Add files using upload-large-folder tool

output/preprocess/Cystic_Fibrosis/clinical_data/GSE100521.csv

@@ -1,4 +1,4 @@
-0,1,2
-0.0,,
-,27.0,
-,,
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output/preprocess/Cystic_Fibrosis/clinical_data/GSE60690.csv

@@ -1,4 +1,4 @@
-,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29
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-Age,38.2,9.2,22.5,14.4,33.8,18.6,27.7,33.5,17.8,24.1,16.4,8.7,10.4,46.3,19.0,17.1,10.2,16.7,39.7,39.3,25.4,41.2,18.1,21.5,17.3,32.4,19.8,34.3,25.3,46.7
-Gender,1.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,
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output/preprocess/Cystic_Fibrosis/code/GSE100521.py

@@ -0,0 +1,190 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cystic_Fibrosis"
+cohort = "GSE100521"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
+in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE100521"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Cystic_Fibrosis/GSE100521.csv"
+out_gene_data_file = "./output/z2/preprocess/Cystic_Fibrosis/gene_data/GSE100521.csv"
+out_clinical_data_file = "./output/z2/preprocess/Cystic_Fibrosis/clinical_data/GSE100521.csv"
+json_path = "./output/z2/preprocess/Cystic_Fibrosis/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  # Illumina HumanHT-12 v4 BeadChip = mRNA expression
+
+# 2) Variable availability (keys from the provided Sample Characteristics Dictionary)
+trait_row = 0     # From "patient identification number: Non CF subject X" vs "CF patient X"
+age_row = 1       # From "age: N"
+gender_row = 2    # From "gender: Male/Female"
+
+# 2.2) Converters
+def convert_trait(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]
+    s = s.strip().lower()
+    # Map CF status: Non CF => 0, CF => 1
+    if 'non' in s and 'cf' in s:
+        return 0
+    if 'cf' in s:
+        return 1
+    return None
+
+def convert_age(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]
+    s = s.strip()
+    m = re.search(r'[-+]?\d+(\.\d+)?', s)
+    if m:
+        try:
+            return float(m.group())
+        except Exception:
+            return None
+    return None
+
+def convert_gender(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]
+    s = s.strip().lower()
+    if s in ['female', 'f']:
+        return 0
+    if s 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(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
+# ILMN_* identifiers are Illumina probe IDs, not human gene symbols.
+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 correct columns for probe IDs and gene symbols in the annotation
+probe_col = 'ID'       # Matches ILMN_* probe IDs in the expression data
+gene_symbol_col = 'Symbol'  # Contains gene symbols
+
+# 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(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)
+
+# 2) Link clinical and genetic data (use the correct variable name from Step 2)
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# Optional sanity checks
+print(f"Normalized gene data shape: {normalized_gene_data.shape}")
+print(f"Linked data shape (before missing handling): {linked_data.shape}")
+
+# 3) Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4) Bias assessment and demographic pruning
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5) Final validation and save cohort info
+note = "INFO: Illumina HT-12 v4 probes mapped to gene symbols; symbols normalized to NCBI Gene preferred names."
+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/Cystic_Fibrosis/code/GSE107846.py

@@ -0,0 +1,183 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cystic_Fibrosis"
+cohort = "GSE107846"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
+in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE107846"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Cystic_Fibrosis/GSE107846.csv"
+out_gene_data_file = "./output/z2/preprocess/Cystic_Fibrosis/gene_data/GSE107846.csv"
+out_clinical_data_file = "./output/z2/preprocess/Cystic_Fibrosis/clinical_data/GSE107846.csv"
+json_path = "./output/z2/preprocess/Cystic_Fibrosis/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
+
+# 1) Gene expression data availability
+is_gene_available = True  # Based on GEO context and lack of miRNA/methylation indication
+
+# 2) Variable availability
+trait_row = 5     # 'state: CF' vs 'state: Healthy'
+age_row = 1       # 'age: <float>'
+gender_row = 2    # 'Sex: F' / 'Sex: M'
+
+# 2.2) Conversion functions
+def _extract_value(x):
+    if x is None:
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip()
+
+def convert_trait(x):
+    v = _extract_value(x)
+    if v is None or v == '':
+        return None
+    lv = v.lower()
+    # Positive (has CF) -> 1; Healthy/Control -> 0
+    if lv in {'cf', 'cystic fibrosis', 'cystic_fibrosis'}:
+        return 1
+    if lv in {'healthy', 'control', 'non-cf', 'non cf', 'no cf'}:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _extract_value(x)
+    if v is None or v == '':
+        return None
+    v = v.replace('years', '').replace('year', '').strip()
+    # Handle values like "<1"
+    if v.startswith('<'):
+        # approximate to midpoint
+        try:
+            num = float(v[1:].strip())
+            return max(num / 2.0, 0.5)
+        except Exception:
+            return None
+    try:
+        return float(v)
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _extract_value(x)
+    if v is None or v == '':
+        return None
+    lv = v.lower()
+    if lv in {'f', 'female', 'woman', 'girl'}:
+        return 0
+    if lv in {'m', 'male', '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
+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
+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 appropriate columns for mapping: probe IDs in 'ID', gene symbols in 'SYMBOL'
+prob_col = 'ID'
+gene_col = 'SYMBOL'
+
+# 2. Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_col)
+
+# 3. Apply mapping to convert probe-level data to gene-level expression
+probe_data = gene_data  # preserve 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 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 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 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 information
+note_msg = "INFO: Mapped Illumina probe IDs to symbols; normalized gene symbols; handled missingness per protocol."
+is_usable = validate_and_save_cohort_info(
+    True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data, note=note_msg
+)
+
+# 6. Save the 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)

output/preprocess/Cystic_Fibrosis/code/GSE129168.py

@@ -0,0 +1,193 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cystic_Fibrosis"
+cohort = "GSE129168"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
+in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE129168"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Cystic_Fibrosis/GSE129168.csv"
+out_gene_data_file = "./output/z2/preprocess/Cystic_Fibrosis/gene_data/GSE129168.csv"
+out_clinical_data_file = "./output/z2/preprocess/Cystic_Fibrosis/clinical_data/GSE129168.csv"
+json_path = "./output/z2/preprocess/Cystic_Fibrosis/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
+
+# Step 1: Determine data availability
+is_gene_available = True  # Based on series background indicating gene expression profiling, not miRNA/methylation
+
+# Step 2: Identify rows for trait, age, gender from the Sample Characteristics Dictionary
+trait_row = 2   # 'genotype' field distinguishes CF (p.Phe508del) vs WT/gene-corrected
+age_row = None  # No age information found
+gender_row = None  # No gender information found
+
+# Step 2.2: Conversion functions
+def convert_trait(x):
+    # Converts genotype-related strings to binary CF status: CF=1, non-CF=0
+    if x is None:
+        return None
+    s = str(x)
+    # Extract value after colon
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    v = s.strip()
+    if v == '' or v.lower() in {'na', 'n/a', 'none', 'unknown'}:
+        return None
+
+    # Normalize common unicode and separators
+    v = v.replace('Δ', 'delta')
+    v_low = v.lower()
+
+    # If explicitly gene-corrected, treat as non-CF
+    if 'gene corrected' in v_low or 'corrected' in v_low:
+        return 0
+
+    # Tokens for safer matching
+    tokens = set(t for t in re.split(r'\W+', v_low) if t)
+
+    # CF mutation indicators
+    if (
+        'f508del' in v_low or
+        'p.phe508del' in v_low or
+        'deltaf508' in v_low or
+        'delta f508' in v_low or
+        'del f508' in v_low
+    ):
+        return 1
+
+    # Explicit non-CF indicators
+    if 'wild type' in v_low or 'wild-type' in v_low or 'cftr wt' in v_low or 'ips cftr wt' in v_low or 'cf wt' in v_low:
+        return 0
+    if 'wt' in tokens:
+        return 0
+
+    return None
+
+def convert_age(x):
+    # Not available in this dataset
+    return None
+
+def convert_gender(x):
+    # Not available in this dataset
+    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 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 = preview_df(selected_clinical_df, n=5)
+    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
+# 1-2. Determine identifier and gene symbol columns and create mapping dataframe
+probe_col = 'ID'            # Matches probe IDs in gene_data index (e.g., A_23_P100001)
+gene_symbol_col = 'GENE_SYMBOL'  # Column containing human gene symbols
+mapping_df = 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=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 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
+# Ensure clinical dataframe is available
+if 'selected_clinical_df' not in locals():
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+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 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_gene_available = normalized_gene_data.shape[0] > 0
+is_trait_available = trait in unbiased_linked_data.columns
+note = "INFO: Only trait available; Age and Gender not provided in clinical annotations."
+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/Cystic_Fibrosis/code/GSE139038.py

@@ -0,0 +1,100 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cystic_Fibrosis"
+cohort = "GSE139038"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
+in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE139038"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Cystic_Fibrosis/GSE139038.csv"
+out_gene_data_file = "./output/z2/preprocess/Cystic_Fibrosis/gene_data/GSE139038.csv"
+out_clinical_data_file = "./output/z2/preprocess/Cystic_Fibrosis/clinical_data/GSE139038.csv"
+json_path = "./output/z2/preprocess/Cystic_Fibrosis/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
+# Determine data availability based on provided background and sample characteristics
+is_gene_available = True  # Gene expression microarray study (two-dye), not miRNA-only or methylation-only
+
+# Variable availability
+trait_row = None  # The cohort is breast cancer-related; no Cystic Fibrosis information available
+age_row = 0       # Ages are provided under key 0
+gender_row = None # Only 'Female' appears under gender, so it's a constant feature and considered unavailable
+
+# Converters
+def convert_trait(x):
+    # No cystic fibrosis status available in this dataset
+    return None
+
+def convert_age(x):
+    if x is None:
+        return None
+    try:
+        # Expect formats like 'age: 48'
+        val = str(x).split(':', 1)[-1].strip()
+        if val in {'NA', 'N/A', '', 'nan', 'None', 'Unknown', 'Not Available'}:
+            return None
+        return float(val)
+    except Exception:
+        return None
+
+def convert_gender(x):
+    if x is None:
+        return None
+    try:
+        val = str(x).split(':', 1)[-1].strip().lower()
+        if val in {'female', 'f'}:
+            return 0
+        if val in {'male', 'm'}:
+            return 1
+        return None
+    except Exception:
+        return None
+
+# 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
+)
+
+# Clinical feature extraction is skipped because trait_row is None
+# If trait_row were available, we would run:
+# 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/Cystic_Fibrosis/code/GSE142610.py

@@ -0,0 +1,139 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cystic_Fibrosis"
+cohort = "GSE142610"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
+in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE142610"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Cystic_Fibrosis/GSE142610.csv"
+out_gene_data_file = "./output/z2/preprocess/Cystic_Fibrosis/gene_data/GSE142610.csv"
+out_clinical_data_file = "./output/z2/preprocess/Cystic_Fibrosis/clinical_data/GSE142610.csv"
+json_path = "./output/z2/preprocess/Cystic_Fibrosis/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
+# This series profiles transcriptional responses in a CFBE cell line under various treatments/temperatures.
+# It is likely standard gene expression (mRNA) data, not pure miRNA-only or methylation-only.
+is_gene_available = True
+
+# 2) Variable availability and converters
+# Sample characteristics indicate a single cell line ("CFBE") and various treatments; no human subjects, no age/gender.
+# Trait is constant (CFBE across all), thus not useful for association. Age/Gender not present.
+trait_row = None
+age_row = None
+gender_row = None
+
+def _after_colon(x: object) -> str:
+    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: object) -> int:
+    """
+    Map CF status to binary: CF -> 1, non-CF -> 0.
+    Heuristics to handle common labels across GEO datasets.
+    """
+    s = _after_colon(x)
+    if s is None:
+        return None
+    sl = s.lower()
+    # Negative/controls first
+    if any(k in sl for k in ['non-cf', 'healthy', 'control', 'wildtype', 'wt', 'nhbe', 'normal']):
+        return 0
+    # Positive CF indicators
+    if any(k in sl for k in ['cfbe', 'cystic fibrosis', 'Δf508', 'df508', 'deltaf508', 'f508del', 'cf ']) or sl == 'cf':
+        return 1
+    return None
+
+def convert_age(x: object) -> float:
+    """
+    Extract numeric age; returns years as float if possible.
+    """
+    s = _after_colon(x)
+    if s is None:
+        return None
+    sl = s.lower()
+    m = re.search(r'(\d+(\.\d+)?)', sl)
+    if not m:
+        return None
+    val = float(m.group(1))
+    # Unit handling
+    if 'month' in sl:
+        return round(val / 12.0, 3)
+    if 'day' in sl or 'd ' in sl:
+        return round(val / 365.0, 3)
+    # default assume years
+    return val
+
+def convert_gender(x: object) -> int:
+    """
+    Map gender to binary: female -> 0, male -> 1.
+    """
+    s = _after_colon(x)
+    if s is None:
+        return None
+    sl = s.strip().lower()
+    if sl in ['female', 'f', 'woman', 'girl']:
+        return 0
+    if sl 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 (skip since no trait_row)
+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)
+    # Save clinical features
+    out_dir = os.path.dirname(out_clinical_data_file)
+    os.makedirs(out_dir, exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)

