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output/preprocess/Polycystic_Ovary_Syndrome/clinical_data/GSE151158.csv

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output/preprocess/Polycystic_Ovary_Syndrome/clinical_data/GSE43322.csv

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output/preprocess/Polycystic_Ovary_Syndrome/clinical_data/GSE87435.csv

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output/preprocess/Polycystic_Ovary_Syndrome/cohort_info.json

@@ -0,0 +1,42 @@
+{
+  "GSE87435": {
+    "is_usable": true,
+    "is_gene_available": true,
+    "is_trait_available": true,
+    "is_available": true,
+    "is_biased": false,
+    "has_age": false,
+    "has_gender": false,
+    "sample_size": 18
+  },
+  "GSE43322": {
+    "is_usable": false,
+    "is_gene_available": true,
+    "is_trait_available": true,
+    "is_available": true,
+    "is_biased": true,
+    "has_age": false,
+    "has_gender": false,
+    "sample_size": 31
+  },
+  "GSE151158": {
+    "is_usable": false,
+    "is_gene_available": true,
+    "is_trait_available": true,
+    "is_available": true,
+    "is_biased": true,
+    "has_age": false,
+    "has_gender": false,
+    "sample_size": 61
+  },
+  "TCGA": {
+    "is_usable": false,
+    "is_gene_available": true,
+    "is_trait_available": true,
+    "is_available": true,
+    "is_biased": true,
+    "has_age": true,
+    "has_gender": false,
+    "sample_size": 308
+  }
+}

output/preprocess/Post-Traumatic_Stress_Disorder/cohort_info.json

@@ -0,0 +1,102 @@
+{
+  "GSE81761": {
+    "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": 109
+  },
+  "GSE77164": {
+    "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": 254
+  },
+  "GSE67663": {
+    "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": 184
+  },
+  "GSE64814": {
+    "is_usable": true,
+    "is_gene_available": true,
+    "is_trait_available": true,
+    "is_available": true,
+    "is_biased": false,
+    "has_age": false,
+    "has_gender": false,
+    "sample_size": 96
+  },
+  "GSE63878": {
+    "is_usable": true,
+    "is_gene_available": true,
+    "is_trait_available": true,
+    "is_available": true,
+    "is_biased": false,
+    "has_age": false,
+    "has_gender": false,
+    "sample_size": 96
+  },
+  "GSE52875": {
+    "is_usable": false,
+    "is_gene_available": false,
+    "is_trait_available": false,
+    "is_available": false,
+    "is_biased": null,
+    "has_age": null,
+    "has_gender": null,
+    "sample_size": null
+  },
+  "GSE44456": {
+    "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": 39
+  },
+  "GSE199841": {
+    "is_usable": true,
+    "is_gene_available": true,
+    "is_trait_available": true,
+    "is_available": true,
+    "is_biased": false,
+    "has_age": true,
+    "has_gender": false,
+    "sample_size": 48
+  },
+  "GSE114852": {
+    "is_usable": true,
+    "is_gene_available": true,
+    "is_trait_available": true,
+    "is_available": true,
+    "is_biased": false,
+    "has_age": false,
+    "has_gender": true,
+    "sample_size": 149
+  },
+  "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
+  }
+}

output/preprocess/Psoriasis/clinical_data/GSE123086.csv

@@ -0,0 +1,4 @@
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output/preprocess/Psoriasis/clinical_data/GSE123088.csv

@@ -0,0 +1,4 @@
+0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29
+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,,,,,,,,,,,,,,
+56.0,,20.0,51.0,37.0,61.0,31.0,41.0,80.0,53.0,73.0,60.0,76.0,77.0,74.0,69.0,81.0,70.0,82.0,67.0,78.0,72.0,66.0,36.0,45.0,65.0,48.0,50.0,24.0,42.0
+1.0,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,

output/preprocess/Psoriasis/clinical_data/GSE158448.csv

@@ -0,0 +1,2 @@
+,GSM4800737,GSM4800738,GSM4800739,GSM4800740,GSM4800741,GSM4800742,GSM4800743,GSM4800744,GSM4800745,GSM4800746,GSM4800747,GSM4800748,GSM4800749,GSM4800750,GSM4800751,GSM4800752,GSM4800753,GSM4800754,GSM4800755,GSM4800756,GSM4800757,GSM4800758,GSM4800759,GSM4800760,GSM4800761,GSM4800762,GSM4800763,GSM4800764,GSM4800765,GSM4800766,GSM4800767,GSM4800768,GSM4800769,GSM4800770,GSM4800771,GSM4800772,GSM4800773,GSM4800774,GSM4800775,GSM4800776,GSM4800777,GSM4800778,GSM4800779,GSM4800780,GSM4800781,GSM4800782,GSM4800783,GSM4800784,GSM4800785,GSM4800786,GSM4800787,GSM4800788,GSM4800789,GSM4800790,GSM4800791,GSM4800792,GSM4800793,GSM4800794,GSM4800795,GSM4800796
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output/preprocess/Psoriasis/clinical_data/GSE162998.csv

@@ -0,0 +1,2 @@
+,GSM4969892,GSM4969893,GSM4969894,GSM4969895,GSM4969896,GSM4969897,GSM4969898,GSM4969899,GSM4969900,GSM4969901,GSM4969902,GSM4969903,GSM4969904,GSM4969905,GSM4969906,GSM4969907,GSM4969908,GSM4969909,GSM4969910,GSM4969911,GSM4969912,GSM4969913,GSM4969914,GSM4969915,GSM4969916,GSM4969917,GSM4969918,GSM4969919,GSM4969920,GSM4969921,GSM4969922,GSM4969923,GSM4969924,GSM4969925,GSM4969926,GSM4969927,GSM4969928,GSM4969929,GSM4969930,GSM4969931,GSM4969932,GSM4969933,GSM4969934,GSM4969935,GSM4969936,GSM4969937,GSM4969938,GSM4969939
+Psoriasis,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

output/preprocess/Psoriasis/clinical_data/GSE178228.csv

@@ -0,0 +1,2 @@
+,GSM5384796,GSM5384797,GSM5384798,GSM5384799,GSM5384800,GSM5384801,GSM5384802,GSM5384803,GSM5384804,GSM5384805,GSM5384806,GSM5384807,GSM5384808,GSM5384809,GSM5384810,GSM5384811,GSM5384812,GSM5384813,GSM5384814,GSM5384815,GSM5384816,GSM5384817,GSM5384818,GSM5384819,GSM5384820,GSM5384821,GSM5384822,GSM5384823,GSM5384824,GSM5384825,GSM5384826,GSM5384827,GSM5384828,GSM5384829,GSM5384830,GSM5384831,GSM5384832,GSM5384833,GSM5384834,GSM5384835,GSM5384836,GSM5384837,GSM5384838,GSM5384839,GSM5384840,GSM5384841,GSM5384842,GSM5384843,GSM5384844,GSM5384845,GSM5384846,GSM5384847,GSM5384848,GSM5384849,GSM5384850,GSM5384851,GSM5384852,GSM5384853,GSM5384854,GSM5384855,GSM5384856,GSM5384857,GSM5384858,GSM5384859,GSM5384860,GSM5384861,GSM5384862,GSM5384863,GSM5384864,GSM5384865,GSM5384866,GSM5384867,GSM5384868,GSM5384869,GSM5384870,GSM5384871,GSM5384872,GSM5384873,GSM5384874,GSM5384875,GSM5384876,GSM5384877,GSM5384878,GSM5384879,GSM5384880,GSM5384881,GSM5384882,GSM5384883,GSM5384884,GSM5384885,GSM5384886,GSM5384887,GSM5384888,GSM5384889,GSM5384890,GSM5384891,GSM5384892,GSM5384893,GSM5384894,GSM5384895,GSM5384896,GSM5384897,GSM5384898,GSM5384899,GSM5384900,GSM5384901,GSM5384902,GSM5384903,GSM5384904,GSM5384905,GSM5384906,GSM5384907,GSM5384908,GSM5384909,GSM5384910,GSM5384911,GSM5384912
+Psoriasis,14.7,2.9,7.2,15.5,3.0,8.2,6.4,3.0,6.0,7.8,10.3,20.1,3.2,5.0,8.39999999999999,2.6,22.8,4.7,13.4,4.3,10.0,10.6,7.2,1.6,10.2,4.4,9.2,12.6,12.0,5.8,5.0,11.9,12.0,3.0,12.7,0.7,4.1,1.8,5.2,2.7,13.2,15.2,13.8999999999999,4.3,16.7,9.4,6.0,11.1,19.2,8.4,12.4,15.6,14.0,22.0,3.6,6.8,8.5,4.6,7.6,2.8,27.7,2.2,4.2,12.0,2.4,12.3999999999999,2.4,31.9,5.3,25.0,1.8,14.9,2.7,15.6,8.4,15.2,5.2,16.7,53.8,5.8,11.8,7.8,22.3,15.3,6.6,2.8,7.2,12.4,17.8,9.0,10.1,7.39999999999999,22.0,10.5,29.8,4.1,18.3,12.0,8.8,16.0,41.4,13.5,6.9,12.4,15.7,15.6,12.3,2.4,14.6,14.9,8.8,22.2,19.2,6.1,11.2,10.6,14.2

