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output/preprocess/Anorexia_Nervosa/code/GSE60190.py

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+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Anorexia_Nervosa"
+cohort = "GSE60190"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Anorexia_Nervosa"
+in_cohort_dir = "../DATA/GEO/Anorexia_Nervosa/GSE60190"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Anorexia_Nervosa/GSE60190.csv"
+out_gene_data_file = "./output/z1/preprocess/Anorexia_Nervosa/gene_data/GSE60190.csv"
+out_clinical_data_file = "./output/z1/preprocess/Anorexia_Nervosa/clinical_data/GSE60190.csv"
+json_path = "./output/z1/preprocess/Anorexia_Nervosa/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+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 2: Dataset Analysis and Clinical Feature Extraction
+# Step 1: Determine gene expression data availability
+is_gene_available = True  # Illumina HumanHT-12 v3 microarray indicates gene expression data
+
+# Step 2: Variable availability and conversion functions
+
+# Trait (Anorexia Nervosa) availability:
+# The dataset provides ED (eating disorder) but does not distinguish AN specifically.
+trait_row = None  # Not available at the required specificity (AN vs BN)
+
+# Age availability
+age_row = 5  # 'age: <float>'
+def convert_age(x):
+    if x is None:
+        return None
+    try:
+        val = x.split(":", 1)[1].strip()
+    except Exception:
+        val = str(x).strip()
+    try:
+        v = float(val)
+        if 0 <= v < 120:
+            return v
+        return None
+    except Exception:
+        return None
+
+# Gender availability
+gender_row = 7  # 'Sex: M' / 'Sex: F'
+def convert_gender(x):
+    if x is None:
+        return None
+    try:
+        val = x.split(":", 1)[1].strip().lower()
+    except Exception:
+        val = str(x).strip().lower()
+    if val in {"m", "male"}:
+        return 1
+    if val in {"f", "female"}:
+        return 0
+    return None
+
+# Placeholder for trait conversion (not used because trait_row is None)
+def convert_trait(x):
+    return None
+
+# Step 3: Initial filtering and save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 4: Clinical feature extraction (skip because trait_row is None)
+# If trait_row becomes available in future, the following block can be used:
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    _ = preview_df(selected_clinical_df)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 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])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify the columns for probe IDs and gene symbols in the annotation
+probe_col = 'ID'       # Matches probe IDs like 'ILMN_1343291'
+gene_symbol_col = 'Symbol'  # Contains human gene symbols
+
+# 2. Build the gene mapping dataframe
+gene_mapping = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3. Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=gene_mapping)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2-6. Proceed only if trait data is available; otherwise, skip linking and final validation
+if ('trait_row' in globals()) and (trait_row is not None):
+    # Build clinical feature dataframe from clinical_data
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+
+    # Link clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+    # Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # Assess bias and remove biased demographic features
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # Final validation and save cohort info
+    is_usable = validate_and_save_cohort_info(
+        True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data
+    )
+
+    # Save linked data if usable
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Anorexia_Nervosa/code/TCGA.py

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+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Anorexia_Nervosa"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Anorexia_Nervosa/TCGA.csv"
+out_gene_data_file = "./output/z1/preprocess/Anorexia_Nervosa/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z1/preprocess/Anorexia_Nervosa/clinical_data/TCGA.csv"
+json_path = "./output/z1/preprocess/Anorexia_Nervosa/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Discover available TCGA cohort directories
+all_entries = os.listdir(tcga_root_dir)
+cohort_dirs = [d for d in all_entries if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+# Define keywords related to the trait to find a relevant cohort (none expected for Anorexia Nervosa in TCGA)
+trait_keywords = {
+    "anorexia", "nervosa", "eating", "appetite", "weight", "body_mass", "bmi", "cachexia"
+}
+
+# Score directories by presence of any keyword
+def score_dir(name: str) -> int:
+    lname = name.lower()
+    return sum(1 for kw in trait_keywords if kw in lname)
+
+scored = [(d, score_dir(d)) for d in cohort_dirs]
+# Select the best match if any positive score
+scored.sort(key=lambda x: x[1], reverse=True)
+selected_dir = scored[0][0] if scored and scored[0][1] > 0 else None
+
+if selected_dir is None:
+    # No suitable TCGA cohort for Anorexia Nervosa; mark as unavailable and complete this task
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    clinical_df = None
+    genetic_df = None
+    print("No suitable TCGA cohort found for the trait; skipping TCGA for this trait.")
+else:
+    cohort_path = os.path.join(tcga_root_dir, selected_dir)
+    clinical_fp, genetic_fp = tcga_get_relevant_filepaths(cohort_path)
+
+    # Load dataframes
+    clinical_df = pd.read_csv(clinical_fp, sep='\t', index_col=0, low_memory=False)
+    genetic_df = pd.read_csv(genetic_fp, sep='\t', index_col=0, low_memory=False)
+
+    # Print clinical column names
+    print(clinical_df.columns.tolist())

output/preprocess/Anorexia_Nervosa/cohort_info.json

@@ -1,22 +1 @@
-{
-  "GSE60190": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": true,
-    "has_age": true,
-    "has_gender": true,
-    "sample_size": 133
-  },
-  "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
-  }
-}
+{"GSE60190": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "TCGA": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}

output/preprocess/Anxiety_disorder/clinical_data/GSE60190.csv

@@ -0,0 +1,4 @@
+,GSM1467273,GSM1467274,GSM1467275,GSM1467276,GSM1467277,GSM1467278,GSM1467279,GSM1467280,GSM1467281,GSM1467282,GSM1467283,GSM1467284,GSM1467285,GSM1467286,GSM1467287,GSM1467288,GSM1467289,GSM1467290,GSM1467291,GSM1467292,GSM1467293,GSM1467294,GSM1467295,GSM1467296,GSM1467297,GSM1467298,GSM1467299,GSM1467300,GSM1467301,GSM1467302,GSM1467303,GSM1467304,GSM1467305,GSM1467306,GSM1467307,GSM1467308,GSM1467309,GSM1467310,GSM1467311,GSM1467312,GSM1467313,GSM1467314,GSM1467315,GSM1467316,GSM1467317,GSM1467318,GSM1467319,GSM1467320,GSM1467321,GSM1467322,GSM1467323,GSM1467324,GSM1467325,GSM1467326,GSM1467327,GSM1467328,GSM1467329,GSM1467330,GSM1467331,GSM1467332,GSM1467333,GSM1467334,GSM1467335,GSM1467336,GSM1467337,GSM1467338,GSM1467339,GSM1467340,GSM1467341,GSM1467342,GSM1467343,GSM1467344,GSM1467345,GSM1467346,GSM1467347,GSM1467348,GSM1467349,GSM1467350,GSM1467351,GSM1467352,GSM1467353,GSM1467354,GSM1467355,GSM1467356,GSM1467357,GSM1467358,GSM1467359,GSM1467360,GSM1467361,GSM1467362,GSM1467363,GSM1467364,GSM1467365,GSM1467366,GSM1467367,GSM1467368,GSM1467369,GSM1467370,GSM1467371,GSM1467372,GSM1467373,GSM1467374,GSM1467375,GSM1467376,GSM1467377,GSM1467378,GSM1467379,GSM1467380,GSM1467381,GSM1467382,GSM1467383,GSM1467384,GSM1467385,GSM1467386,GSM1467387,GSM1467388,GSM1467389,GSM1467390,GSM1467391,GSM1467392,GSM1467393,GSM1467394,GSM1467395,GSM1467396,GSM1467397,GSM1467398,GSM1467399,GSM1467400,GSM1467401,GSM1467402,GSM1467403,GSM1467404,GSM1467405
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output/preprocess/Anxiety_disorder/clinical_data/GSE61672.csv

@@ -1,4 +1,4 @@
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 Gender,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,

output/preprocess/Anxiety_disorder/clinical_data/GSE68526.csv

@@ -1,4 +1,4 @@
-,"""GSM1674313""","""GSM1674314""","""GSM1674315""","""GSM1674316""","""GSM1674317""","""GSM1674318""","""GSM1674319""","""GSM1674320""","""GSM1674321""","""GSM1674322""","""GSM1674323""","""GSM1674324""","""GSM1674325""","""GSM1674326""","""GSM1674327""","""GSM1674328""","""GSM1674329""","""GSM1674330""","""GSM1674331""","""GSM1674332""","""GSM1674333""","""GSM1674334""","""GSM1674335""","""GSM1674336""","""GSM1674337""","""GSM1674338""","""GSM1674339""","""GSM1674340""","""GSM1674341""","""GSM1674342""","""GSM1674343""","""GSM1674344""","""GSM1674345""","""GSM1674346""","""GSM1674347""","""GSM1674348""","""GSM1674349""","""GSM1674350""","""GSM1674351""","""GSM1674352""","""GSM1674353""","""GSM1674354""","""GSM1674355""","""GSM1674356""","""GSM1674357""","""GSM1674358""","""GSM1674359""","""GSM1674360""","""GSM1674361""","""GSM1674362""","""GSM1674363""","""GSM1674364""","""GSM1674365""","""GSM1674366""","""GSM1674367""","""GSM1674368""","""GSM1674369""","""GSM1674370""","""GSM1674371""","""GSM1674372""","""GSM1674373""","""GSM1674374""","""GSM1674375""","""GSM1674376""","""GSM1674377""","""GSM1674378""","""GSM1674379""","""GSM1674380""","""GSM1674381""","""GSM1674382""","""GSM1674383""","""GSM1674384""","""GSM1674385""","""GSM1674386""","""GSM1674387""","""GSM1674388""","""GSM1674389""","""GSM1674390""","""GSM1674391""","""GSM1674392""","""GSM1674393""","""GSM1674394""","""GSM1674395""","""GSM1674396""","""GSM1674397""","""GSM1674398""","""GSM1674399""","""GSM1674400""","""GSM1674401""","""GSM1674402""","""GSM1674403""","""GSM1674404""","""GSM1674405""","""GSM1674406""","""GSM1674407""","""GSM1674408""","""GSM1674409""","""GSM1674410""","""GSM1674411""","""GSM1674412""","""GSM1674413""","""GSM1674414""","""GSM1674415""","""GSM1674416""","""GSM1674417""","""GSM1674418""","""GSM1674419""","""GSM1674420""","""GSM1674421""","""GSM1674422""","""GSM1674423""","""GSM1674424""","""GSM1674425""","""GSM1674426""","""GSM1674427""","""GSM1674428""","""GSM1674429""","""GSM1674430""","""GSM1674431""","""GSM1674432""","""GSM1674433"""
-Anxiety_disorder,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+,GSM1674313,GSM1674314,GSM1674315,GSM1674316,GSM1674317,GSM1674318,GSM1674319,GSM1674320,GSM1674321,GSM1674322,GSM1674323,GSM1674324,GSM1674325,GSM1674326,GSM1674327,GSM1674328,GSM1674329,GSM1674330,GSM1674331,GSM1674332,GSM1674333,GSM1674334,GSM1674335,GSM1674336,GSM1674337,GSM1674338,GSM1674339,GSM1674340,GSM1674341,GSM1674342,GSM1674343,GSM1674344,GSM1674345,GSM1674346,GSM1674347,GSM1674348,GSM1674349,GSM1674350,GSM1674351,GSM1674352,GSM1674353,GSM1674354,GSM1674355,GSM1674356,GSM1674357,GSM1674358,GSM1674359,GSM1674360,GSM1674361,GSM1674362,GSM1674363,GSM1674364,GSM1674365,GSM1674366,GSM1674367,GSM1674368,GSM1674369,GSM1674370,GSM1674371,GSM1674372,GSM1674373,GSM1674374,GSM1674375,GSM1674376,GSM1674377,GSM1674378,GSM1674379,GSM1674380,GSM1674381,GSM1674382,GSM1674383,GSM1674384,GSM1674385,GSM1674386,GSM1674387,GSM1674388,GSM1674389,GSM1674390,GSM1674391,GSM1674392,GSM1674393,GSM1674394,GSM1674395,GSM1674396,GSM1674397,GSM1674398,GSM1674399,GSM1674400,GSM1674401,GSM1674402,GSM1674403,GSM1674404,GSM1674405,GSM1674406,GSM1674407,GSM1674408,GSM1674409,GSM1674410,GSM1674411,GSM1674412,GSM1674413,GSM1674414,GSM1674415,GSM1674416,GSM1674417,GSM1674418,GSM1674419,GSM1674420,GSM1674421,GSM1674422,GSM1674423,GSM1674424,GSM1674425,GSM1674426,GSM1674427,GSM1674428,GSM1674429,GSM1674430,GSM1674431,GSM1674432,GSM1674433
+Anxiety_disorder,1.0,1.0,1.8,1.2,1.4,1.2,1.2,1.0,1.4,1.2,1.8,2.2,1.4,1.8,1.0,1.0,1.0,1.0,1.2,1.0,1.2,1.6,,1.0,1.8,2.8,1.2,2.2,1.8,2.0,,1.6,,1.0,,2.2,1.6,1.4,2.0,1.8,1.0,1.4,,1.4,1.4,1.2,1.0,1.4,1.6,1.0,1.4,1.0,1.4,1.0,1.4,1.2,1.2,1.4,1.0,1.4,,,2.0,1.0,2.4,1.0,,1.2,1.4,,1.6,2.0,1.4,1.0,1.8,2.0,2.0,1.4,3.2,2.0,1.0,1.0,2.6,2.4,,1.0,1.2,1.6,,2.0,1.6,1.4,2.2,1.0,1.4,1.4,1.8,1.0,2.2,1.4,3.2,2.0,2.4,1.0,2.4,1.6,1.0,,1.4,1.0,,1.0,,1.0,1.0,1.0,1.0,1.4,1.8,1.0,1.4
 Age,79.0,79.0,76.0,70.0,65.0,64.0,75.0,70.0,66.0,66.0,93.0,69.0,69.0,67.0,77.0,74.0,73.0,80.0,68.0,83.0,64.0,87.0,87.0,83.0,81.0,84.0,55.0,68.0,62.0,58.0,81.0,76.0,84.0,60.0,87.0,56.0,86.0,81.0,60.0,78.0,78.0,75.0,48.0,82.0,76.0,95.0,69.0,62.0,69.0,75.0,87.0,68.0,73.0,84.0,71.0,85.0,76.0,73.0,76.0,70.0,68.0,64.0,69.0,82.0,75.0,73.0,55.0,61.0,82.0,77.0,70.0,75.0,57.0,79.0,65.0,69.0,62.0,71.0,84.0,74.0,56.0,81.0,94.0,61.0,58.0,73.0,79.0,74.0,79.0,71.0,71.0,88.0,64.0,57.0,59.0,73.0,62.0,51.0,82.0,72.0,82.0,77.0,80.0,69.0,84.0,67.0,81.0,91.0,76.0,62.0,68.0,83.0,89.0,85.0,88.0,87.0,81.0,72.0,66.0,71.0,73.0
-Gender,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+Gender,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0

output/preprocess/Anxiety_disorder/code/GSE119995.py

@@ -0,0 +1,136 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Anxiety_disorder"
+cohort = "GSE119995"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Anxiety_disorder"
+in_cohort_dir = "../DATA/GEO/Anxiety_disorder/GSE119995"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Anxiety_disorder/GSE119995.csv"
+out_gene_data_file = "./output/z1/preprocess/Anxiety_disorder/gene_data/GSE119995.csv"
+out_clinical_data_file = "./output/z1/preprocess/Anxiety_disorder/clinical_data/GSE119995.csv"
+json_path = "./output/z1/preprocess/Anxiety_disorder/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+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 2: Dataset Analysis and Clinical Feature Extraction
+import re
+
+# 1) Gene expression data availability (plasma mRNA expression -> suitable)
+is_gene_available = True
+
+# 2) Variable availability based on provided Sample Characteristics
+# Trait (Anxiety_disorder): all samples are panic disorder patients -> constant -> not usable
+trait_row = None
+
+# Age: not present in provided characteristics
+age_row = None
+
+# Gender: available
+gender_row = 2
+
+# 2.2) Converters
+def _after_colon(val):
+    if val is None:
+        return None
+    if isinstance(val, (int, float)):
+        return val
+    s = str(val)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip()
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    sv = str(v).strip().lower()
+    # Map common control terms to 0
+    control_terms = ['control', 'healthy', 'normal', 'no anxiety', 'no panic', 'non-anxious', 'non anxious']
+    if any(term in sv for term in control_terms):
+        return 0
+    # Map anxiety-related conditions to 1
+    case_terms = ['panic disorder', 'panic-disorder', 'pd', 'anxiety', 'anxiety disorder', 'gad', 'phobia', 'agoraphobia']
+    if any(term in sv for term in case_terms):
+        return 1
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    sv = str(v).strip().lower()
+    if sv in {'na', 'n/a', 'nan', 'none', 'unknown', 'not determined', 'not available', ''}:
+        return None
+    m = re.search(r'[-+]?\d*\.?\d+', sv)
+    if not m:
+        return None
+    try:
+        age = float(m.group())
+        if 0 <= age <= 120:
+            return age
+    except Exception:
+        pass
+    return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    sv = str(v).strip().lower()
+    if sv in {'female', 'f', 'woman', 'women'}:
+        return 0
+    if sv in {'male', 'm', 'man', 'men'}:
+        return 1
+    if sv in {'unknown', 'not determined', 'na', 'n/a', ''}:
+        return None
+    return None
+
+# 3) Save initial metadata (trait availability determined by trait_row is None)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction (skip because trait_row is None)
+# If trait_row were available:
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df, n=5)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)

