefixes
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
+    prefixes_b=['!Sample_', '!Sample_disease_state', '!Sample_description'] 
+)
+
+# Get unique values per clinical feature
+sample_characteristics = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Dataset Background Information:")
+print(f"{background_info}\n")
+
+# Print sample characteristics 
+print("Sample Characteristics:")
+for feature, values in sample_characteristics.items():
+    print(f"Feature: {feature}")
+    print(f"Values: {values}\n")
+# 1. Gene Expression Data Availability
+# Yes - this is gene expression data from Agilent G4112F Whole Human Genome microarray
+is_gene_available = True
+
+# 2. Variable Availability and Data Type 
+
+# 2.1 Data Availability
+# Trait (Atherosclerosis) can be inferred from CAD class in row 22
+trait_row = 22
+# No age data available
+age_row = None  
+# Gender in row 19
+gender_row = 19
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    if x is None:
+        return None
+    # Extract value after colon and strip whitespace
+    val = x.split(':')[-1].strip()
+    # Convert CAD class to binary - 0 for no/minimal disease (class 0), 1 for others (class 1-4)
+    if val == '0':
+        return 0
+    elif val in ['1', '2', '3', '4']:
+        return 1
+    return None
+
+def convert_gender(x):
+    if x is None:
+        return None
+    val = x.split(':')[-1].strip()
+    if val.upper() == 'F':
+        return 0
+    elif val.upper() == 'M':
+        return 1
+    return None
+
+def convert_age(x):
+    # Not used since age is not available
+    return None
+
+# 3. Save metadata
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=trait_row is not None
+)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_data,
+        trait="Atherosclerosis",
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    print("Preview of extracted clinical features:")
+    print(preview_df(clinical_features))
+    clinical_features.to_csv(out_clinical_data_file)
+# Get file paths
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract gene expression data from matrix file
+gene_data = get_genetic_data(matrix_file)
+
+# Print first 20 row IDs and shape of data to help debug 
+print("Shape of gene expression data:", gene_data.shape)
+print("\nFirst few rows of data:")
+print(gene_data.head())
+print("\nFirst 20 gene/probe identifiers:")
+print(gene_data.index[:20])
+
+# Inspect a snippet of raw file to verify identifier format
+import gzip
+with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
+    lines = []
+    for i, line in enumerate(f):
+        if "!series_matrix_table_begin" in line:
+            # Get the next 5 lines after the marker
+            for _ in range(5):
+                lines.append(next(f).strip())
+            break
+print("\nFirst few lines after matrix marker in raw file:")
+for line in lines:
+    print(line)
+requires_gene_mapping = True
+# Extract gene annotation data
+gene_metadata = get_gene_annotation(soft_file)
+
+# Preview the annotation data 
+print("Column names:", gene_metadata.columns.tolist())
+print("\nFirst few rows preview:")
+print(preview_df(gene_metadata))
+# 1. ID and GENE_SYMBOL in annotation data correspond to gene identifiers and symbols
+prob_col = 'ID'  
+gene_col = 'GENE_SYMBOL'
+
+# 2. Extract ID-to-gene mapping columns from annotation data
+mapping_data = get_gene_mapping(gene_metadata, prob_col, gene_col)
+
+# 3. Convert probe-level measurements to gene expression
+gene_data = apply_gene_mapping(gene_data, mapping_data)
+
+# Print shapes to verify transformation 
+print("\nShape after probe-to-gene mapping:", gene_data.shape)
+print("\nFirst few gene symbols:", gene_data.index[:5].tolist())
+# 1. Normalize gene symbols
+gene_data = normalize_gene_symbols_in_index(gene_data)
+gene_data.to_csv(out_gene_data_file)
+
+# Create ID mapping dictionary
+gsm_to_2s = {}
+for col in clinical_features.columns:
+    if col.startswith('2S'):
+        sample_num = col.split('_')[0]  # Get '2S001'
+        gsm_num = int(sample_num[2:])   # Get 1
+        gsm_id = f'GSM2397{157+gsm_num}'  # GSM2397158 for 2S001
+        gsm_to_2s[gsm_id] = col
+
+# Rename gene data columns using mapping
+gene_data = gene_data.rename(columns=gsm_to_2s)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
+
+# 3. Handle missing values 
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check for bias
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True, 
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Study examining gene expression profiles in blood samples of patients with different levels of atherosclerosis severity."
+)
+
+# 6. Save if usable
+if is_usable:
+    linked_data.to_csv(out_data_file)