output/preprocess/Rectal_Cancer/cohort_info.json

@@ -0,0 +1,112 @@
+{
+  "GSE94104": {
+    "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": 80
+  },
+  "GSE40492": {
+    "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": 245
+  },
+  "GSE170999": {
+    "is_usable": false,
+    "is_gene_available": false,
+    "is_trait_available": true,
+    "is_available": false,
+    "is_biased": null,
+    "has_age": null,
+    "has_gender": null,
+    "sample_size": null
+  },
+  "GSE150082": {
+    "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": 39
+  },
+  "GSE145037": {
+    "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
+  },
+  "GSE139255": {
+    "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": 156
+  },
+  "GSE133057": {
+    "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": 33
+  },
+  "GSE123390": {
+    "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": 28
+  },
+  "GSE119409": {
+    "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": 2
+  },
+  "GSE109057": {
+    "is_usable": true,
+    "is_gene_available": true,
+    "is_trait_available": true,
+    "is_available": true,
+    "is_biased": false,
+    "has_age": false,
+    "has_gender": false,
+    "sample_size": 3
+  },
+  "TCGA": {
+    "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": 105
+  }
+}

p1/preprocess/Prostate_Cancer/gene_data/GSE259218.csv

@@ -0,0 +1,9 @@
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+OR4F17,95.95036,92.17675,95.10325,97.2383,95.29253,95.43927,97.35049,89.20792,89.4932,99.43916,91.68876,96.58812,90.92703,94.91365,92.69555,95.66331,92.49176,92.31004,88.7634,93.99573,97.19251,92.02088,96.45043,96.03814,85.89545,94.34533,93.72083,87.66558,94.98042,91.70341,94.19315,95.05149,101.9253,95.44308,94.8525,94.31593,90.55092,98.94518,88.25571,90.87545,97.49011,100.3776,99.07206,93.27406,98.19466,97.44492,89.79266,94.12099
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p1/preprocess/Sarcoma/clinical_data/GSE197147.csv

@@ -0,0 +1,2 @@
+GSM5910186,GSM5910187,GSM5910188,GSM5910189,GSM5910190,GSM5910191,GSM5910192,GSM5910193,GSM5910194,GSM5910195,GSM5910196,GSM5910197,GSM5910198,GSM5910199,GSM5910200,GSM5910201,GSM5910202,GSM5910203,GSM5910204,GSM5910205,GSM5910206,GSM5910207,GSM5910208,GSM5910209,GSM5910210,GSM5910211,GSM5910212,GSM5910213,GSM5910214,GSM5910215,GSM5910216,GSM5910217,GSM5910218,GSM5910219,GSM5910220,GSM5910221,GSM5910222,GSM5910223,GSM5910224,GSM5910225,GSM5910226,GSM5910227,GSM5910228,GSM5910229,GSM5910230,GSM5910231,GSM5910232,GSM5910233,GSM5910234,GSM5910235,GSM5910236,GSM5910237,GSM5910238,GSM5910239,GSM5910240,GSM5910241,GSM5910242,GSM5910243,GSM5910244,GSM5910245,GSM5910246,GSM5910247,GSM5910248,GSM5910249,GSM5910250,GSM5910251,GSM5910252,GSM5910253,GSM5910254,GSM5910255,GSM5910256,GSM5910257,GSM5910258,GSM5910259,GSM5910260,GSM5910261,GSM5910262,GSM5910263,GSM5910264
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p1/preprocess/Sarcoma/code/GSE118336.py

@@ -0,0 +1,251 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sarcoma"
+cohort = "GSE118336"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sarcoma"
+in_cohort_dir = "../DATA/GEO/Sarcoma/GSE118336"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Sarcoma/GSE118336.csv"
+out_gene_data_file = "./output/preprocess/1/Sarcoma/gene_data/GSE118336.csv"
+out_clinical_data_file = "./output/preprocess/1/Sarcoma/clinical_data/GSE118336.csv"
+json_path = "./output/preprocess/1/Sarcoma/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step: Dataset Analysis and Clinical Feature Extraction
+
+# 1. Gene Expression Data Availability
+#    The Series title indicates "HTA2.0 (human transcriptome array) analysis", which suggests
+#    actual gene expression data is available (and not simply miRNA or methylation data).
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+
+#    2.1 Identify keys in the sample characteristics for each variable:
+trait_row = None          # No mention of 'Sarcoma' or a relevant disease key in the dictionary.
+age_row = None            # No age information found.
+gender_row = None         # No gender information found.
+
+#    2.2 Define data-type conversion functions. Even though data is not available,
+#        we provide them as placeholders.
+
+def convert_trait(x: str) -> int:
+    # Not used because trait_row is None, but here's a placeholder function.
+    # Convert to 'binary' if used, or return None for unknown.
+    return None
+
+def convert_age(x: str) -> float:
+    # Not used because age_row is None, placeholder function
+    return None
+
+def convert_gender(x: str) -> int:
+    # Not used because gender_row is None, placeholder function
+    return None
+
+# 3. Save Metadata (initial filtering).
+#    Trait data availability depends on trait_row. Since trait_row is None, is_trait_available=False.
+is_trait_available = False
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Since trait_row is None, we skip clinical feature extraction and do not call geo_select_clinical_features.
+# STEP3
+import gzip
+import pandas as pd
+
+try:
+    # 1. Attempt to extract gene expression data using the library function
+    gene_data = get_genetic_data(matrix_file)
+except KeyError:
+    # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
+    # and rename the first column to "ID".
+    marker = "!series_matrix_table_begin"
+    skip_rows = None
+
+    # Determine how many rows to skip before the matrix data begins
+    with gzip.open(matrix_file, 'rt') as f:
+        for i, line in enumerate(f):
+            if marker in line:
+                skip_rows = i + 1
+                break
+        else:
+            raise ValueError(f"Marker '{marker}' not found in the file.")
+
+    # Read the data from the determined position
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression='gzip',
+        skiprows=skip_rows,
+        comment='!',
+        delimiter='\t',
+        on_bad_lines='skip'
+    )
+
+    # If a different column name is used instead of 'ID_REF', rename appropriately
+    if 'ID_REF' in gene_data.columns:
+        gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
+    else:
+        first_col = gene_data.columns[0]
+        gene_data.rename(columns={first_col: 'ID'}, inplace=True)
+
+    gene_data['ID'] = gene_data['ID'].astype(str)
+    gene_data.set_index('ID', inplace=True)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+requires_gene_mapping = True
+# STEP5
+# 1 & 2. Only extract and preview gene annotation data if the SOFT file exists, otherwise skip.
+if soft_file is None:
+    print("No SOFT file found. Skipping gene annotation extraction.")
+    gene_annotation = pd.DataFrame()
+else:
+    try:
+        # Attempt to extract gene annotation with the default method
+        gene_annotation = get_gene_annotation(soft_file)
+    except UnicodeDecodeError:
+        # Fallback if UTF-8 decoding fails: read with a more lenient encoding and pass the content as a string
+        import gzip
+        with gzip.open(soft_file, 'rt', encoding='latin-1', errors='replace') as f:
+            content = f.read()
+        gene_annotation = filter_content_by_prefix(
+            content,
+            prefixes_a=['^','!','#'],
+            unselect=True,
+            source_type='string',
+            return_df_a=True
+        )[0]
+
+    print("Gene annotation preview:")
+    print(preview_df(gene_annotation))
+# STEP 6: Gene Identifier Mapping
+
+# We'll attempt to map the probe-level data to gene symbols only if there's a genuine overlap
+# between the expression data indices and the annotation IDs.
+
+probe_column_candidates = ["ID", "probeset_id"]
+gene_symbol_column_candidates = ["gene_assignment", "mrna_assignment"]
+
+chosen_probe_col = None
+chosen_symbol_col = None
+
+# 1. Find a probe column with overlap
+for col in probe_column_candidates:
+    if col in gene_annotation.columns:
+        overlap = set(gene_annotation[col]) & set(gene_data.index)
+        if len(overlap) > 0:
+            chosen_probe_col = col
+            break
+
+# 2. Pick a gene symbol column
+for col in gene_symbol_column_candidates:
+    if col in gene_annotation.columns:
+        chosen_symbol_col = col
+        break
+
+# If none found, skip mapping
+if not chosen_probe_col or not chosen_symbol_col:
+    print("No suitable probe or gene symbol columns found in the annotation. Skipping mapping.")
+else:
+    # Build a preliminary mapping DataFrame
+    mapping_df = get_gene_mapping(
+        gene_annotation,
+        prob_col=chosen_probe_col,
+        gene_col=chosen_symbol_col
+    )
+
+    # 3. Check for genuine overlap after dropping invalid entries
+    mapped_ids = set(mapping_df["ID"].unique()) & set(gene_data.index)
+    if len(mapped_ids) == 0:
+        print("No overlapping probe IDs after cleaning. Skipping mapping.")
+    else:
+        # Proceed with the mapping since there is an actual overlap
+        gene_data = apply_gene_mapping(gene_data, mapping_df)
+        print("Gene-level mapping performed successfully.")
+        print("Mapped gene_data shape:", gene_data.shape)
+        print("First few gene symbols after mapping:", gene_data.index[:10].tolist())
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Sarcoma/code/GSE133228.py