output/preprocess/Anxiety_disorder/code/GSE60190.py

@@ -0,0 +1,194 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Anxiety_disorder"
+cohort = "GSE60190"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Anxiety_disorder"
+in_cohort_dir = "../DATA/GEO/Anxiety_disorder/GSE60190"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Anxiety_disorder/GSE60190.csv"
+out_gene_data_file = "./output/z1/preprocess/Anxiety_disorder/gene_data/GSE60190.csv"
+out_clinical_data_file = "./output/z1/preprocess/Anxiety_disorder/clinical_data/GSE60190.csv"
+json_path = "./output/z1/preprocess/Anxiety_disorder/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+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 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import pandas as pd
+
+# 1) Gene expression data availability
+is_gene_available = True  # Illumina HumanHT-12 v3 microarray gene expression platform
+
+# 2) Variable availability and conversion functions
+
+# Trait: Anxiety_disorder
+# Use key 1 ('ocd': Control, OCD, ED). Map OCD to 1; Control and ED to 0.
+trait_row = 1
+def convert_trait(x):
+    if x is None:
+        return None
+    try:
+        val = str(x)
+        if ':' in val:
+            val = val.split(':', 1)[1]
+        val = val.strip().lower()
+    except Exception:
+        return None
+    if val in {'ocd', 'obsessive-compulsive disorder', 'obsessive compulsive disorder'}:
+        return 1
+    if val in {'control', 'ed', 'eating disorder', 'healthy'}:
+        return 0
+    # For unexpected categories, return None
+    return None
+
+# Age: key 5 with continuous values
+age_row = 5
+def convert_age(x):
+    if x is None:
+        return None
+    try:
+        val = str(x)
+        if ':' in val:
+            val = val.split(':', 1)[1]
+        val = val.strip()
+        if val == '' or val.lower() in {'na', 'nan', 'none', 'unknown'}:
+            return None
+        return float(val)
+    except Exception:
+        return None
+
+# Gender: key 7 ('Sex: M'/'Sex: F')
+gender_row = 7
+def convert_gender(x):
+    if x is None:
+        return None
+    try:
+        val = str(x)
+        if ':' in val:
+            val = val.split(':', 1)[1]
+        val = val.strip().lower()
+    except Exception:
+        return None
+    if val in {'f', 'female', 'woman', 'women'}:
+        return 0
+    if val in {'m', 'male', 'man', 'men'}:
+        return 1
+    return None
+
+# 3) Initial metadata saving
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if trait available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    clinical_preview = preview_df(selected_clinical_df, n=5)
+    print("Clinical data preview:", clinical_preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 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])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Decide columns for mapping based on annotation preview:
+# - Probe ID column: 'ID' (e.g., ILMN_1343291)
+# - Gene symbol column: 'Symbol' (official HGNC symbols when available)
+
+# 1-2) Build mapping dataframe from annotation
+gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# 3) Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=gene_mapping)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Assess bias and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+is_gene_available = normalized_gene_data.shape[0] > 0
+is_trait_available = (trait in linked_data.columns)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note="INFO: Illumina HumanHT-12 v3 platform; trait defined as OCD vs non-OCD (Control/ED)."
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Anxiety_disorder/code/GSE60491.py

@@ -0,0 +1,127 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Anxiety_disorder"
+cohort = "GSE60491"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Anxiety_disorder"
+in_cohort_dir = "../DATA/GEO/Anxiety_disorder/GSE60491"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Anxiety_disorder/GSE60491.csv"
+out_gene_data_file = "./output/z1/preprocess/Anxiety_disorder/gene_data/GSE60491.csv"
+out_clinical_data_file = "./output/z1/preprocess/Anxiety_disorder/clinical_data/GSE60491.csv"
+json_path = "./output/z1/preprocess/Anxiety_disorder/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+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 2: Dataset Analysis and Clinical Feature Extraction
+import pandas as pd
+import numpy as np
+
+# 1) Gene expression availability (mRNA expression per background info)
+is_gene_available = True
+
+# 2) Variable availability
+# Trait is Anxiety_disorder, but no diagnostic or proxy field is present in the sample characteristics.
+trait_row = None
+
+# Age and gender availability based on the provided dictionary
+age_row = 0
+gender_row = 1
+
+# 2.2) Conversion functions
+def _extract_value_after_colon(x: str):
+    if x is None:
+        return None
+    if isinstance(x, str):
+        parts = x.split(":", 1)
+        val = parts[1].strip() if len(parts) > 1 else x.strip()
+        return val
+    return x
+
+def convert_trait(x):
+    # No trait available for Anxiety_disorder in this dataset
+    return None
+
+def convert_age(x):
+    v = _extract_value_after_colon(x)
+    if v is None:
+        return None
+    v = v.strip().lower()
+    if v in {"na", "n/a", "none", "missing", ""}:
+        return None
+    try:
+        return float(v)
+    except Exception:
+        return None
+
+def convert_gender(x):
+    # Row 1 contains 'male: 0/1' where 1=male, 0=female
+    v = _extract_value_after_colon(x)
+    if v is None:
+        return None
+    v = str(v).strip().lower()
+    if v in {"na", "n/a", "none", "missing", ""}:
+        return None
+    # Expect numeric 0/1
+    try:
+        iv = int(float(v))
+        if iv == 1:
+            return 1  # male
+        if iv == 0:
+            return 0  # female
+    except Exception:
+        pass
+    # Fallback if textual (not expected here)
+    if v in {"male", "m"}:
+        return 1
+    if v in {"female", "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 because trait_row is None)
+# If trait_row becomes available in the future, uncomment below:
+# if trait_row is not None:
+#     selected_clinical_df = geo_select_clinical_features(
+#         clinical_df=clinical_data,
+#         trait=trait,
+#         trait_row=trait_row,
+#         convert_trait=convert_trait,
+#         age_row=age_row,
+#         convert_age=convert_age,
+#         gender_row=gender_row,
+#         convert_gender=convert_gender
+#     )
+#     preview = preview_df(selected_clinical_df)
+#     selected_clinical_df.to_csv(out_clinical_data_file, index=True)

output/preprocess/Anxiety_disorder/code/GSE61672.py

@@ -0,0 +1,244 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Anxiety_disorder"
+cohort = "GSE61672"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Anxiety_disorder"
+in_cohort_dir = "../DATA/GEO/Anxiety_disorder/GSE61672"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Anxiety_disorder/GSE61672.csv"
+out_gene_data_file = "./output/z1/preprocess/Anxiety_disorder/gene_data/GSE61672.csv"
+out_clinical_data_file = "./output/z1/preprocess/Anxiety_disorder/clinical_data/GSE61672.csv"
+json_path = "./output/z1/preprocess/Anxiety_disorder/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+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 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1) Gene expression data availability
+is_gene_available = True  # Based on series title/summary: blood gene expression profiles
+
+# 2) Converters
+def _after_colon(value: str) -> str:
+    if value is None:
+        return ""
+    s = str(value).strip()
+    if ":" in s:
+        return s.split(":", 1)[1].strip()
+    return s
+
+def convert_trait(value):
+    s = str(value).strip().lower()
+    field = s.split(":", 1)[0].strip() if ":" in s else ""
+    val = _after_colon(s).lower()
+    # Explicitly map "anxiety case/control"
+    if ("anxiety" in field) and ("case/control" in field):
+        if val in ("case", "1", "yes", "patient"):
+            return 1
+        if val in ("control", "0", "no", "healthy"):
+            return 0
+        return None
+    return None
+
+def convert_age(value):
+    val = _after_colon(value)
+    m = re.search(r"-?\d+(\.\d+)?", val)
+    if m:
+        try:
+            return float(m.group(0))
+        except Exception:
+            return None
+    return None
+
+def convert_gender(value):
+    s = str(value).strip().lower()
+    field = s.split(":", 1)[0].strip() if ":" in s else ""
+    val = _after_colon(s).lower()
+
+    if "sex" in field or "gender" in field:
+        if val in ("f", "female", "woman", "women", "girl"):
+            return 0
+        if val in ("m", "male", "man", "men", "boy"):
+            return 1
+    return None
+
+# 2.1 Determine the best trait row between candidates 4 and 5, then optionally fill from the other
+candidate_rows = [4, 5]
+candidate_dfs = {}
+non_na_counts = {}
+
+for r in candidate_rows:
+    # Extract trait only to evaluate coverage
+    df_r = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=r,
+        convert_trait=convert_trait,
+        age_row=None,
+        convert_age=None,
+        gender_row=None,
+        convert_gender=None
+    )
+    candidate_dfs[r] = df_r
+    non_na_counts[r] = df_r.loc[trait].notna().sum()
+
+# Choose the row with higher non-missing count; if tie, prefer 4 as suggested
+best_trait_row = 4 if non_na_counts.get(4, 0) >= non_na_counts.get(5, 0) else 5
+
+# If no trait available at all, mark as unavailable
+trait_row = best_trait_row if max(non_na_counts.values()) > 0 else None
+age_row = 0
+gender_row = 1
+
+# 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 (only if trait is available)
+if is_trait_available:
+    # Primary extraction with the best trait row including age and gender
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Fill missing trait values from the alternative row to reduce missingness
+    alt_row = 5 if trait_row == 4 else 4
+    alt_df = candidate_dfs[alt_row]  # already computed trait-only DataFrame
+    selected_clinical_df.loc[trait] = selected_clinical_df.loc[trait].fillna(alt_df.loc[trait])
+
+    # Preview and save
+    clinical_preview = preview_df(selected_clinical_df, n=5)
+    print(clinical_preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 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])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Decide the identifier and symbol columns from annotation by matching to gene_data index and valid gene symbols
+expr_df = gene_data  # keep original probe-level data
+
+# Candidate columns
+id_candidates = [c for c in ['ID', 'Probe_Id', 'Array_Address_Id'] if c in gene_annotation.columns]
+symbol_candidates = [c for c in ['Symbol', 'ILMN_Gene', 'Gene Symbol', 'Gene_Symbol'] if c in gene_annotation.columns]
+
+# Choose ID column based on maximum overlap with expression probe IDs
+overlap_counts = {}
+for c in id_candidates:
+    ids = gene_annotation[c].astype(str).str.strip()
+    overlap_counts[c] = ids.isin(expr_df.index).sum()
+id_col = max(overlap_counts, key=overlap_counts.get) if overlap_counts else 'ID'
+
+# Choose symbol column based on number of rows with valid human gene symbols
+def valid_symbol_count(series):
+    return series.dropna().astype(str).map(lambda s: len(extract_human_gene_symbols(s)) > 0).sum()
+
+symbol_scores = {c: valid_symbol_count(gene_annotation[c]) for c in symbol_candidates}
+symbol_col = max(symbol_scores, key=symbol_scores.get) if symbol_scores else 'Symbol'
+
+# Build mapping and apply to convert probe-level data to gene-level data
+gene_mapping = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=symbol_col)
+gene_data = apply_gene_mapping(expression_df=expr_df, mapping_df=gene_mapping)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene matrix
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data_pre = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+samples_before = linked_data_pre.shape[0]
+covariate_cols = [trait, 'Age', 'Gender']
+pre_gene_cols = [c for c in linked_data_pre.columns if c not in covariate_cols]
+genes_before = len(pre_gene_cols)
+trait_missing_before = linked_data_pre[trait].isna().sum()
+
+linked_data = handle_missing_values(linked_data_pre, trait)
+
+samples_after = linked_data.shape[0]
+post_gene_cols = [c for c in linked_data.columns if c not in covariate_cols]
+genes_after = len(post_gene_cols)
+
+# 4. Bias check and remove biased demographics
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+note = (
+    f"INFO: Samples before/after filtering: {samples_before}/{samples_after}; "
+    f"Genes before/after filtering: {genes_before}/{genes_after}; "
+    f"Missing {trait} before filtering: {trait_missing_before}."
+)
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=is_trait_available,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Anxiety_disorder/code/GSE68526.py

@@ -0,0 +1,180 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Anxiety_disorder"
+cohort = "GSE68526"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Anxiety_disorder"
+in_cohort_dir = "../DATA/GEO/Anxiety_disorder/GSE68526"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Anxiety_disorder/GSE68526.csv"
+out_gene_data_file = "./output/z1/preprocess/Anxiety_disorder/gene_data/GSE68526.csv"
+out_clinical_data_file = "./output/z1/preprocess/Anxiety_disorder/clinical_data/GSE68526.csv"
+json_path = "./output/z1/preprocess/Anxiety_disorder/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+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 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import pandas as pd
+
+# 1) Gene expression data availability based on background info
+is_gene_available = True  # Gene expression profiling on peripheral blood RNA
+
+# 2) Variable availability and converters
+# - Trait (Anxiety score): key 13 -> 'anxiety: ...' (continuous Beck Anxiety Inventory score)
+# - Age: key 0 -> 'age (yrs): ...'
+# - Gender: key 1 -> 'female: 1/0' (map female=1->0, male=0->1)
+trait_row = 13
+age_row = 0
+gender_row = 1
+
+def _after_colon(x: str) -> str:
+    if x is None:
+        return ''
+    parts = str(x).split(':', 1)
+    return parts[1].strip() if len(parts) > 1 else str(x).strip()
+
+def convert_trait(x):
+    # Anxiety score as continuous value; map 'missing' to None
+    v = _after_colon(x).lower()
+    if v in {'', 'na', 'nan', 'missing', 'none'}:
+        return None
+    try:
+        return float(v)
+    except Exception:
+        return None
+
+def convert_age(x):
+    v = _after_colon(x).lower()
+    # Fix: check exact equality so valid ages are not dropped
+    if v in {'', 'missing', 'na', 'nan', 'none'}:
+        return None
+    try:
+        return float(v)
+    except Exception:
+        # fallback: extract first number
+        import re
+        m = re.search(r'[-+]?\d*\.?\d+', v)
+        return float(m.group()) if m else None
+
+def convert_gender(x):
+    # Input is 'female: 1/0' where 1 indicates female
+    v = _after_colon(x).strip().lower()
+    if v in {'', 'na', 'nan', 'missing', 'none'}:
+        return None
+    if v in {'1', '0'}:
+        return 0 if v == '1' else 1  # female=1->0; male=0->1
+    if v in {'female', 'f'}:
+        return 0
+    if v in {'male', 'm'}:
+        return 1
+    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 (only if trait_row is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print("Clinical features preview:", preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 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])
+
+# Step 4: Gene Identifier Review
+# Based on the observed identifiers (e.g., A1BG, A1CF, A2M, A2ML1, AAAS), these are human gene symbols.
+# Some entries like A2BP1 are older aliases but still gene symbols. Therefore, no mapping is required.
+print("requires_gene_mapping = False")
+
+# Step 5: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize gene symbols and save
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Ensure clinical features are available in scope; load from disk if needed
+if 'selected_clinical_df' not in globals():
+    if os.path.exists(out_clinical_data_file):
+        selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+    else:
+        raise FileNotFoundError("Clinical features not found in memory or on disk.")
+
+# Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values, guarding the presence of the trait column
+if trait not in linked_data.columns:
+    raise KeyError(f"Trait column '{trait}' not found in the linked data.")
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Judge bias and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+is_gene_available = True
+is_trait_available = trait in unbiased_linked_data.columns
+note = "INFO: Gene symbols normalized via NCBI synonyms; duplicates averaged. Clinical features reused from previous step."
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Anxiety_disorder/code/GSE78104.py