p3/preprocess/Atherosclerosis/code/TCGA.py

@@ -0,0 +1,28 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Atherosclerosis"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/Atherosclerosis/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/Atherosclerosis/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/Atherosclerosis/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/Atherosclerosis/cohort_info.json"
+
+# Review directories to find relevant cohort for Atherosclerosis
+cohorts = os.listdir(tcga_root_dir)
+cohorts = [d for d in cohorts if os.path.isdir(os.path.join(tcga_root_dir, d)) and d[0] != '.']
+
+# No suitable cohort found for Atherosclerosis
+# Mark task as completed by recording cohort info
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort="TCGA", 
+    info_path=json_path,
+    is_gene_available=False,
+    is_trait_available=False
+)

p3/preprocess/Atherosclerosis/cohort_info.json

@@ -0,0 +1 @@
+{"GSE90074": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "Study examining gene expression profiles in blood samples of patients with different levels of atherosclerosis severity."}, "GSE87005": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "Study examining gene expression in peripheral blood mononuclear cells from subjects with high vs low insulin resistance (HOMA groups)."}, "GSE83500": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE57691": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "Study examining expression profiles in endometriotic cyst stromal cells versus normal endometrial stromal cells."}, "GSE154851": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "Contains gene expression data but lacks clinical information needed for trait association studies."}, "GSE133601": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "Contains gene expression data but lacks clinical information needed for trait association studies."}, "GSE125771": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "Contains gene expression data but lacks clinical information needed for trait association studies."}, "GSE123088": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "Contains gene expression data but lacks clinical information needed for trait association studies."}, "GSE123086": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "Dataset contains both clinical features and gene expression data."}, "GSE109048": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "Contains gene expression data and trait labels for atherosclerosis."}, "TCGA": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}

p3/preprocess/Atherosclerosis/gene_data/GSE109048.csv

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

p3/preprocess/Atherosclerosis/gene_data/GSE123086.csv

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p3/preprocess/Atherosclerosis/gene_data/GSE123088.csv

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p3/preprocess/Atherosclerosis/gene_data/GSE154851.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1215f5db12fa93de50490eb6ebf6c7cf161522558dfc5030485ed81f07b1728a
+size 17675327

p3/preprocess/Atherosclerosis/gene_data/GSE57691.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:27c2e1eabe66da0727d69144095696cb0eb2f9de8b4a92906f14e584f17b727e
+size 15477956

p3/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/clinical_data/GSE162635.csv

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p3/preprocess/Chronic_obstructive_pulmonary_disease_(COPD)/cohort_info.json

@@ -0,0 +1 @@
+{"GSE84046": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "Dataset contains normalized gene expression data but lacks COPD trait information, so cannot be used for trait association analysis"}, "GSE64599": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 34, "note": "Dataset contains gene expression data from PBMCs comparing healthy controls vs diabetic nephropathy vs ESRD"}, "GSE64593": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 34, "note": "Dataset contains gene expression data from alveolar macrophages comparing HIV- vs HIV+ smokers"}, "GSE32030": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 123, "note": "Dataset contains gene expression data comparing healthy nonsmokers, healthy smokers and smokers with COPD"}, "GSE21359": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 135, "note": "Dataset contains gene expression data comparing healthy nonsmokers, healthy smokers and smokers with COPD"}, "GSE212331": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 87, "note": "Dataset contains gene expression data comparing healthy nonsmokers, healthy smokers and smokers with COPD"}, "GSE210272": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "Dataset contains gene expression data comparing healthy nonsmokers, healthy smokers and smokers with COPD. Using ENSEMBL gene IDs."}, "GSE208662": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 32, "note": "Dataset contains gene expression data comparing healthy nonsmokers, healthy smokers and smokers with COPD"}, "GSE175616": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": true, "has_gender": true, "sample_size": 123, "note": "Dataset contains gene expression data comparing healthy nonsmokers, healthy smokers and smokers with COPD"}, "GSE162635": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 205, "note": "Dataset contains gene expression data comparing healthy nonsmokers, healthy smokers and smokers with COPD"}}