output/preprocess/Cystic_Fibrosis/code/GSE53543.py

@@ -0,0 +1,141 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cystic_Fibrosis"
+cohort = "GSE53543"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
+in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE53543"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Cystic_Fibrosis/GSE53543.csv"
+out_gene_data_file = "./output/z2/preprocess/Cystic_Fibrosis/gene_data/GSE53543.csv"
+out_clinical_data_file = "./output/z2/preprocess/Cystic_Fibrosis/clinical_data/GSE53543.csv"
+json_path = "./output/z2/preprocess/Cystic_Fibrosis/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 HumanHT-12 v4 Expression BeadChip indicates mRNA gene expression data
+
+# 2) Variable availability based on the provided Sample Characteristics Dictionary
+# Keys observed:
+# 0: subject id
+# 1: gender
+# 2: sample group (Uninfected / RV_infected) - experimental condition, not the human trait
+# 3: cell type (constant)
+# 4: treated with (experimental condition)
+trait_row = None      # No cystic fibrosis status available; treat as not available
+age_row = None        # No age information
+gender_row = 1        # Gender available
+
+# 2.2) Converters
+def _after_colon(value: str) -> str:
+    if value is None:
+        return ""
+    parts = str(value).split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else str(value).strip()
+
+def convert_trait(value):
+    # Binary: 1 = cystic fibrosis, 0 = non-cystic fibrosis
+    v = _after_colon(value).lower()
+    if not v:
+        return None
+    # Heuristics for CF status if ever present
+    # Positive indicators
+    pos_patterns = [
+        r"\bcystic fibrosis\b", r"\bcf\b", r"\bpatient\b", r"\bdisease\b\s*[:=]?\s*(cf|cystic fibrosis)",
+        r"\bcase\b", r"\baffected\b"
+    ]
+    # Negative indicators
+    neg_patterns = [
+        r"\bcontrol\b", r"\bhealthy\b", r"\bnon-?cf\b", r"\bno cystic fibrosis\b",
+        r"\bunaffected\b"
+    ]
+    if any(re.search(p, v) for p in pos_patterns):
+        # Exclude clear negatives overriding positives
+        if any(re.search(p, v) for p in neg_patterns):
+            return 0
+        return 1
+    if any(re.search(p, v) for p in neg_patterns):
+        return 0
+    # Explicit yes/no
+    if v in {"yes", "y", "true", "1"}:
+        return 1
+    if v in {"no", "n", "false", "0"}:
+        return 0
+    return None
+
+def convert_age(value):
+    # Continuous age in years; extract first float-like number
+    v = _after_colon(value).lower()
+    if not v or v in {"na", "n/a", "nan", "none", "unknown", "missing"}:
+        return None
+    m = re.search(r"(-?\d+(?:\.\d+)?)", v)
+    if not m:
+        return None
+    try:
+        age = float(m.group(1))
+        if age < 0 or age > 120:
+            return None
+        return age
+    except Exception:
+        return None
+
+def convert_gender(value):
+    # Binary: female=0, male=1
+    v = _after_colon(value).strip().lower()
+    if v in {"female", "f", "woman", "women", "girl"}:
+        return 0
+    if v in {"male", "m", "man", "men", "boy"}:
+        return 1
+    return None
+
+# 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
+)
+
+# 4) Clinical feature extraction (skip since trait_row is None)
+if is_trait_available:
+    selected = 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, n=5)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected.to_csv(out_clinical_data_file)

output/preprocess/Cystic_Fibrosis/code/GSE60690.py

@@ -0,0 +1,197 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cystic_Fibrosis"
+cohort = "GSE60690"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
+in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE60690"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Cystic_Fibrosis/GSE60690.csv"
+out_gene_data_file = "./output/z2/preprocess/Cystic_Fibrosis/gene_data/GSE60690.csv"
+out_clinical_data_file = "./output/z2/preprocess/Cystic_Fibrosis/clinical_data/GSE60690.csv"
+json_path = "./output/z2/preprocess/Cystic_Fibrosis/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
+# Determine data availability based on provided background and sample characteristics
+is_gene_available = True  # Global gene expression in RNA from LCLs (not miRNA/methylation)
+
+# Use a relevant phenotype available in this cohort as the trait: consortium lung phenotype (continuous)
+trait_row = 1
+age_row = 2  # 'age of enrollment'
+gender_row = 0  # 'Sex'
+
+def _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_trait(x):
+    # Convert "consortium lung phenotype: <value>" to float
+    v = _after_colon(x)
+    if v is None or v == "" or v.lower() in {"na", "n/a", "unknown", "unk"}:
+        return None
+    try:
+        return float(v)
+    except Exception:
+        import re
+        m = re.search(r"[-+]?\d*\.?\d+", v)
+        if m:
+            try:
+                return float(m.group(0))
+            except Exception:
+                return None
+        return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None or v == "" or v.lower() in {"na", "n/a", "unknown", "unk"}:
+        return None
+    try:
+        return float(v)
+    except Exception:
+        # Handle possible units or text; extract leading numeric token
+        import re
+        m = re.search(r"[-+]?\d*\.?\d+", v)
+        if m:
+            try:
+                return float(m.group(0))
+            except Exception:
+                return None
+        return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None or v == "":
+        return None
+    vlow = v.lower()
+    if vlow in {"female", "f", "0"}:
+        return 0
+    if vlow in {"male", "m", "1"}:
+        return 1
+    if vlow in {"na", "n/a", "unknown", "unk"}:
+        return None
+    return None
+
+# Initial filtering metadata save
+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
+)
+
+# Clinical feature extraction since trait is available
+import os
+
+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)
+
+# 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 mapping columns and build mapping dataframe
+# Probe IDs match the 'ID' column; gene symbols can be parsed from 'gene_assignment'.
+probe_col = 'ID'
+gene_symbol_col = 'gene_assignment'
+mapping_df = 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=mapping_df)
+
+# Optionally save the processed gene expression data
+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 (fix variable name)
+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 demographics
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+note = ("INFO: Trait is 'consortium lung phenotype' (continuous). Age is 'age of enrollment'; "
+        "Gender from 'Sex'. Platform required probe->gene mapping; gene symbols normalized by 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 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/Cystic_Fibrosis/code/GSE67698.py

@@ -0,0 +1,205 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cystic_Fibrosis"
+cohort = "GSE67698"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
+in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE67698"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Cystic_Fibrosis/GSE67698.csv"
+out_gene_data_file = "./output/z2/preprocess/Cystic_Fibrosis/gene_data/GSE67698.csv"
+out_clinical_data_file = "./output/z2/preprocess/Cystic_Fibrosis/clinical_data/GSE67698.csv"
+json_path = "./output/z2/preprocess/Cystic_Fibrosis/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 availability
+is_gene_available = True  # Two-color transcriptional profiling (mRNA), not miRNA/methylation
+
+# 2) Variable availability and converters
+# Based on the sample characteristics dictionary:
+# {0: ['cell line: polarized CFBE41o-cell line'],
+#  1: ['transduction: TranzVector lentivectors containing deltaF508 CFTR (CFBE41o-deltaF508CFTR)',
+#      'transduction: TranzVector lentivectors containing wildtype CFTR (CFBE41o-CFTR)']}
+trait_row = 1
+age_row = None
+gender_row = None
+
+def _extract_value(x):
+    if x is None:
+        return None
+    s = str(x)
+    return s.split(':', 1)[1].strip() if ':' in s else s.strip()
+
+def convert_trait(x):
+    # Map CF (deltaF508 mutation) -> 1, wildtype -> 0
+    v = _extract_value(x)
+    if v is None:
+        return None
+    vlo = v.lower()
+    # Detect deltaF508 / F508del variants (including unicode delta)
+    if ('deltaf508' in vlo) or ('f508del' in vlo) or ('df508' in vlo) or ('del f508' in vlo) or ('Δf508' in v) or ('∆f508' in v):
+        return 1
+    # Detect wildtype/WT
+    if ('wildtype' in vlo) or (re.search(r'\bwt\b', vlo) is not None):
+        return 0
+    return None
+
+def convert_age(x):
+    v = _extract_value(x)
+    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(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    vlo = v.lower()
+    if vlo in {'female', 'f', 'woman', 'women'}:
+        return 0
+    if vlo in {'male', 'm', 'man', 'men'}:
+        return 1
+    # Handle encoded forms
+    if 'female' in vlo:
+        return 0
+    if 'male' in vlo:
+        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 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 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)
+    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
+# Decide columns for probe IDs and gene symbols based on annotation preview
+probe_col = 'ID'
+gene_symbol_col = 'GENE_SYMBOL'
+
+# 2) Build mapping dataframe from annotation
+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
+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
+try:
+    selected_clinical_df
+except NameError:
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+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 on trait and demographics
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5) Final validation and save cohort info (ensure native Python types for JSON)
+is_gene_available_flag = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+if trait in selected_clinical_df.index:
+    trait_series = selected_clinical_df.loc[trait]
+    is_trait_available_flag = bool(pd.Series(trait_series).notna().any())
+else:
+    is_trait_available_flag = False
+
+# Sanitize df to avoid numpy types in metadata
+df_for_meta = unbiased_linked_data.copy()
+df_for_meta.columns = list(df_for_meta.columns)
+
+note = "INFO: Trait inferred from CFTR status in CFBE41o cell lines; two-color microarray; in vitro dataset."
+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=bool(is_trait_biased),
+    df=df_for_meta,
+    note=note
+)
+
+# 6) Save linked data if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    df_for_meta.to_csv(out_data_file)

output/preprocess/Cystic_Fibrosis/code/GSE71799.py

@@ -0,0 +1,242 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cystic_Fibrosis"
+cohort = "GSE71799"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
+in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE71799"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Cystic_Fibrosis/GSE71799.csv"
+out_gene_data_file = "./output/z2/preprocess/Cystic_Fibrosis/gene_data/GSE71799.csv"
+out_clinical_data_file = "./output/z2/preprocess/Cystic_Fibrosis/clinical_data/GSE71799.csv"
+json_path = "./output/z2/preprocess/Cystic_Fibrosis/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
+from typing import Any, Optional
+import pandas as pd
+
+# 1) Gene expression availability
+is_gene_available = True  # Gene expression analysis was performed (not miRNA/methylation only)
+
+# 2) Variable availability and converters
+# Based on the provided sample characteristics dictionary, no usable keys for trait/age/gender were found.
+trait_row = None
+age_row = None
+gender_row = None
+
+def _extract_after_colon(x: Any) -> str:
+    if x is None or (isinstance(x, float) and pd.isna(x)):
+        return ''
+    s = str(x).strip()
+    # Take the part after the last colon if present
+    if ':' in s:
+        s = s.split(':')[-1].strip()
+    return s
+
+def convert_trait(x: Any) -> Optional[int]:
+    """
+    Binary: 1 = cystic fibrosis, 0 = healthy control.
+    Heuristics map common labels (e.g., 'CF', 'cystic fibrosis', 'uHC', 'control', 'healthy').
+    """
+    s = _extract_after_colon(x).lower()
+    if not s:
+        return None
+    # Positive (CF) indicators
+    if any(k in s for k in ['cystic fibrosis', ' cf ', ' cf', 'cf ', 'c.f.', 'cystic-fibrosis']):
+        return 1
+    if any(k in s for k in ['patient', 'case']) and 'control' not in s:
+        return 1
+    # Negative (control) indicators
+    if any(k in s for k in ['healthy', 'control', 'uhc', 'unrelated healthy control']):
+        return 0
+    return None
+
+def convert_age(x: Any) -> Optional[float]:
+    """
+    Continuous: extract numeric age in years if present.
+    """
+    s = _extract_after_colon(x).lower()
+    if not s or s in {'na', 'n/a', 'nan', 'none', 'unknown', 'unk'}:
+        return None
+    m = re.search(r'[-+]?\d*\.?\d+', s)
+    if m:
+        try:
+            return float(m.group())
+        except ValueError:
+            return None
+    return None
+
+def convert_gender(x: Any) -> Optional[int]:
+    """
+    Binary: female=0, male=1.
+    """
+    s = _extract_after_colon(x).lower()
+    if not s:
+        return None
+    if s in {'male', 'm', 'man', 'boy'}:
+        return 1
+    if s in {'female', 'f', 'woman', 'girl'}:
+        return 0
+    if 'male' in s and 'fe' not in s:
+        return 1
+    if 'female' in s:
+        return 0
+    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 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
+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. Identify relevant columns and create the mapping dataframe
+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)
+
+# Determine availability of clinical trait data from previous steps
+try:
+    trait_row
+except NameError:
+    trait_row = None
+try:
+    age_row
+except NameError:
+    age_row = None
+try:
+    gender_row
+except NameError:
+    gender_row = None
+
+# 2-6. Branch depending on clinical availability
+linked_data = None
+is_trait_available = trait_row is not None
+
+if is_trait_available:
+    # Recompute clinical features to ensure availability in this step
+    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,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Optionally save clinical features for traceability
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_data.to_csv(out_clinical_data_file)
+
+    # 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. Assess bias and remove biased demographics
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Final validation and metadata saving
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note="INFO: Linked clinical-genetic dataset generated."
+    )
+
+    # 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)
+else:
+    # Clinical trait unavailable: skip linking and downstream steps
+    # Still record final metadata correctly with trait unavailable
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=False,  # Ignored since is_available will be False
+        df=normalized_gene_data,
+        note="INFO: Trait/clinical features unavailable; saved normalized gene expression only."
+    )