@@ -0,0 +1,207 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Anxiety_disorder"
+cohort = "GSE78104"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Anxiety_disorder"
+in_cohort_dir = "../DATA/GEO/Anxiety_disorder/GSE78104"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Anxiety_disorder/GSE78104.csv"
+out_gene_data_file = "./output/z1/preprocess/Anxiety_disorder/gene_data/GSE78104.csv"
+out_clinical_data_file = "./output/z1/preprocess/Anxiety_disorder/clinical_data/GSE78104.csv"
+json_path = "./output/z1/preprocess/Anxiety_disorder/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+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 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # mRNA expression profiled by microarray (Agilent lncRNA/mRNA platform)
+
+# 2. Variable Availability and Data Type Conversion
+# From Sample Characteristics Dictionary:
+# trait (Anxiety_disorder) inferred from 'disease state' (OCD vs normal control)
+trait_row = 1
+age_row = 3
+gender_row = 2
+
+def _after_colon(x: str) -> str:
+    if x is None:
+        return ""
+    parts = str(x).split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else str(x).strip()
+
+def convert_trait(x):
+    # Map OCD to 1 (case), normal/healthy control to 0
+    val = _after_colon(x).strip().lower()
+    if val in ("ocd", "obsessive-compulsive disorder", "obsessive compulsive disorder"):
+        return 1
+    if "control" in val:
+        return 0
+    if val in ("patient", "case"):
+        return 1
+    if val in ("normal", "healthy"):
+        return 0
+    return None
+
+def convert_age(x):
+    # Extract numeric age in years; handles patterns like '18y', '18', '18 years'
+    val = _after_colon(x).strip().lower()
+    if val in ("na", "n/a", "unknown", ""):
+        return None
+    m = re.search(r"(\d+(\.\d+)?)", val)
+    if m:
+        try:
+            return float(m.group(1))
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    # Female -> 0, Male -> 1
+    val = _after_colon(x).strip().lower()
+    if val in ("male", "m"):
+        return 1
+    if val in ("female", "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
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 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])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Determine columns for probe IDs and gene symbols based on annotation preview
+probe_col = 'ID'            # Matches probe IDs like 'A_19_P00315459' seen in gene_data index
+gene_symbol_col = 'GeneSymbol'
+
+# Build mapping dataframe from annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import json
+import pandas as pd
+
+# 1. Normalize gene data and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Ensure clinical dataframe is available; fallback to loading from disk if needed
+try:
+    selected_clinical_df
+except NameError:
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 2. Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# Determine availability flags with native Python bools
+is_gene_available_final = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+is_trait_available_final = bool((trait in linked_data.columns) and (linked_data[trait].notna().sum() > 0))
+
+# 3. Handle missing values
+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.
+note = "INFO: Agilent G3 lncRNA/mRNA platform; probe-to-gene mapping via 'GeneSymbol' with synonym normalization."
+
+def _finalize_and_save():
+    return validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=bool(is_gene_available_final),
+        is_trait_available=bool(is_trait_available_final),
+        is_biased=bool(is_trait_biased),
+        df=unbiased_linked_data,
+        note=note
+    )
+
+# Attempt to save; if JSON serialization fails due to non-native bools in existing file, reset and retry once.
+try:
+    is_usable = _finalize_and_save()
+except TypeError:
+    os.makedirs(os.path.dirname(json_path), exist_ok=True)
+    with open(json_path, "w") as f:
+        json.dump({}, f)
+    is_usable = _finalize_and_save()
+
+# 6. If the linked data is usable, save it
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Anxiety_disorder/code/GSE94119.py

@@ -0,0 +1,100 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Anxiety_disorder"
+cohort = "GSE94119"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Anxiety_disorder"
+in_cohort_dir = "../DATA/GEO/Anxiety_disorder/GSE94119"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Anxiety_disorder/GSE94119.csv"
+out_gene_data_file = "./output/z1/preprocess/Anxiety_disorder/gene_data/GSE94119.csv"
+out_clinical_data_file = "./output/z1/preprocess/Anxiety_disorder/clinical_data/GSE94119.csv"
+json_path = "./output/z1/preprocess/Anxiety_disorder/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+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 2: Dataset Analysis and Clinical Feature Extraction
+import re
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Illumina HT-12v4 BeadChip microarray indicates mRNA expression data.
+
+# 2. Variable Availability and Data Type Conversion
+
+# Keys in Sample Characteristics Dictionary
+trait_row = None        # No case/control or diagnosis variability available; all are anxiety disorder patients.
+age_row = None          # Age not provided.
+gender_row = 0          # Gender is available at key 0.
+
+def _extract_after_colon(x: str) -> str:
+    if x is None:
+        return ""
+    parts = str(x).split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else str(x).strip()
+
+def convert_trait(x):
+    # Generic mapping for anxiety-related traits if ever encountered; not used since trait_row is None.
+    v = _extract_after_colon(x).lower()
+    if v in {"case", "patient", "anxiety", "anxiety disorder", "anxiety_disorder", "panic disorder", "specific phobia"}:
+        return 1
+    if v in {"control", "healthy", "no", "none"}:
+        return 0
+    # Heuristics
+    if "panic" in v or "phobia" in v:
+        return 1
+    if "control" in v or "healthy" in v:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _extract_after_colon(x)
+    m = re.search(r"[-+]?\d*\.?\d+", v)
+    if m:
+        try:
+            return float(m.group())
+        except:
+            return None
+    return None
+
+def convert_gender(x):
+    v = _extract_after_colon(x).lower()
+    if v in {"female", "f", "woman", "women"}:
+        return 0
+    if v in {"male", "m", "man", "men"}:
+        return 1
+    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
+# Skipped because trait_row is None (no usable clinical trait data for association)

output/preprocess/Anxiety_disorder/code/TCGA.py

@@ -0,0 +1,52 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Anxiety_disorder"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Anxiety_disorder/TCGA.csv"
+out_gene_data_file = "./output/z1/preprocess/Anxiety_disorder/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z1/preprocess/Anxiety_disorder/clinical_data/TCGA.csv"
+json_path = "./output/z1/preprocess/Anxiety_disorder/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Step 1: Select the most relevant TCGA cohort for Anxiety_disorder (likely none in TCGA cancer cohorts)
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+trait_lower = trait.lower()
+
+# Basic keyword matching for anxiety-related terms
+keywords = ['anxiety', 'anxiet', 'panic', 'gad', 'generalized_anxiety', 'psychi', 'mental']
+candidates = [d for d in subdirs if any(k in d.lower() for k in keywords)]
+
+if not candidates:
+    # No suitable cohort found; record and skip this trait for TCGA
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort='TCGA',
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    print("No TCGA cohort relevant to Anxiety_disorder was found. Skipping TCGA for this trait.")
+else:
+    # If multiple, select the most specific (use longest name as a simple proxy for specificity)
+    selected_dir = sorted(candidates, key=len, reverse=True)[0]
+    selected_dir_path = os.path.join(tcga_root_dir, selected_dir)
+
+    # Step 2: Identify clinical and genetic file paths
+    clinical_path, genetic_path = tcga_get_relevant_filepaths(selected_dir_path)
+
+    # Step 3: Load both files
+    clinical_df = pd.read_csv(clinical_path, sep='\t', index_col=0, low_memory=False)
+    genetic_df = pd.read_csv(genetic_path, sep='\t', index_col=0, low_memory=False)
+
+    # Step 4: Print clinical column names
+    print(clinical_df.columns.tolist())

output/preprocess/Anxiety_disorder/cohort_info.json

@@ -1,82 +1 @@
-{
-  "GSE94119": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  },
-  "GSE78104": {
-    "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": 60
-  },
-  "GSE68526": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  },
-  "GSE61672": {
-    "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": 195
-  },
-  "GSE60491": {
-    "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
-  },
-  "GSE60190": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  },
-  "GSE119995": {
-    "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": 72
-  },
-  "TCGA": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": false,
-    "is_available": false,
-    "is_biased": null,
-    "has_age": null,
-    "has_gender": null,
-    "sample_size": null
-  }
-}
+{"GSE94119": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE78104": {"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": 60, "note": "INFO: Agilent G3 lncRNA/mRNA platform; probe-to-gene mapping via 'GeneSymbol' with synonym normalization."}, "GSE68526": {"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": 107, "note": "INFO: Gene symbols normalized via NCBI synonyms; duplicates averaged. Clinical features reused from previous step."}, "GSE61672": {"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": 336, "note": "INFO: Samples before/after filtering: 546/336; Genes before/after filtering: 9086/9086; Missing Anxiety_disorder before filtering: 210."}, "GSE60491": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE60190": {"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": 133, "note": "INFO: Illumina HumanHT-12 v3 platform; trait defined as OCD vs non-OCD (Control/ED)."}, "GSE119995": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "TCGA": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}

output/preprocess/Arrhythmia/clinical_data/GSE115574.csv

@@ -1,2 +1,2 @@
-0,1
-1.0,
+,GSM3182680,GSM3182681,GSM3182682,GSM3182683,GSM3182684,GSM3182685,GSM3182686,GSM3182687,GSM3182688,GSM3182689,GSM3182690,GSM3182691,GSM3182692,GSM3182693,GSM3182694,GSM3182695,GSM3182696,GSM3182697,GSM3182698,GSM3182699,GSM3182700,GSM3182701,GSM3182702,GSM3182703,GSM3182704,GSM3182705,GSM3182706,GSM3182707,GSM3182708,GSM3182709,GSM3182710,GSM3182711,GSM3182712,GSM3182713,GSM3182714,GSM3182715,GSM3182716,GSM3182717,GSM3182718,GSM3182719,GSM3182720,GSM3182721,GSM3182722,GSM3182723,GSM3182724,GSM3182725,GSM3182726,GSM3182727,GSM3182728,GSM3182729,GSM3182730,GSM3182731,GSM3182732,GSM3182733,GSM3182734,GSM3182735,GSM3182736,GSM3182737,GSM3182738
+Arrhythmia,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

output/preprocess/Arrhythmia/clinical_data/GSE143924.csv

@@ -0,0 +1,2 @@
+,GSM4276706,GSM4276707,GSM4276708,GSM4276709,GSM4276710,GSM4276711,GSM4276712,GSM4276713,GSM4276714,GSM4276715,GSM4276716,GSM4276717,GSM4276718,GSM4276719,GSM4276720,GSM4276721,GSM4276722,GSM4276723,GSM4276724,GSM4276725,GSM4276726,GSM4276727,GSM4276728,GSM4276729,GSM4276730,GSM4276731,GSM4276732,GSM4276733,GSM4276734,GSM4276735
+Arrhythmia,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

output/preprocess/Arrhythmia/clinical_data/GSE182600.csv

@@ -1,4 +1,4 @@
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output/preprocess/Arrhythmia/clinical_data/GSE235307.csv

@@ -1,4 +1,4 @@
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output/preprocess/Arrhythmia/clinical_data/GSE41177.csv

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output/preprocess/Arrhythmia/clinical_data/GSE53622.csv

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output/preprocess/Arrhythmia/clinical_data/GSE93101.csv

@@ -0,0 +1,4 @@
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+Arrhythmia,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+Age,33.4,51.2,51.9,47.8,41.5,67.3,52.8,16.1,78.9,53.2,70.9,59.9,21.9,45.2,52.4,32.3,52.8,55.8,47.0,55.8,57.3,31.7,49.3,66.1,55.9,49.1,63.0,21.0,53.6,50.1,37.4,71.5,56.5
+Gender,0.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0

output/preprocess/Arrhythmia/code/GSE115574.py

@@ -0,0 +1,190 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Arrhythmia"
+cohort = "GSE115574"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Arrhythmia"
+in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE115574"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Arrhythmia/GSE115574.csv"
+out_gene_data_file = "./output/z1/preprocess/Arrhythmia/gene_data/GSE115574.csv"
+out_clinical_data_file = "./output/z1/preprocess/Arrhythmia/clinical_data/GSE115574.csv"
+json_path = "./output/z1/preprocess/Arrhythmia/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+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 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+
+# 1) Gene expression data availability
+is_gene_available = True  # Affymetrix human gene expression microarrays (mRNA), not miRNA-only or methylation-only.
+
+# 2) Variable availability and converters
+
+# Trait (Arrhythmia) is represented by disease state: AFib vs sinus rhythm at row 0.
+trait_row = 0
+
+# Age and gender are not provided in the sample characteristics shown.
+age_row = None
+gender_row = None
+
+def _extract_value(x):
+    if x is None:
+        return None
+    # Handle potential NaNs
+    try:
+        import pandas as pd
+        if pd.isna(x):
+            return None
+    except Exception:
+        pass
+    # Extract substring after the first colon if present
+    s = str(x)
+    parts = s.split(":", 1)
+    val = parts[1] if len(parts) == 2 else parts[0]
+    return val.strip().lower()
+
+def convert_trait(x):
+    """
+    Map Arrhythmia status: AF/AFib -> 1; Sinus rhythm (SR) -> 0; otherwise None.
+    """
+    val = _extract_value(x)
+    if val is None:
+        return None
+
+    # Positive for arrhythmia if 'atrial fibrillation' or 'afib' or ' af ' as a word
+    if ("atrial fibrillation" in val) or re.search(r"\bafib\b", val) or re.search(r"\baf\b", val):
+        return 1
+    # Negative if 'sinus rhythm' or ' sr ' as a word
+    if ("sinus rhythm" in val) or re.search(r"\bsr\b", val):
+        return 0
+
+    return None
+
+# Define but not used since rows are unavailable
+def convert_age(x):
+    return None
+
+def convert_gender(x):
+    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 (only if clinical data is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=None,
+        gender_row=gender_row,
+        convert_gender=None
+    )
+    # Preview and save
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 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])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify the appropriate columns for probe IDs and gene symbols based on the annotation preview
+probe_col = 'ID'
+gene_symbol_col = 'Gene Symbol'
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Optionally save the gene-level data for later steps
+import os
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Check bias and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata saving
+note = "INFO: Trait from disease state (AFib=1 vs SR=0); Age and Gender unavailable in sample characteristics."
+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=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Arrhythmia/code/GSE136992.py

@@ -0,0 +1,123 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Arrhythmia"
+cohort = "GSE136992"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Arrhythmia"
+in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE136992"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Arrhythmia/GSE136992.csv"
+out_gene_data_file = "./output/z1/preprocess/Arrhythmia/gene_data/GSE136992.csv"
+out_clinical_data_file = "./output/z1/preprocess/Arrhythmia/clinical_data/GSE136992.csv"
+json_path = "./output/z1/preprocess/Arrhythmia/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+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 2: Dataset Analysis and Clinical Feature Extraction
+import re
+import math
+
+# 1) Gene expression data availability
+# Background indicates "mRNA expression ... Illumina whole genome gene expression DASL HT assay" -> gene data available.
+is_gene_available = True
+
+# 2) Variable availability and conversion functions
+
+# Trait (Arrhythmia): Not available in this dataset; "condition" is Infection vs Control and does not indicate arrhythmia.
+trait_row = None
+
+# Age: Available at row 2 (values like "age: 0.5 weeks", etc.)
+age_row = 2
+
+# Gender: Available at row 3 ("gender: male/female")
+gender_row = 3
+
+def convert_trait(x):
+    # Trait not available; return None for any input
+    return None
+
+def _extract_value_after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    if ':' in s:
+        return s.split(':', 1)[1].strip()
+    return s.strip()
+
+def convert_age(x):
+    """
+    Convert age string like 'age: 12 weeks' into a float number of weeks.
+    Unknown or unparsable values -> None.
+    """
+    val = _extract_value_after_colon(x)
+    if val is None:
+        return None
+    v = val.lower().strip()
+    # Accept numbers possibly with unit; default unit weeks if not specified
+    # Handle common units
+    m = re.match(r'^([0-9]*\.?[0-9]+)\s*(week|weeks|wk|wks|day|days|d|month|months|mo|year|years|yr|yrs)?$', v)
+    if not m:
+        return None
+    num = float(m.group(1))
+    unit = m.group(2) if m.group(2) else 'weeks'
+    unit = unit.lower()
+    # Convert all to weeks
+    if unit in ['week', 'weeks', 'wk', 'wks']:
+        weeks = num
+    elif unit in ['day', 'days', 'd']:
+        weeks = num / 7.0
+    elif unit in ['month', 'months', 'mo']:
+        weeks = num * (365.25 / 12.0) / 7.0
+    elif unit in ['year', 'years', 'yr', 'yrs']:
+        weeks = num * 52.17857  # approx
+    else:
+        weeks = num  # default to weeks
+    return weeks
+
+def convert_gender(x):
+    """
+    Convert gender to binary: female -> 0, male -> 1; unknown -> None.
+    """
+    val = _extract_value_after_colon(x)
+    if val is None:
+        return None
+    g = val.strip().lower()
+    if g in ['female', 'f']:
+        return 0
+    if g in ['male', 'm']:
+        return 1
+    return None
+
+# 3) Save metadata with 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: skipped because trait_row is None

output/preprocess/Arrhythmia/code/GSE143924.py

@@ -0,0 +1,188 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Arrhythmia"
+cohort = "GSE143924"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Arrhythmia"
+in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE143924"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Arrhythmia/GSE143924.csv"
+out_gene_data_file = "./output/z1/preprocess/Arrhythmia/gene_data/GSE143924.csv"
+out_clinical_data_file = "./output/z1/preprocess/Arrhythmia/clinical_data/GSE143924.csv"
+json_path = "./output/z1/preprocess/Arrhythmia/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+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 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1. Gene expression availability
+is_gene_available = True  # Transcriptome (gene expression) analysis is explicitly stated in background.
+
+# 2. Variable availability keys inferred from Sample Characteristics Dictionary
+trait_row = 1       # patient diagnosis: sinus rhythm after surgery vs postoperative atrial fibrillation (POAF)
+age_row = None      # Not present in provided characteristics
+gender_row = None   # Not present in provided characteristics
+
+# 2.2 Conversion functions
+def _after_colon(value):
+    if value is None:
+        return None
+    s = str(value)
+    parts = s.split(":", 1)
+    s = parts[1] if len(parts) > 1 else parts[0]
+    return s.strip()
+
+def convert_trait(value):
+    """
+    Map arrhythmia status (AF/POAF) to binary:
+    - 1: any mention of atrial fibrillation, fibrillation, AF, or POAF
+    - 0: sinus rhythm / non-POAF / no AF
+    """
+    v = _after_colon(value)
+    if v is None or v == "":
+        return None
+    s = v.lower()
+
+    # Positive (arrhythmia present)
+    if any(k in s for k in ["atrial fibrillation", "fibrillation", "poaf", "af "]):
+        return 1
+    # Negative (no arrhythmia)
+    if "sinus rhythm" in s or "non-poaf" in s or "no atrial fibrillation" in s or s == "sr":
+        return 0
+
+    # Heuristic: 'poaf' abbreviation or 'af' at end/beginning
+    if re.search(r"\b(poaf|af)\b", s):
+        return 1
+
+    return None
+
+def convert_age(value):
+    """
+    Convert age text to continuous float (years). Extract first number found.
+    """
+    v = _after_colon(value)
+    if v is None or v == "":
+        return None
+    m = re.search(r"(\d+(\.\d+)?)", v)
+    if not m:
+        return None
+    try:
+        return float(m.group(1))
+    except Exception:
+        return None
+
+def convert_gender(value):
+    """
+    Convert gender to binary: female->0, male->1.
+    """
+    v = _after_colon(value)
+    if v is None or v == "":
+        return None
+    s = v.strip().lower()
+    if s in ["male", "m"]:
+        return 1
+    if s in ["female", "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 (only if trait is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+
+    # Preview and save
+    preview = preview_df(selected_clinical_df)
+    print("Preview of selected clinical features:", preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 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])
+
+# Step 4: Gene Identifier Review
+# Based on inspection of identifiers like "AACS", "AADAC", "ABCA1", "ABCB1", which are HGNC human gene symbols,
+# mapping is not required.
+print("requires_gene_mapping = False")
+
+# Step 5: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias evaluation (trait required; drop biased demographics)
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+note = "INFO: Balanced POAF vs SR (15/15); no age/gender available."
+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=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Arrhythmia/code/GSE182600.py

