Add files using upload-large-folder tool

output/preprocess/Metabolic_Rate/clinical_data/GSE101492.csv

@@ -1,4 +1,3 @@
 ,GSM2704900,GSM2704901,GSM2704902,GSM2704903,GSM2704904,GSM2704905,GSM2704906,GSM2704907,GSM2704908,GSM2704909,GSM2704910,GSM2704911,GSM2704912,GSM2704913,GSM2704914,GSM2704915,GSM2704916,GSM2704917,GSM2704918,GSM2704919,GSM2704920,GSM2704921,GSM2704922,GSM2704923,GSM2704924,GSM2704925,GSM2704926,GSM2704927,GSM2704928,GSM2704929,GSM2704930,GSM2704931,GSM2704932,GSM2704933,GSM2704934,GSM2704935,GSM2704936,GSM2704937,GSM2704938,GSM2704939,GSM2704940,GSM2704941,GSM2704942,GSM2704943,GSM2704944,GSM2704945,GSM2704946,GSM2704947,GSM2704948,GSM2704949,GSM2704950,GSM2704951,GSM2704952,GSM2704953,GSM2704954,GSM2704955,GSM2704956,GSM2704957,GSM2704958,GSM2704959,GSM2704960,GSM2704961,GSM2704962,GSM2704963,GSM2704964,GSM2704965,GSM2704966,GSM2704967,GSM2704968,GSM2704969,GSM2704970,GSM2704971,GSM2704972,GSM2704973,GSM2704974,GSM2704975,GSM2704976,GSM2704977,GSM2704978,GSM2704979
-Metabolic_Rate,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+Metabolic_Rate,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0
 Age,39.0,28.0,42.0,30.0,42.0,37.0,36.0,33.0,27.0,43.0,37.0,42.0,43.0,39.0,42.0,44.0,25.0,36.0,25.0,25.0,44.0,43.0,44.0,35.0,40.0,25.0,29.0,41.0,34.0,43.0,31.0,41.0,37.0,39.0,29.0,28.0,35.0,37.0,36.0,40.0,30.0,33.0,34.0,40.0,40.0,30.0,38.0,40.0,28.0,39.0,42.0,44.0,40.0,34.0,33.0,41.0,41.0,42.0,36.0,40.0,33.0,39.0,44.0,29.0,28.0,36.0,41.0,43.0,43.0,26.0,33.0,32.0,38.0,31.0,30.0,28.0,27.0,45.0,40.0,25.0
-Gender,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

output/preprocess/Metabolic_Rate/clinical_data/GSE61225.csv

@@ -1,75 +1,4 @@
-,Metabolic_Rate,Age,Gender
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-GSM1499901,0.74230759890745,28.59138,0.0
-GSM1499902,3.936292,28.59138,0.0
+,GSM1499829,GSM1499830,GSM1499831,GSM1499832,GSM1499833,GSM1499834,GSM1499835,GSM1499836,GSM1499837,GSM1499838,GSM1499839,GSM1499840,GSM1499841,GSM1499842,GSM1499843,GSM1499844,GSM1499845,GSM1499846,GSM1499847,GSM1499848,GSM1499849,GSM1499850,GSM1499851,GSM1499852,GSM1499853,GSM1499854,GSM1499855,GSM1499856,GSM1499857,GSM1499858,GSM1499859,GSM1499860,GSM1499861,GSM1499862,GSM1499863,GSM1499864,GSM1499865,GSM1499866,GSM1499867,GSM1499868,GSM1499869,GSM1499870,GSM1499871,GSM1499872,GSM1499873,GSM1499874,GSM1499875,GSM1499876,GSM1499877,GSM1499878,GSM1499879,GSM1499880,GSM1499881,GSM1499882,GSM1499883,GSM1499884,GSM1499885,GSM1499886,GSM1499887,GSM1499888,GSM1499889,GSM1499890,GSM1499891,GSM1499892,GSM1499893,GSM1499894,GSM1499895,GSM1499896,GSM1499897,GSM1499898,GSM1499899,GSM1499900,GSM1499901,GSM1499902
+Metabolic_Rate,0.681873816522757,0.8523423,0.665104620956877,0.8313807,0.786010716020953,0.9825132,0.744848045678339,0.9310601,0.814710221540659,1.018388,0.709058218744461,0.8863227,0.733268847575824,0.9165859,0.728783827412329,0.9109797,0.759969166965226,2.277797,0.863847192835992,2.987472,0.732301833893021,4.727416,0.755339902416559,3.308808,0.757193473208602,0.9464918,0.709058218744461,1.471296,0.812838481910338,3.748088,0.915727555354096,4.644773,0.736619878676601,4.075963,0.73853661923046,3.064927,0.705031456992829,3.250978,0.660887301626415,3.047425,0.760896527420428,0.9511205,0.740808706999891,2.220368,0.744848045678339,1.889017,0.74715416981939,1.722605,0.813342819234442,2.062728,0.718701917558319,2.899763,0.734934524683196,3.727751,0.720948083807537,4.487902,0.881592993199644,2.235817,0.595372813525237,2.33353,0.948882866645629,4.375404,0.638827477795041,2.945704,0.711721049272041,2.461369,0.6815901810508,2.200022,0.814710221540659,1.40877,0.733268847575824,5.917071,0.74230759890745,3.936292
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output/preprocess/Metabolic_Rate/code/GSE101492.py

@@ -0,0 +1,210 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Metabolic_Rate"
+cohort = "GSE101492"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Metabolic_Rate"
+in_cohort_dir = "../DATA/GEO/Metabolic_Rate/GSE101492"
+
+# Output paths
+out_data_file = "./output/z4/preprocess/Metabolic_Rate/GSE101492.csv"
+out_gene_data_file = "./output/z4/preprocess/Metabolic_Rate/gene_data/GSE101492.csv"
+out_clinical_data_file = "./output/z4/preprocess/Metabolic_Rate/clinical_data/GSE101492.csv"
+json_path = "./output/z4/preprocess/Metabolic_Rate/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 (lncRNA expression; not miRNA or methylation)
+is_gene_available = True
+
+# 2) Variable availability and conversion functions
+# Trait proxy in this dataset: insulin sensitivity (insulin sensitive vs insulin resistant) -> row 3
+# Age: row 2 (continuous)
+# Gender: row 1 but constant "female" -> not useful for association (set to None)
+trait_row = 3
+age_row = 2
+gender_row = None  # constant "female" across all samples -> not useful
+
+def _after_colon(val):
+    if val is None:
+        return None
+    s = str(val)
+    parts = s.split(":", 1)
+    return parts[1].strip() if len(parts) > 1 else s.strip()
+
+def convert_trait(x):
+    # Map insulin sensitivity to binary: insulin sensitive -> 0, insulin resistant -> 1
+    v = _after_colon(x)
+    if v is None or v == "":
+        return None
+    v_low = v.lower()
+    if "insulin resistant" in v_low or v_low in {"ir", "resistant"}:
+        return 1
+    if "insulin sensitive" in v_low or v_low in {"is", "sensitive"}:
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None or v == "":
+        return None
+    v = v.lower()
+    m = re.search(r"[-+]?\d*\.?\d+", v)
+    if not m:
+        return None
+    try:
+        return float(m.group(0))
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None or v == "":
+        return None
+    v_low = v.lower()
+    if "female" in v_low or v_low in {"f", "woman", "women"}:
+        return 0
+    if "male" in v_low or v_low in {"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_row is available)
+if is_trait_available:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df, n=5)
+    print("Preview of selected clinical features:", preview)
+    # Save clinical data
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+import re
+
+# Sample of observed gene identifiers from the previous step
+observed_ids = [
+    '18670005', '18670007', '18670009', '18670011', '18670020', '18670022',
+    '18670023', '18670027', '18670028', '18670032', '18670033', '18670036',
+    '18670038', '18670039', '18670041', '18670049', '18670051', '18670053',
+    '18670054', '18670056'
+]
+
+def looks_like_hgnc(symbol: str) -> bool:
+    # HGNC symbols generally include at least one letter; purely numeric IDs are not HGNC symbols.
+    return bool(re.search(r'[A-Za-z]', symbol))
+
+is_hgnc_list = [looks_like_hgnc(x) for x in observed_ids]
+# If the majority are not HGNC-like, mapping is required
+requires_mapping = not (sum(is_hgnc_list) > len(is_hgnc_list) / 2)
+
+print(f"requires_gene_mapping = {requires_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 identifier and gene symbol columns from annotation
+id_col = 'ID'  # matches probe IDs in the expression matrix
+gene_symbol_col = 'gene_assignment'  # contains gene symbol information
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=id_col, gene_col=gene_symbol_col)
+
+# Apply mapping to convert probe-level data to gene-level data
+probe_df = gene_data  # preserve original probe-level data from previous step
+gene_data = apply_gene_mapping(probe_df, mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 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. Missing value handling
+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 save cohort info
+is_gene_available = isinstance(normalized_gene_data, pd.DataFrame) and normalized_gene_data.shape[0] > 0 and normalized_gene_data.shape[1] > 0
+is_trait_available = isinstance(selected_clinical_df, pd.DataFrame) and (trait in selected_clinical_df.index)
+
+note = "INFO: Gender not included because all samples are female in this cohort; trait mapped as insulin sensitivity (0=sensitive, 1=resistant)."
+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/Metabolic_Rate/code/GSE106800.py