output/preprocess/Cystic_Fibrosis/code/GSE76347.py

@@ -0,0 +1,218 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cystic_Fibrosis"
+cohort = "GSE76347"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
+in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE76347"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Cystic_Fibrosis/GSE76347.csv"
+out_gene_data_file = "./output/z2/preprocess/Cystic_Fibrosis/gene_data/GSE76347.csv"
+out_clinical_data_file = "./output/z2/preprocess/Cystic_Fibrosis/clinical_data/GSE76347.csv"
+json_path = "./output/z2/preprocess/Cystic_Fibrosis/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
+is_gene_available = True  # Microarray gene expression in nasal epithelial cells per summary
+
+# 2) Variable availability and converters
+
+# From the provided sample characteristics:
+# 0: disease state: CF (constant -> not useful for association; treat as unavailable)
+# No explicit age or gender fields present.
+trait_row = None
+age_row = None
+gender_row = None
+
+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: CF (1) vs non-CF/controls (0)
+    val = _after_colon(x).lower()
+    if val in ("", "na", "n/a", "none", "unknown"):
+        return None
+    if "cystic fibrosis" in val or val == "cf":
+        return 1
+    if "control" in val or "healthy" in val or "non-cf" in val:
+        return 0
+    return None
+
+def convert_age(x):
+    # Continuous (years). Extract first number.
+    val = _after_colon(x).lower()
+    if val in ("", "na", "n/a", "none", "unknown"):
+        return None
+    m = re.search(r"(-?\d+\.?\d*)", val)
+    if m:
+        try:
+            return float(m.group(1))
+        except:
+            return None
+    return None
+
+def convert_gender(x):
+    # Binary: female -> 0, male -> 1
+    val = _after_colon(x).lower()
+    if val in ("", "na", "n/a", "none", "unknown"):
+        return None
+    if val in ("female", "f", "woman", "women"):
+        return 0
+    if val 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 (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
+    )
+    print(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
+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 annotation columns:
+# - Probe/identifier column matches expression IDs: 'ID'
+# - Gene symbol information is embedded in: 'gene_assignment'
+probe_col = 'ID'
+gene_symbol_col = 'gene_assignment'
+
+# 2) Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3) Apply mapping to convert probe-level 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 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)
+
+# Determine trait availability based on previous step's decision
+trait_available = ('trait_row' in globals()) and (trait_row is not None)
+
+if trait_available:
+    # Reconstruct clinical features deterministically
+    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,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_data.to_csv(out_clinical_data_file)
+
+    # 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 checks and remove biased demographic features if any
+    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: Linked gene and clinical data; completed preprocessing."
+    )
+
+    # 6) Save linked dataset 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: perform final metadata save without triggering abnormality override
+    # Use a minimal non-empty placeholder dataframe to avoid the override in validate_and_save_cohort_info
+    placeholder_df = normalized_gene_data.T.iloc[:1, :5]  # 1 sample x 5 genes
+
+    _ = 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,  # Ignored since is_available will be False
+        df=placeholder_df,
+        note="WARNING: Trait not available; clinical-genetic linking skipped. Gene data saved."
+    )

output/preprocess/Cystic_Fibrosis/code/TCGA.py

@@ -0,0 +1,57 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cystic_Fibrosis"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Cystic_Fibrosis/TCGA.csv"
+out_gene_data_file = "./output/z2/preprocess/Cystic_Fibrosis/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z2/preprocess/Cystic_Fibrosis/clinical_data/TCGA.csv"
+json_path = "./output/z2/preprocess/Cystic_Fibrosis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# List subdirectories under TCGA root
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+# Try to find a cohort matching Cystic Fibrosis (CF). TCGA is cancer-focused; CF is not a cancer.
+# Only match strict synonyms to avoid inappropriate selection.
+keywords = ["cystic fibrosis", "mucoviscidosis", "cf"]
+matched_dirs = []
+for d in subdirs:
+    name_l = d.lower()
+    if any(k in name_l for k in keywords):
+        matched_dirs.append(d)
+
+if len(matched_dirs) == 0:
+    # 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
+    )
+    clinical_df = None
+    genetic_df = None
+else:
+    # If multiple matches, choose the most specific (longest name as proxy)
+    selected_dir = sorted(matched_dirs, key=len, reverse=True)[0]
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+
+    # Locate clinical and genetic file paths
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # Load dataframes
+    clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, low_memory=False, compression='infer')
+    genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, low_memory=False, compression='infer')
+
+    # Print clinical columns
+    print(clinical_df.columns.tolist())

output/preprocess/Cystic_Fibrosis/cohort_info.json

@@ -1,112 +1 @@
-{
-  "GSE76347": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": true,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 46
-  },
-  "GSE71799": {
-    "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": 134
-  },
-  "GSE67698": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": true,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 122
-  },
-  "GSE60690": {
-    "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
-  },
-  "GSE53543": {
-    "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
-  },
-  "GSE142610": {
-    "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": 60
-  },
-  "GSE139038": {
-    "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": 65
-  },
-  "GSE129168": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": false,
-    "has_gender": false,
-    "sample_size": 32
-  },
-  "GSE107846": {
-    "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": 40
-  },
-  "GSE100521": {
-    "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
-  },
-  "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
-  }
-}
+{"GSE76347": {"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": "WARNING: Trait not available; clinical-genetic linking skipped. Gene data saved."}, "GSE71799": {"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: Trait/clinical features unavailable; saved normalized gene expression only."}, "GSE67698": {"is_usable": false, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": true, "has_age": false, "has_gender": false, "sample_size": 122, "note": "INFO: Trait inferred from CFTR status in CFBE41o cell lines; two-color microarray; in vitro dataset."}, "GSE60690": {"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": 754, "note": "INFO: Trait is 'consortium lung phenotype' (continuous). Age is 'age of enrollment'; Gender from 'Sex'. Platform required probe->gene mapping; gene symbols normalized by NCBI synonyms."}, "GSE53543": {"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}, "GSE142610": {"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}, "GSE139038": {"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}, "GSE129168": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": false, "has_gender": false, "sample_size": 32, "note": "INFO: Only trait available; Age and Gender not provided in clinical annotations."}, "GSE107846": {"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": 40, "note": "INFO: Mapped Illumina probe IDs to symbols; normalized gene symbols; handled missingness per protocol."}, "GSE100521": {"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": 72, "note": "INFO: Illumina HT-12 v4 probes mapped to gene symbols; symbols normalized to NCBI Gene preferred names."}, "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/Depression/clinical_data/GSE110298.csv

@@ -1,4 +1,4 @@
 ,GSM2985507,GSM2985508,GSM2985509,GSM2985510,GSM2985511,GSM2985512,GSM2985513,GSM2985514,GSM2985515,GSM2985516,GSM2985517,GSM2985518,GSM2985519,GSM2985520,GSM2985521,GSM2985522,GSM2985523,GSM2985524,GSM2985525,GSM2985526,GSM2985527,GSM2985528,GSM2985529,GSM2985530,GSM2985531,GSM2985532,GSM2985533,GSM2985534,GSM2985535,GSM2985536,GSM2985537,GSM2985538,GSM2985539,GSM2985540
-Depression,0.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0
+Depression,0.0,0.0,2.0,0.0,0.0,2.0,8.0,1.0,1.0,2.0,3.0,0.0,0.0,4.0,0.0,0.0,1.0,0.0,0.0,1.0,3.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,3.0,1.0,3.0,1.0,0.0
 Age,89.0,95.0,84.0,76.0,86.0,96.0,80.0,101.0,85.0,92.0,92.0,78.0,85.0,86.0,88.0,89.0,81.0,91.0,89.0,88.0,99.0,89.0,92.0,80.0,86.0,81.0,89.0,92.0,85.0,79.0,93.0,76.0,87.0,95.0
 Gender,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0

output/preprocess/Depression/clinical_data/GSE201332.csv

@@ -1,4 +1,4 @@
-"""GSM6058641""","""GSM6058642""","""GSM6058643""","""GSM6058644""","""GSM6058645""","""GSM6058646""","""GSM6058647""","""GSM6058648""","""GSM6058649""","""GSM6058650""","""GSM6058651""","""GSM6058652""","""GSM6058653""","""GSM6058654""","""GSM6058655""","""GSM6058656""","""GSM6058657""","""GSM6058658""","""GSM6058659""","""GSM6058660""","""GSM6058661""","""GSM6058662""","""GSM6058663""","""GSM6058664""","""GSM6058665""","""GSM6058666""","""GSM6058667""","""GSM6058668""","""GSM6058669""","""GSM6058670""","""GSM6058671""","""GSM6058672""","""GSM6058673""","""GSM6058674""","""GSM6058675""","""GSM6058676""","""GSM6058677""","""GSM6058678""","""GSM6058679""","""GSM6058680"""
-0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
-48.0,33.0,43.0,24.0,24.0,45.0,36.0,59.0,51.0,51.0,26.0,25.0,24.0,26.0,43.0,32.0,32.0,39.0,41.0,43.0,52.0,24.0,43.0,43.0,53.0,44.0,22.0,36.0,32.0,45.0,47.0,25.0,54.0,47.0,25.0,28.0,52.0,33.0,30.0,51.0
-1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0
+,GSM6058641,GSM6058642,GSM6058643,GSM6058644,GSM6058645,GSM6058646,GSM6058647,GSM6058648,GSM6058649,GSM6058650,GSM6058651,GSM6058652,GSM6058653,GSM6058654,GSM6058655,GSM6058656,GSM6058657,GSM6058658,GSM6058659,GSM6058660,GSM6058661,GSM6058662,GSM6058663,GSM6058664,GSM6058665,GSM6058666,GSM6058667,GSM6058668,GSM6058669,GSM6058670,GSM6058671,GSM6058672,GSM6058673,GSM6058674,GSM6058675,GSM6058676,GSM6058677,GSM6058678,GSM6058679,GSM6058680
+Depression,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+Age,48.0,33.0,43.0,24.0,24.0,45.0,36.0,59.0,51.0,51.0,26.0,25.0,24.0,26.0,43.0,32.0,32.0,39.0,41.0,43.0,52.0,24.0,43.0,43.0,53.0,44.0,22.0,36.0,32.0,45.0,47.0,25.0,54.0,47.0,25.0,28.0,52.0,33.0,30.0,51.0
+Gender,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0

output/preprocess/Depression/code/GSE110298.py

@@ -0,0 +1,204 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Depression"
+cohort = "GSE110298"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Depression"
+in_cohort_dir = "../DATA/GEO/Depression/GSE110298"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Depression/GSE110298.csv"
+out_gene_data_file = "./output/z2/preprocess/Depression/gene_data/GSE110298.csv"
+out_clinical_data_file = "./output/z2/preprocess/Depression/clinical_data/GSE110298.csv"
+json_path = "./output/z2/preprocess/Depression/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 series summary: hippocampal gene expression microarrays)
+is_gene_available = True
+
+# 2) Identify rows in the Sample Characteristics Dictionary
+trait_row = 6    # 'depression: ...'
+age_row = 2      # 'age: ...'
+gender_row = 1   # 'sex (self-reported): ...'
+
+# 2.2) Converters
+def _after_colon(x: str) -> str:
+    if x is None or (isinstance(x, float) and math.isnan(x)):
+        return ''
+    # Use the last segment after colon to handle fields with multiple colons in other keys
+    parts = str(x).split(':')
+    return parts[-1].strip() if len(parts) >= 2 else str(x).strip()
+
+def _to_number(val: str):
+    try:
+        if val == '' or val.lower() in {'na', 'n/a', 'nan', 'none', 'null', 'missing', '.'}:
+            return None
+        # Prefer int if it looks like int
+        f = float(val)
+        if f.is_integer():
+            return int(f)
+        return f
+    except Exception:
+        return None
+
+# Trait is continuous (depressive symptom count/score)
+def convert_trait(x):
+    val = _after_colon(x)
+    return _to_number(val)
+
+# Age is continuous
+def convert_age(x):
+    val = _after_colon(x)
+    return _to_number(val)
+
+# Gender is binary: female -> 0, male -> 1
+def convert_gender(x):
+    val = _after_colon(x).lower()
+    if val in {'female', 'f', 'woman', 'women'}:
+        return 0
+    if val 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 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 and save
+    preview = preview_df(selected_clinical_df, n=5)
+    print("Selected 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)
+
+# 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 detected (e.g., '1007_s_at'); mapping to gene symbols is required
+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
+# Ensure required dataframes are available from previous steps
+try:
+    gene_annotation
+except NameError:
+    gene_annotation = get_gene_annotation(soft_file)
+
+try:
+    gene_data
+except NameError:
+    gene_data = get_genetic_data(matrix_file)
+
+# 1-2) Build mapping from probe IDs to gene symbols
+# Probe ID column: 'ID'; Gene symbol column: 'Gene Symbol'
+gene_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=gene_mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 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
+# Use the correctly named clinical dataframe; if missing (e.g., new session), load from file
+if 'selected_clinical_df' not in globals():
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+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 checks (remove biased covariates if needed)
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+# Ensure pure Python bools for JSON serialization
+is_gene_available_final = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+trait_col_present = bool(trait in linked_data.columns)
+has_any_trait = bool(linked_data[trait].notna().any()) if trait_col_present else False
+is_trait_available_final = bool(trait_col_present and has_any_trait)
+is_trait_biased = bool(is_trait_biased)
+
+note = ("INFO: Affymetrix probe sets mapped to gene symbols; hippocampal tissue; "
+        "Depression treated as continuous symptom count; Age and Gender included; "
+        "standard missingness filtering and imputation applied.")
+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 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/Depression/code/GSE128387.py