p3/preprocess/HIV_Resistance/GSE33580.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0765943151958fb8392662fbd94e440f6061dc568837bbbed1d2c8f7f407cbe6
+size 17574906

p3/preprocess/HIV_Resistance/clinical_data/GSE33580.csv

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+HIV_Resistance,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

p3/preprocess/HIV_Resistance/clinical_data/GSE46599.csv

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+HIV_Resistance,0.0,0.0,,,0.0,0.0,,,0.5,0.5,,,0.0,0.0,,,1.0,1.0,,,0.5,0.5,,,1.0,1.0,1.0,,,,1.0,1.0,1.0,,,,1.0,1.0,,,1.0,1.0,,,0.0,0.0,,

p3/preprocess/HIV_Resistance/code/GSE117748.py

@@ -0,0 +1,71 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "HIV_Resistance"
+cohort = "GSE117748"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/HIV_Resistance"
+in_cohort_dir = "../DATA/GEO/HIV_Resistance/GSE117748"
+
+# Output paths
+out_data_file = "./output/preprocess/3/HIV_Resistance/GSE117748.csv"
+out_gene_data_file = "./output/preprocess/3/HIV_Resistance/gene_data/GSE117748.csv"
+out_clinical_data_file = "./output/preprocess/3/HIV_Resistance/clinical_data/GSE117748.csv"
+json_path = "./output/preprocess/3/HIV_Resistance/cohort_info.json"
+
+# Get relevant file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Get dictionary of unique values per row in clinical data
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Background Information:")
+print("-" * 50)
+print(background_info)
+print("\n")
+
+# Print clinical data unique values
+print("Sample Characteristics:")
+print("-" * 50)
+for row, values in unique_values_dict.items():
+    print(f"{row}:")
+    print(f"  {values}")
+    print()
+# 1. Gene Expression Data Availability
+# This is a miRNA study on cell lines (based on the title and sample characteristics)
+is_gene_available = False 
+
+# 2.1 Data Availability
+# From sample characteristics, no human trait, age or gender data available
+trait_row = None
+age_row = None  
+gender_row = None
+
+# 2.2 Data Type Conversion functions (not used but defined for completeness)
+def convert_trait(x):
+    return None
+
+def convert_age(x):
+    return None
+    
+def convert_gender(x):
+    return None
+
+# 3. Save Metadata
+# Validate and save cohort info - initial filtering 
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=False  # trait_row is None
+)
+
+# 4. Clinical Feature Extraction
+# Skip since trait_row is None