@@ -0,0 +1,179 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Metabolic_Rate"
+cohort = "GSE106800"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Metabolic_Rate"
+in_cohort_dir = "../DATA/GEO/Metabolic_Rate/GSE106800"
+
+# Output paths
+out_data_file = "./output/z4/preprocess/Metabolic_Rate/GSE106800.csv"
+out_gene_data_file = "./output/z4/preprocess/Metabolic_Rate/gene_data/GSE106800.csv"
+out_clinical_data_file = "./output/z4/preprocess/Metabolic_Rate/clinical_data/GSE106800.csv"
+json_path = "./output/z4/preprocess/Metabolic_Rate/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  # Microarray analysis on skeletal muscle biopsies (not miRNA/methylation)
+
+# 2) Variable availability and conversion functions
+
+# Trait: Metabolic_Rate is not explicitly available in sample characteristics; cannot be reliably inferred
+trait_row = None
+
+def convert_trait(x):
+    # Trait not available in this cohort
+    return None
+
+# Age: available at key 2
+age_row = 2
+
+def convert_age(x):
+    if x is None:
+        return None
+    # Extract substring after colon and parse float
+    try:
+        val = x.split(":", 1)[1].strip()
+        # Remove any non-numeric characters except dot and minus
+        val = re.sub(r"[^0-9.\-eE]", " ", val).strip()
+        # Sometimes units or extra spaces exist; take first numeric token
+        token = re.findall(r"[-+]?\d*\.?\d+(?:[eE][-+]?\d+)?", val)
+        return float(token[0]) if token else None
+    except Exception:
+        return None
+
+# Gender: only 'male' reported across all samples -> constant feature, consider not available
+gender_row = None
+
+def convert_gender(x):
+    if x is None:
+        return None
+    try:
+        val = x.split(":", 1)[1].strip().lower()
+    except Exception:
+        val = str(x).strip().lower()
+    if val in ["male", "m"]:
+        return 1
+    if val in ["female", "f"]:
+        return 0
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction: skipped because trait_row is None (no clinical trait data available)
+# If trait_row becomes available in future, use geo_select_clinical_features and preview_df, then save to 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 mapping based on previous previews:
+# Probe identifier column: 'ID' (matches numeric IDs in expression data)
+# Gene symbol information column: 'gene_assignment'
+
+# 1-2. Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
+
+# 3. Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Optionally save gene-level data for downstream steps
+import os
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_data.to_csv(out_gene_data_file)
+
+# Step 7: Data Normalization and Linking
+# 1. Normalize the obtained gene data and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+import os
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2-6. Link clinical and genetic data only if clinical trait data exists; otherwise, finalize with trait unavailable
+is_usable = False
+proceed_with_linking = False
+try:
+    # Proceed only if selected_clinical_data exists and contains the trait row
+    proceed_with_linking = (selected_clinical_data is not None) and (trait in getattr(selected_clinical_data, 'index', []))
+except NameError:
+    proceed_with_linking = False
+
+if proceed_with_linking:
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+    linked_data = handle_missing_values(linked_data, trait)
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note="INFO: Clinical trait available and data linked."
+    )
+    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 validation without linking
+    placeholder_df = normalized_gene_data.T
+    _ = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=False,
+        df=placeholder_df,
+        note="INFO: Trait not available; saved normalized gene expression only."
+    )

output/preprocess/Metabolic_Rate/code/GSE151683.py

@@ -0,0 +1,110 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Metabolic_Rate"
+cohort = "GSE151683"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Metabolic_Rate"
+in_cohort_dir = "../DATA/GEO/Metabolic_Rate/GSE151683"
+
+# Output paths
+out_data_file = "./output/z4/preprocess/Metabolic_Rate/GSE151683.csv"
+out_gene_data_file = "./output/z4/preprocess/Metabolic_Rate/gene_data/GSE151683.csv"
+out_clinical_data_file = "./output/z4/preprocess/Metabolic_Rate/clinical_data/GSE151683.csv"
+json_path = "./output/z4/preprocess/Metabolic_Rate/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  # DNA microarray analysis of whole blood
+
+# 2) Variable availability and conversion functions
+# From Sample Characteristics:
+#  - Age available at row 2
+#  - Gender is constant male only -> not useful, set to None
+#  - Trait (Metabolic_Rate) not present -> None
+trait_row = None
+age_row = 2
+gender_row = None
+
+def _after_colon(s: str) -> str:
+    if s is None:
+        return ''
+    parts = str(s).split(':', 1)
+    return parts[1].strip() if len(parts) == 2 else str(s).strip()
+
+def convert_trait(x):
+    # Metabolic_Rate not available in this dataset; return None if not numeric
+    val = _after_colon(x)
+    try:
+        return float(val)
+    except Exception:
+        # Try to extract a number anywhere in the string
+        m = re.search(r"[-+]?\d*\.?\d+(?:[eE][-+]?\d+)?", val)
+        return float(m.group(0)) if m else None
+
+def convert_age(x):
+    val = _after_colon(x)
+    try:
+        return float(val)
+    except Exception:
+        m = re.search(r"\d{1,3}", val)
+        return float(m.group(0)) if m else None
+
+def convert_gender(x):
+    val = _after_colon(x).lower()
+    if val in {'male', 'm'}:
+        return 1
+    if val in {'female', 'f'}:
+        return 0
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (skip because trait_row is None)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    _ = preview_df(selected_clinical_df)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)

output/preprocess/Metabolic_Rate/code/GSE23025.py

@@ -0,0 +1,134 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Metabolic_Rate"
+cohort = "GSE23025"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Metabolic_Rate"
+in_cohort_dir = "../DATA/GEO/Metabolic_Rate/GSE23025"
+
+# Output paths
+out_data_file = "./output/z4/preprocess/Metabolic_Rate/GSE23025.csv"
+out_gene_data_file = "./output/z4/preprocess/Metabolic_Rate/gene_data/GSE23025.csv"
+out_clinical_data_file = "./output/z4/preprocess/Metabolic_Rate/clinical_data/GSE23025.csv"
+json_path = "./output/z4/preprocess/Metabolic_Rate/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
+# Affymetrix HG U133 Plus 2.0 Arrays indicate mRNA gene expression data are available.
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+
+# Based on the provided sample characteristics, there are no explicit fields for metabolic rate, age, or gender.
+trait_row = None      # Metabolic_Rate not present
+age_row = None        # Age not present
+gender_row = None     # Gender not present
+
+# Helper to extract value after colon and strip
+def _after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip() if s is not None else None
+
+# 2.2 Conversion functions
+
+# Metabolic_Rate: choose continuous; robust numeric parsing if ever encountered; otherwise None.
+def convert_trait(x):
+    val = _after_colon(x)
+    if val is None or val == '':
+        return None
+    # Extract first float-like number
+    m = re.search(r'[-+]?\d*\.?\d+(?:[eE][-+]?\d+)?', val)
+    if m:
+        try:
+            return float(m.group(0))
+        except Exception:
+            return None
+    return None
+
+# Age: continuous
+def convert_age(x):
+    val = _after_colon(x)
+    if val is None or val == '':
+        return None
+    m = re.search(r'[-+]?\d*\.?\d+(?:[eE][-+]?\d+)?', val)
+    if m:
+        try:
+            return float(m.group(0))
+        except Exception:
+            return None
+    return None
+
+# Gender: binary female->0, male->1
+def convert_gender(x):
+    val = _after_colon(x)
+    if val is None or val == '':
+        return None
+    v = val.strip().lower()
+    # Normalize common variants
+    if v in {'male', 'm', 'man', 'boy'}:
+        return 1
+    if v in {'female', 'f', 'woman', 'girl'}:
+        return 0
+    if v in {'unknown', 'na', 'n/a', 'not available', 'undisclosed', 'other'}:
+        return None
+    # Heuristic: if contains 'male' or 'female' substrings
+    if 'male' in v:
+        return 1
+    if 'female' in v:
+        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 (no clinical trait available)
+# If in future trait_row becomes available, the following template can 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)
+# os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+# selected_clinical_df.to_csv(out_clinical_data_file)

output/preprocess/Metabolic_Rate/code/GSE26440.py

@@ -0,0 +1,103 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Metabolic_Rate"
+cohort = "GSE26440"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Metabolic_Rate"
+in_cohort_dir = "../DATA/GEO/Metabolic_Rate/GSE26440"
+
+# Output paths
+out_data_file = "./output/z4/preprocess/Metabolic_Rate/GSE26440.csv"
+out_gene_data_file = "./output/z4/preprocess/Metabolic_Rate/gene_data/GSE26440.csv"
+out_clinical_data_file = "./output/z4/preprocess/Metabolic_Rate/clinical_data/GSE26440.csv"
+json_path = "./output/z4/preprocess/Metabolic_Rate/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
+is_gene_available = True  # Genome-wide expression profiling of whole blood RNA (gene expression microarray)
+trait_row = None          # No field corresponding to Metabolic_Rate in sample characteristics
+age_row = 2               # 'age (years): ...'
+gender_row = None         # No gender information available
+
+# Converters
+def _extract_after_colon(x):
+    if x is None:
+        return None
+    try:
+        parts = str(x).split(":", 1)
+        return parts[1].strip() if len(parts) > 1 else str(x).strip()
+    except Exception:
+        return None
+
+def convert_trait(x):
+    # No trait available for Metabolic_Rate in this cohort
+    return None
+
+def convert_age(x):
+    val = _extract_after_colon(x)
+    if val is None:
+        return None
+    val_lower = val.strip().lower()
+    if val_lower in {"", "na", "n/a", "null", "none", "unknown"}:
+        return None
+    # Extract numeric value
+    try:
+        # Keep only the leading numeric portion
+        import re
+        match = re.search(r"[-+]?\d*\.?\d+", val_lower)
+        return float(match.group()) if match else None
+    except Exception:
+        return None
+
+def convert_gender(x):
+    # Not available in this dataset
+    return None
+
+# Initial filtering metadata
+is_trait_available = trait_row is not None
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Clinical feature extraction (only if trait is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    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, index=True)