@@ -0,0 +1,247 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sarcoma"
+cohort = "GSE133228"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sarcoma"
+in_cohort_dir = "../DATA/GEO/Sarcoma/GSE133228"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Sarcoma/GSE133228.csv"
+out_gene_data_file = "./output/preprocess/1/Sarcoma/gene_data/GSE133228.csv"
+out_clinical_data_file = "./output/preprocess/1/Sarcoma/clinical_data/GSE133228.csv"
+json_path = "./output/preprocess/1/Sarcoma/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Gene Expression Data Availability
+is_gene_available = True  # Based on background info, we assume these data measure gene expression
+
+# 2) Variable Availability and Data Type Conversion
+
+# From the sample characteristics:
+#   0 -> ['gender: Male', 'gender: Female']
+#   1 -> ['age: 3', 'age: 11', 'age: 4', 'age: 25', ...] (multiple distinct ages)
+#   2 -> ['tumor type: primary tumor'] (only one value)
+
+# The trait "Sarcoma" is not explicitly found in any row, and row 2 has only one unique value.
+# Hence, trait_row = None (not useful for a variation-based analysis).
+trait_row = None
+
+# Age data is in row=1 with multiple distinct values
+age_row = 1
+
+# Gender data is in row=0 with multiple distinct values
+gender_row = 0
+
+# 2.2) Define data type converters
+
+def convert_trait(value: str) -> int:
+    # Not used because trait_row is None, but define for consistency.
+    # If we had data, we might extract part after the colon and map accordingly.
+    return None
+
+def convert_age(value: str) -> float:
+    # Typical pattern: "age: 25"
+    # Split by colon and take the numeric part
+    parts = value.split(':')
+    if len(parts) == 2:
+        try:
+            return float(parts[1].strip())
+        except ValueError:
+            return None
+    return None
+
+def convert_gender(value: str) -> int:
+    # Typical pattern: "gender: Male"/"gender: Female"
+    # Convert Female->0, Male->1, otherwise None
+    parts = value.split(':')
+    if len(parts) == 2:
+        g = parts[1].strip().lower()
+        if g == 'male':
+            return 1
+        elif g == 'female':
+            return 0
+    return None
+
+# 3) Save Metadata (initial filtering)
+# Trait data availability depends on whether trait_row is None
+is_trait_available = (trait_row is not None)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction
+# Since trait_row is None, we skip extracting clinical features
+# STEP3
+import gzip
+import pandas as pd
+
+try:
+    # 1. Attempt to extract gene expression data using the library function
+    gene_data = get_genetic_data(matrix_file)
+except KeyError:
+    # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
+    # and rename the first column to "ID".
+    marker = "!series_matrix_table_begin"
+    skip_rows = None
+
+    # Determine how many rows to skip before the matrix data begins
+    with gzip.open(matrix_file, 'rt') as f:
+        for i, line in enumerate(f):
+            if marker in line:
+                skip_rows = i + 1
+                break
+        else:
+            raise ValueError(f"Marker '{marker}' not found in the file.")
+
+    # Read the data from the determined position
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression='gzip',
+        skiprows=skip_rows,
+        comment='!',
+        delimiter='\t',
+        on_bad_lines='skip'
+    )
+
+    # If a different column name is used instead of 'ID_REF', rename appropriately
+    if 'ID_REF' in gene_data.columns:
+        gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
+    else:
+        first_col = gene_data.columns[0]
+        gene_data.rename(columns={first_col: 'ID'}, inplace=True)
+
+    gene_data['ID'] = gene_data['ID'].astype(str)
+    gene_data.set_index('ID', inplace=True)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These identifiers (e.g., "100009676_at", "10000_at") appear to be microarray probe IDs, not standard human gene symbols.
+# Typically, such probe IDs need to be mapped to the corresponding gene symbols.
+
+print("requires_gene_mapping = True")
+# STEP5
+# 1 & 2. Only extract and preview gene annotation data if the SOFT file exists, otherwise skip.
+if soft_file is None:
+    print("No SOFT file found. Skipping gene annotation extraction.")
+    gene_annotation = pd.DataFrame()
+else:
+    try:
+        # Attempt to extract gene annotation with the default method
+        gene_annotation = get_gene_annotation(soft_file)
+    except UnicodeDecodeError:
+        # Fallback if UTF-8 decoding fails: read with a more lenient encoding and pass the content as a string
+        import gzip
+        with gzip.open(soft_file, 'rt', encoding='latin-1', errors='replace') as f:
+            content = f.read()
+        gene_annotation = filter_content_by_prefix(
+            content,
+            prefixes_a=['^','!','#'],
+            unselect=True,
+            source_type='string',
+            return_df_a=True
+        )[0]
+
+    print("Gene annotation preview:")
+    print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1) Decide which key in the gene annotation dataframe corresponds to the probe IDs 
+#    (same as those in the gene expression data) and which key corresponds to the gene symbol.
+#    From our preview, "ID" in the annotation matches the probe IDs in the gene expression data,
+#    while "Description" appears to hold gene names/symbols (albeit as descriptive text).
+
+prob_col = "ID"
+gene_col = "Description"
+
+# 2) Get a gene mapping dataframe using these columns.
+mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)
+
+# 3) Convert probe-level measurements to gene-level data.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Optional: Inspect the resulting gene_data shape
+print("Mapped gene expression data shape:", gene_data.shape)
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Sarcoma/code/GSE142162.py