p3/preprocess/HIV_Resistance/code/GSE33580.py

@@ -0,0 +1,166 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "HIV_Resistance"
+cohort = "GSE33580"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/HIV_Resistance"
+in_cohort_dir = "../DATA/GEO/HIV_Resistance/GSE33580"
+
+# Output paths
+out_data_file = "./output/preprocess/3/HIV_Resistance/GSE33580.csv"
+out_gene_data_file = "./output/preprocess/3/HIV_Resistance/gene_data/GSE33580.csv"
+out_clinical_data_file = "./output/preprocess/3/HIV_Resistance/clinical_data/GSE33580.csv"
+json_path = "./output/preprocess/3/HIV_Resistance/cohort_info.json"
+
+# Get relevant file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Get dictionary of unique values per row in clinical data
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Background Information:")
+print("-" * 50)
+print(background_info)
+print("\n")
+
+# Print clinical data unique values
+print("Sample Characteristics:")
+print("-" * 50)
+for row, values in unique_values_dict.items():
+    print(f"{row}:")
+    print(f"  {values}")
+    print()
+# 1. Gene Expression Data Availability
+# Yes - the background info mentions "gene expression analysis" and "Affymetrix microarrays"
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# Trait (HIV Resistance) is in row 1
+trait_row = 1
+# Age data not available 
+age_row = None
+# Gender data not available - appears to be all female from background info
+gender_row = None
+
+def convert_trait(x):
+    """Convert HIV status to binary (1=resistant, 0=negative)"""
+    if not isinstance(x, str):
+        return None
+    x = x.lower().split(': ')[-1]
+    if 'resistant' in x:
+        return 1
+    elif 'negative' in x:
+        return 0
+    return None
+
+def convert_age(x):
+    """Placeholder since age not available"""
+    return None
+    
+def convert_gender(x):
+    """Placeholder since gender not available"""
+    return None
+
+# 3. Save Metadata
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=trait_row is not None)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview the extracted features
+    preview = preview_df(clinical_features)
+    print("Preview of clinical features:")
+    print(preview)
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 probe IDs
+print("First 20 probe IDs:")
+print(genetic_data.index[:20])
+# The identifiers shown are Affymetrix probe IDs (e.g. '1007_s_at', '1053_at')
+# These need to be mapped to HGNC gene symbols for standardization
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Preview column names and first few values
+preview_dict = preview_df(gene_annotation)
+print("Column names and preview values:")
+for col, values in preview_dict.items():
+    print(f"\n{col}:")
+    print(values)
+# 1. Identify columns for mapping
+# 'ID' in gene annotation matches probe IDs in gene expression data
+# 'Gene Symbol' contains the gene symbols we want to map to
+prob_col = 'ID'
+gene_col = 'Gene Symbol'
+
+# 2. Get mapping dataframe
+mapping_data = get_gene_mapping(gene_annotation, prob_col, gene_col)
+
+# 3. Apply mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_data)
+
+# Preview result
+print("\nFirst few rows and columns of gene expression data:")
+print(gene_data.iloc[:5, :5])
+# 1. Normalize gene symbols and save normalized gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Read the processed clinical data file 
+clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# Link clinical and genetic data using the normalized gene data
+linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)
+
+# Handle missing values systematically
+linked_data = handle_missing_values(linked_data, trait)
+
+# Detect bias in trait and demographic features, remove biased demographic features
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Validate data quality and save cohort info
+note = "Gene expression data from glucocorticoid sensitivity study."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note=note
+)
+
+# Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)
+else:
+    print(f"Dataset {cohort} did not pass quality validation and will not be saved.")