output/preprocess/Metabolic_Rate/code/GSE40589.py

@@ -0,0 +1,224 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Metabolic_Rate"
+cohort = "GSE40589"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Metabolic_Rate"
+in_cohort_dir = "../DATA/GEO/Metabolic_Rate/GSE40589"
+
+# Output paths
+out_data_file = "./output/z4/preprocess/Metabolic_Rate/GSE40589.csv"
+out_gene_data_file = "./output/z4/preprocess/Metabolic_Rate/gene_data/GSE40589.csv"
+out_clinical_data_file = "./output/z4/preprocess/Metabolic_Rate/clinical_data/GSE40589.csv"
+json_path = "./output/z4/preprocess/Metabolic_Rate/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 (from series title and context, this is a gene expression study)
+is_gene_available = True
+
+# 2) Variable availability based on the provided Sample Characteristics Dictionary
+# Only key 0 exists and it is tissue info, so trait/age/gender are not available.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2) Conversion functions
+
+def _get_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 _extract_first_number(s):
+    if s is None:
+        return None
+    match = re.search(r'[-+]?\d*\.?\d+', s)
+    return float(match.group()) if match else None
+
+def convert_trait(x):
+    # Metabolic_Rate is typically continuous; attempt to extract a numeric value if present.
+    s = _get_value_after_colon(x)
+    if s is None:
+        return None
+    num = _extract_first_number(s)
+    return num
+
+def convert_age(x):
+    # Age is continuous; extract a plausible age number (0 < age < 120)
+    s = _get_value_after_colon(x)
+    if s is None:
+        return None
+    num = _extract_first_number(s)
+    if num is None:
+        return None
+    # Basic sanity check for human age
+    if 0 < num < 120:
+        return num
+    return None
+
+def convert_gender(x):
+    # Binary: female -> 0, male -> 1
+    s = _get_value_after_colon(x)
+    if s is None:
+        return None
+    v = s.strip().lower()
+    # Check female first to avoid substring confusion with male
+    female_tokens = {'f', 'female', 'woman', 'girl', 'fem'}
+    male_tokens = {'m', 'male', 'man', 'boy', 'masc'}
+    # Normalize single-letter tokens or words
+    if v in female_tokens or any(tok in v for tok in ['female']):
+        return 0
+    if v in male_tokens or any(tok in v for tok in ['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 (skip because trait_row is None)
+if trait_row is not None:
+    selected_clinical = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age 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 and save
+    _ = preview_df(selected_clinical, n=5)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+# The identifiers like 'A_23_P100001' are Agilent 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
+# Select appropriate columns for probe IDs and gene symbols based on the annotation preview
+probe_col = 'ID'
+gene_symbol_col = 'GENE_SYMBOL'
+
+# Build the 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 data
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save 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 from earlier step
+is_trait_available = (trait_row is not None)
+
+if is_trait_available:
+    # Ensure clinical features are available; if not, extract them now
+    try:
+        selected_clinical
+    except NameError:
+        selected_clinical = geo_select_clinical_features(
+            clinical_df=clinical_data,
+            trait=trait,
+            trait_row=trait_row,
+            convert_trait=convert_trait,
+            age_row=age_row,
+            convert_age=convert_age if age_row is not None else None,
+            gender_row=gender_row,
+            convert_gender=convert_gender if gender_row is not None else None
+        )
+        os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+        selected_clinical.to_csv(out_clinical_data_file)
+
+    # 2. Link clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical, 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 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=''
+    )
+
+    # 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:
+    # No trait data; record metadata and skip linking/validation of linked data
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False
+    )

output/preprocess/Metabolic_Rate/code/GSE40873.py

@@ -0,0 +1,118 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Metabolic_Rate"
+cohort = "GSE40873"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Metabolic_Rate"
+in_cohort_dir = "../DATA/GEO/Metabolic_Rate/GSE40873"
+
+# Output paths
+out_data_file = "./output/z4/preprocess/Metabolic_Rate/GSE40873.csv"
+out_gene_data_file = "./output/z4/preprocess/Metabolic_Rate/gene_data/GSE40873.csv"
+out_clinical_data_file = "./output/z4/preprocess/Metabolic_Rate/clinical_data/GSE40873.csv"
+json_path = "./output/z4/preprocess/Metabolic_Rate/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 background: genome-wide gene expression analysis)
+is_gene_available = True
+
+# 2) Variable availability and converters
+# From the Sample Characteristics Dictionary:
+# 0: disease state (constant), 1: tissue (constant), 2: MFS time (days), 3: event (MO occurrence), 4: patient id
+# The project trait here is "Metabolic_Rate", which is not present or inferable in this dataset.
+trait_row = None
+age_row = None
+gender_row = None
+
+def _after_colon(value):
+    if value is None:
+        return None
+    s = str(value)
+    if ":" in s:
+        s = s.split(":", 1)[1]
+    return s.strip()
+
+def _to_float(s):
+    if s is None:
+        return None
+    m = re.search(r'[-+]?\d*\.?\d+(?:[eE][-+]?\d+)?', str(s))
+    if m:
+        try:
+            return float(m.group(0))
+        except Exception:
+            return None
+    return None
+
+# Assuming metabolic rate would be continuous if available
+def convert_trait(value):
+    # Extract numeric value after colon if any; return float or None
+    s = _after_colon(value)
+    return _to_float(s)
+
+def convert_age(value):
+    # Expecting age in years; extract numeric
+    s = _after_colon(value)
+    return _to_float(s)
+
+def convert_gender(value):
+    # Map female->0, male->1; otherwise None
+    s = _after_colon(value)
+    if s is None:
+        return None
+    s_low = str(s).strip().lower()
+    if s_low in {"female", "f", "woman", "women"}:
+        return 0
+    if s_low 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 if age_row is not None else None,
+        gender_row=gender_row,
+        convert_gender=convert_gender if gender_row is not None else None
+    )
+    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, index=True)

output/preprocess/Metabolic_Rate/code/GSE41168.py

@@ -0,0 +1,124 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Metabolic_Rate"
+cohort = "GSE41168"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Metabolic_Rate"
+in_cohort_dir = "../DATA/GEO/Metabolic_Rate/GSE41168"
+
+# Output paths
+out_data_file = "./output/z4/preprocess/Metabolic_Rate/GSE41168.csv"
+out_gene_data_file = "./output/z4/preprocess/Metabolic_Rate/gene_data/GSE41168.csv"
+out_clinical_data_file = "./output/z4/preprocess/Metabolic_Rate/clinical_data/GSE41168.csv"
+json_path = "./output/z4/preprocess/Metabolic_Rate/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import re
+
+# 1) Gene expression data availability
+# Background and design indicate mRNA expression profiling in muscle and adipose tissues.
+is_gene_available = True
+
+# 2) Variable availability based on provided Sample Characteristics:
+# {0: ['tissue: muscle', 'tissue: adipose tissue'],
+#  1: ['sample group: calorie restrictive', 'sample group: placebo', 'sample group: resveratrol'],
+#  2: ['treatment: before', 'treatment: after'],
+#  3: ['gender: Female']}
+#
+# - Trait (Metabolic_Rate): Not present/inferable at per-sample level -> unavailable
+# - Age: Not present -> unavailable
+# - Gender: Constant 'Female' across all samples -> considered unavailable
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2) Define conversion functions
+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):
+    # Continuous. Extract numeric value if present; otherwise None.
+    v = _after_colon(x)
+    if v is None or v == "" or v.lower() in {"na", "n/a", "none", "unknown"}:
+        return None
+    m = re.search(r"[-+]?\d*\.?\d+(?:[eE][-+]?\d+)?", v)
+    try:
+        return float(m.group()) if m else None
+    except Exception:
+        return None
+
+def convert_age(x):
+    # Continuous. Extract age in years (integer/float).
+    v = _after_colon(x)
+    if v is None or v == "" or v.lower() in {"na", "n/a", "none", "unknown"}:
+        return None
+    m = re.search(r"(\d*\.?\d+)", v)
+    try:
+        return float(m.group()) if m else None
+    except Exception:
+        return None
+
+def convert_gender(x):
+    # Binary: female -> 0, male -> 1
+    v = _after_colon(x)
+    if v is None:
+        return None
+    val = v.strip().lower()
+    if val in {"f", "female", "woman", "women"}:
+        return 0
+    if val in {"m", "male", "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
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)