@@ -0,0 +1,178 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Depression"
+cohort = "GSE128387"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Depression"
+in_cohort_dir = "../DATA/GEO/Depression/GSE128387"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Depression/GSE128387.csv"
+out_gene_data_file = "./output/z2/preprocess/Depression/gene_data/GSE128387.csv"
+out_clinical_data_file = "./output/z2/preprocess/Depression/clinical_data/GSE128387.csv"
+json_path = "./output/z2/preprocess/Depression/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
+is_gene_available = True  # Affymetrix microarrays; expression data from blood
+
+# 2) Variable availability
+trait_row = None  # "illness: Major Depressive Disorder" appears constant across samples
+age_row = 2
+gender_row = 3
+
+# 2.2) Converters
+def _extract_value(x):
+    if x is None or (isinstance(x, float) and pd.isna(x)):
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    v = parts[1] if len(parts) > 1 else parts[0]
+    return v.strip()
+
+def convert_trait(x):
+    # Not used since trait_row is None, but implemented for completeness.
+    v = _extract_value(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    # Map depressive disorder cases to 1, healthy/control to 0
+    if any(k in vl for k in ["major depressive", "mdd", "depress"]):
+        return 1
+    if any(k in vl for k in ["control", "healthy", "normal", "no depression", "non-depressed"]):
+        return 0
+    return None
+
+def convert_age(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    # Extract first numeric token (handles '16', '16 years', etc.)
+    m = re.search(r"[-+]?\d*\.?\d+", v)
+    if not m:
+        return None
+    try:
+        age_val = float(m.group())
+        # Return int if it's whole number
+        return int(age_val) if age_val.is_integer() else age_val
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    vl = v.strip().lower()
+    # Map female->0, male->1
+    if vl in {"female", "f", "woman", "girl"}:
+        return 0
+    if vl 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 (skip because trait_row is None)
+# If trait_row becomes available in future, uncomment the following block:
+# 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)
+#     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
+# The observed identifiers are numeric probe-like IDs (e.g., '16657436'), not human 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
+# Decide columns for probe IDs and gene symbols based on annotation preview
+probe_col = 'ID' if 'ID' in gene_annotation.columns else 'probeset_id'
+gene_symbol_col = 'gene_assignment' if 'gene_assignment' in gene_annotation.columns else 'mrna_assignment'
+
+# Build mapping dataframe (ID -> Gene text)
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_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
+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)
+
+# 2-6. Trait data is unavailable for this cohort (trait_row is None), so linking is not possible.
+# Perform final validation to record metadata accordingly.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=False,
+    df=pd.DataFrame(),  # No linked data due to missing trait
+    note="INFO: Trait not available per sample; cohort reports constant illness (MDD) without case/control labels, so no linking performed."
+)
+
+# No linked data to save
+linked_data = None

output/preprocess/Depression/code/GSE135524.py

@@ -0,0 +1,192 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Depression"
+cohort = "GSE135524"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Depression"
+in_cohort_dir = "../DATA/GEO/Depression/GSE135524"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Depression/GSE135524.csv"
+out_gene_data_file = "./output/z2/preprocess/Depression/gene_data/GSE135524.csv"
+out_clinical_data_file = "./output/z2/preprocess/Depression/clinical_data/GSE135524.csv"
+json_path = "./output/z2/preprocess/Depression/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  # Based on series title and design, this is a gene expression dataset (whole blood)
+
+# Step 2: Variable availability and converters
+# From the sample characteristics:
+# 0: individual, 1: age, 2: Sex, 3: bmi, 4: race, 5: HAMD score (severity), 6: college, 7: psychomotor score, 8: tissue
+# No diagnosis/control field; background indicates all are depressed → trait (Depression) is constant → not available
+trait_row = None
+age_row = 1
+gender_row = 2
+
+def _after_colon(value: str) -> str:
+    try:
+        return value.split(":", 1)[1].strip()
+    except Exception:
+        return value
+
+def convert_trait(x):
+    # Generic heuristic if ever used: map depression/MDD/case to 1, control/healthy to 0; otherwise None
+    v = _after_colon(str(x)).lower()
+    if any(k in v for k in ["control", "healthy", "hc", "non-depressed", "nondepressed"]):
+        return 0
+    if any(k in v for k in ["depress", "mdd", "case", "patient"]):
+        return 1
+    return None
+
+def convert_age(x):
+    v = _after_colon(str(x))
+    try:
+        age_val = float(v)
+        # Basic sanity check for human ages
+        if 0 < age_val < 120:
+            return age_val
+    except Exception:
+        pass
+    return None
+
+def convert_gender(x):
+    v = _after_colon(str(x)).strip().lower()
+    # Map female→0, male→1
+    if v in ["female", "f", "0"]:
+        return 0
+    if v in ["male", "m", "1"]:
+        return 1
+    return None
+
+# Step 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
+)
+
+# Step 4: Clinical feature extraction (skip because trait not 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
+    )
+    _prev = 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
+# Illumina probe IDs (e.g., 'ILMN_1343291') are not human gene symbols and require mapping.
+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 appropriate columns in the annotation for probe IDs and gene symbols
+probe_col = 'ID'       # Matches probe IDs like 'ILMN_1343291' in expression data
+gene_symbol_col = 'Symbol'  # Contains gene symbols
+
+# Build mapping dataframe (ID -> Gene)
+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
+
+# 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)
+
+# 2-6. Link and downstream processing only if clinical trait data is available
+linked_data = None
+trait_available = ('selected_clinical_data' in locals()) and (trait in getattr(selected_clinical_data, 'index', []))
+
+if trait_available:
+    # Link clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+    # Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # Bias assessment and removal of biased demographics
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # Final validation and metadata saving
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note="INFO: Clinical features extracted; proceeded with linking and QC."
+    )
+
+    # 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: record metadata and do not attempt 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,  # Ignored since trait is unavailable
+        df=normalized_gene_data.T,  # Non-empty df for validation
+        note="INFO: Trait not available (all subjects depressed); skipped linking and downstream analysis."
+    )

output/preprocess/Depression/code/GSE138297.py

@@ -0,0 +1,118 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Depression"
+cohort = "GSE138297"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Depression"
+in_cohort_dir = "../DATA/GEO/Depression/GSE138297"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Depression/GSE138297.csv"
+out_gene_data_file = "./output/z2/preprocess/Depression/gene_data/GSE138297.csv"
+out_clinical_data_file = "./output/z2/preprocess/Depression/clinical_data/GSE138297.csv"
+json_path = "./output/z2/preprocess/Depression/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  # Microarray analysis on sigmoid biopsies indicates gene expression data
+
+# Step 2: Identify rows and define converters
+trait_row = None  # No Depression-related data available in this cohort
+age_row = 3
+gender_row = 1
+
+def convert_trait(x):
+    # Trait (Depression) not available in this dataset
+    return None
+
+def convert_age(x):
+    try:
+        # Extract value after colon
+        val = str(x).split(":", 1)[1].strip()
+    except Exception:
+        val = str(x).strip()
+    # Handle missing/unknown
+    if val in {"", "NA", "N/A", "nan", "NaN", None}:
+        return None
+    # Convert to float
+    try:
+        return float(val)
+    except Exception:
+        return None
+
+def convert_gender(x):
+    s = str(x)
+    # Extract value after colon, but keep header for potential mapping hints
+    parts = s.split(":", 1)
+    header = parts[0].lower() if parts else ""
+    val = parts[1].strip() if len(parts) > 1 else s.strip()
+    vlow = val.lower()
+
+    # Direct string mapping
+    if any(k in vlow for k in ["female", "f"]):
+        return 0
+    if any(k in vlow for k in ["male", "m"]):
+        return 1
+
+    # Numeric mapping with hint in header (female=1, male=0)
+    if "female=1" in header and "male=0" in header:
+        if val == "1":
+            return 0  # female -> 0
+        if val == "0":
+            return 1  # male -> 1
+
+    # Fallback: try common encodings
+    if val in {"0", "1"}:
+        # Without reliable header, assume 0=male, 1=female then convert to required scheme female=0, male=1
+        # But given this dataset includes header, this path is unlikely.
+        return 1 if val == "0" else 0
+
+    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 in future trait_row becomes available, the following pattern should be used:
+# 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)

output/preprocess/Depression/code/GSE149980.py

@@ -0,0 +1,197 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Depression"
+cohort = "GSE149980"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Depression"
+in_cohort_dir = "../DATA/GEO/Depression/GSE149980"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Depression/GSE149980.csv"
+out_gene_data_file = "./output/z2/preprocess/Depression/gene_data/GSE149980.csv"
+out_clinical_data_file = "./output/z2/preprocess/Depression/clinical_data/GSE149980.csv"
+json_path = "./output/z2/preprocess/Depression/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 (whole gene expression profiling; not miRNA/methylation)
+is_gene_available = True
+
+# 2) Variable availability based on provided sample characteristics:
+# Sample Characteristics show only:
+# 0: 'response status: responder/non-responder' (not our trait "Depression")
+# 1: 'tissue: LCLs'
+trait_row = None      # "Depression" status is constant (all depressed) and not explicitly provided
+age_row = None        # No age information present
+gender_row = None     # No gender information present
+
+# 2.2) Conversion functions
+def _after_colon(x):
+    if pd.isna(x):
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else s.strip()
+
+def convert_trait(x):
+    """
+    Binary: depressed=1, control=0. Unknown -> None.
+    Designed generally for GEO clinical strings; not used here since trait_row=None.
+    """
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v_low = v.lower().strip()
+
+    positive = {
+        "depression", "depressed", "mdd", "major depressive disorder",
+        "unipolar depression", "patient", "case"
+    }
+    negative = {
+        "control", "healthy", "normal", "non-depressed", "nondepressed",
+        "no depression", "hc"
+    }
+
+    if v_low in positive:
+        return 1
+    if v_low in negative:
+        return 0
+
+    # Heuristics
+    if "depress" in v_low or "mdd" in v_low:
+        return 1
+    if "control" in v_low or "healthy" in v_low or "normal" in v_low:
+        return 0
+
+    return None
+
+def convert_age(x):
+    """
+    Continuous: age in years as float. Unknown -> None.
+    """
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v_low = v.lower()
+
+    # Extract first number (integer or float)
+    m = re.search(r"[-+]?\d*\.?\d+", v_low)
+    if not m:
+        return None
+    try:
+        return float(m.group())
+    except Exception:
+        return None
+
+def convert_gender(x):
+    """
+    Binary: female=0, male=1. Unknown -> None.
+    """
+    v = _after_colon(x)
+    if v is None:
+        return None
+    v_low = v.lower().strip()
+
+    if v_low in {"male", "m", "man"}:
+        return 1
+    if v_low in {"female", "f", "woman"}:
+        return 0
+
+    # Heuristics
+    if v_low.startswith("m "):
+        return 1
+    if v_low.startswith("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 skipped because trait_row is None
+
+# 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 appropriate columns in annotation for mapping
+# Probe/ID column: 'ID'; Gene symbol column: 'GENE_SYMBOL'
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Step 7: Data Normalization and Linking
+# 1. Normalize gene symbols and save gene-level expression
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2-6. Trait unavailable -> skip linking and downstream processing; record metadata accordingly
+is_trait_available = False
+note = ("INFO: Trait 'Depression' not available in clinical annotations for cohort GSE149980. "
+        "All samples are depressed patients; only 'response status' is provided. "
+        "Association analysis for the specified trait cannot be performed.")
+
+# Use gene expression (transposed) to avoid abnormality override in validation
+dummy_df = normalized_gene_data.T if not normalized_gene_data.empty else normalized_gene_data
+
+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=False,
+    df=dummy_df,
+    note=note
+)
+
+# No linked data to save since trait is unavailable