@@ -0,0 +1,235 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sarcoma"
+cohort = "GSE142162"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sarcoma"
+in_cohort_dir = "../DATA/GEO/Sarcoma/GSE142162"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Sarcoma/GSE142162.csv"
+out_gene_data_file = "./output/preprocess/1/Sarcoma/gene_data/GSE142162.csv"
+out_clinical_data_file = "./output/preprocess/1/Sarcoma/clinical_data/GSE142162.csv"
+json_path = "./output/preprocess/1/Sarcoma/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Determine gene expression availability
+is_gene_available = True  # Based on Affymetrix hgu133Plus2 arrays and "Expression profiling" indication
+
+# 2) Identify data availability (rows) and define conversion functions
+
+# For this dataset, the trait is effectively constant ("tumor type: primary tumor"), so it's not useful
+# for an association study. Hence, trait_row is None.
+trait_row = None
+
+# Age is variable under key 1
+age_row = 1
+
+# Gender is variable under key 0
+gender_row = 0
+
+# 2.2) Data type conversion functions
+
+def convert_trait(value: str):
+    """
+    This dataset does not contain meaningful variation for the primary trait 'Sarcoma'.
+    We'll return None for all inputs.
+    """
+    return None
+
+def convert_age(value: str):
+    """
+    Converts age values after the colon to a continuous numeric type.
+    Unknown values are returned as None.
+    Example input: "age: 25"
+    """
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    raw_val = parts[1].strip()
+    if not raw_val.isdigit():
+        return None
+    return float(raw_val)
+
+def convert_gender(value: str):
+    """
+    Converts gender to a binary variable:
+      Female -> 0
+      Male   -> 1
+    Unknown values are returned as None.
+    Example input: "gender: Male"
+    """
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    raw_val = parts[1].strip().lower()
+    if raw_val == 'male':
+        return 1
+    elif raw_val == 'female':
+        return 0
+    return None
+
+# 3) Conduct initial dataset filtering and save metadata
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Since trait_row is None, we skip clinical feature extraction.
+# STEP3
+import gzip
+import pandas as pd
+
+try:
+    # 1. Attempt to extract gene expression data using the library function
+    gene_data = get_genetic_data(matrix_file)
+except KeyError:
+    # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
+    # and rename the first column to "ID".
+    marker = "!series_matrix_table_begin"
+    skip_rows = None
+
+    # Determine how many rows to skip before the matrix data begins
+    with gzip.open(matrix_file, 'rt') as f:
+        for i, line in enumerate(f):
+            if marker in line:
+                skip_rows = i + 1
+                break
+        else:
+            raise ValueError(f"Marker '{marker}' not found in the file.")
+
+    # Read the data from the determined position
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression='gzip',
+        skiprows=skip_rows,
+        comment='!',
+        delimiter='\t',
+        on_bad_lines='skip'
+    )
+
+    # If a different column name is used instead of 'ID_REF', rename appropriately
+    if 'ID_REF' in gene_data.columns:
+        gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
+    else:
+        first_col = gene_data.columns[0]
+        gene_data.rename(columns={first_col: 'ID'}, inplace=True)
+
+    gene_data['ID'] = gene_data['ID'].astype(str)
+    gene_data.set_index('ID', inplace=True)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+print("requires_gene_mapping = True")
+# STEP5
+# 1 & 2. Only extract and preview gene annotation data if the SOFT file exists, otherwise skip.
+if soft_file is None:
+    print("No SOFT file found. Skipping gene annotation extraction.")
+    gene_annotation = pd.DataFrame()
+else:
+    try:
+        # Attempt to extract gene annotation with the default method
+        gene_annotation = get_gene_annotation(soft_file)
+    except UnicodeDecodeError:
+        # Fallback if UTF-8 decoding fails: read with a more lenient encoding and pass the content as a string
+        import gzip
+        with gzip.open(soft_file, 'rt', encoding='latin-1', errors='replace') as f:
+            content = f.read()
+        gene_annotation = filter_content_by_prefix(
+            content,
+            prefixes_a=['^','!','#'],
+            unselect=True,
+            source_type='string',
+            return_df_a=True
+        )[0]
+
+    print("Gene annotation preview:")
+    print(preview_df(gene_annotation))
+# Gene Identifier Mapping
+probe_col = "ID"          # column in gene_annotation that matches the probe IDs in gene_data
+gene_symbol_col = "Description"  # column in gene_annotation containing the gene symbol or descriptive info
+
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Sarcoma/code/GSE159847.py

@@ -0,0 +1,237 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sarcoma"
+cohort = "GSE159847"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sarcoma"
+in_cohort_dir = "../DATA/GEO/Sarcoma/GSE159847"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Sarcoma/GSE159847.csv"
+out_gene_data_file = "./output/preprocess/1/Sarcoma/gene_data/GSE159847.csv"
+out_clinical_data_file = "./output/preprocess/1/Sarcoma/clinical_data/GSE159847.csv"
+json_path = "./output/preprocess/1/Sarcoma/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # This dataset likely contains gene expression data (Affymetrix/Agilent transcriptome).
+
+# 2. Variable Availability and Data Type Conversion
+
+# Since all samples are "complex sarcomas" (one trait), there's effectively no variation in the trait.
+trait_row = None
+
+# We do see multiple ages under key=1, so age is available.
+age_row = 1
+
+# We see males and females under key=0, so gender is available.
+gender_row = 0
+
+# Conversion functions
+def convert_trait(value: str):
+    # Not used since trait_row is None, but defined as per instructions
+    return None
+
+def convert_age(value: str):
+    # Example value: "age: 73"
+    try:
+        val = value.split(":", 1)[1].strip()
+        return float(val)
+    except:
+        return None
+
+def convert_gender(value: str):
+    # Example value: "Sex: M" or "Sex: F"
+    val = value.split(":", 1)[1].strip().lower()
+    if val == "m":
+        return 1
+    elif val == "f":
+        return 0
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Skip this step if trait_row is None
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait="Sarcoma",
+        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:", preview_df(selected_clinical_df))
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+import gzip
+import pandas as pd
+
+try:
+    # 1. Attempt to extract gene expression data using the library function
+    gene_data = get_genetic_data(matrix_file)
+except KeyError:
+    # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
+    # and rename the first column to "ID".
+    marker = "!series_matrix_table_begin"
+    skip_rows = None
+
+    # Determine how many rows to skip before the matrix data begins
+    with gzip.open(matrix_file, 'rt') as f:
+        for i, line in enumerate(f):
+            if marker in line:
+                skip_rows = i + 1
+                break
+        else:
+            raise ValueError(f"Marker '{marker}' not found in the file.")
+
+    # Read the data from the determined position
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression='gzip',
+        skiprows=skip_rows,
+        comment='!',
+        delimiter='\t',
+        on_bad_lines='skip'
+    )
+
+    # If a different column name is used instead of 'ID_REF', rename appropriately
+    if 'ID_REF' in gene_data.columns:
+        gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
+    else:
+        first_col = gene_data.columns[0]
+        gene_data.rename(columns={first_col: 'ID'}, inplace=True)
+
+    gene_data['ID'] = gene_data['ID'].astype(str)
+    gene_data.set_index('ID', inplace=True)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These appear to be microarray probe IDs that are not standard human gene symbols.
+print("requires_gene_mapping = True")
+# STEP5
+# 1 & 2. Only extract and preview gene annotation data if the SOFT file exists, otherwise skip.
+if soft_file is None:
+    print("No SOFT file found. Skipping gene annotation extraction.")
+    gene_annotation = pd.DataFrame()
+else:
+    try:
+        # Attempt to extract gene annotation with the default method
+        gene_annotation = get_gene_annotation(soft_file)
+    except UnicodeDecodeError:
+        # Fallback if UTF-8 decoding fails: read with a more lenient encoding and pass the content as a string
+        import gzip
+        with gzip.open(soft_file, 'rt', encoding='latin-1', errors='replace') as f:
+            content = f.read()
+        gene_annotation = filter_content_by_prefix(
+            content,
+            prefixes_a=['^','!','#'],
+            unselect=True,
+            source_type='string',
+            return_df_a=True
+        )[0]
+
+    print("Gene annotation preview:")
+    print(preview_df(gene_annotation))
+# STEP6: Gene Identifier Mapping
+
+# 1. Identify the matching columns in gene_annotation for probe ID and gene symbol
+#    Based on the preview, 'ID' corresponds to the probe IDs (e.g., A_23_P1000xx),
+#    and 'GENE_SYMBOL' holds the gene symbol.
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# 2. Apply the mapping to convert probe-level data into gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# 3. Check a small part of the resulting gene_data
+print("Mapped gene expression data shape:", gene_data.shape)
+print("First 5 genes in gene_data index:", gene_data.index[:5].tolist())
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Sarcoma/code/GSE159848.py