output/preprocess/Metabolic_Rate/code/GSE61225.py

@@ -0,0 +1,201 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Metabolic_Rate"
+cohort = "GSE61225"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Metabolic_Rate"
+in_cohort_dir = "../DATA/GEO/Metabolic_Rate/GSE61225"
+
+# Output paths
+out_data_file = "./output/z4/preprocess/Metabolic_Rate/GSE61225.csv"
+out_gene_data_file = "./output/z4/preprocess/Metabolic_Rate/gene_data/GSE61225.csv"
+out_clinical_data_file = "./output/z4/preprocess/Metabolic_Rate/clinical_data/GSE61225.csv"
+json_path = "./output/z4/preprocess/Metabolic_Rate/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) Determine gene expression data availability based on background info
+is_gene_available = True  # Illumina HumanHT-12v3 Expression-BeadChip => gene expression microarray
+
+# 2) Identify variable availability (row indices from Sample Characteristics Dictionary)
+trait_row = 4     # 'metabolic equivalents: ...'
+age_row = 6       # 'age: ...'
+gender_row = 5    # 'gender: female/male'
+
+# 2.2) Define conversion functions
+def _extract_value(cell):
+    if cell is None:
+        return None
+    try:
+        # typical format "field: value"
+        parts = str(cell).split(":", 1)
+        val = parts[1] if len(parts) > 1 else parts[0]
+        val = val.strip()
+        if val == "" or val.lower() in {"na", "n/a", "nan", "none", "unknown", "?", "missing"}:
+            return None
+        return val
+    except Exception:
+        return None
+
+def convert_trait(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    try:
+        return float(v)
+    except Exception:
+        return None
+
+def convert_age(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    try:
+        return float(v)
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    s = str(v).strip().lower()
+    # map to binary: female -> 0, male -> 1
+    if s in {"female", "f", "woman", "women"}:
+        return 0
+    if s in {"male", "m", "man", "men"}:
+        return 1
+    return None
+
+# 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
+)
+
+# 4) Clinical feature extraction (only if trait is available)
+if is_trait_available:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    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
+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 the appropriate columns for mapping:
+# - Probe identifiers in expression data: 'ILMN_...' => matches 'ID' in gene_annotation
+# - Gene symbols: use 'ILMN_Gene' from gene_annotation
+identifier_col = 'ID'
+gene_symbol_col = 'ILMN_Gene'
+
+# 2) Build the mapping dataframe (ID -> Gene)
+mapping_df = get_gene_mapping(gene_annotation, prob_col=identifier_col, gene_col=gene_symbol_col)
+
+# 3) Apply mapping: 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
+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 (load from file if not in memory)
+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. Assess bias and remove biased demographic features
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort metadata
+# Ensure pure Python bools are passed to the validator
+is_gene_available = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+is_trait_available = bool((trait in linked_data.columns) and bool(linked_data[trait].notna().any()))
+is_trait_biased = bool(is_trait_biased)
+
+note_details = (
+    f"genes_before={int(gene_data.shape[0]) if isinstance(gene_data, pd.DataFrame) else 'NA'}, "
+    f"genes_after_norm={int(normalized_gene_data.shape[0])}, "
+    f"samples_linked={int(unbiased_linked_data.shape[0])}"
+)
+note = f"INFO: Gene symbols normalized with NCBI synonyms; clinical features (Age, Gender) extracted from GEO characteristics; {note_details}."
+
+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/Metabolic_Rate/code/GSE89231.py

@@ -0,0 +1,147 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Metabolic_Rate"
+cohort = "GSE89231"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Metabolic_Rate"
+in_cohort_dir = "../DATA/GEO/Metabolic_Rate/GSE89231"
+
+# Output paths
+out_data_file = "./output/z4/preprocess/Metabolic_Rate/GSE89231.csv"
+out_gene_data_file = "./output/z4/preprocess/Metabolic_Rate/gene_data/GSE89231.csv"
+out_clinical_data_file = "./output/z4/preprocess/Metabolic_Rate/clinical_data/GSE89231.csv"
+json_path = "./output/z4/preprocess/Metabolic_Rate/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 data availability
+is_gene_available = True  # Gene expression profiling is described in the background (not miRNA/methylation)
+trait_row = None          # No human Metabolic_Rate data available in cell line study
+age_row = None            # No human age data available
+gender_row = None         # No human gender data available
+
+# Step 2: Define conversion functions
+def _extract_value(x):
+    if x is None:
+        return None
+    s = str(x).strip()
+    if ':' in s:
+        # take substring after the last colon to be robust against headers with colons
+        s = s.split(':', 1)[1].strip()
+    return s if s != '' else None
+
+def convert_trait(x):
+    # Metabolic_Rate not available; implement a general numeric parser if ever used
+    s = _extract_value(x)
+    if s is None:
+        return None
+    # Try to parse as float
+    try:
+        return float(s)
+    except:
+        # Try to extract first numeric token
+        import re
+        m = re.search(r'[-+]?\d*\.?\d+(?:[eE][-+]?\d+)?', s)
+        if m:
+            try:
+                return float(m.group(0))
+            except:
+                return None
+        return None
+
+def convert_age(x):
+    s = _extract_value(x)
+    if s is None:
+        return None
+    s_lower = s.lower()
+    # Map common unknowns
+    if s_lower in {'na', 'n/a', 'nan', 'none', 'unknown', 'not available', 'missing'}:
+        return None
+    import re
+    # Handle age ranges by taking midpoint
+    range_match = re.findall(r'(\d+(?:\.\d+)?)', s_lower)
+    if '-' in s_lower or 'to' in s_lower:
+        nums = [float(n) for n in range_match] if range_match else []
+        if len(nums) >= 2:
+            return (nums[0] + nums[1]) / 2.0
+    # Detect units
+    if 'month' in s_lower or 'mo' in s_lower:
+        # Convert months to years
+        nums = [float(n) for n in range_match] if range_match else []
+        if nums:
+            return nums[0] / 12.0
+    # Default: first number in years
+    if range_match:
+        try:
+            return float(range_match[0])
+        except:
+            return None
+    return None
+
+def convert_gender(x):
+    s = _extract_value(x)
+    if s is None:
+        return None
+    s_lower = s.strip().lower()
+    # Standardize common representations
+    if s_lower in {'male', 'm', 'man', 'boy'}:
+        return 1
+    if s_lower in {'female', 'f', 'woman', 'girl'}:
+        return 0
+    if s_lower in {'unknown', 'na', 'n/a', 'none', 'nan', 'not available', ''}:
+        return None
+    # Heuristic: if startswith 'm' assume male; 'f' assume female
+    if s_lower.startswith('m'):
+        return 1
+    if s_lower.startswith('f'):
+        return 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 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)
+    # Save clinical data
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)

output/preprocess/Metabolic_Rate/code/TCGA.py

@@ -0,0 +1,68 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Metabolic_Rate"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z4/preprocess/Metabolic_Rate/TCGA.csv"
+out_gene_data_file = "./output/z4/preprocess/Metabolic_Rate/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z4/preprocess/Metabolic_Rate/clinical_data/TCGA.csv"
+json_path = "./output/z4/preprocess/Metabolic_Rate/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Step 1: Find a suitable TCGA cohort directory for the trait "Metabolic_Rate"
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+# Define trait-related keywords. For "Metabolic_Rate", TCGA cohort names are cancer-type specific and unlikely to match.
+trait_keywords = [
+    "metabolic rate", "metabolism", "metabolic", "bmr", "basal metabolic rate",
+    "energy expenditure", "resting metabolic rate", "oxygen consumption", "vo2",
+    "glycolysis", "oxidative", "mitochondrial"
+]
+
+# Rank subdirectories by presence of keywords; pick the best match if any
+candidates = []
+for d in subdirs:
+    dl = d.lower()
+    score = sum(1 for kw in trait_keywords if kw in dl)
+    if score > 0:
+        candidates.append((score, d))
+
+selected_dir = None
+if candidates:
+    # Choose the most specific (highest score, then shortest name)
+    candidates.sort(key=lambda x: (-x[0], len(x[1])))
+    selected_dir = candidates[0][1]
+
+# Initialize variables for later steps
+clinical_df = None
+genetic_df = None
+
+if selected_dir is None:
+    # No suitable directory found; mark this trait as skipped/unavailable
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+else:
+    # Step 2: Identify file paths for clinical and genetic data
+    cohort_path = os.path.join(tcga_root_dir, selected_dir)
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_path)
+
+    # Step 3: Load both files as 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')
+
+    # Step 4: Print clinical column names
+    print(list(clinical_df.columns))