p3/preprocess/HIV_Resistance/code/GSE46599.py

@@ -0,0 +1,160 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "HIV_Resistance"
+cohort = "GSE46599"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/HIV_Resistance"
+in_cohort_dir = "../DATA/GEO/HIV_Resistance/GSE46599"
+
+# Output paths
+out_data_file = "./output/preprocess/3/HIV_Resistance/GSE46599.csv"
+out_gene_data_file = "./output/preprocess/3/HIV_Resistance/gene_data/GSE46599.csv"
+out_clinical_data_file = "./output/preprocess/3/HIV_Resistance/clinical_data/GSE46599.csv"
+json_path = "./output/preprocess/3/HIV_Resistance/cohort_info.json"
+
+# Get relevant file paths
+soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
+
+# Extract background info and clinical data from the matrix file
+background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
+
+# Get dictionary of unique values per row in clinical data
+unique_values_dict = get_unique_values_by_row(clinical_data)
+
+# Print background info
+print("Background Information:")
+print("-" * 50)
+print(background_info)
+print("\n")
+
+# Print clinical data unique values
+print("Sample Characteristics:")
+print("-" * 50)
+for row, values in unique_values_dict.items():
+    print(f"{row}:")
+    print(f"  {values}")
+    print()
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Yes, this is gene expression data studying ISGs, not miRNA/methylation
+
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Data Availability
+trait_row = 4  # HIV resistance status in row 4
+age_row = None  # Age not available
+gender_row = None  # Gender not available
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x):
+    if not isinstance(x, str):
+        return None
+    val = x.split(': ')[-1].lower()
+    if 'resistant' == val:
+        return 1
+    elif 'partially resistant' == val:
+        return 0.5  
+    elif 'permissive' == val:
+        return 0
+    elif 'untreated' == val:
+        return None
+    return None
+
+def convert_age(x):
+    return None  # Not used
+
+def convert_gender(x):
+    return None  # Not used
+
+# 3. Save Metadata
+validate_and_save_cohort_info(is_final=False, 
+                            cohort=cohort,
+                            info_path=json_path,
+                            is_gene_available=is_gene_available,
+                            is_trait_available=trait_row is not None)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    clinical_features = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    
+    # Preview extracted features
+    preview = preview_df(clinical_features)
+    print("Preview of clinical features:")
+    print(preview)
+    
+    # Save to CSV
+    clinical_features.to_csv(out_clinical_data_file)
+# Extract gene expression data
+genetic_data = get_genetic_data(matrix_file_path)
+
+# Print first 20 probe IDs
+print("First 20 probe IDs:")
+print(genetic_data.index[:20])
+# These are Illumina BeadArray probe IDs (starting with ILMN_), not gene symbols
+requires_gene_mapping = True
+# Extract gene annotation from SOFT file
+gene_annotation = get_gene_annotation(soft_file_path)
+
+# Preview column names and first few values
+preview_dict = preview_df(gene_annotation)
+print("Column names and preview values:")
+for col, values in preview_dict.items():
+    print(f"\n{col}:")
+    print(values)
+# Get gene mapping from annotation data
+# 'ID' column contains probe IDs (ILMN_*) matching gene expression data
+# 'Symbol' column contains gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# Apply gene mapping to convert probe-level data to gene expression data
+gene_data = apply_gene_mapping(genetic_data, mapping_df)
+
+# Preview first few genes
+print("\nFirst few genes after mapping:")
+print(gene_data.head().index)
+# 1. Normalize gene symbols and save normalized gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Read the processed clinical data file 
+clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# Link clinical and genetic data using the normalized gene data
+linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)
+
+# Handle missing values systematically
+linked_data = handle_missing_values(linked_data, trait)
+
+# Detect bias in trait and demographic features, remove biased demographic features
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Validate data quality and save cohort info
+note = "Gene expression data from glucocorticoid sensitivity study."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note=note
+)
+
+# Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    linked_data.to_csv(out_data_file)
+else:
+    print(f"Dataset {cohort} did not pass quality validation and will not be saved.")

p3/preprocess/HIV_Resistance/code/TCGA.py

@@ -0,0 +1,31 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "HIV_Resistance"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/3/HIV_Resistance/TCGA.csv"
+out_gene_data_file = "./output/preprocess/3/HIV_Resistance/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/3/HIV_Resistance/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/3/HIV_Resistance/cohort_info.json"
+
+# Review available cohorts and check for HIV resistance relevance
+available_cohorts = os.listdir(tcga_root_dir)
+cohorts = [c for c in available_cohorts if not c.startswith('.') and not c.endswith('.ipynb')]
+
+# No suitable cohort found for HIV resistance in TCGA cancer datasets
+is_gene_available = False  
+is_trait_available = False
+
+# Record that this trait cannot be studied with TCGA data
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=is_gene_available, 
+    is_trait_available=is_trait_available
+)

p3/preprocess/HIV_Resistance/cohort_info.json

@@ -0,0 +1 @@
+{"GSE46599": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 24, "note": "Gene expression data from glucocorticoid sensitivity study."}, "GSE33580": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 86, "note": "Gene expression data from glucocorticoid sensitivity study."}, "GSE117748": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "TCGA": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}

p3/preprocess/HIV_Resistance/gene_data/GSE33580.csv

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

p3/preprocess/HIV_Resistance/gene_data/GSE46599.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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