@@ -0,0 +1,234 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sarcoma"
+cohort = "GSE159848"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sarcoma"
+in_cohort_dir = "../DATA/GEO/Sarcoma/GSE159848"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Sarcoma/GSE159848.csv"
+out_gene_data_file = "./output/preprocess/1/Sarcoma/gene_data/GSE159848.csv"
+out_clinical_data_file = "./output/preprocess/1/Sarcoma/clinical_data/GSE159848.csv"
+json_path = "./output/preprocess/1/Sarcoma/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Determine if gene expression data is available
+is_gene_available = True  # The dataset is described as "Expression data from 50 mixoid liposarcomas"
+
+# 2. Identify availability of variables and define their data-conversion functions
+# According to the dictionary, row 0 has sex: M/F, and row 1 has age
+# For the trait "Sarcoma," the dataset is entirely mixoid liposarcomas (no variation), so we consider it unavailable.
+trait_row = None
+age_row = 1
+gender_row = 0
+
+def convert_trait(value: str):
+    """
+    Since trait is not available/variable in this dataset, always return None.
+    """
+    return None
+
+def convert_age(value: str):
+    """
+    Extract the age value after the colon, converting it to float if possible.
+    Return None for invalid or unknown values.
+    """
+    val = value.split(':')[-1].strip()
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    """
+    Extract the gender value (M or F) after the colon and convert to binary:
+      M or male -> 1
+      F or female -> 0
+    Others -> None
+    """
+    val = value.split(':')[-1].strip().lower()
+    if val in ['m', 'male']:
+        return 1
+    elif val in ['f', 'female']:
+        return 0
+    else:
+        return None
+
+# 3. Save metadata with initial filtering
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Since trait_row is None, skip extraction of clinical features
+# STEP3
+import gzip
+import pandas as pd
+
+try:
+    # 1. Attempt to extract gene expression data using the library function
+    gene_data = get_genetic_data(matrix_file)
+except KeyError:
+    # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
+    # and rename the first column to "ID".
+    marker = "!series_matrix_table_begin"
+    skip_rows = None
+
+    # Determine how many rows to skip before the matrix data begins
+    with gzip.open(matrix_file, 'rt') as f:
+        for i, line in enumerate(f):
+            if marker in line:
+                skip_rows = i + 1
+                break
+        else:
+            raise ValueError(f"Marker '{marker}' not found in the file.")
+
+    # Read the data from the determined position
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression='gzip',
+        skiprows=skip_rows,
+        comment='!',
+        delimiter='\t',
+        on_bad_lines='skip'
+    )
+
+    # If a different column name is used instead of 'ID_REF', rename appropriately
+    if 'ID_REF' in gene_data.columns:
+        gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
+    else:
+        first_col = gene_data.columns[0]
+        gene_data.rename(columns={first_col: 'ID'}, inplace=True)
+
+    gene_data['ID'] = gene_data['ID'].astype(str)
+    gene_data.set_index('ID', inplace=True)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the observed probe IDs (e.g., "A_23_P100001"), these are not standard human gene symbols.
+# Thus, gene-to-symbol mapping is required.
+
+print("requires_gene_mapping = True")
+# STEP5
+# 1 & 2. Only extract and preview gene annotation data if the SOFT file exists, otherwise skip.
+if soft_file is None:
+    print("No SOFT file found. Skipping gene annotation extraction.")
+    gene_annotation = pd.DataFrame()
+else:
+    try:
+        # Attempt to extract gene annotation with the default method
+        gene_annotation = get_gene_annotation(soft_file)
+    except UnicodeDecodeError:
+        # Fallback if UTF-8 decoding fails: read with a more lenient encoding and pass the content as a string
+        import gzip
+        with gzip.open(soft_file, 'rt', encoding='latin-1', errors='replace') as f:
+            content = f.read()
+        gene_annotation = filter_content_by_prefix(
+            content,
+            prefixes_a=['^','!','#'],
+            unselect=True,
+            source_type='string',
+            return_df_a=True
+        )[0]
+
+    print("Gene annotation preview:")
+    print(preview_df(gene_annotation))
+# STEP6: Gene Identifier Mapping
+
+# 1. Identify the matching columns in the gene annotation dataframe.
+#    From the preview, the probe identifiers are under "ID" and the gene symbols are under "GENE_SYMBOL".
+probe_col = "ID"
+symbol_col = "GENE_SYMBOL"
+
+# 2. Create the mapping dataframe from probe to gene symbol.
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=symbol_col)
+
+# 3. Convert probe-level measurements into gene expression data using the mapping dataframe.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional) Print the shape and a small index preview to verify successful mapping
+print("Mapped gene_data shape:", gene_data.shape)
+print("First 20 gene symbols in mapped gene_data:", list(gene_data.index[:20]))
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Sarcoma/code/GSE162785.py

@@ -0,0 +1,218 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sarcoma"
+cohort = "GSE162785"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sarcoma"
+in_cohort_dir = "../DATA/GEO/Sarcoma/GSE162785"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Sarcoma/GSE162785.csv"
+out_gene_data_file = "./output/preprocess/1/Sarcoma/gene_data/GSE162785.csv"
+out_clinical_data_file = "./output/preprocess/1/Sarcoma/clinical_data/GSE162785.csv"
+json_path = "./output/preprocess/1/Sarcoma/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Determine gene expression data availability
+is_gene_available = True  # Microarray analysis suggests gene expression data is available
+
+# 2) Variable availability and data type conversion
+
+# From the sample characteristics dictionary:
+# {0: ['cell line: A673', 'cell line: CHLA-10', 'cell line: EW7', 'cell line: SK-N-MC']}
+# There is only one key (0), whose values all refer to Ewing sarcoma cell lines. For our trait of interest ("Sarcoma"),
+# this dataset effectively has just one value for all samples (i.e., all are Ewing Sarcoma), so it is considered constant
+# and therefore not available for association analysis.
+trait_row = None   # No variability in the trait
+age_row = None     # No age data available
+gender_row = None  # No gender data available
+
+# Although the rows are None, we still define the conversion functions as requested.
+def convert_trait(value: str):
+    # Not used here because trait_row is None
+    # Typically, we would parse the string after the colon and convert, but there's no relevant data to parse.
+    return None
+
+def convert_age(value: str):
+    # Not used here because age_row is None
+    return None
+
+def convert_gender(value: str):
+    # Not used here because gender_row is None
+    return None
+
+# 3) Save metadata (initial filtering)
+# Trait data is considered unavailable since trait_row is None.
+is_trait_available = False
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction is skipped because trait_row is None (no clinical data for trait).
+# STEP3
+import gzip
+import pandas as pd
+
+try:
+    # 1. Attempt to extract gene expression data using the library function
+    gene_data = get_genetic_data(matrix_file)
+except KeyError:
+    # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
+    # and rename the first column to "ID".
+    marker = "!series_matrix_table_begin"
+    skip_rows = None
+
+    # Determine how many rows to skip before the matrix data begins
+    with gzip.open(matrix_file, 'rt') as f:
+        for i, line in enumerate(f):
+            if marker in line:
+                skip_rows = i + 1
+                break
+        else:
+            raise ValueError(f"Marker '{marker}' not found in the file.")
+
+    # Read the data from the determined position
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression='gzip',
+        skiprows=skip_rows,
+        comment='!',
+        delimiter='\t',
+        on_bad_lines='skip'
+    )
+
+    # If a different column name is used instead of 'ID_REF', rename appropriately
+    if 'ID_REF' in gene_data.columns:
+        gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
+    else:
+        first_col = gene_data.columns[0]
+        gene_data.rename(columns={first_col: 'ID'}, inplace=True)
+
+    gene_data['ID'] = gene_data['ID'].astype(str)
+    gene_data.set_index('ID', inplace=True)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+print("requires_gene_mapping = True")
+# STEP5
+# 1 & 2. Only extract and preview gene annotation data if the SOFT file exists, otherwise skip.
+if soft_file is None:
+    print("No SOFT file found. Skipping gene annotation extraction.")
+    gene_annotation = pd.DataFrame()
+else:
+    try:
+        # Attempt to extract gene annotation with the default method
+        gene_annotation = get_gene_annotation(soft_file)
+    except UnicodeDecodeError:
+        # Fallback if UTF-8 decoding fails: read with a more lenient encoding and pass the content as a string
+        import gzip
+        with gzip.open(soft_file, 'rt', encoding='latin-1', errors='replace') as f:
+            content = f.read()
+        gene_annotation = filter_content_by_prefix(
+            content,
+            prefixes_a=['^','!','#'],
+            unselect=True,
+            source_type='string',
+            return_df_a=True
+        )[0]
+
+    print("Gene annotation preview:")
+    print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns in the annotation dataframe that match our probe IDs and contain gene symbols.
+#    From the preview, the "ID" column matches the numerical probe identifiers, and "gene_assignment" contains gene symbols.
+prob_col = "ID"
+gene_col = "gene_assignment"
+
+# 2. Create a mapping dataframe with these two columns.
+mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_col)
+
+# 3. Convert the probe-level expression data to gene-level expression data using this mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional) Print shape and a small preview of the resulting mapped data
+print("Mapped gene_data shape:", gene_data.shape)
+print("Mapped gene_data preview:\n", gene_data.head())
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Sarcoma/code/GSE162789.py