output/preprocess/Migraine/clinical_data/GSE67311.csv

@@ -0,0 +1,2 @@
+,GSM1644447,GSM1644448,GSM1644449,GSM1644450,GSM1644451,GSM1644452,GSM1644453,GSM1644454,GSM1644455,GSM1644456,GSM1644457,GSM1644458,GSM1644459,GSM1644460,GSM1644461,GSM1644462,GSM1644463,GSM1644464,GSM1644465,GSM1644466,GSM1644467,GSM1644468,GSM1644469,GSM1644470,GSM1644471,GSM1644472,GSM1644473,GSM1644474,GSM1644475,GSM1644476,GSM1644477,GSM1644478,GSM1644479,GSM1644480,GSM1644481,GSM1644482,GSM1644483,GSM1644484,GSM1644485,GSM1644486,GSM1644487,GSM1644488,GSM1644489,GSM1644490,GSM1644491,GSM1644492,GSM1644493,GSM1644494,GSM1644495,GSM1644496,GSM1644497,GSM1644498,GSM1644499,GSM1644500,GSM1644501,GSM1644502,GSM1644503,GSM1644504,GSM1644505,GSM1644506,GSM1644507,GSM1644508,GSM1644509,GSM1644510,GSM1644511,GSM1644512,GSM1644513,GSM1644514,GSM1644515,GSM1644516,GSM1644517,GSM1644518,GSM1644519,GSM1644520,GSM1644521,GSM1644522,GSM1644523,GSM1644524,GSM1644525,GSM1644526,GSM1644527,GSM1644528,GSM1644529,GSM1644530,GSM1644531,GSM1644532,GSM1644533,GSM1644534,GSM1644535,GSM1644536,GSM1644537,GSM1644538,GSM1644539,GSM1644540,GSM1644541,GSM1644542,GSM1644543,GSM1644544,GSM1644545,GSM1644546,GSM1644547,GSM1644548,GSM1644549,GSM1644550,GSM1644551,GSM1644552,GSM1644553,GSM1644554,GSM1644555,GSM1644556,GSM1644557,GSM1644558,GSM1644559,GSM1644560,GSM1644561,GSM1644562,GSM1644563,GSM1644564,GSM1644565,GSM1644566,GSM1644567,GSM1644568,GSM1644569,GSM1644570,GSM1644571,GSM1644572,GSM1644573,GSM1644574,GSM1644575,GSM1644576,GSM1644577,GSM1644578,GSM1644579,GSM1644580,GSM1644581,GSM1644582,GSM1644583,GSM1644584,GSM1644585,GSM1644586,GSM1644587,GSM1644588
+Migraine,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,0.0,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,,1.0,1.0,1.0,1.0,0.0,1.0,,1.0,,1.0,1.0,0.0,0.0,,0.0,1.0,0.0,1.0,0.0,0.0,0.0,,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,,1.0,1.0,0.0

output/preprocess/Migraine/code/GSE67311.py

@@ -0,0 +1,258 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Migraine"
+cohort = "GSE67311"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Migraine"
+in_cohort_dir = "../DATA/GEO/Migraine/GSE67311"
+
+# Output paths
+out_data_file = "./output/z4/preprocess/Migraine/GSE67311.csv"
+out_gene_data_file = "./output/z4/preprocess/Migraine/gene_data/GSE67311.csv"
+out_clinical_data_file = "./output/z4/preprocess/Migraine/clinical_data/GSE67311.csv"
+json_path = "./output/z4/preprocess/Migraine/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+
+# 1) Gene expression data availability
+is_gene_available = True  # Affymetrix Human Gene 1.1 ST arrays -> gene expression microarray
+
+# 2) Variable availability and converters
+
+# Trait: Migraine
+# From sample characteristics: key 4 has 'migraine: Yes/No/-'
+if 4 in clinical_data.index:
+    trait_row = 4
+elif "4" in clinical_data.index:
+    trait_row = "4"
+else:
+    trait_row = None
+
+# Age and Gender are not present in the characteristics dictionary
+age_row = None
+gender_row = None
+
+# Conversion functions
+def _after_colon(value):
+    if value is None:
+        return None
+    s = str(value)
+    parts = s.split(":", 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    return val.strip()
+
+def convert_trait(value):
+    v = _after_colon(value)
+    if v is None or v == "" or v == "-" or v.lower() in {"na", "n/a", "unknown"}:
+        return None
+    vl = v.strip().lower()
+    if vl in {"yes", "y", "1", "true"}:
+        return 1
+    if vl in {"no", "n", "0", "false"}:
+        return 0
+    # Fallback: try numeric
+    try:
+        num = float(vl)
+        if num == 1.0:
+            return 1
+        if num == 0.0:
+            return 0
+    except:
+        pass
+    return None
+
+def convert_age(value):
+    # Not available in this dataset; function provided for interface completeness.
+    v = _after_colon(value)
+    if v is None:
+        return None
+    nums = re.findall(r"[-+]?\d*\.?\d+", v)
+    if not nums:
+        return None
+    try:
+        age = float(nums[0])
+        if age <= 0 or age > 120:
+            return None
+        return age
+    except:
+        return None
+
+def convert_gender(value):
+    # Not available in this dataset; function provided for interface completeness.
+    v = _after_colon(value)
+    if v is None or v == "" or v == "-" or v.lower() in {"na", "n/a", "unknown"}:
+        return None
+    vl = v.strip().lower()
+    # female -> 0, male -> 1
+    if vl in {"female", "f", "woman", "women"}:
+        return 0
+    if vl 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, preview, and save
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df, n=5)
+    print("Selected clinical 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
+# The observed identifiers like '7892501' are Affymetrix/array probe set 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 mapping columns based on previous previews:
+# - Probe IDs in expression data match 'ID' in annotation.
+# - Gene symbols are embedded in 'gene_assignment' in annotation.
+
+# 1-2) Build mapping dataframe from annotation
+mapping_df = get_gene_mapping(annotation=gene_annotation, prob_col='ID', gene_col='gene_assignment')
+
+# 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 json
+
+# 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 correct clinical variable from Step 2)
+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
+covariate_cols = [trait, 'Age', 'Gender']
+gene_cols = [c for c in unbiased_linked_data.columns if c not in covariate_cols]
+is_gene_available_final = bool((len(gene_cols) > 0) and (len(unbiased_linked_data) > 0))
+is_trait_available_final = bool((trait in unbiased_linked_data.columns) and unbiased_linked_data[trait].notna().any())
+
+note = "INFO: Age and Gender not available in this cohort. Probes mapped via 'gene_assignment'; gene symbols normalized with NCBI synonyms."
+
+# Try library function first; if serialization fails, fallback to manual JSON update with sanitized types.
+try:
+    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=bool(is_trait_biased),
+        df=unbiased_linked_data,
+        note=note
+    )
+except Exception:
+    # Fallback: replicate core logic of final validation and write JSON with sanitized (primitive) types
+    is_gene_av = bool(is_gene_available_final)
+    is_trait_av = bool(is_trait_available_final)
+
+    # Detect abnormality in data and override flags similar to library behavior
+    if len(unbiased_linked_data) <= 0 or len(unbiased_linked_data.columns) <= 4:
+        is_gene_av = False
+        if len(unbiased_linked_data) <= 0:
+            is_trait_av = False
+    is_available = bool(is_gene_av and is_trait_av)
+    is_usable = bool(is_available and (is_trait_biased is False))
+
+    # Build sanitized record (cast booleans to int for maximum compatibility)
+    new_record = {
+        "is_usable": int(is_usable),
+        "is_gene_available": int(is_gene_av),
+        "is_trait_available": int(is_trait_av),
+        "is_available": int(is_available),
+        "is_biased": (int(is_trait_biased) if is_available else None),
+        "has_age": (int('Age' in unbiased_linked_data.columns) if is_available else None),
+        "has_gender": (int('Gender' in unbiased_linked_data.columns) if is_available else None),
+        "sample_size": (int(len(unbiased_linked_data)) if is_available else None),
+        "note": note
+    }
+
+    os.makedirs(os.path.dirname(json_path), exist_ok=True)
+    if not os.path.exists(json_path):
+        with open(json_path, 'w') as f:
+            json.dump({}, f)
+
+    with open(json_path, 'r') as f:
+        records = json.load(f)
+    records[cohort] = new_record
+
+    temp_path = json_path + ".tmp"
+    with open(temp_path, 'w') as f:
+        json.dump(records, f)
+    os.replace(temp_path, json_path)
+
+# 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/Migraine/code/TCGA.py

@@ -0,0 +1,53 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Migraine"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z4/preprocess/Migraine/TCGA.csv"
+out_gene_data_file = "./output/z4/preprocess/Migraine/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z4/preprocess/Migraine/clinical_data/TCGA.csv"
+json_path = "./output/z4/preprocess/Migraine/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Step 1: find the most appropriate cohort directory for Migraine (likely none in TCGA cancer cohorts)
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+migraine_synonyms = [
+    "migraine", "headache", "cephalalgia", "trigeminal autonomic", "cluster headache",
+    "hemicrania", "hemicrania continua", "sunct", "suna", "paroxysmal hemicrania"
+]
+selected_dir = None
+for d in subdirs:
+    dl = d.lower()
+    if any(term in dl for term in migraine_synonyms):
+        selected_dir = d
+        break
+
+if selected_dir is None:
+    # No suitable cohort for migraine in TCGA; mark as skipped
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+else:
+    # Step 2: identify clinical and genetic file paths
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # Step 3: load dataframes
+    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)
+
+    # Step 4: print clinical column names
+    print(clinical_df.columns.tolist())

output/preprocess/Migraine/cohort_info.json

@@ -1,22 +1 @@
-{
-  "GSE67311": {
-    "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": 133
-  },
-  "TCGA": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": true,
-    "has_gender": true,
-    "sample_size": 702
-  }
-}
+{"GSE67311": {"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": 133, "note": "INFO: Age and Gender not available in this cohort. Probes mapped via 'gene_assignment'; gene symbols normalized with NCBI synonyms."}, "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/Mitochondrial_Disorders/clinical_data/GSE42986.csv