@@ -0,0 +1,195 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Arrhythmia"
+cohort = "GSE182600"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Arrhythmia"
+in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE182600"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Arrhythmia/GSE182600.csv"
+out_gene_data_file = "./output/z1/preprocess/Arrhythmia/gene_data/GSE182600.csv"
+out_clinical_data_file = "./output/z1/preprocess/Arrhythmia/clinical_data/GSE182600.csv"
+json_path = "./output/z1/preprocess/Arrhythmia/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+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 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import math
+import pandas as pd
+
+# 1) Gene expression data availability (based on background description)
+is_gene_available = True  # Transcriptomic profiling / genome-wide gene expression
+
+# 2) Variable availability based on the provided sample characteristics dictionary
+trait_row = 0    # 'disease state: ... includes Arrhythmia'
+age_row = 1      # 'age: <float>'
+gender_row = 2   # 'gender: M/F'
+
+# 2.2) Conversion helpers
+def _after_colon(x):
+    if x is None or (isinstance(x, float) and math.isnan(x)):
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip()
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    v_low = v.lower()
+    # 1 for Arrhythmia, 0 for other disease states
+    if 'arrhythmia' == v_low:
+        return 1
+    # If clearly another disease state, map to 0
+    other_states = [
+        'acute myocardial infarction', 'acute myocarditis', 'congestive heart failure',
+        'dilated cardiomyopathy', 'dilated cardiomyopathy, dcmp', 'aortic dissection'
+    ]
+    if v_low in other_states:
+        return 0
+    # Default: if it contains arrhythmia substring
+    if 'arrhythmia' in v_low:
+        return 1
+    return 0  # Treat other known disease states as 0
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    try:
+        age = float(v)
+        if 0 < age < 120:
+            return age
+        return None
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    v_low = v.strip().lower()
+    if v_low in ['m', 'male', 'man']:
+        return 1
+    if v_low in ['f', 'female', 'woman']:
+        return 0
+    return None
+
+# 3) Save initial metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction, preview, and save
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 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])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify the appropriate columns in the annotation for probe IDs and gene symbols
+probe_id_col = 'ID'       # Matches probe identifiers like ILMN_1343291
+gene_symbol_col = 'Symbol'  # Contains gene symbols
+
+# 2) Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_id_col, gene_col=gene_symbol_col)
+
+# 3) Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias check and remove biased demographic features if necessary
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+is_gene_available_flag = (normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0)
+is_trait_available_flag = True  # Clinical trait was extracted previously
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_flag,
+    is_trait_available=is_trait_available_flag,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=""
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Arrhythmia/code/GSE235307.py

@@ -0,0 +1,236 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Arrhythmia"
+cohort = "GSE235307"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Arrhythmia"
+in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE235307"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Arrhythmia/GSE235307.csv"
+out_gene_data_file = "./output/z1/preprocess/Arrhythmia/gene_data/GSE235307.csv"
+out_clinical_data_file = "./output/z1/preprocess/Arrhythmia/clinical_data/GSE235307.csv"
+json_path = "./output/z1/preprocess/Arrhythmia/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+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 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1) Gene expression availability based on background info
+is_gene_available = True  # Series title indicates gene expression profiling (not miRNA/methylation)
+
+# 2) Variable availability (from the provided Sample Characteristics Dictionary)
+trait_row = 5        # 'cardiac rhythm after 1 year follow-up: ...'
+age_row = 2          # 'age: ...'
+gender_row = 1       # 'gender: ...'
+
+# 2.2) Conversion utilities
+def _after_colon(x):
+    if x is None or (isinstance(x, float) and pd.isna(x)):
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip()
+
+def convert_trait(x):
+    """
+    Binary:
+      - 1: Atrial fibrillation (AF)
+      - 0: Sinus rhythm
+      - None: unknown/other
+    """
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.strip().lower()
+    if 'atrial fibrillation' in vl or 'a-fib' in vl or (('atrial' in vl) and ('fibrillation' in vl)) or vl == 'af':
+        return 1
+    if 'sinus' in vl and 'rhythm' in vl:
+        return 0
+    if vl == 'sr':
+        return 0
+    return None
+
+def convert_age(x):
+    """
+    Continuous age in years. Extract first numeric token; return float if valid (0 < age <= 120), else None.
+    """
+    v = _after_colon(x)
+    if v is None:
+        return None
+    m = re.search(r'(\d+(\.\d+)?)', v)
+    if not m:
+        return None
+    try:
+        age_val = float(m.group(1))
+    except Exception:
+        return None
+    if 0 < age_val <= 120:
+        return age_val
+    return None
+
+def convert_gender(x):
+    """
+    Binary gender:
+      - 1: Male
+      - 0: Female
+      - None: unknown/other
+    """
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.strip().lower()
+    if vl in ['male', 'm', 'man']:
+        return 1
+    if vl in ['female', 'f', 'woman', 'women']:
+        return 0
+    return None
+
+# 3) Save metadata via 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 (only if clinical data is available)
+if is_trait_available:
+    if 'clinical_data' in locals():
+        selected_clinical_df = geo_select_clinical_features(
+            clinical_df=clinical_data,
+            trait=trait,
+            trait_row=trait_row,
+            convert_trait=convert_trait,
+            age_row=age_row,
+            convert_age=convert_age,
+            gender_row=gender_row,
+            convert_gender=convert_gender
+        )
+        clinical_preview = preview_df(selected_clinical_df)
+        print("Clinical preview:", clinical_preview)
+
+        os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+        selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+    else:
+        print("WARNING: 'clinical_data' not found in environment. Skipping clinical feature extraction.")
+
+# Step 3: Gene Data Extraction
+# 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])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Determine appropriate identifier columns based on overlap with expression data indices
+id_candidates = [col for col in ['ID', 'NAME', 'SPOT_ID'] if col in gene_annotation.columns]
+expr_ids = set(gene_data.index.astype(str))
+
+best_id_col = None
+best_overlap = -1
+for col in id_candidates:
+    ann_ids = set(gene_annotation[col].astype(str))
+    overlap = len(expr_ids & ann_ids)
+    if overlap > best_overlap:
+        best_overlap = overlap
+        best_id_col = col
+
+# Fallback to 'ID' if nothing better is found (shouldn't happen given preview)
+if best_id_col is None:
+    best_id_col = 'ID'
+
+# Gene symbol column
+gene_symbol_col = 'GENE_SYMBOL'
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=best_id_col, gene_col=gene_symbol_col)
+
+# Apply mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize gene symbols and save gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+# Use the in-memory clinical dataframe if available; otherwise reload from disk and restore row labels.
+if 'selected_clinical_df' not in locals():
+    if os.path.exists(out_clinical_data_file):
+        tmp = pd.read_csv(out_clinical_data_file)
+        # The saved file had index=False, so restore expected row index if shapes match
+        if tmp.shape[0] == 3:
+            tmp.index = [trait, 'Age', 'Gender']
+        selected_clinical_df = tmp
+    else:
+        raise RuntimeError("Clinical data not found in memory or on disk.")
+
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Assess bias and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort metadata
+note = f"INFO: Gene symbols normalized using NCBI synonyms. Linked {normalized_gene_data.shape[1]} samples and {normalized_gene_data.shape[0]} genes before QC."
+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=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Arrhythmia/code/GSE41177.py

@@ -0,0 +1,182 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Arrhythmia"
+cohort = "GSE41177"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Arrhythmia"
+in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE41177"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Arrhythmia/GSE41177.csv"
+out_gene_data_file = "./output/z1/preprocess/Arrhythmia/gene_data/GSE41177.csv"
+out_clinical_data_file = "./output/z1/preprocess/Arrhythmia/clinical_data/GSE41177.csv"
+json_path = "./output/z1/preprocess/Arrhythmia/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+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 2: Dataset Analysis and Clinical Feature Extraction
+import re
+import pandas as pd
+
+# 1) Gene expression availability
+is_gene_available = True  # Microarray gene expression per background info
+
+# 2) Variable availability and converters
+# Sample Characteristics Dictionary indices:
+# 0: organ, 1: gender, 2: age, 3: af duration (months)
+trait_row = 3        # Use AF duration as a continuous arrhythmia-related trait (months)
+age_row = 2
+gender_row = 1
+
+def _after_colon(value: str) -> str:
+    if value is None or (isinstance(value, float) and pd.isna(value)):
+        return ""
+    s = str(value)
+    parts = s.split(":", 1)
+    return parts[1].strip() if len(parts) > 1 else s.strip()
+
+def convert_trait(value):
+    # Convert AF duration to months (continuous). Examples: '>1M', '0M', '10M', '110M'
+    s = _after_colon(value)
+    if not s:
+        return None
+    s = s.strip()
+    # Match optional comparator and a number followed by 'M' or 'm'
+    m = re.search(r'([<>]=?)?\s*([0-9]+)\s*[mM]\b', s)
+    if m:
+        sign = m.group(1) or ""
+        num = int(m.group(2))
+        # Use numeric bound; for '>1M' map to 1 (lower bound) to keep it numeric
+        return float(num)
+    # Fallback: any digits interpreted as months
+    m2 = re.search(r'([0-9]+)', s)
+    if m2:
+        return float(m2.group(1))
+    return None
+
+def convert_age(value):
+    # Convert 'age: 43Y' to 43 (years, continuous)
+    s = _after_colon(value)
+    if not s:
+        return None
+    s = s.strip()
+    m = re.search(r'([0-9]+)\s*[yY]?\b', s)
+    if m:
+        return float(m.group(1))
+    return None
+
+def convert_gender(value):
+    # Binary: female -> 0, male -> 1
+    s = _after_colon(value).lower()
+    if not s:
+        return None
+    if "female" in s:
+        return 0
+    if "male" in s:
+        return 1
+    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 (only if trait data is available)
+if is_trait_available:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+
+# Step 3: Gene Data Extraction
+# 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])
+
+# Step 4: Gene Identifier Review
+# Affymetrix probe set IDs like "1007_s_at", "1053_at" indicate non-gene-symbol identifiers.
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify the columns for probe IDs and gene symbols from the annotation preview:
+# Probe IDs: 'ID' (e.g., '1007_s_at'); Gene symbols: 'Gene Symbol'
+
+# 1-2) Build mapping dataframe from annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# 3) Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Assess bias and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+note_text = "INFO: Trait is AF duration (months); paired tissue samples (LA-PV junction vs LAA)."
+is_usable = validate_and_save_cohort_info(
+    True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data, note=note_text
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Arrhythmia/code/GSE47727.py

@@ -0,0 +1,139 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Arrhythmia"
+cohort = "GSE47727"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Arrhythmia"
+in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE47727"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Arrhythmia/GSE47727.csv"
+out_gene_data_file = "./output/z1/preprocess/Arrhythmia/gene_data/GSE47727.csv"
+out_clinical_data_file = "./output/z1/preprocess/Arrhythmia/clinical_data/GSE47727.csv"
+json_path = "./output/z1/preprocess/Arrhythmia/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+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 2: Dataset Analysis and Clinical Feature Extraction
+import re
+
+# 1) Gene expression data availability
+# Platform: Illumina HumanHT-12 v3.0 gene expression microarray -> gene data available
+is_gene_available = True
+
+# 2) Variable availability and conversion functions
+
+# Trait (Arrhythmia) availability:
+# Sample characteristics only include age, gender, and tissue; no disease status.
+# Background indicates "control participants" only -> trait not variable (constant/absent).
+trait_row = None  # Not available
+
+# Age availability
+age_row = 0  # 'age (yrs): ...'
+
+# Gender availability
+gender_row = 1  # 'gender: female' / 'gender: male'
+
+# Conversion functions
+def convert_trait(x):
+    # Generic mapper for Arrhythmia/AF presence; not used here since trait_row is None.
+    if x is None:
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    v = s.strip().lower()
+    # Positive mappings
+    pos_terms = {
+        'arrhythmia', 'atrial fibrillation', 'af', 'yes', 'present', 'case', '1', 'true', 'y'
+    }
+    neg_terms = {
+        'no arrhythmia', 'no af', 'none', 'no', 'absent', 'control', '0', 'false', 'n', 'healthy', 'normal'
+    }
+    if v in pos_terms:
+        return 1
+    if v in neg_terms:
+        return 0
+    # Heuristics for strings containing keywords
+    if any(k in v for k in ['atrial fibrillation', 'af', 'arrhythmia']):
+        # If explicitly negated, map to 0
+        if any(k in v for k in ['no ', 'absent', 'without', 'free of']):
+            return 0
+        return 1
+    return None
+
+def convert_age(x):
+    if x is None:
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    s = s.strip()
+    m = re.search(r'(\d+(\.\d+)?)', s)
+    if not m:
+        return None
+    try:
+        return float(m.group(1))
+    except Exception:
+        return None
+
+def convert_gender(x):
+    if x is None:
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    v = s.strip().lower()
+    if v in ['female', 'f', '0']:
+        return 0
+    if v in ['male', 'm', '1']:
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (skip because trait_row is None)
+# If in future a trait field is identified, uncomment the following block:
+# if trait_row is not None:
+#     selected_df = geo_select_clinical_features(
+#         clinical_df=clinical_data,
+#         trait=trait,
+#         trait_row=trait_row,
+#         convert_trait=convert_trait,
+#         age_row=age_row,
+#         convert_age=convert_age,
+#         gender_row=gender_row,
+#         convert_gender=convert_gender
+#     )
+#     preview = preview_df(selected_df, n=5)
+#     os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+#     selected_df.to_csv(out_clinical_data_file)