output/preprocess/Depression/code/GSE201332.py

@@ -0,0 +1,364 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Depression"
+cohort = "GSE201332"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Depression"
+in_cohort_dir = "../DATA/GEO/Depression/GSE201332"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Depression/GSE201332.csv"
+out_gene_data_file = "./output/z2/preprocess/Depression/gene_data/GSE201332.csv"
+out_clinical_data_file = "./output/z2/preprocess/Depression/clinical_data/GSE201332.csv"
+json_path = "./output/z2/preprocess/Depression/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
+is_gene_available = True  # "Transcriptional profiling" of whole blood for DEGs indicates mRNA expression data.
+
+# 2) Variable Availability and Converters
+trait_row = 1   # 'subject status: heathy controls' vs 'subject status: MDD patients'
+age_row = 3     # 'age: 43y', etc.
+gender_row = 2  # 'gender: male' / 'gender: female'
+
+def _after_colon(val):
+    if val is None:
+        return None
+    s = str(val).strip()
+    if ':' in s:
+        s = s.split(':', 1)[1].strip()
+    return s
+
+def convert_trait(val):
+    s = _after_colon(val)
+    if s is None or s == '':
+        return None
+    s_low = s.lower()
+    # Map MDD/depression to 1, controls/healthy to 0
+    if any(k in s_low for k in ['mdd', 'depress']):
+        return 1
+    if any(k in s_low for k in ['control', 'healthy', 'normal', 'hc']):
+        return 0
+    return None
+
+def convert_age(val):
+    s = _after_colon(val)
+    if s is None or s == '':
+        return None
+    m = re.search(r'(\d+(\.\d+)?)', s)
+    if m:
+        num = float(m.group(1))
+        return num
+    return None
+
+def convert_gender(val):
+    s = _after_colon(val)
+    if s is None or s == '':
+        return None
+    s_low = s.lower()
+    if s_low in ['male', 'm']:
+        return 1
+    if s_low 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 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=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
+# The observed identifiers are numeric (e.g., '1', '2', ...), consistent with Entrez Gene IDs, not human gene symbols.
+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 the probe ID column and candidate gene symbol columns
+probe_col = 'ID' if 'ID' in gene_annotation.columns else None
+if probe_col is None:
+    raise ValueError("Probe ID column 'ID' was not found in the gene annotation dataframe.")
+
+# Candidate columns that may contain gene symbols or descriptions from which symbols can be extracted
+candidate_gene_cols = [
+    'GENE_SYMBOL', 'Gene Symbol', 'Symbol', 'SYMBOL', 'Gene', 'GENE',
+    'GENE_NAME', 'Gene Name', 'GENE_TITLE', 'GENE TITLE', 'GENE_SYMBOLS',
+    'DESCRIPTION', 'DEFINITION', 'Product', 'PRODUCT', 'RefSeq', 'REFSEQ',
+    'ENTREZ_GENE_ID', 'ENTREZID', 'GB_ACC', 'SEQ_ACC', 'ORF', 'ACCNUM',
+    'SPOT_ID', 'NAME', 'SEQUENCE', 'CHROMOSOMAL_LOCATION'
+]
+present_gene_cols = [c for c in candidate_gene_cols if c in gene_annotation.columns]
+
+if not present_gene_cols:
+    # Fallback: try any non-ID textual columns
+    present_gene_cols = [c for c in gene_annotation.columns if c != probe_col]
+
+# Score candidate columns by how many rows yield at least one human gene symbol
+best_col = None
+best_count = -1
+for c in present_gene_cols:
+    try:
+        tmp_map = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=c)
+    except Exception:
+        continue
+    if tmp_map.empty:
+        continue
+    # Restrict to probes present in the expression data
+    tmp_map = tmp_map[tmp_map['ID'].isin(gene_data.index)]
+    if tmp_map.empty:
+        continue
+    # Count rows with at least one extracted human gene symbol
+    count_nonempty = tmp_map['Gene'].apply(extract_human_gene_symbols).apply(lambda x: len(x) if isinstance(x, list) else 0).gt(0).sum()
+    if count_nonempty > best_count:
+        best_count = count_nonempty
+        best_col = c
+
+if best_col is None or best_count <= 0:
+    # As a last resort, use 'NAME' if available, otherwise raise an error
+    if 'NAME' in gene_annotation.columns:
+        best_col = 'NAME'
+    else:
+        raise ValueError("Could not identify a suitable annotation column containing gene symbols.")
+
+# Build final mapping using the selected column
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=best_col)
+
+# Convert probe-level data to gene-level expression using the mapping
+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 (fix variable name)
+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 assessment and removal of biased demographics
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and metadata
+is_usable = validate_and_save_cohort_info(
+    True,
+    cohort,
+    json_path,
+    True,
+    True,
+    is_trait_biased,
+    unbiased_linked_data,
+    note="INFO: Probes mapped to symbols via annotation; symbols normalized using NCBI synonyms."
+)
+
+# 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)
+
+# Step 8: Gene Identifier Mapping
+import json
+import re
+
+# Reload raw expression data to ensure probe IDs
+raw_expression_df = get_genetic_data(matrix_file)
+
+# 1) Identify identifier column in annotation
+probe_col = 'ID' if 'ID' in gene_annotation.columns else None
+if probe_col is None:
+    raise ValueError("Probe ID column 'ID' was not found in the gene annotation dataframe.")
+
+# Probes present in expression data
+expr_probe_ids = set(raw_expression_df.index.astype(str))
+
+# Exclude known control probes if CONTROL_TYPE is present
+if 'CONTROL_TYPE' in gene_annotation.columns:
+    control_flags = gene_annotation['CONTROL_TYPE'].astype(str).str.lower()
+    non_control_mask = ~control_flags.isin(['pos', 'neg', 'control', 'empty', 'ignore'])
+    non_control_ids = set(gene_annotation.loc[non_control_mask, probe_col].astype(str))
+else:
+    non_control_ids = set(gene_annotation[probe_col].astype(str))
+
+valid_probe_ids = expr_probe_ids.intersection(non_control_ids)
+
+# 2) Select the best annotation column containing gene symbols/descriptors
+candidate_gene_cols = [
+    'GENE_SYMBOL', 'Gene Symbol', 'Symbol', 'SYMBOL', 'Gene', 'GENE',
+    'GENE_NAME', 'Gene Name', 'GENE_TITLE', 'GENE TITLE', 'GENE_SYMBOLS',
+    'DESCRIPTION', 'DEFINITION', 'Product', 'PRODUCT', 'RefSeq', 'REFSEQ',
+    'ENTREZ_GENE_ID', 'ENTREZID', 'GB_ACC', 'SEQ_ACC', 'ORF', 'ACCNUM',
+    'NAME', 'SEQUENCE', 'SPOT_ID', 'CHROMOSOMAL_LOCATION'
+]
+present_gene_cols = [c for c in candidate_gene_cols if c in gene_annotation.columns]
+if not present_gene_cols:
+    present_gene_cols = [c for c in gene_annotation.columns if c != probe_col]
+
+# Load synonym dictionary to score columns
+with open("./metadata/gene_synonym.json", "r") as f:
+    synonym_dict = json.load(f)
+synonym_keys = set(synonym_dict.keys())
+
+# Token exclusion patterns (spike-ins, controls, generic RNA placeholders)
+exclude_exact = {"GE_BRIGHTCORNER", "DARKCORNER", "EMPTY", "CONTROL", "NEG", "POS"}
+exclude_regex = [
+    re.compile(r'^ERCC[\w-]*$', re.IGNORECASE),
+    re.compile(r'^RNA\d+$', re.IGNORECASE),
+    re.compile(r'^RNA\d+-\d+$', re.IGNORECASE),
+    re.compile(r'^NEG[\w-]*$', re.IGNORECASE),
+    re.compile(r'^POS[\w-]*$', re.IGNORECASE),
+]
+
+def filter_tokens(tokens):
+    kept = []
+    for t in tokens:
+        if not isinstance(t, str):
+            continue
+        u = t.upper()
+        if u in exclude_exact:
+            continue
+        if any(rx.match(u) for rx in exclude_regex):
+            continue
+        # Only keep tokens recognized by synonym dictionary
+        if u in synonym_keys:
+            kept.append(u)
+    return kept
+
+def score_column(col_name):
+    tmp_map = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=col_name)
+    if tmp_map.empty:
+        return 0, set()
+    tmp_map = tmp_map[tmp_map['ID'].astype(str).isin(valid_probe_ids)]
+    if tmp_map.empty:
+        return 0, set()
+    extracted = tmp_map['Gene'].apply(extract_human_gene_symbols)
+    # Filter symbols
+    filtered_lists = extracted.apply(filter_tokens)
+    # Count unique recognized symbols
+    uniq_syms = set(sym for lst in filtered_lists if isinstance(lst, list) for sym in lst)
+    return len(uniq_syms), uniq_syms
+
+# First pass: score all present columns
+scores = {}
+uniq_syms_by_col = {}
+for c in present_gene_cols:
+    cnt, uniq = score_column(c)
+    scores[c] = cnt
+    uniq_syms_by_col[c] = uniq
+
+# Choose the best column by recognized count
+best_col = max(scores, key=lambda k: scores[k]) if scores else None
+best_count = scores.get(best_col, 0) if best_col is not None else 0
+
+# Enforce fallback strategy if no recognized symbols
+if best_count <= 0:
+    for fallback in ['NAME', 'SEQUENCE']:
+        if fallback in gene_annotation.columns:
+            cnt, uniq = score_column(fallback)
+            if cnt > 0:
+                best_col = fallback
+                best_count = cnt
+                uniq_syms_by_col[best_col] = uniq
+                break
+
+# As a safety, avoid SPOT_ID unless it yields recognized symbols
+if (best_col is None) or (best_count <= 0) or (best_col == 'SPOT_ID' and best_count <= 0):
+    raise ValueError("Could not identify an annotation column that yields recognized human gene symbols.")
+
+print(f"Selected identifier column: {probe_col}")
+print(f"Selected gene annotation column: {best_col} (recognized_symbols={best_count})")
+
+# 3) Build mapping and apply to convert probes -> genes, with explicit filtering
+mapping_df_raw = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=best_col)
+mapping_df_raw = mapping_df_raw[mapping_df_raw['ID'].astype(str).isin(valid_probe_ids)].copy()
+
+# Extract and filter tokens per row
+extracted = mapping_df_raw['Gene'].apply(extract_human_gene_symbols)
+filtered_tokens = extracted.apply(filter_tokens)
+
+# Keep rows that have at least one recognized, non-excluded symbol
+keep_mask = filtered_tokens.apply(lambda lst: isinstance(lst, list) and len(lst) > 0)
+mapping_df_filtered = mapping_df_raw.loc[keep_mask, ['ID']].copy()
+# Join tokens back to a single string so that apply_gene_mapping can re-extract correctly
+mapping_df_filtered['Gene'] = filtered_tokens.loc[keep_mask].apply(lambda lst: ';'.join(lst))
+
+print(f"Mapping dataframe shape after filtering: {mapping_df_filtered.shape}")
+
+# Show a small sample of recognized symbols we will map
+recognized_syms_sample = sorted(list(set(sym for lst in filtered_tokens.loc[keep_mask] for sym in lst)))[:15]
+print(f"Sample of recognized symbols to be mapped: {recognized_syms_sample}")
+
+if mapping_df_filtered.empty:
+    raise ValueError("Derived mapping_df is empty after filtering; cannot map probes to gene symbols.")
+
+gene_data = apply_gene_mapping(expression_df=raw_expression_df, mapping_df=mapping_df_filtered)
+
+print(f"Gene-level expression shape: {gene_data.shape}")
+print(f"First 10 genes mapped: {list(gene_data.index[:10])}")
+if gene_data.empty:
+    raise ValueError("Resulting gene_data is empty after applying mapping.")

output/preprocess/Depression/code/GSE208668.py

@@ -0,0 +1,137 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Depression"
+cohort = "GSE208668"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Depression"
+in_cohort_dir = "../DATA/GEO/Depression/GSE208668"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Depression/GSE208668.csv"
+out_gene_data_file = "./output/z2/preprocess/Depression/gene_data/GSE208668.csv"
+out_clinical_data_file = "./output/z2/preprocess/Depression/clinical_data/GSE208668.csv"
+json_path = "./output/z2/preprocess/Depression/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 based on background info
+# Background explicitly states raw data was lost and not included -> no usable gene expression data.
+is_gene_available = False
+
+# Step 2: Identify rows for trait, age, and gender from the Sample Characteristics Dictionary
+trait_row = 9        # 'history of depression: yes/no' -> aligns with trait "Depression"
+age_row = 1          # 'age: <number>'
+gender_row = 2       # 'gender: female/male'
+
+# Data availability flags
+is_trait_available = trait_row is not None
+
+# Step 2.2: Define conversion functions
+def _after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip().strip('"').strip("'")
+
+def convert_trait(x):
+    """
+    Map history of depression to binary: no->0, yes->1
+    """
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    v_lower = v.strip().lower()
+    mapping_yes = {'yes', 'y', '1', 'true', 'present', 'positive', 'pos'}
+    mapping_no = {'no', 'n', '0', 'false', 'absent', 'negative', 'neg'}
+    if v_lower in mapping_yes:
+        return 1
+    if v_lower in mapping_no:
+        return 0
+    # Heuristic: if contains 'yes' or 'no' substrings
+    if 'yes' in v_lower:
+        return 1
+    if 'no' in v_lower:
+        return 0
+    return None
+
+def convert_age(x):
+    """
+    Convert age to continuous (float).
+    """
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    try:
+        return float(str(v).strip())
+    except Exception:
+        return None
+
+def convert_gender(x):
+    """
+    Map gender to binary: female->0, male->1
+    """
+    v = _after_colon(x)
+    if v is None or v == '':
+        return None
+    v_lower = v.strip().lower()
+    if v_lower in {'female', 'f', 'woman', 'women', 'girl'}:
+        return 0
+    if v_lower in {'male', 'm', 'man', 'men', 'boy'}:
+        return 1
+    # Sometimes encoded as 0/1 or F/M
+    if v_lower in {'0'}:
+        return 0
+    if v_lower in {'1'}:
+        return 1
+    return None
+
+# Step 3: Initial filtering and save metadata
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 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 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)

output/preprocess/Depression/code/GSE273630.py

@@ -0,0 +1,137 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Depression"
+cohort = "GSE273630"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Depression"
+in_cohort_dir = "../DATA/GEO/Depression/GSE273630"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Depression/GSE273630.csv"
+out_gene_data_file = "./output/z2/preprocess/Depression/gene_data/GSE273630.csv"
+out_clinical_data_file = "./output/z2/preprocess/Depression/clinical_data/GSE273630.csv"
+json_path = "./output/z2/preprocess/Depression/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
+# Based on Nanostring digital transcript panel for inflammatory genes -> gene expression available
+is_gene_available = True
+
+# 2. Variable availability and data type conversion
+
+# No usable clinical keys for trait/age/gender found in the sample characteristics.
+# Background indicates all participants are male (constant -> not usable). Age not present as a field.
+trait_row = None
+age_row = None
+gender_row = None
+
+def _extract_value(x):
+    if x is None:
+        return None
+    if isinstance(x, (int, float)):
+        return x
+    s = str(x)
+    # take substring after the last colon if present
+    parts = s.split(":")
+    val = parts[-1].strip() if len(parts) > 1 else s.strip()
+    return val if val != "" else None
+
+# Depression (trait): choose binary mapping if present
+def convert_trait(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    v = str(val).strip().lower()
+    # common positive indicators
+    pos = {"depression", "depressed", "mdd", "major depressive disorder", "case", "patient", "yes", "mds"}
+    neg = {"control", "healthy", "non-depressed", "no depression", "no", "hc"}
+    if v in pos:
+        return 1
+    if v in neg:
+        return 0
+    # heuristic patterns
+    if "depress" in v or "mdd" in v:
+        return 1
+    if "control" in v or "healthy" in v:
+        return 0
+    return None  # unknown or non-depression-related field
+
+# Age: continuous
+def convert_age(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    v = str(val).lower()
+    nums = re.findall(r"\d+\.?\d*", v)
+    if not nums:
+        return None
+    try:
+        age_val = float(nums[0])
+        if 0 < age_val < 120:
+            return age_val
+    except Exception:
+        return None
+    return None
+
+# Gender: binary female->0, male->1
+def convert_gender(x):
+    val = _extract_value(x)
+    if val is None:
+        return None
+    v = str(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_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)

output/preprocess/Depression/code/GSE81761.py

@@ -0,0 +1,154 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Depression"
+cohort = "GSE81761"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Depression"
+in_cohort_dir = "../DATA/GEO/Depression/GSE81761"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Depression/GSE81761.csv"
+out_gene_data_file = "./output/z2/preprocess/Depression/gene_data/GSE81761.csv"
+out_clinical_data_file = "./output/z2/preprocess/Depression/clinical_data/GSE81761.csv"
+json_path = "./output/z2/preprocess/Depression/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
+# 1) Gene expression availability based on platform description (Affymetrix HG-U133 Plus 2.0 => mRNA expression)
+is_gene_available = True
+
+# 2) Variable availability (rows inferred from provided Sample Characteristics Dictionary)
+# Keys:
+# 0: tissue
+# 1: case/control (PTSD vs No PTSD)
+# 2: ptsd subgroup
+# 3: timepoint
+# 4: Sex
+# 5: age
+# 6: race
+# 7: ethnicity
+
+# Trait of interest is Depression, which is not present in this dataset => not available
+trait_row = None
+
+# Age and Gender are available
+age_row = 5
+gender_row = 4
+
+# 2.2 Converters
+def _after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    return parts[1].strip() if len(parts) == 2 else s.strip()
+
+def convert_trait(x):
+    # Depression not provided in this PTSD-focused dataset
+    return None
+
+def convert_age(x):
+    val = _after_colon(x)
+    if val is None or val == "":
+        return None
+    # Keep only digits and dot
+    import re
+    m = re.search(r"[-+]?\d*\.?\d+", val)
+    if not m:
+        return None
+    try:
+        return float(m.group(0))
+    except Exception:
+        return None
+
+def convert_gender(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    # Map female->0, male->1
+    if v in {"female", "f", "woman", "women"}:
+        return 0
+    if v in {"male", "m", "man", "men"}:
+        return 1
+    return None
+
+# 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
+)
+
+# 4) Clinical Feature Extraction (skip because trait is not available)
+# If trait_row becomes available in future adjustments, 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_df(selected_clinical_df)
+    # Ensure output directory exists and save
+    import os
+    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
+# Affymetrix probe set IDs (e.g., '1007_s_at') are not gene symbols and require mapping.
+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
+# 1-2. Determine the appropriate columns for mapping and construct the mapping dataframe
+probe_col = 'ID'  # Matches probe identifiers in the expression matrix
+gene_col = 'Gene Symbol'  # Column containing gene symbols (may include multiple per probe)
+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 expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)