@@ -1,4 +1,4 @@
 ,GSM1054461,GSM1054462,GSM1054463,GSM1054464,GSM1054465,GSM1054466,GSM1054467,GSM1054468,GSM1054469,GSM1054470,GSM1054471,GSM1054472,GSM1054473,GSM1054474,GSM1054475,GSM1054476,GSM1054477,GSM1054478,GSM1054479,GSM1054480,GSM1054481,GSM1054482,GSM1054483,GSM1054484,GSM1054485,GSM1054486,GSM1054487,GSM1054488,GSM1054489,GSM1054490,GSM1054491,GSM1054492,GSM1054493,GSM1054494,GSM1054495,GSM1054496,GSM1054497,GSM1054498,GSM1054499,GSM1054500,GSM1054501,GSM1054502,GSM1054503,GSM1054504,GSM1054505,GSM1054506,GSM1054507,GSM1054508,GSM1054509,GSM1054510,GSM1054511,GSM1054512,GSM1054513
-Mitochondrial_Disorders,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,,,,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,,0.0,,,0.0,0.0,0.0,1.0,,1.0,1.0,0.0
+Mitochondrial_Disorders,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,,1.0,1.0,,,0.0,0.0,0.0,,1.0,,,0.0,0.0,0.0,1.0,0.0,,1.0,1.0,1.0,1.0,0.0,,1.0,0.0,,0.0,1.0,0.0,1.0,1.0,,1.0,1.0,1.0
 Age,0.76,20.0,20.0,16.0,1.0,0.75,0.75,3.0,3.0,0.2,0.9,2.0,6.0,10.0,4.0,0.3,8.0,72.0,54.0,23.0,0.75,60.0,67.0,59.0,59.0,11.0,46.0,42.0,2.0,,,,4.0,0.76,20.0,5.0,16.0,5.0,1.0,0.75,3.0,30.0,2.0,36.0,39.0,6.0,10.0,4.0,0.3,0.1,8.0,11.0,0.7
 Gender,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0

output/preprocess/Mitochondrial_Disorders/code/GSE22651.py

@@ -0,0 +1,132 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Mitochondrial_Disorders"
+cohort = "GSE22651"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Mitochondrial_Disorders"
+in_cohort_dir = "../DATA/GEO/Mitochondrial_Disorders/GSE22651"
+
+# Output paths
+out_data_file = "./output/z4/preprocess/Mitochondrial_Disorders/GSE22651.csv"
+out_gene_data_file = "./output/z4/preprocess/Mitochondrial_Disorders/gene_data/GSE22651.csv"
+out_clinical_data_file = "./output/z4/preprocess/Mitochondrial_Disorders/clinical_data/GSE22651.csv"
+json_path = "./output/z4/preprocess/Mitochondrial_Disorders/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 HT12 v3 mRNA expression platform suggests gene expression is available.
+
+# 2) Variable availability and conversion functions
+
+# Based on the provided sample characteristics dictionary, trait status (FRDA vs control) is not explicitly or reliably available.
+trait_row = None
+
+# Age appears inconsistently and mostly absent/constant (e.g., only "age: 47 years" once), so treat as unavailable.
+age_row = None
+
+# Gender information is split across different rows (e.g., 'gender: male' under key 0 and 'gender: female' under key 1),
+# making it not available as a single consistent feature.
+gender_row = None
+
+def _after_colon(x):
+    if x is None:
+        return None
+    if isinstance(x, str):
+        parts = x.split(":", 1)
+        val = parts[1] if len(parts) > 1 else parts[0]
+        val = val.strip()
+        return val if val else None
+    return None
+
+def convert_trait(x):
+    # Heuristic mapping for FRDA vs control if ever needed:
+    # Return 1 for FRDA, 0 for control, None if unknown.
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    # Clear FRDA indicators
+    if "friedreich" in vl or "frda" in vl or "patient" in vl:
+        return 1
+    # Likely controls (common control lines/tissues)
+    ctrl_markers = ["embryonic stem cell", "hes-", "hsf", "h9", "keratinocyte", "huvec", "mesenchymal_stem_cells", "hs27", "hdf"]
+    if any(m in vl for m in ctrl_markers):
+        return 0
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    if vl in {"na", "unknown", ""}:
+        return None
+    m = re.search(r'(\d{1,3})', vl)
+    if not m:
+        return None
+    age = int(m.group(1))
+    if 0 <= age <= 120:
+        return age
+    return None
+
+def convert_gender(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.strip().lower()
+    if vl in {"male", "m"}:
+        return 1
+    if vl in {"female", "f"}:
+        return 0
+    return None
+
+# 3) Save metadata via initial filtering
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (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
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)

output/preprocess/Mitochondrial_Disorders/code/GSE30933.py

@@ -0,0 +1,185 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Mitochondrial_Disorders"
+cohort = "GSE30933"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Mitochondrial_Disorders"
+in_cohort_dir = "../DATA/GEO/Mitochondrial_Disorders/GSE30933"
+
+# Output paths
+out_data_file = "./output/z4/preprocess/Mitochondrial_Disorders/GSE30933.csv"
+out_gene_data_file = "./output/z4/preprocess/Mitochondrial_Disorders/gene_data/GSE30933.csv"
+out_clinical_data_file = "./output/z4/preprocess/Mitochondrial_Disorders/clinical_data/GSE30933.csv"
+json_path = "./output/z4/preprocess/Mitochondrial_Disorders/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import pandas as pd
+
+# 1) Gene expression data availability
+is_gene_available = True  # Microarray gene expression in PBMCs
+
+# 2) Variable availability and conversion functions
+
+# Keys from Sample Characteristics Dictionary
+trait_row = 0         # 'disease status: Normal/Carrier/FRDA'
+age_row = None        # Not available
+gender_row = None     # Not available
+
+def _extract_value(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()
+    return s if s != '' else None
+
+def convert_trait(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    v_low = v.lower()
+    # Map FRDA patients to 1; carriers and normals to 0
+    if any(k in v_low for k in ['frda', 'friedreich']):
+        return 1
+    if any(k in v_low for k in ['normal', 'control', 'healthy', 'carrier']):
+        return 0
+    return None
+
+def convert_age(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    # extract first numeric token as age in years
+    try:
+        # keep digits and dot
+        import re
+        m = re.search(r'[-+]?\d*\.?\d+', v)
+        return float(m.group()) if m else None
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    v_low = v.lower()
+    if v_low in ['male', 'm']:
+        return 1
+    if v_low in ['female', 'f']:
+        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 (only if trait is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview
+    preview = preview_df(selected_clinical_df, n=5)
+    print("Clinical features preview:", preview)
+
+    # Save
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Map probe IDs to gene symbols using annotation columns: 'ID' (probe) and 'SYMBOL' (gene symbol)
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='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
+import os
+
+# 1. Normalize the obtained gene data and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine whether the trait and demographic features are severely biased, and remove biased features.
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+note = "INFO: Age and Gender not available in clinical data. Trait derived from 'disease 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 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/Mitochondrial_Disorders/code/GSE42986.py

@@ -0,0 +1,209 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Mitochondrial_Disorders"
+cohort = "GSE42986"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Mitochondrial_Disorders"
+in_cohort_dir = "../DATA/GEO/Mitochondrial_Disorders/GSE42986"
+
+# Output paths
+out_data_file = "./output/z4/preprocess/Mitochondrial_Disorders/GSE42986.csv"
+out_gene_data_file = "./output/z4/preprocess/Mitochondrial_Disorders/gene_data/GSE42986.csv"
+out_clinical_data_file = "./output/z4/preprocess/Mitochondrial_Disorders/clinical_data/GSE42986.csv"
+json_path = "./output/z4/preprocess/Mitochondrial_Disorders/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  # Affymetrix Human Exon 1.0 ST indicates mRNA expression profiling
+
+# 2) Variable availability (rows inferred from the provided Sample Characteristics Dictionary)
+trait_row = 4     # 'informatic analysis group: Mito Disease Group' vs 'Control Group' (exclude poor quality/outlier)
+age_row = 3       # 'age (years): ...'
+gender_row = 2    # 'gender: F/M'
+
+# 2.2) Conversion helpers
+def _after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip() if s is not None else None
+
+def convert_trait(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.strip().lower()
+
+    # Primary mapping for row 4
+    if vl in {"mito disease group", "mito disease", "disease", "patient", "rc disease group"}:
+        return 1
+    if vl in {"control group", "control", "healthy", "normal"}:
+        return 0
+    if "exclude" in vl or "outlier" in vl or "poor quality" in vl:
+        return None
+
+    # Heuristic fallback if a different row is encountered inadvertently
+    if "no respiratory chain complex deficiency" in vl:
+        return 0
+    if "complex" in vl or "mtdna depletion" in vl:
+        return 1
+    if "not determined" in vl or "not measured" in vl:
+        return None
+
+    return None
+
+def convert_age(x):
+    v = _after_colon(x)
+    if v is None:
+        return None
+    vl = v.strip().lower()
+    if vl in {"not obtained", "na", "n/a", "unknown", ""}:
+        return None
+    try:
+        return float(vl)
+    except Exception:
+        m = re.search(r"[-+]?\d*\.?\d+(?:[eE][-+]?\d+)?", vl)
+        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:
+        return None
+    vl = v.strip().lower()
+    if vl in {"f", "female"}:
+        return 0
+    if vl in {"m", "male"}:
+        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 (only if clinical data is available)
+if is_trait_available:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+
+    preview = preview_df(selected_clinical_df, 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 (e.g., '10000_at') 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
+# 1-2. Build mapping from probe IDs to gene symbols using the appropriate columns
+# Probe identifiers: 'ID' (e.g., '10000_at'); Gene symbols: 'Symbol' (e.g., 'A1BG')
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# 3. Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+
+# 1. Normalize gene symbols and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Bias assessment and removal of biased covariates
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# Prepare availability flags for final validation (ensure pure Python bools)
+is_gene_available_final = bool((normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0))
+is_trait_available_final = bool((trait in linked_data.columns) and linked_data[trait].notna().any())
+is_trait_biased = bool(is_trait_biased)
+
+note = "INFO: Samples labeled 'Excluded' or 'outlier' in the informatic analysis group were set to missing trait and removed during missing-value handling."
+
+# 5. Final validation and save cohort info
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=str(cohort),
+    info_path=str(json_path),
+    is_gene_available=bool(is_gene_available_final),
+    is_trait_available=bool(is_trait_available_final),
+    is_biased=bool(is_trait_biased),
+    df=unbiased_linked_data,
+    note=str(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/Mitochondrial_Disorders/code/GSE65399.py