output/preprocess/Arrhythmia/code/GSE53622.py

@@ -0,0 +1,309 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Arrhythmia"
+cohort = "GSE53622"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Arrhythmia"
+in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE53622"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Arrhythmia/GSE53622.csv"
+out_gene_data_file = "./output/z1/preprocess/Arrhythmia/gene_data/GSE53622.csv"
+out_clinical_data_file = "./output/z1/preprocess/Arrhythmia/clinical_data/GSE53622.csv"
+json_path = "./output/z1/preprocess/Arrhythmia/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+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 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import pandas as pd
+
+# 1) Assess gene expression availability (lncRNA microarray is acceptable as gene expression)
+is_gene_available = True
+
+# 2) Variable availability
+trait_row = 10  # 'arrhythmia: yes/no'
+age_row = 1     # 'age: float'
+gender_row = 2  # 'Sex: male/female'
+
+# 2) Conversion functions
+def _after_colon(x):
+    if x is None or (isinstance(x, float) and pd.isna(x)):
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    return val.strip()
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v_l = v.strip().lower()
+    if v_l in {"yes", "y", "1", "true", "present"}:
+        return 1
+    if v_l in {"no", "n", "0", "false", "absent"}:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    try:
+        age = float(v)
+        # basic sanity check for human age
+        if 0 <= age <= 120:
+            return age
+        return None
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v_l = v.strip().lower()
+    if v_l in {"female", "f", "woman", "women"}:
+        return 0
+    if v_l in {"male", "m", "man", "men"}:
+        return 1
+    return None
+
+# 3) Save initial metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if clinical trait is available)
+if is_trait_available:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Observe preview
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+
+    # Save clinical data
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 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])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Step 6 (revised): Map probe IDs to human gene symbols using platform (GPL) annotation
+
+import os
+import pandas as pd
+
+def try_mapping_with_annotation(ann_df: pd.DataFrame, expr_df: pd.DataFrame):
+    # Identify the probe ID column with maximum overlap to expression index
+    id_overlaps = []
+    for col in ann_df.columns:
+        try:
+            overlap = ann_df[col].astype(str).str.strip().isin(expr_df.index).sum()
+            id_overlaps.append((col, int(overlap)))
+        except Exception:
+            continue
+    if not id_overlaps:
+        return None, None, None
+    id_overlaps.sort(key=lambda x: x[1], reverse=True)
+    prob_col, prob_overlap = id_overlaps[0]
+    if prob_overlap == 0:
+        return None, None, None
+
+    # Identify gene symbol column by content-based detection (max extractable human gene symbols)
+    gene_col = None
+    max_nonempty = -1
+    for col in ann_df.columns:
+        if col == prob_col:
+            continue
+        try:
+            extracted = ann_df[col].astype(str).map(extract_human_gene_symbols)
+            nonempty = extracted.map(lambda x: len(x) if isinstance(x, list) else 0).gt(0).sum()
+            if nonempty > max_nonempty:
+                max_nonempty = nonempty
+                gene_col = col
+        except Exception:
+            continue
+
+    if gene_col is None or max_nonempty <= 0:
+        return None, None, None
+
+    # Build mapping and apply
+    mapping_df = get_gene_mapping(ann_df, prob_col=prob_col, gene_col=gene_col)
+    mapping_df = mapping_df[mapping_df['ID'].isin(expr_df.index)]
+    if len(mapping_df) == 0:
+        return None, None, None
+
+    gene_level_df = apply_gene_mapping(expression_df=expr_df, mapping_df=mapping_df)
+    return gene_level_df, prob_col, gene_col
+
+
+# Find candidate GPL SOFT files (prefer within cohort dir, then trait dir)
+candidate_softs = []
+for search_dir in [in_cohort_dir, in_trait_dir]:
+    if os.path.isdir(search_dir):
+        for fname in os.listdir(search_dir):
+            low = fname.lower()
+            if ('gpl' in low) and ('soft' in low):
+                candidate_softs.append(os.path.join(search_dir, fname))
+
+# Ensure uniqueness and stable order
+candidate_softs = list(dict.fromkeys(candidate_softs))
+
+mapping_done = False
+used_gpl = None
+used_prob_col = None
+used_gene_col = None
+
+# Try GPL annotations first
+for gpl_path in candidate_softs:
+    try:
+        platform_annotation = get_gene_annotation(gpl_path)
+        result = try_mapping_with_annotation(platform_annotation, gene_data)
+        if result[0] is not None:
+            gene_data, used_prob_col, used_gene_col = result
+            used_gpl = gpl_path
+            mapping_done = True
+            print(f"Mapping succeeded with GPL annotation: {os.path.basename(gpl_path)}")
+            print(f"Probe ID column: {used_prob_col} | Gene symbol column: {used_gene_col}")
+            print(f"Gene-level matrix shape: {gene_data.shape}")
+            break
+    except Exception as e:
+        # Try next GPL if this one fails to parse properly
+        print(f"Warning: Failed to use {gpl_path} due to error: {e}")
+
+# Fallback: attempt with the previously loaded SOFT (may be series-level; often insufficient)
+if not mapping_done:
+    try:
+        ann_series = gene_annotation.copy()
+        result = try_mapping_with_annotation(ann_series, gene_data)
+        if result[0] is not None:
+            gene_data, used_prob_col, used_gene_col = result
+            mapping_done = True
+            print("Mapping succeeded using the previously loaded SOFT annotation (non-GPL).")
+            print(f"Probe ID column: {used_prob_col} | Gene symbol column: {used_gene_col}")
+            print(f"Gene-level matrix shape: {gene_data.shape}")
+    except Exception as e:
+        print(f"Warning: Fallback mapping with series SOFT failed due to error: {e}")
+
+if not mapping_done:
+    raise RuntimeError("Probe-to-gene mapping failed: no suitable GPL annotation found or no valid mapping could be derived.")
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save gene matrix
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data on sample IDs
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+is_gene_available_final = (normalized_gene_data.shape[0] > 0)
+if is_gene_available_final:
+    processed_linked = handle_missing_values(linked_data, trait)
+else:
+    # Avoid dropping all samples when there are no gene columns
+    df = linked_data.copy()
+    # Drop samples with missing trait
+    df = df.dropna(subset=[trait])
+    # Impute Age with mean if present
+    if 'Age' in df.columns:
+        df['Age'] = df['Age'].astype(float)
+        df['Age'] = df['Age'].fillna(df['Age'].mean())
+    # Impute Gender with mode if present
+    if 'Gender' in df.columns:
+        mode_result = df['Gender'].mode()
+        if len(mode_result) > 0:
+            df['Gender'] = df['Gender'].fillna(mode_result[0])
+        else:
+            df = df.drop(columns=['Gender'])
+    processed_linked = df
+
+# 4. Bias check and drop biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(processed_linked, trait)
+
+# 5. Final validation and save cohort info
+is_trait_available_final = (trait in selected_clinical_df.index) and (not selected_clinical_df.loc[trait].isna().all())
+
+note = ""
+if not is_gene_available_final:
+    used_gene_col_str = globals().get("used_gene_col", None)
+    used_prob_col_str = globals().get("used_prob_col", None)
+    used_gpl_str = os.path.basename(globals().get("used_gpl", "")) if globals().get("used_gpl", None) else "non-GPL SOFT"
+    details = []
+    if used_prob_col_str:
+        details.append(f"probe column '{used_prob_col_str}'")
+    if used_gene_col_str:
+        details.append(f"gene column '{used_gene_col_str}'")
+    detail_str = ", ".join(details) if details else "unknown columns"
+    note = f"WARNING: Gene mapping likely failed (gene matrix empty after normalization). Previous mapping used {detail_str} from {used_gpl_str}."
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available_final,
+    is_trait_available=is_trait_available_final,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Arrhythmia/code/GSE55231.py

@@ -0,0 +1,219 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Arrhythmia"
+cohort = "GSE55231"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Arrhythmia"
+in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE55231"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Arrhythmia/GSE55231.csv"
+out_gene_data_file = "./output/z1/preprocess/Arrhythmia/gene_data/GSE55231.csv"
+out_clinical_data_file = "./output/z1/preprocess/Arrhythmia/clinical_data/GSE55231.csv"
+json_path = "./output/z1/preprocess/Arrhythmia/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+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 2: Dataset Analysis and Clinical Feature Extraction
+import re
+
+# 1) Gene expression data availability
+is_gene_available = True  # Illumina Human HT12 v4 expression profiling (mRNA), not miRNA/methylation
+
+# 2) Variable availability and data type conversion
+
+# Availability (from Sample Characteristics Dictionary)
+trait_row = None  # No arrhythmia-related phenotype available; donors are non-diseased, no case/control info
+age_row = 2       # 'age: <number>'
+gender_row = 0    # 'gender: male/female'
+
+# Converters
+def _after_colon(value):
+    if value is None:
+        return None
+    parts = str(value).split(":", 1)
+    return parts[1].strip() if len(parts) > 1 else str(value).strip()
+
+def convert_trait(x):
+    """
+    Binary: 1 = arrhythmia present, 0 = no arrhythmia.
+    Heuristics to map a wide range of arrhythmia-related terms if present.
+    Unknown/irrelevant values -> None.
+    """
+    v = _after_colon(x)
+    if v is None or v == "":
+        return None
+    s = v.lower()
+
+    # Explicit negatives
+    neg_markers = ["no arrhythmia", "arrhythmia: no", "absence of arrhythmia", "no history of arrhythmia",
+                   "control", "healthy", "non-diseased", "normal"]
+    if any(m in s for m in neg_markers):
+        return 0
+
+    # Explicit positives
+    pos_terms = [
+        "arrhythmia", "atrial fibrillation", "af", "atrial flutter", "ventricular tachycardia", "vt",
+        "ventricular fibrillation", "vf", "svt", "supraventricular tachycardia", "long qt", "brugada",
+        "wpw", "wolff-parkinson-white"
+    ]
+    if any(term in s for term in pos_terms):
+        # If explicitly says 'no', treat as negative (safeguard)
+        if "no " in s or "absent" in s:
+            return 0
+        return 1
+
+    # Generic yes/no patterns
+    if re.search(r"\byes\b|\bpresent\b|\bcase\b|\bpatient\b", s):
+        return 1
+    if re.search(r"\bno\b|\babsent\b|\bcontrol\b|\bhealthy\b|\bnon-diseased\b|\bnormal\b", s):
+        return 0
+
+    return None
+
+def convert_age(x):
+    """
+    Continuous age in years (float). Extracts first numeric token after colon.
+    """
+    v = _after_colon(x)
+    if v is None or v == "":
+        return None
+    # Find first number (integer or float)
+    m = re.search(r"[-+]?\d*\.?\d+", v)
+    if not m:
+        return None
+    try:
+        return float(m.group())
+    except Exception:
+        return None
+
+def convert_gender(x):
+    """
+    Binary: female = 0, male = 1
+    """
+    v = _after_colon(x)
+    if v is None or v == "":
+        return None
+    s = v.strip().lower()
+    if s in ["female", "f", "woman", "girl"]:
+        return 0
+    if s in ["male", "m", "man", "boy"]:
+        return 1
+    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
+# Skipped because trait_row is None (no clinical trait data available for Arrhythmia in this cohort)
+
+# Step 3: Gene Data Extraction
+# 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])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify the appropriate columns for mapping: probe IDs and gene symbols
+probe_id_col = 'ID'
+gene_symbol_col = 'Symbol'
+
+# Create the mapping dataframe from the annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_id_col, gene_col=gene_symbol_col)
+
+# Apply the mapping to convert probe-level data to gene-level data
+probe_data = gene_data  # backup the probe-level data
+gene_data = apply_gene_mapping(probe_data, mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize gene symbols, drop unrecognized, aggregate duplicates, and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2-6. Proceed with linking and downstream steps only if clinical data (selected_clinical_data) exists
+if 'selected_clinical_data' in globals() and isinstance(selected_clinical_data, pd.DataFrame):
+    # 2. Link clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Bias evaluation and removal of biased covariates
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final validation and save cohort info
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note="INFO: Trait and covariates processed; gene symbols normalized."
+    )
+
+    # 6. Save linked data only if usable
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        unbiased_linked_data.to_csv(out_data_file)
+else:
+    # No clinical trait data available; skip linking and downstream steps
+    linked_data = None
+    # Use a non-empty placeholder (samples x genes) to avoid false 'abnormality' override
+    placeholder_df = normalized_gene_data.T
+    _ = 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=placeholder_df,
+        note="INFO: No trait data available; only gene-level expression normalized and saved."
+    )

output/preprocess/Arrhythmia/code/GSE93101.py

@@ -0,0 +1,204 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Arrhythmia"
+cohort = "GSE93101"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Arrhythmia"
+in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE93101"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Arrhythmia/GSE93101.csv"
+out_gene_data_file = "./output/z1/preprocess/Arrhythmia/gene_data/GSE93101.csv"
+out_clinical_data_file = "./output/z1/preprocess/Arrhythmia/clinical_data/GSE93101.csv"
+json_path = "./output/z1/preprocess/Arrhythmia/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+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 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import math
+
+# 1) Gene expression availability
+is_gene_available = True  # Transcriptome data per background info
+
+# 2) Variable availability (rows identified from the provided sample characteristics)
+trait_row = 0     # 'course' field indicates underlying condition; includes 'Arrhythmia'
+age_row = 1       # 'age'
+gender_row = 2    # 'gender'
+
+# 2.2) Conversion functions
+def _extract_value(x):
+    if x is None:
+        return None
+    s = str(x).strip()
+    if ':' in s:
+        s = s.split(':', 1)[1].strip()
+    return s if s != '' else None
+
+def convert_trait(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    if 'arrhythmia' in vl:
+        return 1
+    # Known non-arrhythmia etiologies -> negative class
+    if any(k in vl for k in ['myocardial infarction', 'myocarditis', 'dilated cardiomyopathy', 'congestive heart failure', 'aortic dissection', 'dcmp']):
+        return 0
+    # Default: if it's a non-empty course but not arrhythmia, treat as 0
+    return 0
+
+def convert_age(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    try:
+        val = float(v)
+        # Basic plausibility filter
+        if 0 <= val <= 120:
+            return val
+        return None
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    if vl in ['f', 'female', 'woman', 'girl']:
+        return 0
+    if vl in ['m', 'male', 'man', 'boy']:
+        return 1
+    return None
+
+# 3) Initial filtering metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if trait is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 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])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify the appropriate columns for probe IDs and gene symbols in the annotation data
+id_col = 'ID'        # Matches probe identifiers like ILMN_#######
+symbol_col = 'Symbol' # Gene symbols
+
+# Optional: filter out control probes if 'Species' column is present
+anno_for_map = gene_annotation
+if 'Species' in anno_for_map.columns:
+    anno_for_map = anno_for_map[anno_for_map['Species'].astype(str).str.lower() != 'ilmn controls'.lower()]
+
+# Build mapping dataframe (probe ID -> symbol)
+mapping_df = get_gene_mapping(anno_for_map, prob_col=id_col, gene_col=symbol_col)
+
+# Apply mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Assess bias and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Fallbacks for metadata flags if not present
+try:
+    is_gene_available
+except NameError:
+    is_gene_available = True if isinstance(normalized_gene_data, pd.DataFrame) and normalized_gene_data.shape[0] > 0 else False
+
+try:
+    is_trait_available
+except NameError:
+    is_trait_available = True if 'selected_clinical_df' in locals() else False
+
+# Optional note about dataset
+note = "INFO: ECMO cardiogenic shock cohort; trait derived from 'course' field (Arrhythmia vs. other etiologies)."
+
+# 5. Final validation and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available,
+    is_biased=is_trait_biased,
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Arrhythmia/code/TCGA.py

@@ -0,0 +1,70 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Arrhythmia"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Arrhythmia/TCGA.csv"
+out_gene_data_file = "./output/z1/preprocess/Arrhythmia/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z1/preprocess/Arrhythmia/clinical_data/TCGA.csv"
+json_path = "./output/z1/preprocess/Arrhythmia/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Discover available TCGA subdirectories
+available_subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+# Attempt to find a cohort relevant to Arrhythmia (cardiac rhythm disorders)
+keywords_specific = [
+    'arrhythmia', 'atrial_fibrillation', 'brugada', 'long_qt', 'ventricular_tachycardia',
+    'supraventricular', 'cardiac_conduction', 'torsades', 'wolff', 'wolff-parkinson-white'
+]
+keywords_general = ['cardiac', 'cardio', 'heart', 'myocard']
+
+def find_best_cohort(subdirs, specific_kw, general_kw):
+    scored = []
+    for sd in subdirs:
+        sdl = sd.lower()
+        score = 0
+        if any(k in sdl for k in specific_kw):
+            score += 2
+        if any(k in sdl for k in general_kw):
+            score += 1
+        if score > 0:
+            scored.append((score, sd))
+    if not scored:
+        return None
+    scored.sort(reverse=True)  # highest score first
+    return scored[0][1]
+
+selected_subdir = find_best_cohort(available_subdirs, keywords_specific, keywords_general)
+
+if selected_subdir is None:
+    print(f"No suitable TCGA cohort directory found for trait '{trait}'. Skipping this trait.")
+    # Record metadata for skipping
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+else:
+    cohort_dir = os.path.join(tcga_root_dir, selected_subdir)
+    clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # Load files
+    clinical_df = pd.read_csv(clinical_path, sep='\t', index_col=0, low_memory=False)
+    genetic_df = pd.read_csv(genetic_path, sep='\t', index_col=0, low_memory=False)
+
+    # Print clinical column names for inspection
+    print(f"Selected cohort directory: {selected_subdir}")
+    print("Clinical data columns:")
+    print(list(clinical_df.columns))