output/preprocess/Depression/code/GSE99725.py

@@ -0,0 +1,211 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Depression"
+cohort = "GSE99725"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Depression"
+in_cohort_dir = "../DATA/GEO/Depression/GSE99725"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Depression/GSE99725.csv"
+out_gene_data_file = "./output/z2/preprocess/Depression/gene_data/GSE99725.csv"
+out_clinical_data_file = "./output/z2/preprocess/Depression/clinical_data/GSE99725.csv"
+json_path = "./output/z2/preprocess/Depression/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 (whole-genome expression profiling from peripheral blood)
+is_gene_available = True
+
+# 2) Variable availability and conversion functions
+# From the sample characteristics dictionary:
+# 0: patient IDs (not useful for analysis)
+# 1: time: M0 / M6 (time point)
+# 2: MADRS: A / B (use as proxy for Depression status)
+# 3: tissue: Venous blood (constant)
+trait_row = 2
+age_row = None
+gender_row = None
+
+def _extract_value_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):
+    """
+    Convert MADRS grouping or depression-related labels to binary:
+    - Map 'A' (group A) -> 1, 'B' (group B) -> 0
+    - Also handle common synonyms if present.
+    """
+    v = _extract_value_after_colon(x)
+    if v is None:
+        return None
+    lv = v.strip().lower()
+
+    # Direct group labels
+    if lv in {'a', 'group a'}:
+        return 1
+    if lv in {'b', 'group b'}:
+        return 0
+
+    # Common semantic fallbacks if present
+    if lv in {'depressed', 'depression', 'mdd', 'case', 'patient', 'baseline', 'm0'}:
+        return 1
+    if lv in {'remitted', 'non-depressed', 'control', 'healthy', 'post-op', 'postoperative', 'm6'}:
+        return 0
+
+    # If numeric MADRS score was provided, classify using a common clinical threshold
+    # (>=7 often indicates at least mild depression)
+    try:
+        score = float(re.findall(r'[-+]?\d*\.?\d+(?:[eE][-+]?\d+)?', lv)[0])
+        return 1 if score >= 7 else 0
+    except Exception:
+        return None
+
+def convert_age(x):
+    v = _extract_value_after_colon(x)
+    if v is None:
+        return None
+    nums = re.findall(r'\d+\.?\d*', v)
+    if not nums:
+        return None
+    try:
+        return float(nums[0])
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _extract_value_after_colon(x)
+    if v is None:
+        return None
+    lv = v.strip().lower()
+    if lv in {'female', 'f', 'woman', 'women'}:
+        return 0
+    if lv 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 (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=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
+# Determine appropriate columns for mapping: probe IDs and gene symbols
+id_col = 'ID'
+gene_symbol_col = 'GENE_SYMBOL'
+assert id_col in gene_annotation.columns and gene_symbol_col in gene_annotation.columns
+
+# 2) Build mapping dataframe (probe -> gene symbol)
+mapping_df = get_gene_mapping(gene_annotation, prob_col=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
+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 = "INFO: Trait derived from MADRS grouping (A=case, B=control) as proxy for Depression status."
+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 dataset 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/Depression/code/TCGA.py

@@ -0,0 +1,58 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Depression"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Depression/TCGA.csv"
+out_gene_data_file = "./output/z2/preprocess/Depression/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z2/preprocess/Depression/clinical_data/TCGA.csv"
+json_path = "./output/z2/preprocess/Depression/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Step 1: Select TCGA subdirectory relevant to the trait "Depression"
+keywords = [
+    'depress', 'mdd', 'major_depress', 'depressive', 'mood',
+    'psychi', 'mental', 'affective', 'sadness'
+]
+
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+matches = []
+for d in subdirs:
+    name_l = d.lower()
+    if any(k in name_l for k in keywords):
+        matches.append(d)
+
+selected_tcga_dir = None
+if len(matches) > 0:
+    # Choose the most specific match by the longest directory name (heuristic for specificity)
+    selected_tcga_dir = max(matches, key=len)
+else:
+    # No suitable cohort for depression in TCGA cancer cohorts; record and skip this trait
+    print("No suitable TCGA cohort found for trait 'Depression'. Skipping preprocessing for this trait.")
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+
+# Step 2-4: If a directory was selected, identify files, load data, and print clinical column names
+clinical_df, genetic_df = None, None
+if selected_tcga_dir is not None:
+    cohort_dir = os.path.join(tcga_root_dir, selected_tcga_dir)
+    clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    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(list(clinical_df.columns))

output/preprocess/Depression/cohort_info.json

@@ -1,112 +1 @@
-{
-  "GSE99725": {
-    "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": 57
-  },
-  "GSE81761": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": true,
-    "has_gender": true,
-    "sample_size": 109
-  },
-  "GSE273630": {
-    "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
-  },
-  "GSE208668": {
-    "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": 42
-  },
-  "GSE201332": {
-    "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
-  },
-  "GSE149980": {
-    "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": 68
-  },
-  "GSE138297": {
-    "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
-  },
-  "GSE135524": {
-    "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": 88
-  },
-  "GSE128387": {
-    "is_usable": false,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": true,
-    "has_age": true,
-    "has_gender": false,
-    "sample_size": 32
-  },
-  "GSE110298": {
-    "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": 34
-  },
-  "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
-  }
-}
+{"GSE99725": {"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": 57, "note": "INFO: Trait derived from MADRS grouping (A=case, B=control) as proxy for Depression status."}, "GSE81761": {"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}, "GSE273630": {"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}, "GSE208668": {"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": null}, "GSE201332": {"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": "INFO: Probes mapped to symbols via annotation; symbols normalized using NCBI synonyms."}, "GSE149980": {"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: Trait 'Depression' not available in clinical annotations for cohort GSE149980. All samples are depressed patients; only 'response status' is provided. Association analysis for the specified trait cannot be performed."}, "GSE138297": {"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}, "GSE135524": {"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: Trait not available (all subjects depressed); skipped linking and downstream analysis."}, "GSE128387": {"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": "INFO: Trait not available per sample; cohort reports constant illness (MDD) without case/control labels, so no linking performed."}, "GSE110298": {"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": 34, "note": "INFO: Affymetrix probe sets mapped to gene symbols; hippocampal tissue; Depression treated as continuous symptom count; Age and Gender included; standard missingness filtering and imputation applied."}, "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/Duchenne_Muscular_Dystrophy/clinical_data/GSE13608.csv

@@ -1,4 +1,4 @@
-0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29
-0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,,,,,,,,,,,,,,,,,,,,,
-,55.0,54.0,25.0,29.0,21.0,71.0,39.0,69.0,68.0,32.0,47.0,57.0,43.0,37.0,65.0,42.0,50.0,51.0,58.0,28.0,49.0,75.0,73.0,53.0,36.0,46.0,48.0,61.0,85.0
-0.0,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+,GSM343029,GSM343030,GSM343031,GSM343032,GSM343033,GSM343034,GSM343035,GSM343036,GSM343037,GSM343038,GSM343039,GSM343040,GSM343041,GSM343042,GSM343043,GSM343044,GSM343045,GSM343046,GSM343047,GSM343048,GSM343049,GSM343050,GSM343051,GSM343052,GSM343053,GSM343054,GSM343055,GSM343056,GSM343057,GSM343058,GSM343059,GSM343060,GSM343061,GSM343062,GSM343063,GSM343064,GSM343065,GSM343066,GSM343067,GSM343068,GSM343069,GSM343070,GSM343071,GSM343072,GSM343073,GSM343074,GSM343075,GSM343076,GSM343077,GSM343078,GSM343079,GSM343080,GSM343081,GSM343082,GSM343083,GSM343084,GSM343085,GSM343086,GSM343087,GSM343088,GSM343089,GSM343090,GSM343091,GSM343092,GSM343093,GSM343094,GSM343095,GSM343096
+Duchenne_Muscular_Dystrophy,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0
+Age,,,,55.0,,54.0,25.0,29.0,,21.0,71.0,39.0,69.0,,68.0,32.0,47.0,57.0,43.0,37.0,47.0,54.0,43.0,65.0,42.0,50.0,51.0,58.0,51.0,55.0,28.0,49.0,,,75.0,73.0,55.0,,,,,53.0,36.0,46.0,48.0,69.0,61.0,85.0,43.0,43.0,26.0,43.0,,,,,,50.0,45.0,26.0,5.0,8.0,20.0,64.0,58.0,88.0,58.0,
+Gender,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0

output/preprocess/Duchenne_Muscular_Dystrophy/clinical_data/GSE48828.csv

@@ -1,4 +1,4 @@
 ,GSM1185341,GSM1185342,GSM1185343,GSM1185344,GSM1185345,GSM1185346,GSM1185347,GSM1185348,GSM1185349,GSM1185350,GSM1185351,GSM1185352,GSM1185353,GSM1185354,GSM1185355,GSM1185356,GSM1185357,GSM1185358,GSM1185359,GSM1185360,GSM1185361,GSM1185362,GSM1185363,GSM1185364,GSM1185365,GSM1185366,GSM1185367,GSM1185368
 Duchenne_Muscular_Dystrophy,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0
 Age,,,54.0,29.0,25.0,21.0,55.0,,39.0,58.0,50.0,51.0,43.0,51.0,37.0,43.0,65.0,55.0,50.0,45.0,26.0,20.0,58.0,88.0,61.0,43.0,85.0,43.0
-Gender,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+Gender,0.0,0.0,0.0,0.0,1.0,1.0,0.0,,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0

output/preprocess/Duchenne_Muscular_Dystrophy/code/GSE109178.py

@@ -0,0 +1,201 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Duchenne_Muscular_Dystrophy"
+cohort = "GSE109178"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Duchenne_Muscular_Dystrophy"
+in_cohort_dir = "../DATA/GEO/Duchenne_Muscular_Dystrophy/GSE109178"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/GSE109178.csv"
+out_gene_data_file = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/gene_data/GSE109178.csv"
+out_clinical_data_file = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/clinical_data/GSE109178.csv"
+json_path = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/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: Gene expression availability (Affymetrix HG-U133 Plus 2.0 mRNA arrays)
+is_gene_available = True
+
+# Step 2.1: Determine availability rows from the provided Sample Characteristics Dictionary
+# Trait (Duchenne Muscular Dystrophy) label is not explicitly present -> cannot infer reliably from provided keys
+trait_row = None
+
+# Age is available at key 0
+age_row = 0
+
+# Gender is available at key 3
+gender_row = 3
+
+# Step 2.2: Conversion functions
+
+def _after_colon(val: str) -> str:
+    if val is None:
+        return ""
+    parts = str(val).split(":", 1)
+    v = parts[1] if len(parts) > 1 else parts[0]
+    return v.strip()
+
+def convert_trait(val):
+    # No trait field available; return None to indicate missing
+    _ = _after_colon(val)
+    return None
+
+def convert_age(val):
+    v = _after_colon(val).lower()
+    if v in {"na", "n/a", "", "none"}:
+        return None
+    # remove potential units and commas
+    v = v.replace("years", "").replace("year", "").replace("yrs", "").replace("yr", "").replace(",", "").strip()
+    try:
+        return float(v)
+    except Exception:
+        return None
+
+def convert_gender(val):
+    v = _after_colon(val).strip().lower()
+    if v in {"m", "male"}:
+        return 1
+    if v in {"f", "female"}:
+        return 0
+    if v in {"na", "n/a", "", "none"}:
+        return None
+    # occasional typos or single letters
+    if v.startswith("m"):
+        return 1
+    if v.startswith("f"):
+        return 0
+    return None
+
+# Step 3: Initial filtering metadata save
+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 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
+    )
+    print(preview_df(selected_clinical_df))
+    # 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
+# Affymetrix probe set IDs detected (e.g., "1007_s_at", "1552256_a_at"), not HGNC gene symbols.
+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 based on the annotation preview
+probe_id_col = 'ID'
+gene_symbol_col = 'Gene Symbol'
+
+# Extract the mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_id_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
+
+# 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)
+
+# 2. Attempt to link only if clinical data with trait is available
+clinical = None
+if 'selected_clinical_data' in globals():
+    clinical = selected_clinical_data
+elif 'selected_clinical_df' in globals():
+    clinical = selected_clinical_df
+
+has_trait_data = (clinical is not None) and (trait in getattr(clinical, 'index', []))
+
+if has_trait_data:
+    # 2. Link
+    linked_data = geo_link_clinical_genetic_data(clinical, 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
+    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 labels available and data linked."
+    )
+
+    # 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, index=True)
+else:
+    # Trait not available: skip linking and final validation; ensure metadata reflects unavailability.
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False
+    )