@@ -0,0 +1,129 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Mitochondrial_Disorders"
+cohort = "GSE65399"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Mitochondrial_Disorders"
+in_cohort_dir = "../DATA/GEO/Mitochondrial_Disorders/GSE65399"
+
+# Output paths
+out_data_file = "./output/z4/preprocess/Mitochondrial_Disorders/GSE65399.csv"
+out_gene_data_file = "./output/z4/preprocess/Mitochondrial_Disorders/gene_data/GSE65399.csv"
+out_clinical_data_file = "./output/z4/preprocess/Mitochondrial_Disorders/clinical_data/GSE65399.csv"
+json_path = "./output/z4/preprocess/Mitochondrial_Disorders/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: Assess gene expression data availability based on background information
+is_gene_available = True  # Illumina HT12v4 Gene Expression BeadArray indicates mRNA expression data
+
+# Step 2: Determine availability of trait, age, and gender from the sample characteristics dictionary
+# Trait and gender are not available; "time point" can be used as developmental age.
+trait_row = None
+age_row = 1  # "time point" (e.g., 8wk, 20wk, d24)
+gender_row = None
+
+# Step 2.2: Define conversion functions
+import re
+import pandas as pd
+
+def _parse_value(x):
+    if x is None or (isinstance(x, float) and pd.isna(x)):
+        return None
+    if isinstance(x, str):
+        parts = x.split(":", 1)
+        v = parts[1].strip() if len(parts) > 1 else x.strip()
+        if v == "" or v.lower() in {"na", "n/a", "unknown", "missing", "not available", "nd"}:
+            return None
+        return v
+    return x
+
+def convert_trait(x):
+    _ = _parse_value(x)
+    return None
+
+def convert_age(x):
+    # Parse developmental time to weeks as float.
+    v = _parse_value(x)
+    if v is None:
+        return None
+    s = str(v).strip().lower()
+    # Examples: "20wk", "20 wk", "18w", "d24", "24d"
+    m = re.search(r'(\d+(?:\.\d+)?)\s*wk?', s)
+    if m:
+        try:
+            return float(m.group(1))
+        except ValueError:
+            return None
+    m = re.search(r'^d(\d+(?:\.\d+)?)$', s)
+    if m:
+        try:
+            return float(m.group(1)) / 7.0
+        except ValueError:
+            return None
+    m = re.search(r'(\d+(?:\.\d+)?)\s*d', s)
+    if m:
+        try:
+            return float(m.group(1)) / 7.0
+        except ValueError:
+            return None
+    return None
+
+def convert_gender(x):
+    v = _parse_value(x)
+    if v is None:
+        return None
+    v_low = str(v).strip().lower()
+    if v_low in {"female", "f", "woman", "girl"}:
+        return 0
+    if v_low in {"male", "m", "man", "boy"}:
+        return 1
+    return None
+
+# Step 3: Initial filtering and save metadata
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 4: Clinical feature extraction (skip since 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
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)

output/preprocess/Mitochondrial_Disorders/code/TCGA.py

@@ -0,0 +1,65 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Mitochondrial_Disorders"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z4/preprocess/Mitochondrial_Disorders/TCGA.csv"
+out_gene_data_file = "./output/z4/preprocess/Mitochondrial_Disorders/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z4/preprocess/Mitochondrial_Disorders/clinical_data/TCGA.csv"
+json_path = "./output/z4/preprocess/Mitochondrial_Disorders/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Step 1: Find the most relevant TCGA cohort directory for the trait
+all_entries = os.listdir(tcga_root_dir)
+subdirs = [d for d in all_entries if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+# Keywords related to mitochondrial disorders; keep them specific to avoid false positives
+keywords = [
+    "mitochond", "mitochondrial", "mtdna", "mt-dna", "respiratory_chain", "oxphos", "oxidative_phosphorylation",
+    "mitophagy", "electron_transport_chain"
+]
+
+matches = []
+for d in subdirs:
+    name_lower = d.lower()
+    if any(k in name_lower for k in keywords):
+        matches.append(d)
+
+selected_dir = None
+if matches:
+    # If multiple, choose the most specific match by the longest match length
+    def best_score(dirname: str) -> int:
+        name_lower = dirname.lower()
+        return max((len(k) for k in keywords if k in name_lower), default=0)
+    selected_dir = max(matches, key=best_score)
+
+if not selected_dir:
+    # No suitable TCGA cohort for mitochondrial disorders; record and skip
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+    print("No suitable TCGA cohort directory found for the trait. Skipping this trait.")
+else:
+    # Step 2: Identify clinical and genetic files in the selected directory
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # Step 3: Load both files as DataFrames
+    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)
+
+    # Step 4: Print clinical data column names for inspection
+    print(list(clinical_df.columns))

output/preprocess/Mitochondrial_Disorders/cohort_info.json

@@ -1,52 +1 @@
-{
-  "GSE65399": {
-    "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
-  },
-  "GSE42986": {
-    "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": 46
-  },
-  "GSE30933": {
-    "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": 108
-  },
-  "GSE22651": {
-    "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
-  }
-}
+{"GSE65399": {"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}, "GSE42986": {"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": 43, "note": "INFO: Samples labeled 'Excluded' or 'outlier' in the informatic analysis group were set to missing trait and removed during missing-value handling."}, "GSE30933": {"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": 108, "note": "INFO: Age and Gender not available in clinical data. Trait derived from 'disease status'."}, "GSE22651": {"is_usable": false, "is_gene_available": true, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}, "TCGA": {"is_usable": false, "is_gene_available": false, "is_trait_available": false, "is_available": false, "is_biased": null, "has_age": null, "has_gender": null, "sample_size": null, "note": null}}

output/preprocess/Multiple_Endocrine_Neoplasia_Type_2/clinical_data/GSE19987.csv

@@ -1,2 +1,2 @@
-0,1,2,3,4,5,6
-0.0,0.0,0.0,0.0,1.0,1.0,0.0
+,GSM62248,GSM62249,GSM62250,GSM62251,GSM62252,GSM62253,GSM62254,GSM62255,GSM62256,GSM62257,GSM62258,GSM62259,GSM62260,GSM62261,GSM62262,GSM62263,GSM62264,GSM62265,GSM62266,GSM62267,GSM62268,GSM62269,GSM62270,GSM62271,GSM62272,GSM62273,GSM62274,GSM62275,GSM62276,GSM62277,GSM62278,GSM62279,GSM62280,GSM62281,GSM62282,GSM62283,GSM62284,GSM62285,GSM62286,GSM62287,GSM62288,GSM62289,GSM62290,GSM62291,GSM62292,GSM62293,GSM62294,GSM62295,GSM62296,GSM62297,GSM62298,GSM62299,GSM62300,GSM62301,GSM62302,GSM62303,GSM62304,GSM62305,GSM62306,GSM62307,GSM62308,GSM62309,GSM62310,GSM62311,GSM62312,GSM62313,GSM62314,GSM62315,GSM62316,GSM62317,GSM62318,GSM62319,GSM62320,GSM62321,GSM62322
+Multiple_Endocrine_Neoplasia_Type_2,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0

output/preprocess/Multiple_Endocrine_Neoplasia_Type_2/code/GSE19987.py

@@ -0,0 +1,200 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Multiple_Endocrine_Neoplasia_Type_2"
+cohort = "GSE19987"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Multiple_Endocrine_Neoplasia_Type_2"
+in_cohort_dir = "../DATA/GEO/Multiple_Endocrine_Neoplasia_Type_2/GSE19987"
+
+# Output paths
+out_data_file = "./output/z4/preprocess/Multiple_Endocrine_Neoplasia_Type_2/GSE19987.csv"
+out_gene_data_file = "./output/z4/preprocess/Multiple_Endocrine_Neoplasia_Type_2/gene_data/GSE19987.csv"
+out_clinical_data_file = "./output/z4/preprocess/Multiple_Endocrine_Neoplasia_Type_2/clinical_data/GSE19987.csv"
+json_path = "./output/z4/preprocess/Multiple_Endocrine_Neoplasia_Type_2/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import os
+import re
+
+# 1) Gene expression data availability
+is_gene_available = True  # Expression profiling study of tumors; likely mRNA expression data
+
+# 2) Variable availability
+# From the sample characteristics:
+# 0: tumor type (constant: pheochromocytoma)
+# 1: genetic class (includes 'MEN2A' among others) -> can infer MEN2 status
+# 2: tumor location (not trait, not age, not gender)
+trait_row = 1
+age_row = None
+gender_row = None
+
+# 2.2) Data type conversion functions
+def _after_colon(x):
+    if x is None:
+        return None
+    s = str(x)
+    parts = s.split(':', 1)
+    val = parts[-1].strip() if len(parts) > 1 else s.strip()
+    return val if val != '' else None
+
+def convert_trait(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    # Map MEN2 syndromes to 1; others to 0
+    if 'men2' in v:
+        return 1
+    return 0
+
+def convert_age(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = val.lower()
+    # Extract first numeric token as age
+    m = re.search(r'(\d+(\.\d+)?)', v)
+    if m:
+        try:
+            return float(m.group(1))
+        except:
+            return None
+    return None
+
+def convert_gender(x):
+    val = _after_colon(x)
+    if val is None:
+        return None
+    v = val.strip().lower()
+    if v in {'male', 'm'}:
+        return 1
+    if v in {'female', 'f'}:
+        return 0
+    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 (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("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
+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 the appropriate columns for probe IDs and gene symbols based on the annotation preview
+probe_col = 'ID'
+gene_symbol_col = 'Gene Symbol'
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# Apply mapping to convert probe-level expression to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Step 7: Data Normalization and Linking
+import os
+import pandas as pd
+
+# 1. Normalize the obtained gene data and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data
+# Use the correct clinical variable; fallback to load if not in memory
+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)
+
+# Compute availability flags based on data
+is_gene_available = (normalized_gene_data.shape[0] > 0) and (normalized_gene_data.shape[1] > 0)
+is_trait_available = trait in linked_data.columns
+
+# 3. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Conduct final quality validation and save the cohort information.
+note = "INFO: Trait inferred from 'genetic class' (MEN2 vs others); Age/Gender not provided in series matrix."
+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. If the linked data is usable, save it to 'out_data_file'.
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Multiple_Endocrine_Neoplasia_Type_2/code/TCGA.py