output/preprocess/Arrhythmia/cohort_info.json

@@ -1,112 +1 @@
-{
-  "GSE93101": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": true,
-    "has_age": true,
-    "has_gender": true,
-    "sample_size": 33
-  },
-  "GSE55231": {
-    "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
-  },
-  "GSE53622": {
-    "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
-  },
-  "GSE47727": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": true,
-    "has_age": true,
-    "has_gender": true,
-    "sample_size": 122
-  },
-  "GSE41177": {
-    "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": 38
-  },
-  "GSE235307": {
-    "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": 119
-  },
-  "GSE182600": {
-    "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": 78
-  },
-  "GSE143924": {
-    "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": 30
-  },
-  "GSE136992": {
-    "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": 60
-  },
-  "GSE115574": {
-    "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": 59
-  },
-  "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": 553
-  }
-}
+{"GSE93101": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": true, "has_gender": true, "sample_size": 33, "note": "INFO: ECMO cardiogenic shock cohort; trait derived from 'course' field (Arrhythmia vs. other etiologies)."}, "GSE55231": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": "INFO: No trait data available; only gene-level expression normalized and saved."}, "GSE53622": {"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, "note": "WARNING: Gene mapping likely failed (gene matrix empty after normalization). Previous mapping used probe column 'ID', gene column 'Control Type' from non-GPL SOFT."}, "GSE47727": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE41177": {"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": 38, "note": "INFO: Trait is AF duration (months); paired tissue samples (LA-PV junction vs LAA)."}, "GSE235307": {"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": 119, "note": "INFO: Gene symbols normalized using NCBI synonyms. Linked 119 samples and 19847 genes before QC."}, "GSE182600": {"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": 78, "note": ""}, "GSE143924": {"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": 30, "note": "INFO: Balanced POAF vs SR (15/15); no age/gender available."}, "GSE136992": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "GSE115574": {"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": 59, "note": "INFO: Trait from disease state (AFib=1 vs SR=0); Age and Gender unavailable in sample characteristics."}, "TCGA": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}

output/preprocess/Arrhythmia/gene_data/GSE53622.csv

@@ -1,2 +1 @@
 Gene,GSM1296956,GSM1296957,GSM1296958,GSM1296959,GSM1296960,GSM1296961,GSM1296962,GSM1296963,GSM1296964,GSM1296965,GSM1296966,GSM1296967,GSM1296968,GSM1296969,GSM1296970,GSM1296971,GSM1296972,GSM1296973,GSM1296974,GSM1296975,GSM1296976,GSM1296977,GSM1296978,GSM1296979,GSM1296980,GSM1296981,GSM1296982,GSM1296983,GSM1296984,GSM1296985,GSM1296986,GSM1296987,GSM1296988,GSM1296989,GSM1296990,GSM1296991,GSM1296992,GSM1296993,GSM1296994,GSM1296995,GSM1296996,GSM1296997,GSM1296998,GSM1296999,GSM1297000,GSM1297001,GSM1297002,GSM1297003,GSM1297004,GSM1297005,GSM1297006,GSM1297007,GSM1297008,GSM1297009,GSM1297010,GSM1297011,GSM1297012,GSM1297013,GSM1297014,GSM1297015,GSM1297016,GSM1297017,GSM1297018,GSM1297019,GSM1297020,GSM1297021,GSM1297022,GSM1297023,GSM1297024,GSM1297025,GSM1297026,GSM1297027,GSM1297028,GSM1297029,GSM1297030,GSM1297031,GSM1297032,GSM1297033,GSM1297034,GSM1297035,GSM1297036,GSM1297037,GSM1297038,GSM1297039,GSM1297040,GSM1297041,GSM1297042,GSM1297043,GSM1297044,GSM1297045,GSM1297046,GSM1297047,GSM1297048,GSM1297049,GSM1297050,GSM1297051,GSM1297052,GSM1297053,GSM1297054,GSM1297055,GSM1297056,GSM1297057,GSM1297058,GSM1297059,GSM1297060,GSM1297061,GSM1297062,GSM1297063,GSM1297064,GSM1297065,GSM1297066,GSM1297067,GSM1297068,GSM1297069,GSM1297070,GSM1297071,GSM1297072,GSM1297073,GSM1297074,GSM1297075
-CYP2D7,8.079096,8.139438,9.026626,8.791721,8.473965,8.342405,9.739359,7.9230347,8.583124,8.02764,8.788752,8.261488,9.436542,8.793202,8.7394085,8.246852,8.832868,8.059782,9.033502,8.699034,8.6981735,8.912555,9.671825,9.04456,8.546082,8.485996,8.531118,7.918057,8.615377,8.59308,8.368535,8.339417,8.049576,8.49325,8.829485,7.7764406,9.171941,8.380742,8.910029,8.761705,9.288424,8.152321,8.996962,8.421651,9.306223,8.427002,9.186377,9.067205,9.29377,7.674825,8.886724,8.288611,8.391205,8.328333,9.4850645,8.638261,8.818429,8.208845,8.661726,8.337332,8.728982,9.014844,9.993892,9.300717,8.882419,9.034479,9.13243,8.48914,10.105189,8.870117,9.026258,8.353404,9.018257,9.150945,8.951023,8.625228,9.165361,9.344513,9.973912,9.161152,10.080526,9.153526,8.81058,8.382329,9.047473,8.5763,8.87176,8.508767,8.644913,8.179255,8.425197,8.379519,9.144303,9.500169,9.029782,8.204135,8.694639,8.394274,9.329949,9.138085,8.912275,8.730211,9.467376,9.151735,9.105803,8.961231,9.132017,8.702591,8.70009,8.58842,9.246985,8.295102,8.890052,8.258354,8.630826,9.123106,9.033117,8.654027,8.588141,8.872706

output/preprocess/Asthma/clinical_data/GSE123086.csv

@@ -0,0 +1,4 @@
+,GSM3494884,GSM3494885,GSM3494886,GSM3494887,GSM3494888,GSM3494889,GSM3494890,GSM3494891,GSM3494892,GSM3494893,GSM3494894,GSM3494895,GSM3494896,GSM3494897,GSM3494898,GSM3494899,GSM3494900,GSM3494901,GSM3494902,GSM3494903,GSM3494904,GSM3494905,GSM3494906,GSM3494907,GSM3494908,GSM3494909,GSM3494910,GSM3494911,GSM3494912,GSM3494913,GSM3494914,GSM3494915,GSM3494916,GSM3494917,GSM3494918,GSM3494919,GSM3494920,GSM3494921,GSM3494922,GSM3494923,GSM3494924,GSM3494925,GSM3494926,GSM3494927,GSM3494928,GSM3494929,GSM3494930,GSM3494931,GSM3494932,GSM3494933,GSM3494934,GSM3494935,GSM3494936,GSM3494937,GSM3494938,GSM3494939,GSM3494940,GSM3494941,GSM3494942,GSM3494943,GSM3494944,GSM3494945,GSM3494946,GSM3494947,GSM3494948,GSM3494949,GSM3494950,GSM3494951,GSM3494952,GSM3494953,GSM3494954,GSM3494955,GSM3494956,GSM3494957,GSM3494958,GSM3494959,GSM3494960,GSM3494961,GSM3494962,GSM3494963,GSM3494964,GSM3494965,GSM3494966,GSM3494967,GSM3494968,GSM3494969,GSM3494970,GSM3494971,GSM3494972,GSM3494973,GSM3494974,GSM3494975,GSM3494976,GSM3494977,GSM3494978,GSM3494979,GSM3494980,GSM3494981,GSM3494982,GSM3494983,GSM3494984,GSM3494985,GSM3494986,GSM3494987,GSM3494988,GSM3494989,GSM3494990,GSM3494991,GSM3494992,GSM3494993,GSM3494994,GSM3494995,GSM3494996,GSM3494997,GSM3494998,GSM3494999,GSM3495000,GSM3495001,GSM3495002,GSM3495003,GSM3495004,GSM3495005,GSM3495006,GSM3495007,GSM3495008,GSM3495009,GSM3495010,GSM3495011,GSM3495012,GSM3495013,GSM3495014,GSM3495015,GSM3495016,GSM3495017,GSM3495018,GSM3495019,GSM3495020,GSM3495021,GSM3495022,GSM3495023,GSM3495024,GSM3495025,GSM3495026,GSM3495027,GSM3495028,GSM3495029,GSM3495030,GSM3495031,GSM3495032,GSM3495033,GSM3495034,GSM3495035,GSM3495036,GSM3495037,GSM3495038,GSM3495039,GSM3495040,GSM3495041,GSM3495042,GSM3495043,GSM3495044,GSM3495045,GSM3495046,GSM3495047,GSM3495048,GSM3495049
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output/preprocess/Asthma/clinical_data/GSE123088.csv

@@ -1,4 +1,4 @@
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+Asthma,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,0.0,,,0.0,,0.0,,,0.0,,0.0,0.0,,,0.0,,,,,,0.0,0.0,0.0,,,,,,0.0,,,,,0.0,0.0
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output/preprocess/Asthma/clinical_data/GSE182797.csv

@@ -1,4 +1,3 @@
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-Asthma,0.0,0.0,0.0,1.0,1.0,,0.0,1.0,1.0,,1.0,0.0,1.0,1.0,1.0,,0.0,1.0,1.0,1.0,1.0,0.0,,1.0,1.0,,0.0,1.0,1.0,1.0,0.0,,0.0,1.0,1.0,1.0,1.0,1.0,1.0,,0.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,,0.0,0.0,,1.0,,1.0,0.0,1.0,1.0,1.0,,,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,,0.0,1.0,0.0,,0.0,1.0,1.0,1.0,0.0
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 Age,38.33,38.08,48.83,33.42,46.08,45.58,28.0,30.83,39.25,60.17,52.75,25.75,60.67,64.67,54.83,57.67,47.0,47.5,24.25,47.67,47.58,18.42,41.33,24.5,47.08,47.5,41.17,48.83,47.17,59.83,42.58,56.67,37.5,58.58,24.75,52.75,55.33,56.17,52.75,40.67,19.17,42.5,57.08,40.58,40.67,55.75,43.17,59.58,56.25,46.42,47.08,51.75,53.5,52.58,52.25,45.58,52.67,50.5,60.08,44.67,57.58,53.17,51.33,46.17,26.58,60.17,54.67,57.75,28.42,33.08,50.33,37.83,44.25,58.83,48.25,43.08,41.17,51.75,53.58,41.5
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output/preprocess/Asthma/clinical_data/GSE182798.csv

@@ -1,4 +1,3 @@
 ,GSM5530417,GSM5530418,GSM5530419,GSM5530420,GSM5530421,GSM5530422,GSM5530423,GSM5530424,GSM5530425,GSM5530426,GSM5530427,GSM5530428,GSM5530429,GSM5530430,GSM5530431,GSM5530432,GSM5530433,GSM5530434,GSM5530435,GSM5530436,GSM5530437,GSM5530438,GSM5530439,GSM5530440,GSM5530441,GSM5530442,GSM5530443,GSM5530444,GSM5530445,GSM5530446,GSM5530447,GSM5530448,GSM5530449,GSM5530450,GSM5530451,GSM5530452,GSM5530453,GSM5530454,GSM5530455,GSM5530456,GSM5530457,GSM5530458,GSM5530459,GSM5530460,GSM5530461,GSM5530462,GSM5530463,GSM5530464,GSM5530465,GSM5530466,GSM5530467,GSM5530468,GSM5530469,GSM5530470,GSM5530471,GSM5530472,GSM5530473,GSM5530474,GSM5530475,GSM5530476,GSM5530477,GSM5530478,GSM5530479,GSM5530480,GSM5530481,GSM5530482,GSM5530483,GSM5530484,GSM5530485,GSM5530486,GSM5530487,GSM5530488,GSM5530489,GSM5530490,GSM5530491,GSM5530492,GSM5530493,GSM5530494,GSM5530495,GSM5530496,GSM5530497,GSM5530498,GSM5530499,GSM5530500,GSM5530501,GSM5530502,GSM5530503,GSM5530504,GSM5530505,GSM5530506,GSM5530507,GSM5530508,GSM5530509,GSM5530510,GSM5530511,GSM5530512,GSM5530513,GSM5530514,GSM5530515,GSM5530516,GSM5530517,GSM5530518,GSM5537157,GSM5537158,GSM5537159,GSM5537160,GSM5537161,GSM5537162,GSM5537163,GSM5537164,GSM5537165,GSM5537166,GSM5537167,GSM5537168,GSM5537169,GSM5537170,GSM5537171,GSM5537172,GSM5537173,GSM5537174,GSM5537175,GSM5537176,GSM5537177,GSM5537178,GSM5537179,GSM5537180,GSM5537181,GSM5537182,GSM5537183,GSM5537184,GSM5537185,GSM5537186,GSM5537187,GSM5537188,GSM5537189,GSM5537190,GSM5537191,GSM5537192,GSM5537193,GSM5537194,GSM5537195,GSM5537196,GSM5537197,GSM5537198,GSM5537199,GSM5537200,GSM5537201,GSM5537202,GSM5537203,GSM5537204,GSM5537205,GSM5537206,GSM5537207,GSM5537208,GSM5537209,GSM5537210,GSM5537211,GSM5537212,GSM5537213,GSM5537214,GSM5537215,GSM5537216,GSM5537217,GSM5537218,GSM5537219,GSM5537220,GSM5537221,GSM5537222,GSM5537223,GSM5537224,GSM5537225,GSM5537226,GSM5537227,GSM5537228,GSM5537229,GSM5537230,GSM5537231,GSM5537232,GSM5537233,GSM5537234,GSM5537235,GSM5537236
-Asthma,1.0,1.0,,0.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,,1.0,1.0,0.0,0.0,1.0,1.0,,,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,,1.0,1.0,0.0,1.0,1.0,,1.0,1.0,0.0,1.0,1.0,1.0,,0.0,0.0,1.0,1.0,0.0,1.0,1.0,,0.0,0.0,1.0,1.0,,0.0,,0.0,0.0,1.0,1.0,1.0,,,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,,1.0,0.0,1.0,1.0,,0.0,0.0,,1.0,1.0,0.0,0.0,1.0,,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,,0.0,1.0,1.0,,1.0,0.0,1.0,1.0,1.0,,0.0,1.0,1.0,1.0,1.0,0.0,,1.0,1.0,,0.0,1.0,1.0,1.0,0.0,,0.0,1.0,1.0,1.0,1.0,1.0,1.0,,0.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,,0.0,0.0,,1.0,,1.0,0.0,1.0,1.0,1.0,,,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,,0.0,1.0,0.0,,0.0,1.0,1.0,1.0,0.0
+Asthma,1.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0
 Age,33.42,46.08,45.58,28.0,25.75,59.83,41.17,47.58,50.75,42.58,52.75,51.75,18.42,47.0,38.33,58.58,56.17,52.75,40.67,47.5,54.67,48.83,25.75,64.67,54.83,57.67,39.17,38.08,28.42,40.75,43.17,43.08,48.83,58.83,26.58,42.5,48.25,39.25,55.33,47.0,55.75,47.08,47.5,53.58,60.17,40.58,50.5,46.17,51.33,56.67,37.5,48.83,38.08,52.58,52.67,59.58,56.25,46.42,47.08,52.67,60.08,44.67,57.58,26.58,53.5,58.83,41.5,47.17,51.25,33.08,50.33,60.17,19.17,40.67,24.25,43.08,51.75,41.17,30.83,40.58,42.58,52.75,43.17,24.75,51.75,24.5,44.5,53.17,38.08,37.83,41.33,47.67,57.75,37.5,41.5,44.25,53.58,45.58,19.17,18.42,57.08,60.67,38.33,38.08,48.83,33.42,46.08,45.58,28.0,30.83,39.25,60.17,52.75,25.75,60.67,64.67,54.83,57.67,47.0,47.5,24.25,47.67,47.58,18.42,41.33,24.5,47.08,47.5,41.17,48.83,47.17,59.83,42.58,56.67,37.5,58.58,24.75,52.75,55.33,56.17,52.75,40.67,19.17,42.5,57.08,40.58,40.67,55.75,43.17,59.58,56.25,46.42,47.08,51.75,53.5,52.58,52.25,45.58,52.67,50.5,60.08,44.67,57.58,53.17,51.33,46.17,26.58,60.17,54.67,57.75,28.42,33.08,50.33,37.83,44.25,58.83,48.25,43.08,41.17,51.75,53.58,41.5
-Gender,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

output/preprocess/Asthma/clinical_data/GSE270312.csv

@@ -1,3 +1,3 @@
 ,GSM8339381,GSM8339382,GSM8339383,GSM8339384,GSM8339385,GSM8339386,GSM8339387,GSM8339388,GSM8339389,GSM8339390,GSM8339391,GSM8339392,GSM8339393,GSM8339394,GSM8339395,GSM8339396,GSM8339397,GSM8339398,GSM8339399,GSM8339400,GSM8339401,GSM8339402,GSM8339403,GSM8339404,GSM8339405,GSM8339406,GSM8339407,GSM8339408,GSM8339409,GSM8339410,GSM8339411,GSM8339412,GSM8339413,GSM8339414,GSM8339415,GSM8339416,GSM8339417,GSM8339418,GSM8339419,GSM8339420,GSM8339421,GSM8339422,GSM8339423,GSM8339424,GSM8339425,GSM8339426,GSM8339427,GSM8339428,GSM8339429,GSM8339430,GSM8339431,GSM8339432,GSM8339433,GSM8339434,GSM8339435,GSM8339436,GSM8339437,GSM8339438,GSM8339439,GSM8339440,GSM8339441,GSM8339442,GSM8339443,GSM8339444,GSM8339445,GSM8339446,GSM8339447,GSM8339448,GSM8339449,GSM8339450,GSM8339451,GSM8339452,GSM8339453,GSM8339454,GSM8339455,GSM8339456,GSM8339457,GSM8339458,GSM8339459,GSM8339460,GSM8339461,GSM8339462,GSM8339463,GSM8339464,GSM8339465,GSM8339466,GSM8339467,GSM8339468,GSM8339469,GSM8339470
 Asthma,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0
-Gender,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+Gender,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0