@@ -0,0 +1,245 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sarcoma"
+cohort = "GSE162789"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sarcoma"
+in_cohort_dir = "../DATA/GEO/Sarcoma/GSE162789"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Sarcoma/GSE162789.csv"
+out_gene_data_file = "./output/preprocess/1/Sarcoma/gene_data/GSE162789.csv"
+out_clinical_data_file = "./output/preprocess/1/Sarcoma/clinical_data/GSE162789.csv"
+json_path = "./output/preprocess/1/Sarcoma/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Determine if gene expression data is available
+is_gene_available = True  # Based on the dataset description focusing on Ewing sarcoma pathogenesis via HDAC
+
+# 2) Identify and set availability of trait, age, and gender
+#    Inspecting the sample characteristics, we see only one key (0).
+#    All samples appear to have the same trait (Ewing sarcoma), so there's no variation -> trait not available
+trait_row = None
+
+#    Age is available for 2 samples (14, 20). The other 4 do not mention age but we will treat them as missing
+age_row = 0
+
+#    Gender is only explicitly "female" for 2 samples and unknown for the rest, so there's effectively only one unique known value
+gender_row = None
+
+# 2) Define converters for trait, age, and gender
+def convert_trait(x: str) -> int:
+    """
+    Convert the trait from string to a binary or categorical label.
+    Not used here because trait_row is None, but defined for completeness.
+    """
+    # Example logic (convert to 1 if "Ewing sarcoma" appears, else None):
+    parts = x.split(":")
+    if len(parts) > 1:
+        val = parts[1].strip().lower()
+        if "ewing sarcoma" in val:
+            return 1
+    return None
+
+def convert_age(x: str) -> float:
+    """
+    Convert the age from string to a continuous float.
+    If no age information is present, return None.
+    """
+    # Example logic to parse "14 year old"
+    match = re.search(r'(\d+)\s*year\s*old', x.lower())
+    if match:
+        return float(match.group(1))
+    return None
+
+def convert_gender(x: str) -> int:
+    """
+    Convert the gender from string to binary (female -> 0, male -> 1).
+    If unknown, return None.
+    """
+    parts = x.split(":")
+    val = parts[-1].strip().lower() if len(parts) > 1 else x.lower()
+    if "female" in val:
+        return 0
+    elif "male" in val:
+        return 1
+    return None
+
+# 3) Save metadata using initial filtering
+is_trait_available = (trait_row is not None)
+
+# Since this is just the initial filtering, set is_final=False
+_ = 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) Because trait_row is None, we skip clinical feature extraction
+#    (No further steps for clinical data since the trait is considered not available.)
+# STEP3
+import gzip
+import pandas as pd
+
+try:
+    # 1. Attempt to extract gene expression data using the library function
+    gene_data = get_genetic_data(matrix_file)
+except KeyError:
+    # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
+    # and rename the first column to "ID".
+    marker = "!series_matrix_table_begin"
+    skip_rows = None
+
+    # Determine how many rows to skip before the matrix data begins
+    with gzip.open(matrix_file, 'rt') as f:
+        for i, line in enumerate(f):
+            if marker in line:
+                skip_rows = i + 1
+                break
+        else:
+            raise ValueError(f"Marker '{marker}' not found in the file.")
+
+    # Read the data from the determined position
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression='gzip',
+        skiprows=skip_rows,
+        comment='!',
+        delimiter='\t',
+        on_bad_lines='skip'
+    )
+
+    # If a different column name is used instead of 'ID_REF', rename appropriately
+    if 'ID_REF' in gene_data.columns:
+        gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
+    else:
+        first_col = gene_data.columns[0]
+        gene_data.rename(columns={first_col: 'ID'}, inplace=True)
+
+    gene_data['ID'] = gene_data['ID'].astype(str)
+    gene_data.set_index('ID', inplace=True)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the numeric identifiers (e.g., 7892501, 7892502, etc.), these look like probe IDs rather than official gene symbols.
+# Therefore, they need to be mapped to standard human gene symbols.
+print("requires_gene_mapping = True")
+# STEP5
+# 1 & 2. Only extract and preview gene annotation data if the SOFT file exists, otherwise skip.
+if soft_file is None:
+    print("No SOFT file found. Skipping gene annotation extraction.")
+    gene_annotation = pd.DataFrame()
+else:
+    try:
+        # Attempt to extract gene annotation with the default method
+        gene_annotation = get_gene_annotation(soft_file)
+    except UnicodeDecodeError:
+        # Fallback if UTF-8 decoding fails: read with a more lenient encoding and pass the content as a string
+        import gzip
+        with gzip.open(soft_file, 'rt', encoding='latin-1', errors='replace') as f:
+            content = f.read()
+        gene_annotation = filter_content_by_prefix(
+            content,
+            prefixes_a=['^','!','#'],
+            unselect=True,
+            source_type='string',
+            return_df_a=True
+        )[0]
+
+    print("Gene annotation preview:")
+    print(preview_df(gene_annotation))
+# STEP 6: Gene Identifier Mapping
+
+# 1. Identify the annotation columns that match the expression data IDs and the gene symbols.
+#    From inspection, "ID" corresponds to the probe ID (same format as gene_data.index),
+#    and "mrna_assignment" appears to have clearer references to actual gene symbols (e.g. "OR4F4", "SEPT14").
+
+# 2. Get the gene mapping DataFrame using "mrna_assignment" as the gene symbol source
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="mrna_assignment")
+
+# 3. Convert probe-level measurements to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Let's see the result
+print("Gene data shape after mapping:", gene_data.shape)
+print("First 5 gene indices after mapping:", gene_data.index[:5].tolist())
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Sarcoma/code/GSE197147.py