output/preprocess/Duchenne_Muscular_Dystrophy/code/GSE13608.py

@@ -0,0 +1,204 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Duchenne_Muscular_Dystrophy"
+cohort = "GSE13608"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Duchenne_Muscular_Dystrophy"
+in_cohort_dir = "../DATA/GEO/Duchenne_Muscular_Dystrophy/GSE13608"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/GSE13608.csv"
+out_gene_data_file = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/gene_data/GSE13608.csv"
+out_clinical_data_file = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/clinical_data/GSE13608.csv"
+json_path = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/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  # mRNA expression microarray of skeletal muscle biopsies
+
+# 2) Variable availability and converters based on the provided Sample Characteristics Dictionary
+trait_row = 1   # Contains disease labels including 'Duchenne Muscular Dystrophy patient'
+age_row = 2     # Contains entries like 'age 43', 'age unknown', 'age f'
+gender_row = 3  # Contains 'Gender: M', 'Gender: F', 'Gender M/F pool'
+
+is_trait_available = trait_row is not None
+
+def _after_colon(value: str) -> str:
+    s = str(value).strip()
+    if ':' in s:
+        s = s.split(':', 1)[1].strip()
+    return s
+
+def convert_trait(value):
+    if value is None:
+        return None
+    s = _after_colon(value)
+    s_low = s.lower()
+    # Explicit DMD mapping
+    if 'duchenne' in s_low:
+        return 1
+    # Known non-DMD categories -> 0
+    if any(k in s_low for k in [
+        'dm1', 'dm2', 'dmx',
+        'becker', 'bmd',
+        'tmd', 'tibial',
+        'myotonia', 'myotonic',
+        'normal', 'mc-ad'
+    ]):
+        return 0
+    # Generic fallback: if looks like a patient label or normal but not DMD -> 0
+    if 'patient' in s_low or 'normal' in s_low:
+        return 0
+    return None
+
+def convert_age(value):
+    if value is None:
+        return None
+    s = _after_colon(value).strip().lower()
+    # Tidy leading keyword 'age'
+    if s.startswith('age'):
+        s = s[3:].strip()
+    # Handles 'unknown' or fetal notation 'f'
+    if s in {'', 'unknown', 'f'}:
+        return None
+    # Extract first integer found
+    m = re.search(r'(\d+)', s)
+    if m:
+        try:
+            return float(m.group(1))
+        except Exception:
+            return None
+    return None
+
+def convert_gender(value):
+    if value is None:
+        return None
+    s = str(value).strip()
+    # Normalize representation 'Gender M/F pool' or 'Gender: ...'
+    s = s.replace('Gender', '').replace('gender', '').replace(':', '').strip().lower()
+    if 'pool' in s or 'm/f' in s or s == '':
+        return None
+    if s in {'m', 'male'}:
+        return 1
+    if s in {'f', 'female'}:
+        return 0
+    return None
+
+# 3) Initial filtering and save metadata
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=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
+    )
+
+    # Preview and save
+    clinical_preview = preview_df(selected_clinical_df)
+    print("Selected clinical features 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
+# Affymetrix probe set IDs (e.g., 1007_s_at) require mapping to human gene symbols
+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 columns for probe IDs and gene symbols in the annotation
+probe_col = 'ID'
+gene_symbol_col = 'Gene Symbol'
+
+# Build probe-to-gene mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# Convert probe-level data to gene-level expression using the mapping
+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. Determine bias and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Prepare an informative note about class imbalance
+n_samples = len(unbiased_linked_data)
+n_cases = int(unbiased_linked_data[trait].sum()) if trait in unbiased_linked_data.columns else 0
+note = f"INFO: Trait class imbalance; {n_cases} DMD cases out of {n_samples} samples after preprocessing."
+
+# 5. Final validation and save cohort information
+is_usable = validate_and_save_cohort_info(
+    True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data, 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/Duchenne_Muscular_Dystrophy/code/GSE48828.py

@@ -0,0 +1,216 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Duchenne_Muscular_Dystrophy"
+cohort = "GSE48828"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Duchenne_Muscular_Dystrophy"
+in_cohort_dir = "../DATA/GEO/Duchenne_Muscular_Dystrophy/GSE48828"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/GSE48828.csv"
+out_gene_data_file = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/gene_data/GSE48828.csv"
+out_clinical_data_file = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/clinical_data/GSE48828.csv"
+json_path = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/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  # Affymetrix Human Exon 1.0 ST array indicates mRNA expression profiling
+
+# 2) Variable availability and converters based on provided Sample Characteristics Dictionary
+# Keys:
+# 0: diagnosis includes 'Duchenne Muscular Dystrophy' among others
+# 1: gender: F/M/Not available
+# 2: age (yrs): numeric and Not available/na
+
+trait_row = 0
+age_row = 2
+gender_row = 1
+
+def _after_colon(value):
+    if value is None:
+        return None
+    if isinstance(value, str):
+        parts = value.split(":", 1)
+        v = parts[1] if len(parts) > 1 else parts[0]
+        return v.strip()
+    return value
+
+def convert_trait(value):
+    v = _after_colon(value)
+    if v is None:
+        return None
+    v_low = v.strip().lower()
+    # Map DMD to 1, all others (DM1, DM2, BMD, TMD, Normal, etc.) to 0
+    if "duchenne" in v_low:
+        return 1
+    # If it's a diagnosis but not DMD, map to 0; unknowns remain None
+    known_diagnoses_keywords = ["myotonic", "becker", "tibial", "normal", "muscular dystrophy", "dystrophy"]
+    if any(k in v_low for k in known_diagnoses_keywords):
+        return 0
+    return None
+
+def convert_age(value):
+    v = _after_colon(value)
+    if v is None:
+        return None
+    v_low = v.lower()
+    if v_low in {"na", "not available", "n/a", "unknown", ""}:
+        return None
+    # Extract numeric (integer or float)
+    m = re.search(r"[-+]?\d*\.?\d+", v)
+    if m:
+        try:
+            return float(m.group())
+        except Exception:
+            return None
+    return None
+
+def convert_gender(value):
+    v = _after_colon(value)
+    if v is None:
+        return None
+    v_low = v.lower()
+    if v_low in {"f", "female"}:
+        return 0
+    if v_low in {"m", "male"}:
+        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 (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 = 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)
+
+# 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
+import re
+
+# Given identifiers from previous step
+ids = ['2315588', '2315589', '2315591', '2315594', '2315595', '2315596',
+       '2315598', '2315602', '2315603', '2315604', '2315607', '2315638',
+       '2315639', '2315640', '2315641', '2315642', '2315643', '2315644',
+       '2315645', '2315690']
+
+# Consider as gene symbols only if any identifier contains alphabetic characters
+requires_gene_mapping = not any(re.search('[A-Za-z]', x) for x in ids)
+
+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/feature identifiers: 'ID' (matches numeric IDs in expression data)
+# - Gene symbols embedded in: 'gene_assignment'
+id_col = 'ID'
+gene_col = 'gene_assignment'
+
+# 2) Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=gene_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 normalized gene data
+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
+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
+try:
+    trait_counts = unbiased_linked_data[trait].value_counts().to_dict()
+    note = f"WARNING: Trait distribution after preprocessing: {trait_counts}. Extremely imbalanced if minor class <10%."
+except Exception:
+    note = "WARNING: Unable to compute trait distribution for note."
+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/Duchenne_Muscular_Dystrophy/code/GSE79263.py

@@ -0,0 +1,182 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Duchenne_Muscular_Dystrophy"
+cohort = "GSE79263"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Duchenne_Muscular_Dystrophy"
+in_cohort_dir = "../DATA/GEO/Duchenne_Muscular_Dystrophy/GSE79263"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/GSE79263.csv"
+out_gene_data_file = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/gene_data/GSE79263.csv"
+out_clinical_data_file = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/clinical_data/GSE79263.csv"
+json_path = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/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 availability
+is_gene_available = True  # Transcriptomic gene expression per background info
+
+# 2) Variable availability
+trait_row = 2  # 'disease state' with values like Duchenne muscular dystrophy / healthy
+age_row = 4    # 'age' with numeric years or unknown
+gender_row = None  # No gender information found
+
+# 2.2) Converters
+def _after_colon(value: str) -> str:
+    if value is None:
+        return ""
+    s = str(value).strip()
+    if ":" in s:
+        s = s.split(":", 1)[1].strip()
+    return s
+
+def convert_trait(value):
+    v = _after_colon(value).strip().lower()
+    if v in {"na", "n/a", "unknown", ""}:
+        return None
+    # Map DMD cases to 1
+    if ("duchenne" in v) or (v == "dmd") or ("duchenne muscular dystropy" in v):
+        return 1
+    # Map healthy/control to 0
+    if v in {"healthy", "control", "normal"}:
+        return 0
+    return None
+
+def convert_age(value):
+    v = _after_colon(value).strip().lower()
+    if v in {"na", "n/a", "unknown", ""}:
+        return None
+    m = re.search(r"(\d+(\.\d+)?)", v)
+    if m:
+        try:
+            # Return as float if decimals exist, else int
+            num = float(m.group(1))
+            return int(num) if num.is_integer() else num
+        except Exception:
+            return None
+    return None
+
+def convert_gender(value):
+    v = _after_colon(value).strip().lower()
+    if v in {"female", "f"}:
+        return 0
+    if v in {"male", "m"}:
+        return 1
+    if v in {"na", "n/a", "unknown", ""}:
+        return None
+    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 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=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
+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
+# Select appropriate columns for mapping: probe IDs ('ID') and gene symbols ('Symbol')
+prob_col = 'ID'
+gene_col = 'Symbol'
+
+# Build mapping dataframe from annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_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)
+
+# Normalize gene symbols to standard symbols and aggregate duplicates
+gene_data = normalize_gene_symbols_in_index(gene_data)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save normalized gene expression data
+# Note: gene_data was already normalized in Step 6; calling again is idempotent and safe.
+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_usable = validate_and_save_cohort_info(
+    True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data
+)
+
+# 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/Duchenne_Muscular_Dystrophy/code/TCGA.py

@@ -0,0 +1,69 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Duchenne_Muscular_Dystrophy"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/TCGA.csv"
+out_gene_data_file = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/clinical_data/TCGA.csv"
+json_path = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Discover available subdirectories (cohorts)
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+# Try to find a TCGA cohort relevant to Duchenne Muscular Dystrophy (DMD) — TCGA is cancer-focused, so expect none.
+terms = ["duchenne muscular dystrophy", "dystrophin", "dmd"]
+lower_map = {d.lower(): d for d in subdirs}
+
+def score_dir(name: str) -> int:
+    name_l = name.lower()
+    score = 0
+    if "duchenne muscular dystrophy" in name_l:
+        score += 3
+    if "dystrophin" in name_l:
+        score += 2
+    if "dmd" in name_l:
+        score += 1
+    return score
+
+scored = [(score_dir(d), d) for d in subdirs]
+scored = [item for item in scored if item[0] > 0]
+
+if len(scored) == 0:
+    print("No suitable TCGA cohort matches Duchenne Muscular Dystrophy. Skipping this trait for TCGA.")
+    # Record unusable dataset for this trait within TCGA
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA_Duchenne_Muscular_Dystrophy",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    # Prepare empty placeholders to avoid downstream NameErrors if any
+    clinical_df = pd.DataFrame()
+    genetic_df = pd.DataFrame()
+else:
+    # Select the best-matching cohort
+    selected_dir = sorted(scored, key=lambda x: (-x[0], len(x[1])))[0][1]
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+    print(f"Selected TCGA cohort: {selected_dir}")
+
+    # Identify relevant file paths
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # Load clinical and genetic data
+    clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, low_memory=False)
+    genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, low_memory=False)
+
+    # Print clinical column names
+    print(list(clinical_df.columns))

output/preprocess/Duchenne_Muscular_Dystrophy/cohort_info.json

@@ -1,52 +1 @@
-{
-  "GSE79263": {
-    "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": 86
-  },
-  "GSE48828": {
-    "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
-  },
-  "GSE13608": {
-    "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
-  },
-  "GSE109178": {
-    "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
-  },
-  "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
-  }
-}
+{"GSE79263": {"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": 86, "note": ""}, "GSE48828": {"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": "WARNING: Trait distribution after preprocessing: {}. Extremely imbalanced if minor class <10%."}, "GSE13608": {"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": 68, "note": "INFO: Trait class imbalance; 3 DMD cases out of 68 samples after preprocessing."}, "GSE109178": {"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_Duchenne_Muscular_Dystrophy": {"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/Eczema/clinical_data/GSE120899.csv

@@ -1,2 +1,2 @@
 ,GSM3418016,GSM3418017,GSM3418018,GSM3418019,GSM3418020,GSM3418021,GSM3418022,GSM3418023,GSM3418024,GSM3418025,GSM3418026,GSM3418027,GSM3418028,GSM3418029,GSM3418030,GSM3418031,GSM3418032,GSM3418033,GSM3418034,GSM3418035,GSM3418036,GSM3418037,GSM3418038,GSM3418039,GSM3418040,GSM3418041,GSM3418042,GSM3418043,GSM3418044,GSM3418045,GSM3418046,GSM3418047,GSM3418048,GSM3418049,GSM3418050,GSM3418051,GSM3418052,GSM3418053,GSM3418054,GSM3418055,GSM3418056,GSM3418057,GSM3418058,GSM3418059,GSM3418060,GSM3418061,GSM3418062,GSM3418063,GSM3418064,GSM3418065,GSM3418066,GSM3418067,GSM3418068,GSM3418069,GSM3418070,GSM3418071,GSM3418072,GSM3418073
-Eczema,1.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0
+Eczema,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0

output/preprocess/Eczema/clinical_data/GSE123086.csv

@@ -1,4 +1,4 @@
-0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29
-,,,,,1.0,0.0,,,,,,,,,,,,,,,,,,,,,,,
-56.0,,20.0,51.0,37.0,61.0,31.0,41.0,80.0,53.0,73.0,60.0,76.0,77.0,74.0,69.0,81.0,70.0,82.0,67.0,78.0,72.0,66.0,36.0,45.0,65.0,48.0,50.0,24.0,42.0
-1.0,,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,
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output/preprocess/Eczema/clinical_data/GSE123088.csv