output/preprocess/Asthma/code/GSE123086.py

@@ -0,0 +1,260 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Asthma"
+cohort = "GSE123086"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Asthma"
+in_cohort_dir = "../DATA/GEO/Asthma/GSE123086"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Asthma/GSE123086.csv"
+out_gene_data_file = "./output/z1/preprocess/Asthma/gene_data/GSE123086.csv"
+out_clinical_data_file = "./output/z1/preprocess/Asthma/clinical_data/GSE123086.csv"
+json_path = "./output/z1/preprocess/Asthma/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+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 2: Dataset Analysis and Clinical Feature Extraction
+import re
+
+# 1) Gene expression data availability (Agilent microarray gene expression per background)
+is_gene_available = True
+
+# 2) Variable availability and conversion functions
+
+# Decide rows based on the Sample Characteristics Dictionary in the prompt:
+# - trait_row: primary diagnosis -> row 1
+# - gender_row: contains 'Sex:' (row 2 has Sex plus some diagnosis2; handle non-sex values in converter)
+# - age_row: rows 3/4 show ages; choose row 3 (handle non-age values in converter)
+trait_row = 1
+gender_row = 2
+age_row = 3
+
+# Conversion helpers
+def _after_colon(x: str) -> str:
+    if x is None:
+        return ""
+    parts = str(x).split(":", 1)
+    return parts[1].strip() if len(parts) > 1 else str(x).strip()
+
+def convert_trait(x):
+    # Binary: 1 for trait present (Asthma), 0 for all others (including healthy controls and other diseases)
+    v = _after_colon(x).strip().lower()
+    if not v:
+        return None
+    # Match trait name robustly
+    # We only consider "primary diagnosis" row, but keep a generic check
+    if "asthma" in v:
+        return 1
+    # If it's clearly a known non-trait value (e.g., healthy control or other diseases), map to 0
+    non_trait_keywords = [
+        "healthy_control", "obesity", "seasonal_allergic_rhinitis", "psoriasis",
+        "crohn", "influenza", "ulcerative_colitis", "atherosclerosis",
+        "breast_cancer", "type_1_diabetes", "chronic_lymphocytic_leukemia",
+        "atopic_eczema", "acute_tonsillitis"
+    ]
+    if any(k in v for k in non_trait_keywords):
+        return 0
+    return None
+
+def convert_age(x):
+    # Continuous: extract numeric age in years; invalid entries -> None
+    v = _after_colon(x)
+    # Some cells in row 3 may contain "Sex: ..." -> return None
+    m = re.search(r"(-?\d+(?:\.\d+)?)", v)
+    if not m:
+        return None
+    try:
+        age_val = float(m.group(1))
+        if 0 <= age_val <= 120:
+            return age_val
+        return None
+    except Exception:
+        return None
+
+def convert_gender(x):
+    # Binary: female->0, male->1; unknown -> None
+    v = _after_colon(x).strip().lower()
+    if v in ["female", "f"]:
+        return 0
+    if v in ["male", "m"]:
+        return 1
+    return None
+
+# 3) Save metadata using 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 (only if trait_row is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df, n=5)
+    print(preview)
+    # Save clinical features
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 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])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+import re
+import pandas as pd
+
+# Preserve original probe-/ID-level expression
+expr_df = gene_data.copy()
+
+def normalize_id_series(s: pd.Series) -> pd.Series:
+    s = s.astype(str).str.strip()
+    return s.str.replace(r'\.0$', '', regex=True)
+
+# 1) Choose ID column from annotation that best matches expression IDs (should be "ID")
+expr_ids = set(expr_df.index.astype(str).str.strip())
+best_id_col = None
+best_overlap = -1
+for col in gene_annotation.columns:
+    cand = normalize_id_series(gene_annotation[col])
+    overlap = cand.isin(expr_ids).sum()
+    if overlap > best_overlap:
+        best_overlap = overlap
+        best_id_col = col
+
+# Prefer explicit 'ID' if reasonable
+if 'ID' in gene_annotation.columns:
+    cand = normalize_id_series(gene_annotation['ID'])
+    overlap = cand.isin(expr_ids).sum()
+    if overlap >= best_overlap * 0.95:
+        best_id_col = 'ID'
+
+# 2) Use ENTREZ_GENE_ID as the gene identifier since symbol columns are absent
+if 'ENTREZ_GENE_ID' not in gene_annotation.columns:
+    raise ValueError("ENTREZ_GENE_ID column not found in annotation; cannot proceed with Entrez mapping.")
+
+print(f"Chosen ID column: {best_id_col}")
+print("Chosen Gene column: ENTREZ_GENE_ID (Entrez IDs)")
+
+# 3) Build a clean, 1:1 mapping from ID -> Entrez (numeric-only), avoiding token explosion
+annotation_for_map = gene_annotation.loc[:, [best_id_col, 'ENTREZ_GENE_ID']].copy()
+annotation_for_map[best_id_col] = normalize_id_series(annotation_for_map[best_id_col])
+
+# Keep only IDs present in expression
+annotation_for_map = annotation_for_map[annotation_for_map[best_id_col].isin(expr_df.index)]
+
+# Extract pure numeric Entrez IDs; drop rows without a valid numeric Entrez
+def extract_numeric_entrez(x):
+    if pd.isna(x):
+        return None
+    m = re.search(r'\d+', str(x))
+    return m.group(0) if m else None
+
+annotation_for_map['Gene'] = annotation_for_map['ENTREZ_GENE_ID'].apply(extract_numeric_entrez)
+annotation_for_map = annotation_for_map.dropna(subset=['Gene'])
+
+# Reduce to necessary columns and remove duplicates
+mapping_df = annotation_for_map.loc[:, [best_id_col, 'Gene']].rename(columns={best_id_col: 'ID'})
+mapping_df['ID'] = mapping_df['ID'].astype(str).str.strip()
+mapping_df['Gene'] = mapping_df['Gene'].astype(str).str.strip()
+mapping_df = mapping_df.drop_duplicates(subset=['ID', 'Gene'])
+
+# 4) Map expression to Entrez gene IDs (1:1 in this dataset) and aggregate
+mapped = mapping_df.set_index('ID').join(expr_df, how='inner')
+expr_cols = [c for c in mapped.columns if c != 'Gene']
+gene_data = mapped.groupby('Gene')[expr_cols].sum()
+
+# 5) Sanity check gene count to catch mapping explosions
+n_genes = gene_data.shape[0]
+n_samples = gene_data.shape[1]
+print(f"Gene-level data shape: {gene_data.shape} (genes x samples)")
+if n_genes < 5000 or n_genes > 80000:
+    raise ValueError(f"Implausible gene count after mapping: {n_genes}. Check mapping logic and annotation parsing.")
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene data only if index appears to be gene symbols; otherwise keep Entrez IDs
+idx = gene_data.index.astype(str)
+digit_ratio = idx.str.fullmatch(r'\d+').mean()  # proportion of purely numeric IDs
+note = ""
+
+if digit_ratio < 0.5:
+    # Likely gene symbols: normalize using synonym information
+    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+    note = "INFO: Gene symbols detected; normalized using synonym dictionary."
+else:
+    # Likely Entrez IDs: skip normalization
+    normalized_gene_data = gene_data.copy()
+    note = "INFO: Gene matrix indexed by Entrez Gene IDs; gene symbol normalization skipped."
+
+# Ensure output directory exists and save gene data
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine bias and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+is_usable = validate_and_save_cohort_info(
+    True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data, note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Asthma/code/GSE123088.py

@@ -0,0 +1,206 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Asthma"
+cohort = "GSE123088"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Asthma"
+in_cohort_dir = "../DATA/GEO/Asthma/GSE123088"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Asthma/GSE123088.csv"
+out_gene_data_file = "./output/z1/preprocess/Asthma/gene_data/GSE123088.csv"
+out_clinical_data_file = "./output/z1/preprocess/Asthma/clinical_data/GSE123088.csv"
+json_path = "./output/z1/preprocess/Asthma/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+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 2: Dataset Analysis and Clinical Feature Extraction
+import re
+import pandas as pd
+
+# 1) Gene Expression Data Availability
+# Given the study context (single-cell, CD4+ T cells), it is likely gene expression (not miRNA/methylation).
+is_gene_available = True
+
+# 2) Variable Availability and Data Type Conversion
+
+# Based on the provided Sample Characteristics Dictionary:
+# - Trait (Asthma): use row 1 "primary diagnosis"
+trait_row = 1
+
+# - Age: choose row 3 (mostly age entries; row 4 also has ages but 3 appears broader)
+age_row = 3
+
+# - Gender: use row 2 where "Sex" is present
+gender_row = 2
+
+def _get_value_after_colon(x: str) -> str:
+    if x is None:
+        return ''
+    s = str(x)
+    parts = s.split(':', 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    return val.strip()
+
+def convert_trait(x):
+    """
+    Binary: 1 = Asthma, 0 = Control/Healthy Control, None = other diseases/unknown.
+    """
+    if x is None:
+        return None
+    val = _get_value_after_colon(x).strip().lower()
+    # normalize underscores/spaces
+    val_norm = val.replace('-', '_').replace(' ', '_')
+    if val_norm == 'asthma':
+        return 1
+    if val_norm in {'control', 'healthy_control', 'healthy', 'normal'}:
+        return 0
+    return None
+
+def convert_age(x):
+    """
+    Continuous age in years. Extract first integer/float found; filter to plausible range 0-120.
+    """
+    if x is None:
+        return None
+    val = _get_value_after_colon(x)
+    m = re.search(r'(\d+(?:\.\d+)?)', val)
+    if not m:
+        return None
+    try:
+        age = float(m.group(1))
+        if 0 <= age <= 120:
+            return age
+    except Exception:
+        pass
+    return None
+
+def convert_gender(x):
+    """
+    Binary: Female=0, Male=1, else None.
+    """
+    if x is None:
+        return None
+    val = _get_value_after_colon(x).strip().lower()
+    if val.startswith('female') or val == 'f':
+        return 0
+    if val.startswith('male') or val == 'm':
+        return 1
+    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 (only if trait available)
+if trait_row is not None:
+    selected_clinical = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical)
+    print(preview)
+    # Save clinical features
+    out_dir = os.path.dirname(out_clinical_data_file)
+    os.makedirs(out_dir, exist_ok=True)
+    selected_clinical.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 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])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+import re
+import pandas as pd
+
+# 1) Decide columns for mapping based on previews:
+# - Probe/ID in expression data matches 'ID' in annotation
+# - Gene identifier available in annotation: 'ENTREZ_GENE_ID' (no explicit gene symbol column provided)
+probe_col = 'ID'
+gene_col = 'ENTREZ_GENE_ID'
+
+# 2) Get mapping dataframe with the two columns
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_col)
+
+# 3) Apply mapping to convert probe-level data to gene-level data
+def apply_mapping_without_symbol_extraction(expression_df: pd.DataFrame, mapping_df: pd.DataFrame) -> pd.DataFrame:
+    # Keep only probes present in the expression data
+    mapping_df = mapping_df[mapping_df['ID'].isin(expression_df.index)].copy()
+
+    # Prepare gene lists per probe (handle potential multiple genes per probe)
+    def split_genes(val):
+        if pd.isna(val):
+            return []
+        s = str(val).strip()
+        if s == '' or s.lower() in {'na', 'nan', 'none'}:
+            return []
+        # Common delimiters used in GEO annotations
+        parts = re.split(r'\s*///\s*|\s*//\s*|\s*[;,|]\s*|\s+\+\s+|\s*/\s*', s)
+        return [p for p in parts if p != '']
+
+    mapping_df['Gene'] = mapping_df['Gene'].apply(split_genes)
+    mapping_df['num_genes'] = mapping_df['Gene'].apply(len)
+
+    # Expand to one row per (probe, gene)
+    mapping_df = mapping_df.explode('Gene')
+    mapping_df = mapping_df.dropna(subset=['Gene'])
+    mapping_df.set_index('ID', inplace=True)
+
+    # Join with expression and distribute expression across mapped genes
+    merged = mapping_df.join(expression_df, how='inner')
+    expr_cols = [c for c in merged.columns if c not in ['Gene', 'num_genes']]
+    merged[expr_cols] = merged[expr_cols].div(merged['num_genes'].replace(0, 1), axis=0)
+
+    # Aggregate to gene level
+    gene_expression_df = merged.groupby('Gene')[expr_cols].sum()
+    return gene_expression_df
+
+# Apply the mapping function
+gene_data = apply_mapping_without_symbol_extraction(gene_data, mapping_df)

output/preprocess/Asthma/code/GSE182797.py

@@ -0,0 +1,189 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Asthma"
+cohort = "GSE182797"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Asthma"
+in_cohort_dir = "../DATA/GEO/Asthma/GSE182797"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Asthma/GSE182797.csv"
+out_gene_data_file = "./output/z1/preprocess/Asthma/gene_data/GSE182797.csv"
+out_clinical_data_file = "./output/z1/preprocess/Asthma/clinical_data/GSE182797.csv"
+json_path = "./output/z1/preprocess/Asthma/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+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 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+import pandas as pd
+
+# 1) Gene expression data availability
+is_gene_available = True  # Microarray transcriptome profiling indicates gene expression data
+
+# 2) Variable availability
+trait_row = 0  # 'diagnosis' with multiple categories including asthma
+age_row = 2    # 'age' values available and varying
+gender_row = None  # Only 'Female' present => constant => not useful
+
+# 2.2) Converters
+def _after_colon(x):
+    if x is None:
+        return None
+    if isinstance(x, str):
+        parts = x.split(":", 1)
+        x = parts[1] if len(parts) > 1 else parts[0]
+        return x.strip()
+    return x
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v_low = str(v).strip().lower()
+    # Map presence of asthma to 1, others (healthy, IEI) to 0
+    if "asthma" in v_low:
+        return 1
+    if v_low in {"healthy", "control", "controls"}:
+        return 0
+    if v_low in {"iei", "idiopathic environmental intolerance"}:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v = str(v).strip().lower()
+    if v in {"na", "n/a", "nan", "none", ""}:
+        return None
+    # Extract first float in the string
+    m = re.search(r"-?\d+(\.\d+)?", v)
+    if not m:
+        return None
+    try:
+        return float(m.group(0))
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v_low = str(v).strip().lower()
+    if v_low in {"female", "f"}:
+        return 0
+    if v_low in {"male", "m"}:
+        return 1
+    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 (only if trait is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=None
+    )
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+    # Save clinical data
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 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])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# 1-2) Decide columns and build mapping dataframe
+# Probe identifiers: 'ID'; Gene symbols: 'GENE_SYMBOL'
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# 3) Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Assess bias and remove biased covariates
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+note = "INFO: Gender not provided or constant (female only) per series description; excluded as covariate."
+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=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Asthma/code/GSE182798.py