@@ -0,0 +1,244 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Sarcoma"
+cohort = "GSE197147"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Sarcoma"
+in_cohort_dir = "../DATA/GEO/Sarcoma/GSE197147"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Sarcoma/GSE197147.csv"
+out_gene_data_file = "./output/preprocess/1/Sarcoma/gene_data/GSE197147.csv"
+out_clinical_data_file = "./output/preprocess/1/Sarcoma/clinical_data/GSE197147.csv"
+json_path = "./output/preprocess/1/Sarcoma/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Determine if the dataset contains gene expression data
+is_gene_available = True  # The series description explicitly mentions "Gene expression profiling"
+
+# 2. Identify variable availability and define row indices
+#    Only one key (0) is present with multiple histotypes ("HB","NB","RMS","WT").
+#    We'll map "RMS" to the trait of interest (Sarcoma=1) and others to 0.
+trait_row = 0  # We can infer the Sarcoma trait from 'histotype: RMS' vs others
+age_row = None  # No age information in the sample dictionary
+gender_row = None  # No gender information in the sample dictionary
+
+# 2.2 Define conversion functions
+def convert_trait(value: str):
+    # Extract the part after the colon.
+    val = value.split(':')[-1].strip().lower()
+    # Map RMS => 1 (Sarcoma), others => 0
+    if val == 'rms':
+        return 1
+    elif val in ['hb', 'nb', 'wt']:
+        return 0
+    return None  # For any unexpected values
+
+def convert_age(value: str):
+    # No rows found for age, so no real conversion logic needed
+    return None
+
+def convert_gender(value: str):
+    # No rows found for gender, so no real conversion logic needed
+    return None
+
+# 2.1 Check if the trait data is available
+is_trait_available = (trait_row is not None)
+
+# 3. Save metadata with 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=is_trait_available
+)
+
+# 4. If trait data is available (trait_row != None), extract clinical features
+if trait_row is not None:
+    selected_clinical = geo_select_clinical_features(
+        clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview the extracted clinical features
+    print(preview_df(selected_clinical, n=5))
+    # Save to CSV
+    selected_clinical.to_csv(out_clinical_data_file, index=False)
+# STEP3
+import gzip
+import pandas as pd
+
+try:
+    # 1. Attempt to extract gene expression data using the library function
+    gene_data = get_genetic_data(matrix_file)
+except KeyError:
+    # Fallback: the expected "ID_REF" column may be absent, so manually parse the file
+    # and rename the first column to "ID".
+    marker = "!series_matrix_table_begin"
+    skip_rows = None
+
+    # Determine how many rows to skip before the matrix data begins
+    with gzip.open(matrix_file, 'rt') as f:
+        for i, line in enumerate(f):
+            if marker in line:
+                skip_rows = i + 1
+                break
+        else:
+            raise ValueError(f"Marker '{marker}' not found in the file.")
+
+    # Read the data from the determined position
+    gene_data = pd.read_csv(
+        matrix_file,
+        compression='gzip',
+        skiprows=skip_rows,
+        comment='!',
+        delimiter='\t',
+        on_bad_lines='skip'
+    )
+
+    # If a different column name is used instead of 'ID_REF', rename appropriately
+    if 'ID_REF' in gene_data.columns:
+        gene_data.rename(columns={'ID_REF': 'ID'}, inplace=True)
+    else:
+        first_col = gene_data.columns[0]
+        gene_data.rename(columns={first_col: 'ID'}, inplace=True)
+
+    gene_data['ID'] = gene_data['ID'].astype(str)
+    gene_data.set_index('ID', inplace=True)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the provided gene expression data index (e.g., 'TC0100006437.hg.1'), 
+# these appear to be probe or platform-specific identifiers rather than standard human gene symbols.
+# Therefore, we conclude that these IDs must be mapped to standard gene symbols for proper analysis.
+
+print("requires_gene_mapping = True")
+# STEP5
+# 1 & 2. Only extract and preview gene annotation data if the SOFT file exists, otherwise skip.
+if soft_file is None:
+    print("No SOFT file found. Skipping gene annotation extraction.")
+    gene_annotation = pd.DataFrame()
+else:
+    try:
+        # Attempt to extract gene annotation with the default method
+        gene_annotation = get_gene_annotation(soft_file)
+    except UnicodeDecodeError:
+        # Fallback if UTF-8 decoding fails: read with a more lenient encoding and pass the content as a string
+        import gzip
+        with gzip.open(soft_file, 'rt', encoding='latin-1', errors='replace') as f:
+            content = f.read()
+        gene_annotation = filter_content_by_prefix(
+            content,
+            prefixes_a=['^','!','#'],
+            unselect=True,
+            source_type='string',
+            return_df_a=True
+        )[0]
+
+    print("Gene annotation preview:")
+    print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify which annotation columns store the gene IDs and gene symbol references.
+#    From the preview, it appears the column "ID" matches the row index of our gene expression data,
+#    and the column "SPOT_ID.1" contains gene symbol references.
+probe_id_col = "ID"
+gene_symbol_col = "SPOT_ID.1"
+
+# 2. Get a gene mapping dataframe from the annotation dataframe.
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation,
+    prob_col=probe_id_col,
+    gene_col=gene_symbol_col
+)
+
+# 3. Convert probe-level measurements into gene-level expression.
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Print a small preview to confirm the result
+print("Mapped gene expression data preview:")
+print(gene_data.head())
+import os
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# First, check if the clinical CSV file exists. If it does not, we cannot proceed with trait-based linking.
+if not os.path.exists(out_clinical_data_file):
+    # No trait data file => dataset is not usable for trait analysis
+    df_null = pd.DataFrame()
+    is_biased = True  # Arbitrary boolean to satisfy function requirement
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=is_biased,
+        df=df_null,
+        note="No trait data file found; dataset not usable for trait analysis."
+    )
+
+else:
+    # 1. Normalize the mapped gene expression data using known gene symbol synonyms, then save.
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    normalized_gene_data.to_csv(out_gene_data_file)
+
+    # 2. Load the previously extracted clinical CSV.
+    selected_clinical_df = pd.read_csv(out_clinical_data_file)
+    # If we had a single-row trait, rename row 0 to the trait name (example usage).
+    selected_clinical_df = selected_clinical_df.rename(index={0: trait})
+
+    # Combine these as our final clinical data; in this dataset, we only have trait info (if any).
+    combined_clinical_df = selected_clinical_df
+
+    # Link the clinical and genetic data by matching sample IDs in columns.
+    linked_data = geo_link_clinical_genetic_data(combined_clinical_df, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data (drop incomplete rows/columns, then impute).
+    processed_data = handle_missing_values(linked_data, trait)
+
+    # 4. Check trait bias and remove any biased demographic features (if any).
+    trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+    # 5. Final validation and metadata saving.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=processed_data,
+        note="Completed trait-based preprocessing."
+    )
+
+    # 6. If final dataset is usable, save. Otherwise, skip.
+    if is_usable:
+        processed_data.to_csv(out_data_file)

p1/preprocess/Schizophrenia/clinical_data/GSE120340.csv

@@ -0,0 +1,2 @@
+GSM3398477,GSM3398478,GSM3398479,GSM3398480,GSM3398481,GSM3398482,GSM3398483,GSM3398484,GSM3398485,GSM3398486,GSM3398487,GSM3398488,GSM3398489,GSM3398490,GSM3398491,GSM3398492,GSM3398493,GSM3398494,GSM3398495,GSM3398496,GSM3398497,GSM3398498,GSM3398499,GSM3398500,GSM3398501,GSM3398502,GSM3398503,GSM3398504,GSM3398505,GSM3398506
+0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

p1/preprocess/Schizophrenia/clinical_data/GSE120342.csv

@@ -0,0 +1,2 @@
+GSM3398507,GSM3398508,GSM3398509,GSM3398510,GSM3398511,GSM3398512,GSM3398513,GSM3398514,GSM3398515,GSM3398516,GSM3398517
+0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0

p1/preprocess/Schizophrenia/clinical_data/GSE145554.csv

@@ -0,0 +1,4 @@
+GSM4321410,GSM4321411,GSM4321412,GSM4321413,GSM4321414,GSM4321415,GSM4321416,GSM4321417,GSM4321418,GSM4321419,GSM4321420,GSM4321421,GSM4321422,GSM4321423,GSM4321424,GSM4321425,GSM4321426,GSM4321427,GSM4321428,GSM4321429,GSM4321430,GSM4321431,GSM4321432,GSM4321433,GSM4321434,GSM4321435,GSM4321436,GSM4321437,GSM4321438,GSM4321439,GSM4321440,GSM4321441,GSM4321442,GSM4321443,GSM4321444,GSM4321445,GSM4321446,GSM4321447,GSM4321448,GSM4321449,GSM4321450,GSM4321451,GSM4321452
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p1/preprocess/Schizophrenia/code/GSE119288.py