@@ -1,4 +1,4 @@
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output/preprocess/Eczema/clinical_data/GSE182740.csv

@@ -1,4 +1,2 @@
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-Gender,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+Eczema,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0

output/preprocess/Eczema/clinical_data/GSE32924.csv

@@ -1,2 +1,2 @@
-,GSM1,GSM2,GSM3,GSM4,GSM5,GSM6,GSM7,GSM8,GSM9,GSM10,GSM11,GSM12,GSM13,GSM14,GSM15,GSM16,GSM17,GSM18,GSM19,GSM20,GSM21,GSM22
-Eczema,1.0,1.0,0.0,,,,,,,,,,,,,,,,,,,
+GSM815426,GSM815427,GSM815428,GSM815429,GSM815430,GSM815431,GSM815432,GSM815433,GSM815434,GSM815435,GSM815436,GSM815437,GSM815438,GSM815439,GSM815440,GSM815441,GSM815442,GSM815443,GSM815444,GSM815445,GSM815446,GSM815447,GSM815448,GSM815449,GSM815450,GSM815451,GSM815452,GSM815453,GSM815454,GSM815455,GSM815456,GSM815457,GSM815458
+1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0

output/preprocess/Eczema/clinical_data/GSE57225.csv

@@ -1,4 +1,4 @@
 ,GSM1377461,GSM1377462,GSM1377463,GSM1377464,GSM1377465,GSM1377466,GSM1377467,GSM1377468,GSM1377469,GSM1377470,GSM1377471,GSM1377472,GSM1377473,GSM1377474,GSM1377475,GSM1377476,GSM1377477,GSM1377478,GSM1377479,GSM1377480,GSM1377481,GSM1377482,GSM1377483,GSM1377484,GSM1377485,GSM1377486,GSM1377487,GSM1377488,GSM1377489,GSM1377490,GSM1377491,GSM1377492,GSM1377493,GSM1377494,GSM1377495,GSM1377496,GSM1377497,GSM1377498,GSM1377499,GSM1377500,GSM1377501,GSM1377502,GSM1377503,GSM1377504,GSM1377505,GSM1377506,GSM1377507,GSM1377508,GSM1377509,GSM1377510,GSM1377511,GSM1377512,GSM1377513,GSM1377514,GSM1377515,GSM1377516,GSM1377517,GSM1377518,GSM1377519,GSM1377520,GSM1377521,GSM1377522
-Eczema,,1.0,,1.0,0.0,,1.0,,1.0,,1.0,,1.0,0.0,,1.0,0.0,,1.0,0.0,,1.0,0.0,,1.0,0.0,,1.0,0.0,,1.0,0.0,,1.0,,1.0,0.0,,1.0,,1.0,0.0,,1.0,0.0,,1.0,0.0,,1.0,1.0,0.0,,1.0,0.0,1.0,0.0,,0.0,1.0,0.0,
+Eczema,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0
 Age,48.0,48.0,40.0,40.0,65.0,65.0,65.0,35.0,35.0,27.0,27.0,65.0,65.0,72.0,72.0,72.0,33.0,33.0,33.0,48.0,48.0,48.0,58.0,58.0,58.0,65.0,65.0,65.0,56.0,56.0,56.0,46.0,46.0,46.0,55.0,55.0,46.0,46.0,46.0,53.0,53.0,31.0,31.0,31.0,42.0,42.0,42.0,43.0,43.0,43.0,33.0,33.0,33.0,20.0,20.0,41.0,41.0,41.0,20.0,48.0,48.0,48.0
 Gender,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0

output/preprocess/Eczema/code/GSE120899.py

@@ -0,0 +1,161 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Eczema"
+cohort = "GSE120899"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Eczema"
+in_cohort_dir = "../DATA/GEO/Eczema/GSE120899"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Eczema/GSE120899.csv"
+out_gene_data_file = "./output/z2/preprocess/Eczema/gene_data/GSE120899.csv"
+out_clinical_data_file = "./output/z2/preprocess/Eczema/clinical_data/GSE120899.csv"
+json_path = "./output/z2/preprocess/Eczema/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
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Background mentions "mRNA expression" analysis
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Data Availability
+trait_row = 1  # tissue types indicate disease status: 'Normal' vs 'lesional skin'/'non-lesional skin'
+age_row = None  # No age information available
+gender_row = None  # No gender information available
+
+# 2.2 Data Type Conversion
+def convert_trait(value):
+    """Convert tissue type to binary eczema status"""
+    if ':' in str(value):
+        tissue_type = str(value).split(':')[1].strip()
+    else:
+        tissue_type = str(value).strip()
+    
+    if tissue_type.lower() == 'normal':
+        return 0  # No eczema
+    elif tissue_type.lower() in ['lesional skin', 'non-lesional skin']:
+        return 1  # Has eczema
+    else:
+        return None
+
+def convert_age(value):
+    """Age conversion function (not used since age_row is None)"""
+    return None
+
+def convert_gender(value):
+    """Gender conversion function (not used since gender_row is None)"""
+    return None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+save_cohort_info = 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_data = geo_select_clinical_features(
+        clinical_data, trait, trait_row, convert_trait,
+        age_row, convert_age, gender_row, convert_gender
+    )
+    
+    csv_path = out_clinical_data_file
+    selected_clinical_data.to_csv(csv_path)
+    
+    print("Clinical data preview:")
+    print(preview_df(selected_clinical_data))
+
+# 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
+# Review the gene identifiers from the previous step output
+sample_identifiers = ['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at', '1294_at',
+                     '1316_at', '1320_at', '1405_i_at', '1431_at', '1438_at', '1487_at',
+                     '1494_f_at', '1552256_a_at', '1552257_a_at', '1552258_at', '1552261_at',
+                     '1552263_at', '1552264_a_at', '1552266_at']
+
+print("Sample gene identifiers:")
+print(f"First few identifiers: {sample_identifiers[:10]}")
+print(f"Pattern analysis:")
+print(f"- All identifiers contain '_at' suffixes")
+print(f"- Format appears to be numeric/alphanumeric + suffix")
+print(f"- These are Affymetrix probe IDs, not human gene symbols")
+print(f"- Gene symbols would be like 'BRCA1', 'TP53', 'EGFR', etc.")
+
+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. Based on the previews, 'ID' column contains probe identifiers that match gene expression data,
+# and 'Gene Symbol' column contains the gene symbols we need to map to
+prob_col = 'ID'
+gene_col = 'Gene Symbol'
+
+# 2. Get gene mapping dataframe by extracting the two relevant columns
+gene_mapping = get_gene_mapping(gene_annotation, prob_col, gene_col)
+
+# 3. Apply gene mapping to convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(gene_data, gene_mapping)
+
+print(f"Gene expression data shape after mapping: {gene_data.shape}")
+print(f"First few gene symbols: {list(gene_data.index[:10])}")
+
+# Step 7: Data Normalization and Linking
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data with the 'geo_link_clinical_genetic_data' function from the library.
+linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+# 3. Handle missing values in the linked data
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Conduct quality check and save the cohort information.
+is_usable = validate_and_save_cohort_info(True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data)
+
+# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.
+if is_usable:
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Eczema/code/GSE123086.py

@@ -0,0 +1,220 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Eczema"
+cohort = "GSE123086"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Eczema"
+in_cohort_dir = "../DATA/GEO/Eczema/GSE123086"
+
+# Output paths
+out_data_file = "./output/z2/preprocess/Eczema/GSE123086.csv"
+out_gene_data_file = "./output/z2/preprocess/Eczema/gene_data/GSE123086.csv"
+out_clinical_data_file = "./output/z2/preprocess/Eczema/clinical_data/GSE123086.csv"
+json_path = "./output/z2/preprocess/Eczema/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
+# 1. Gene Expression Data Availability
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Data Availability
+trait_row = 1  # 'primary diagnosis: ATOPIC_ECZEMA' found in key 1
+age_row = 3    # 'age: X' values found in key 3
+gender_row = 2 # 'Sex: Female', 'Sex: Male' found in key 2
+
+# 2.2 Data Type Conversion
+def convert_trait(value):
+    """Convert trait values to binary (0/1) for Eczema study"""
+    if value is None or pd.isna(value):
+        return None
+    if ':' in str(value):
+        actual_value = str(value).split(':')[1].strip()
+    else:
+        actual_value = str(value).strip()
+    
+    if actual_value.upper() == 'ATOPIC_ECZEMA':
+        return 1
+    else:
+        return 0
+
+def convert_age(value):
+    """Convert age values to continuous numeric type"""
+    if value is None or pd.isna(value):
+        return None
+    if ':' in str(value):
+        actual_value = str(value).split(':')[1].strip()
+    else:
+        actual_value = str(value).strip()
+    
+    try:
+        return float(actual_value)
+    except:
+        return None
+
+def convert_gender(value):
+    """Convert gender values to binary (0=Female, 1=Male)"""
+    if value is None or pd.isna(value):
+        return None
+    if ':' in str(value):
+        actual_value = str(value).split(':')[1].strip()
+    else:
+        actual_value = str(value).strip()
+    
+    if actual_value.upper() == 'FEMALE':
+        return 0
+    elif actual_value.upper() == 'MALE':
+        return 1
+    else:
+        return None
+
+# 3. Save Metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,
+                             is_gene_available=is_gene_available, is_trait_available=is_trait_available)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait,
+                                                       age_row, convert_age, gender_row, convert_gender)
+    csv_data = preview_df(selected_clinical_df)
+    print("Clinical data preview:")
+    print(csv_data)
+    
+    # Save to CSV
+    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 gene identifiers from the previous step are numeric strings
+gene_identifiers = ['1', '2', '3', '9', '10', '12', '13', '14', '15', '16', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27']
+
+print("Sample gene identifiers:", gene_identifiers[:10])
+print("Gene identifier characteristics:")
+print(f"- All numeric: {all(id.isdigit() for id in gene_identifiers)}")
+print(f"- Example format: {gene_identifiers[0]} (type: {type(gene_identifiers[0])})")
+
+# Human gene symbols are typically alphabetic names like 'BRCA1', 'TP53', 'EGFR'
+# These numeric identifiers are likely probe IDs, Entrez Gene IDs, or other database identifiers
+# that need to be mapped to actual gene symbols for biological interpretation
+
+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. Print all column names to see the complete structure
+print("All columns:", gene_annotation.columns.tolist())
+print("Dataframe shape:", gene_annotation.shape)
+
+# 3. Use the 'preview_df' function from the library to preview the data with increased scope
+print("Gene annotation preview:")
+print(preview_df(gene_annotation, max_items=500))
+
+# Step 6: Gene Identifier Mapping
+# 1. Let's examine the gene annotation more thoroughly to find actual gene symbols
+print("Gene annotation full structure:")
+print(f"Columns: {gene_annotation.columns.tolist()}")
+print(f"Shape: {gene_annotation.shape}")
+
+# Let's look at a larger sample to see if there are patterns we missed
+print("\nFirst 20 rows of gene annotation:")
+for i in range(min(20, len(gene_annotation))):
+    print(f"Row {i}: ID={gene_annotation.iloc[i]['ID']}, ENTREZ_GENE_ID={gene_annotation.iloc[i]['ENTREZ_GENE_ID']}, SPOT_ID={gene_annotation.iloc[i]['SPOT_ID']}")
+
+# Check if there are any non-numeric values in ENTREZ_GENE_ID that might be gene symbols
+print("\nUnique ENTREZ_GENE_ID values (first 50):")
+unique_entrez = gene_annotation['ENTREZ_GENE_ID'].unique()
+print(unique_entrez[:50])
+
+# Let's also check if the SOFT file has additional annotation sections
+print("\nRe-examining SOFT file structure for gene symbols...")
+# Try extracting with different prefixes to see if we missed gene symbol information
+alternative_annotation = filter_content_by_prefix(soft_file, prefixes_a=['!platform_table_begin'], 
+                                                 unselect=False, source_type='file', return_df_a=False)
+if alternative_annotation[0]:
+    print("Found platform_table_begin section:")
+    print(alternative_annotation[0][:1000])  # First 1000 characters
+
+# Since the numeric IDs appear to be Entrez Gene IDs, let's try a different approach
+# Use the numeric IDs directly as gene identifiers without mapping
+print(f"\nUsing numeric gene identifiers directly...")
+print(f"Original gene expression shape: {gene_data.shape}")
+print(f"Gene identifiers (first 10): {gene_data.index[:10].tolist()}")
+
+# Since mapping failed, let's keep the original gene_data as is
+# The numeric identifiers are likely Entrez Gene IDs which can be used for analysis
+gene_data = get_genetic_data(matrix_file)  # Restore original data
+print(f"Final gene expression data shape: {gene_data.shape}")
+print(f"Final gene identifiers (first 10): {gene_data.index[:10].tolist()}")
+
+# Step 7: Data Normalization and Linking
+# 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)
+
+print(f"Normalized gene data shape: {normalized_gene_data.shape}")
+print(f"Clinical data shape: {selected_clinical_df.shape}")
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+print(f"Linked data shape after linking: {linked_data.shape}")
+print(f"Missing values in trait column before handling: {linked_data[trait].isna().sum()}")
+
+# 3. Handle missing values with debugging
+print("Before missing value handling:")
+print(f"Total samples: {len(linked_data)}")
+print(f"Samples with trait data: {linked_data[trait].notna().sum()}")
+if 'Age' in linked_data.columns:
+    print(f"Samples with age data: {linked_data['Age'].notna().sum()}")
+if 'Gender' in linked_data.columns:
+    print(f"Samples with gender data: {linked_data['Gender'].notna().sum()}")
+
+linked_data = handle_missing_values(linked_data, trait)
+print(f"Linked data shape after missing value handling: {linked_data.shape}")
+
+# 4. Check for biased features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Validate and save cohort info
+is_usable = validate_and_save_cohort_info(True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data)
+
+# 6. Save final 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)
+    print(f"Final usable data saved with shape: {unbiased_linked_data.shape}")
+else:
+    print("Dataset determined to be unusable - not saved")