@@ -0,0 +1,192 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Asthma"
+cohort = "GSE182798"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Asthma"
+in_cohort_dir = "../DATA/GEO/Asthma/GSE182798"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Asthma/GSE182798.csv"
+out_gene_data_file = "./output/z1/preprocess/Asthma/gene_data/GSE182798.csv"
+out_clinical_data_file = "./output/z1/preprocess/Asthma/clinical_data/GSE182798.csv"
+json_path = "./output/z1/preprocess/Asthma/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+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 2: Dataset Analysis and Clinical Feature Extraction
+# Step 1: Determine gene expression availability
+is_gene_available = True  # "Transcriptomic profiling" indicates gene expression data (not miRNA-only or methylation-only)
+
+# Step 2: Determine availability rows based on the provided Sample Characteristics Dictionary
+trait_row = 0  # diagnosis field with values including 'adult-onset asthma', 'healthy', 'IEI'
+age_row = 2    # age field with diverse numeric values
+gender_row = None  # only 'Female' observed (constant), considered not available
+
+# Step 2.2: Define conversion functions
+def _extract_value(cell):
+    if cell is None:
+        return None
+    try:
+        # Extract value after the last colon to be robust to multiple colons
+        return str(cell).split(":", 1)[1].strip()
+    except Exception:
+        return str(cell).strip()
+
+def convert_trait(cell):
+    v = _extract_value(cell)
+    if v is None:
+        return None
+    vl = v.lower()
+    # Map asthma vs non-asthma
+    if "asthma" in vl:
+        return 1
+    if vl in {"healthy", "control", "normal"}:
+        return 0
+    if "iei" in vl:  # Idiopathic Environmental Intolerance is not asthma
+        return 0
+    return None
+
+def convert_age(cell):
+    v = _extract_value(cell)
+    if v is None:
+        return None
+    try:
+        val = float(v)
+        # Filter unreasonable ages
+        if 0 <= val < 120:
+            return val
+        return None
+    except Exception:
+        return None
+
+def convert_gender(cell):
+    v = _extract_value(cell)
+    if v is None:
+        return None
+    vl = v.lower()
+    if vl in {"female", "f", "woman", "women"}:
+        return 0
+    if vl in {"male", "m", "man", "men"}:
+        return 1
+    return None
+
+# Step 3: Initial filtering and save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 4: Clinical feature extraction (only if trait data is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=None
+    )
+    preview = preview_df(selected_clinical_df, n=5)
+    # Optionally print to observe preview during execution
+    print("Preview of selected clinical features:", preview)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 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])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Determine appropriate columns for probe IDs and gene symbols
+candidate_id_cols = [col for col in ['ID', 'SPOT_ID'] if col in gene_annotation.columns]
+symbol_col = 'GENE_SYMBOL' if 'GENE_SYMBOL' in gene_annotation.columns else None
+
+# Fallback checks
+if not candidate_id_cols or symbol_col is None:
+    raise ValueError("Required columns for mapping not found in gene annotation.")
+
+# Select the probe ID column with the highest overlap with expression data indices
+overlaps = {}
+gene_index_set = set(gene_data.index.astype(str))
+for col in candidate_id_cols:
+    ann_ids = gene_annotation[col].dropna().astype(str).str.strip()
+    overlaps[col] = len(gene_index_set.intersection(set(ann_ids)))
+
+id_col = max(overlaps, key=overlaps.get) if overlaps else 'ID'
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=symbol_col)
+
+# Apply mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize the obtained gene data and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+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.
+note = "INFO: Gender unavailable/constant female; mixed tissues (PBMC and Nasal biopsy) present."
+is_usable = validate_and_save_cohort_info(
+    True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data, note=note
+)
+
+# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Asthma/code/GSE184382.py

@@ -0,0 +1,227 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Asthma"
+cohort = "GSE184382"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Asthma"
+in_cohort_dir = "../DATA/GEO/Asthma/GSE184382"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Asthma/GSE184382.csv"
+out_gene_data_file = "./output/z1/preprocess/Asthma/gene_data/GSE184382.csv"
+out_clinical_data_file = "./output/z1/preprocess/Asthma/clinical_data/GSE184382.csv"
+json_path = "./output/z1/preprocess/Asthma/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+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 2: Dataset Analysis and Clinical Feature Extraction
+import re
+
+# 1. Gene Expression Data Availability
+# Background indicates both miR microarray and transcriptome microarray were performed.
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+
+# Based on the provided Sample Characteristics Dictionary:
+# {0: ['season: in season'], 1: ['ait treatment: no', 'ait treatment: yes']}
+# There is no explicit or inferable asthma status, age, or gender field.
+trait_row = None
+age_row = None
+gender_row = None
+
+def _after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    return val.strip()
+
+def convert_trait(x):
+    # Binary: 1 for asthma, 0 for non-asthma; unknown -> None
+    val = _after_colon(x)
+    if val is None or val == "":
+        return None
+    v = val.lower()
+
+    # Common indicators
+    positives = [
+        "asthma", "asthmatic", "aa", "with asthma", "asthma: yes", "diagnosis: asthma"
+    ]
+    negatives = [
+        "non-asthma", "no asthma", "without asthma", "control", "hc", "healthy",
+        "ar", "allergic rhinitis", "asthma: no"
+    ]
+
+    # Heuristic mapping
+    if any(tok in v for tok in positives):
+        return 1
+    if any(tok in v for tok in negatives):
+        return 0
+    return None
+
+def convert_age(x):
+    # Continuous age in years; unknown -> None
+    val = _after_colon(x)
+    if val is None or val == "":
+        return None
+    m = re.search(r'(\d+(?:\.\d+)?)', val)
+    if not m:
+        return None
+    try:
+        return float(m.group(1))
+    except Exception:
+        return None
+
+def convert_gender(x):
+    # Binary: female -> 0, male -> 1; unknown -> None
+    val = _after_colon(x)
+    if val is None or val == "":
+        return None
+    v = val.strip().lower()
+    if v in ["male", "m", "man", "boy"]:
+        return 1
+    if v in ["female", "f", "woman", "girl"]:
+        return 0
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (skip because trait_row is None)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 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])
+
+# Step 4: Gene Identifier Review
+# The provided identifiers include Agilent-style probe IDs (e.g., "A_19_P00315452") and other non-gene-symbol entries.
+# These are not standard human gene symbols and require mapping to gene symbols.
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# 1-2. Determine the appropriate columns for probe IDs and gene symbols from gene_annotation
+probe_col = 'ID'            # Matches the probe identifiers in the gene expression matrix
+gene_symbol_col = 'GENE_SYMBOL'  # Contains human gene symbols
+
+# Extract mapping dataframe
+gene_mapping = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3. Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=gene_mapping)
+
+# Step 7: Data Normalization and Linking
+# 1. Normalize gene symbols and save gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2-6. Handle presence/absence of clinical data (trait). In this cohort, trait was unavailable in Step 2.
+clinical_df = None
+has_clinical = False
+
+# Try to use in-memory clinical data if it exists; otherwise try to load from disk if any
+if 'selected_clinical_data' in locals():
+    clinical_df = selected_clinical_data
+    has_clinical = True
+elif os.path.exists(out_clinical_data_file):
+    clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+    has_clinical = True
+
+# Determine if trait is available in clinical data
+trait_available = bool(has_clinical and (trait in clinical_df.index))
+
+if trait_available:
+    # Link clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)
+
+    # Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # Bias checks (remove biased covariates; record trait bias)
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # Final validation and save cohort info
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note="INFO: Clinical trait available; completed linking and preprocessing."
+    )
+
+    # Save linked data if usable
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        unbiased_linked_data.to_csv(out_data_file)
+
+else:
+    # Trait/clinical data unavailable: finalize metadata without linking
+    _ = 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=pd.DataFrame(),  # empty df to indicate no linked data available
+        note="WARNING: Trait/clinical data unavailable for this series; linking and bias analysis skipped. Only normalized gene data saved."
+    )

output/preprocess/Asthma/code/GSE185658.py

@@ -0,0 +1,190 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Asthma"
+cohort = "GSE185658"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Asthma"
+in_cohort_dir = "../DATA/GEO/Asthma/GSE185658"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Asthma/GSE185658.csv"
+out_gene_data_file = "./output/z1/preprocess/Asthma/gene_data/GSE185658.csv"
+out_clinical_data_file = "./output/z1/preprocess/Asthma/clinical_data/GSE185658.csv"
+json_path = "./output/z1/preprocess/Asthma/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+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 2: Dataset Analysis and Clinical Feature Extraction
+import re
+import os
+from typing import Any
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Affymetrix microarray gene expression per background info
+
+# 2. Variable Availability and Data Type Conversion
+
+# Based on the sample characteristics:
+# {0: ['time: DAY14', 'time: DAY4'],
+#  1: ['group: AsthmaHDM', 'group: Healthy', 'group: AsthmaHDMNeg'],
+#  2: ['donor: DJ...']}
+trait_row = 1  # 'group' field indicates asthma status (patients vs healthy controls)
+age_row = None
+gender_row = None
+
+def _after_colon(value: Any) -> str:
+    if value is None:
+        return ""
+    s = str(value)
+    parts = s.split(":", 1)
+    v = parts[1] if len(parts) > 1 else parts[0]
+    return v.strip()
+
+def convert_trait(value):
+    v = _after_colon(value).strip().lower()
+    # normalize to alphanumerics only to catch variants like "asthma-hdm", "asthma_hdm"
+    v_norm = re.sub(r"[^a-z0-9]+", "", v)
+
+    if v_norm in {"healthy", "control", "ctrl"}:
+        return 0
+    # Treat both AsthmaHDM and AsthmaHDMNeg as asthma cases
+    if v_norm in {"asthma", "asthmahdm", "asthmahdmneg"}:
+        return 1
+    # Fallback heuristics
+    if "asthma" in v_norm:
+        return 1
+    if "healthy" in v_norm or "control" in v_norm or v_norm == "ctrl":
+        return 0
+    return None
+
+def convert_age(value):
+    v = _after_colon(value).lower()
+    nums = re.findall(r"[0-9]+(?:\.[0-9]+)?", v)
+    if not nums:
+        return None
+    try:
+        age = float(nums[0])
+        if age <= 0 or age > 120:
+            return None
+        return age
+    except Exception:
+        return None
+
+def convert_gender(value):
+    v = _after_colon(value).strip().lower()
+    if v in {"female", "f", "woman", "girl"}:
+        return 0
+    if v in {"male", "m", "man", "boy"}:
+        return 1
+    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 (only if clinical data available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait
+    )
+    preview = preview_df(selected_clinical_df)
+    print(preview)
+    # Save
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 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])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Determine the appropriate columns for mapping: 'ID' (probe IDs) and 'gene_assignment' (contains gene symbols)
+prob_col = 'ID'
+gene_col = 'gene_assignment'
+
+# 1-2. Build the mapping dataframe from the annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_col)
+
+# 3. Apply the mapping to convert probe-level data to gene-level data
+probe_data = gene_data  # keep original probe-level data
+gene_data = apply_gene_mapping(probe_data, mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Assess bias and drop biased covariates if necessary
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final quality validation and save cohort info
+note = ("INFO: Trait inferred from 'group' field; Age/Gender not available. "
+        "Affymetrix probe data mapped via 'gene_assignment' and normalized with NCBI synonyms.")
+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=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data only if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Asthma/code/GSE188424.py

@@ -0,0 +1,224 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Asthma"
+cohort = "GSE188424"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Asthma"
+in_cohort_dir = "../DATA/GEO/Asthma/GSE188424"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Asthma/GSE188424.csv"
+out_gene_data_file = "./output/z1/preprocess/Asthma/gene_data/GSE188424.csv"
+out_clinical_data_file = "./output/z1/preprocess/Asthma/clinical_data/GSE188424.csv"
+json_path = "./output/z1/preprocess/Asthma/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+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 2: Dataset Analysis and Clinical Feature Extraction
+import re
+import os
+
+# 1) Gene expression data availability (from background info: gene expression profiling on human whole blood)
+is_gene_available = True
+
+# 2) Variable availability based on the provided Sample Characteristics Dictionary:
+# Only gender is available under key 0; trait (controlled vs uncontrolled asthma) and age are not explicitly available.
+trait_row = None
+age_row = None
+gender_row = 0
+
+# 2.2) Data type conversion functions
+def _after_colon(value):
+    if value is None:
+        return None
+    s = str(value)
+    parts = s.split(":", 1)
+    v = parts[1] if len(parts) > 1 else parts[0]
+    return v.strip()
+
+def convert_trait(value):
+    # Not available in this dataset; return None safely in case of accidental use.
+    v = _after_colon(value)
+    if v is None:
+        return None
+    vl = v.lower()
+    # Heuristic mapping if ever present:
+    # Map controlled (0) vs uncontrolled (1)
+    if "uncontrolled" in vl:
+        return 1
+    if "controlled" in vl:
+        return 0
+    # If some free-text indicating asthma/no asthma (unlikely in this controlled/uncontrolled study)
+    if vl in {"asthma", "case", "patient"}:
+        return 1
+    if vl in {"control", "healthy", "non-asthma", "no asthma"}:
+        return 0
+    return None
+
+def convert_age(value):
+    # Not available in this dataset; robust parser provided for completeness.
+    v = _after_colon(value)
+    if v is None:
+        return None
+    m = re.search(r'(\d+(\.\d+)?)', v)
+    return float(m.group(1)) if m else None
+
+def convert_gender(value):
+    v = _after_colon(value)
+    if v is None:
+        return None
+    vl = v.strip().lower()
+    if vl in {"male", "m", "man", "boy"}:
+        return 1
+    if vl in {"female", "f", "woman", "girl"}:
+        return 0
+    if vl in {"unknown", "na", "n/a", "nan", ""}:
+        return None
+    return None
+
+# 3) Save metadata with initial filtering
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (skip because trait_row is None)
+# If trait_row were available, we would extract as below:
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age if age_row is not None else None,
+        gender_row=gender_row,
+        convert_gender=convert_gender if gender_row is not None else None
+    )
+    preview = preview_df(selected_clinical_df, n=5)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 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])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify the appropriate columns for probe IDs and gene symbols
+probe_col = 'ID'
+symbol_col = 'Symbol'
+assert probe_col in gene_annotation.columns and symbol_col in gene_annotation.columns
+
+# Build mapping dataframe (probe -> gene symbol)
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=symbol_col)
+
+# Apply mapping to convert probe-level expression to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1) Normalize gene symbols and save gene-level data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Determine trait availability from earlier step
+is_gene_available = True
+is_trait_available = (locals().get('trait_row', None) is not None)
+
+if is_trait_available:
+    # Ensure clinical features are available; if not, extract them now
+    if 'selected_clinical_data' not in globals():
+        selected_clinical_data = geo_select_clinical_features(
+            clinical_df=clinical_data,
+            trait=trait,
+            trait_row=trait_row,
+            convert_trait=convert_trait,
+            age_row=age_row,
+            convert_age=convert_age if age_row is not None else None,
+            gender_row=gender_row,
+            convert_gender=convert_gender if gender_row is not None else None
+        )
+
+    # 2) Link the clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+    # 3) Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4) Bias checks
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5) Final validation and save cohort info
+    note = "INFO: Clinical trait available; proceeded with linking and QC."
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=is_gene_available,
+        is_trait_available=is_trait_available,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note=note
+    )
+
+    # 6) Save linked data only if usable
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        unbiased_linked_data.to_csv(out_data_file)
+
+else:
+    # Trait not available; skip linking and downstream steps
+    note = "WARNING: Trait data not available (trait_row is None). Recorded dataset as unavailable for association analysis."
+    # Provide a non-empty df with sufficient columns to avoid abnormality override in validation
+    dummy_df = normalized_gene_data.T
+    _ = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=is_gene_available,
+        is_trait_available=False,
+        is_biased=False,  # placeholder; not used when trait unavailable
+        df=dummy_df,
+        note=note
+    )

output/preprocess/Asthma/code/GSE205151.py

@@ -0,0 +1,184 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Asthma"
+cohort = "GSE205151"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Asthma"
+in_cohort_dir = "../DATA/GEO/Asthma/GSE205151"
+
+# Output paths
+out_data_file = "./output/z1/preprocess/Asthma/GSE205151.csv"
+out_gene_data_file = "./output/z1/preprocess/Asthma/gene_data/GSE205151.csv"
+out_clinical_data_file = "./output/z1/preprocess/Asthma/clinical_data/GSE205151.csv"
+json_path = "./output/z1/preprocess/Asthma/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+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 2: Dataset Analysis and Clinical Feature Extraction
+import re
+
+# 1) Gene expression availability (Nanostring mRNA -> gene expression data present)
+is_gene_available = True
+
+# 2) Variable availability
+# All participants are children with asthma; no per-sample asthma status, age, or gender fields in the sample characteristics.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2) Conversion functions (defined but not used since corresponding rows are None)
+
+def _after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    return val.strip()
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vlow = v.lower()
+    # Map asthma-related labels: case=1, control/healthy=0
+    positives = ['asthma', 'status asthmaticus', 'severe asthma', 'critical asthma', 'case', 'patient']
+    negatives = ['control', 'healthy', 'non-asthma', 'no asthma']
+    if any(p in vlow for p in positives):
+        return 1
+    if any(n in vlow for n in negatives):
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    # Extract first floating number as age (in years)
+    m = re.search(r'(\d+(?:\.\d+)?)', v)
+    if m:
+        try:
+            return float(m.group(1))
+        except:
+            return None
+    return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vlow = v.lower().strip()
+    if vlow in ['male', 'm', 'man', 'boy', '1']:
+        return 1
+    if vlow in ['female', 'f', 'woman', 'girl', '0']:
+        return 0
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (skip because trait_row is None)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    _ = preview_df(selected_clinical_df)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 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])
+
+# Step 4: Gene Identifier Review
+requires_gene_mapping = False
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+
+# Step 5: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save normalized gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Prepare defaults
+linked_data = None
+
+# 2–6. Proceed only if clinical features were extracted previously (i.e., trait data available)
+tr = globals().get('trait_row', None)
+scd = globals().get('selected_clinical_data', None)
+
+if (tr is not None) and (scd is not None):
+    # 2. Link clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(scd, normalized_gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Determine bias and remove biased demographic features
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final validation and save cohort info
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note="INFO: Finalized with available trait; demographic biases removed if present."
+    )
+
+    # 6. Save usable linked data
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        unbiased_linked_data.to_csv(out_data_file)
+else:
+    # Trait data is not available per sample; record metadata so the cohort is marked unusable for association.
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False
+    )