@@ -0,0 +1,120 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Schizophrenia"
+cohort = "GSE119288"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Schizophrenia"
+in_cohort_dir = "../DATA/GEO/Schizophrenia/GSE119288"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Schizophrenia/GSE119288.csv"
+out_gene_data_file = "./output/preprocess/1/Schizophrenia/gene_data/GSE119288.csv"
+out_clinical_data_file = "./output/preprocess/1/Schizophrenia/clinical_data/GSE119288.csv"
+json_path = "./output/preprocess/1/Schizophrenia/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Determine if gene expression data is available
+is_gene_available = True  # Based on "expression-based" transcriptomic study
+# 2) Identify rows for trait, age, and gender in the sample characteristics dictionary
+#    None indicates the data is not available or is constant (useless for association).
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2) Define data conversion functions (though they won't be used here since data is missing).
+def convert_trait(value: str) -> Optional[int]:
+    # Placeholder for a binary trait conversion (not used due to missing data).
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    # Placeholder for a continuous age conversion (not used due to missing data).
+    return None
+
+def convert_gender(value: str) -> Optional[int]:
+    # Placeholder for a binary gender conversion (not used due to missing data).
+    return None
+
+# 3) Conduct initial filtering and save metadata about dataset usability
+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) Since trait_row is None, we skip clinical feature extraction.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These probe set IDs (e.g., '1007_s_at') are not human gene symbols but Affymetrix identifiers.
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns matching the probe IDs and the gene symbols.
+#    From the annotation preview, "ID" matches the probe set IDs in the expression data,
+#    and we will assume "SPOT_ID" contains gene identifiers or symbols (though here they appear identical
+#    to "ID" in the preview, other rows might differ or contain additional mappings).
+
+prob_col = 'ID'
+gene_col = 'SPOT_ID'
+
+# 2. Create a gene mapping dataframe using the identified columns.
+gene_mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_col)
+
+# 3. Convert the probe-level expression data into gene-level expression data using the mapping.
+gene_data = apply_gene_mapping(gene_data, gene_mapping_df)
+
+# Optionally, display a small portion of the resulting DataFrame to verify.
+print(gene_data.head())
+# STEP7
+
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Since trait_row is None (no trait data available), we cannot link to clinical data or perform
+# downstream steps requiring a trait variable. Therefore, we skip steps 2, 3, and 4.
+
+# 5. We perform partial validation (is_final=False) to record the dataset has gene data but no trait.
+#    That way, we are not required to provide a df or is_biased argument.
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False
+)
+
+# 6. Since trait data is unavailable, we do NOT save a linked dataset.

p1/preprocess/Schizophrenia/code/GSE119289.py

@@ -0,0 +1,124 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Schizophrenia"
+cohort = "GSE119289"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Schizophrenia"
+in_cohort_dir = "../DATA/GEO/Schizophrenia/GSE119289"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Schizophrenia/GSE119289.csv"
+out_gene_data_file = "./output/preprocess/1/Schizophrenia/gene_data/GSE119289.csv"
+out_clinical_data_file = "./output/preprocess/1/Schizophrenia/clinical_data/GSE119289.csv"
+json_path = "./output/preprocess/1/Schizophrenia/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Determine if gene expression data is available
+is_gene_available = True  # Based on "transcriptomic drug screening," it appears to be gene expression data
+
+# Step 2: Identify rows and define data conversion functions for trait, age, and gender
+trait_row = None   # No Schizophrenia-related info found
+age_row = None     # No age-related info found
+gender_row = None  # No gender-related info found
+
+# Since we have no actual data, the conversion functions simply return None
+def convert_trait(value: str):
+    return None
+
+def convert_age(value: str):
+    return None
+
+def convert_gender(value: str):
+    return None
+
+# Step 3: Initial filtering and saving cohort info
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 4: Clinical feature extraction is skipped because trait_row is None
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# The identifiers appear to be Affymetrix probe IDs, not standard human gene symbols.
+# Therefore, they likely require mapping to gene symbols.
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP - Gene Identifier Mapping
+
+# 1. Determine which columns in the annotation represent the probe identifiers and the gene symbols.
+#    Based on the prior preview, "ID" appears to match our expression data's index (probe IDs),
+#    but we do not see a separate column containing actual gene symbols. For demonstration, we'll
+#    assume "SPOT_ID" is intended as the gene symbol column, although it is the same as "ID" in the preview.
+
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="SPOT_ID")
+
+# 2. Convert probe-level measurements into gene expression data using the mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# 3. Print a small preview of the resulting gene expression data.
+print("Converted gene expression data shape:", gene_data.shape)
+print("Gene expression data preview:")
+print(preview_df(gene_data))
+# STEP7
+# Since there is no trait data (trait_row is None), we cannot link clinical data or perform trait-based analysis.
+# Nonetheless, we must do a final validation to record that the dataset is unusable for trait-related analysis.
+
+import pandas as pd
+
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Prepare a dummy DataFrame for final validation,
+#    and set is_biased to False (arbitrary choice) to meet function requirements.
+dummy_df = pd.DataFrame()
+
+# 3. Perform final quality validation, marking trait as unavailable,
+#    which leads to the dataset being recorded as unusable (is_usable=False).
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=False,
+    df=dummy_df,
+    note="Trait data not available; only gene expression is present."
+)
+
+# 4. Since the dataset is unusable for trait analysis, do not produce any final linked output.

p1/preprocess/Schizophrenia/code/GSE120340.py

@@ -0,0 +1,144 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Schizophrenia"
+cohort = "GSE120340"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Schizophrenia"
+in_cohort_dir = "../DATA/GEO/Schizophrenia/GSE120340"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Schizophrenia/GSE120340.csv"
+out_gene_data_file = "./output/preprocess/1/Schizophrenia/gene_data/GSE120340.csv"
+out_clinical_data_file = "./output/preprocess/1/Schizophrenia/clinical_data/GSE120340.csv"
+json_path = "./output/preprocess/1/Schizophrenia/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Affymetrix whole-genome expression microarray indicates gene expression data
+
+# 2.1 Variable Availability
+trait_row = 0     # Row with multiple disease states, including SCZ
+age_row = None    # No age data in the dictionary
+gender_row = None # No gender data in the dictionary
+
+# 2.2 Data Type Conversion
+def convert_trait(value: str):
+    parts = value.split(':', 1)
+    if len(parts) == 2:
+        v = parts[1].strip().lower()
+        # Mark SCZ as 1, others as 0
+        return 1 if v == 'scz' else 0
+    return None
+
+def convert_age(value: str):
+    # No age data is available
+    return None
+
+def convert_gender(value: str):
+    # No gender data is available
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if trait data is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Observe output
+    print(preview_df(selected_clinical_df))
+    # Save extracted clinical features
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the observed identifiers (e.g., 10002_at), these are Affymetrix probe set IDs, not standard human gene symbols.
+# Therefore, a mapping step to human gene symbols is needed.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns in gene_annotation that match the probe IDs and the gene symbols.
+#    From the preview, "ID" matches the probe ID used in gene_data, and "Description" appears
+#    to contain the gene's name/symbol information (albeit in a descriptive form).
+
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Description')
+
+# 2. Convert probe-level measurements to gene-level measurements by applying the mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# For confirmation, let's see the shape of the resulting gene_data
+print("Mapped gene_data shape:", gene_data.shape)
+# STEP7
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.
+#    Fixing the variable name from 'selected_clinical_data' to 'selected_clinical_df'
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values in the linked data
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Conduct quality check and save the cohort information.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=linked_data
+)
+
+# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.
+if is_usable:
+    unbiased_linked_data.to_csv(out_data_file)