@@ -0,0 +1,209 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Multiple_Endocrine_Neoplasia_Type_2"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z4/preprocess/Multiple_Endocrine_Neoplasia_Type_2/TCGA.csv"
+out_gene_data_file = "./output/z4/preprocess/Multiple_Endocrine_Neoplasia_Type_2/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z4/preprocess/Multiple_Endocrine_Neoplasia_Type_2/clinical_data/TCGA.csv"
+json_path = "./output/z4/preprocess/Multiple_Endocrine_Neoplasia_Type_2/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Step 1: Select the most relevant TCGA cohort directory for Multiple Endocrine Neoplasia Type 2 (MEN2)
+# MEN2 commonly presents with pheochromocytoma/paraganglioma; thus PCPG is the most specific match.
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+selected_dir = None
+priority_keywords = [
+    "pheochromocytoma_paraganglioma",  # PCPG
+    "pcpg",
+    "thyroid_cancer",                  # secondary relevance (MTC is rare in TCGA THCA)
+    "(thca)",
+    "thyroid"
+]
+
+lower_subdirs = {d.lower(): d for d in subdirs}
+for kw in priority_keywords:
+    for d_lower, d_orig in lower_subdirs.items():
+        if kw in d_lower:
+            selected_dir = d_orig
+            break
+    if selected_dir:
+        break
+
+if selected_dir is None:
+    # No suitable directory found; record and stop
+    _ = validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+else:
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+
+    # Step 2: Identify clinical and genetic data file paths
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # Step 3: Load both files
+    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)
+
+    # Step 4: Print clinical column names
+    print(list(clinical_df.columns))
+
+# Step 2: Find Candidate Demographic Features
+import os
+import pandas as pd
+
+# Provided column names from previous step
+column_names = ['_INTEGRATION', '_PATIENT', '_cohort', '_primary_disease', '_primary_site', 'age_at_initial_pathologic_diagnosis', 'bcr_followup_barcode', 'bcr_patient_barcode', 'bcr_sample_barcode', 'ct_scan', 'days_to_birth', 'days_to_collection', 'days_to_death', 'days_to_initial_pathologic_diagnosis', 'days_to_last_followup', 'days_to_new_tumor_event_after_initial_treatment', 'disease_detected_on_screening', 'eastern_cancer_oncology_group', 'form_completion_date', 'gender', 'histological_type', 'history_of_neoadjuvant_treatment', 'history_pheo_or_para_anatomic_site', 'history_pheo_or_para_include_benign', 'icd_10', 'icd_o_3_histology', 'icd_o_3_site', 'informed_consent_verified', 'initial_weight', 'is_ffpe', 'karnofsky_performance_score', 'laterality', 'lost_follow_up', 'lymph_node_examined_count', 'new_neoplasm_confirmed_diagnosis_method_name', 'new_neoplasm_event_occurrence_anatomic_site', 'new_neoplasm_event_type', 'new_neoplasm_occurrence_anatomic_site_text', 'new_tumor_event_after_initial_treatment', 'number_of_lymphnodes_positive_by_he', 'oct_embedded', 'other_dx', 'outside_adrenal', 'pathology_report_file_name', 'patient_id', 'performance_status_scale_timing', 'person_neoplasm_cancer_status', 'postoperative_rx_tx', 'primary_lymph_node_presentation_assessment', 'primary_therapy_outcome_success', 'radiation_therapy', 'sample_type', 'sample_type_id', 'tissue_prospective_collection_indicator', 'tissue_retrospective_collection_indicator', 'tissue_source_site', 'tumor_tissue_site', 'tumor_tissue_site_other', 'vial_number', 'vital_status', 'year_of_initial_pathologic_diagnosis', '_GENOMIC_ID_TCGA_PCPG_exp_HiSeqV2_PANCAN', '_GENOMIC_ID_TCGA_PCPG_mutation_bcm_gene', '_GENOMIC_ID_TCGA_PCPG_mutation_broad_gene', '_GENOMIC_ID_TCGA_PCPG_hMethyl450', '_GENOMIC_ID_TCGA_PCPG_gistic2thd', '_GENOMIC_ID_TCGA_PCPG_exp_HiSeqV2', '_GENOMIC_ID_TCGA_PCPG_exp_HiSeqV2_exon', '_GENOMIC_ID_TCGA_PCPG_PDMRNAseqCNV', '_GENOMIC_ID_TCGA_PCPG_miRNA_HiSeq', '_GENOMIC_ID_data/public/TCGA/PCPG/miRNA_HiSeq_gene', '_GENOMIC_ID_TCGA_PCPG_mutation_bcgsc_gene', '_GENOMIC_ID_TCGA_PCPG_RPPA', '_GENOMIC_ID_TCGA_PCPG_mutation_ucsc_maf_gene', '_GENOMIC_ID_TCGA_PCPG_gistic2', '_GENOMIC_ID_TCGA_PCPG_PDMRNAseq', '_GENOMIC_ID_TCGA_PCPG_exp_HiSeqV2_percentile']
+
+# Identify candidate columns
+lower_cols = [c.lower() for c in column_names]
+candidate_age_cols = [c for c in column_names if 'age' in c.lower() or 'birth' in c.lower()]
+candidate_gender_cols = [c for c in column_names if 'gender' in c.lower() or 'sex' in c.lower()]
+
+# Strictly required formatted output
+print(f"candidate_age_cols = {candidate_age_cols}")
+print(f"candidate_gender_cols = {candidate_gender_cols}")
+
+# Try to obtain clinical_df; if not present, load from TCGA PCPG cohort
+clinical_df = globals().get('clinical_df', None)
+if clinical_df is None:
+    try:
+        # Prefer PCPG if available
+        cohorts = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+        if 'PCPG' in cohorts:
+            cohort_dir = os.path.join(tcga_root_dir, 'PCPG')
+        else:
+            # Fallback to any cohort directory
+            cohort_dir = os.path.join(tcga_root_dir, cohorts[0]) if cohorts else None
+
+        if cohort_dir:
+            clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)
+            clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, dtype=str)
+    except Exception:
+        clinical_df = None
+
+# Preview extracted candidate columns if clinical_df is available
+if clinical_df is not None:
+    age_cols_existing = [c for c in candidate_age_cols if c in clinical_df.columns]
+    gender_cols_existing = [c for c in candidate_gender_cols if c in clinical_df.columns]
+
+    if age_cols_existing:
+        age_preview = preview_df(clinical_df[age_cols_existing], n=5)
+        print(age_preview)
+    else:
+        print({})
+
+    if gender_cols_existing:
+        gender_preview = preview_df(clinical_df[gender_cols_existing], n=5)
+        print(gender_preview)
+    else:
+        print({})
+else:
+    # If clinical data cannot be loaded, print empty previews to keep output predictable
+    print({})
+    print({})
+
+# Step 3: Select Demographic Features
+# Deterministic selection of demographic columns based on candidate lists
+
+# Safely access candidate lists
+try:
+    _candidate_age_cols = candidate_age_cols if isinstance(candidate_age_cols, list) else []
+except NameError:
+    _candidate_age_cols = []
+try:
+    _candidate_gender_cols = candidate_gender_cols if isinstance(candidate_gender_cols, list) else []
+except NameError:
+    _candidate_gender_cols = []
+
+age_col = None
+gender_col = None
+
+# Prefer direct age field; fall back to days_to_birth if needed
+preferred_age_order = [
+    "age_at_initial_pathologic_diagnosis",
+    "age",
+    "age_at_diagnosis",
+    "years_to_birth",
+    "days_to_birth",
+]
+for c in preferred_age_order:
+    if c in _candidate_age_cols:
+        age_col = c
+        break
+
+# Prefer 'gender', then 'sex'
+preferred_gender_order = ["gender", "sex"]
+for c in preferred_gender_order:
+    if c in _candidate_gender_cols:
+        gender_col = c
+        break
+
+print(f"Selected age_col: {age_col}")
+print(f"Selected gender_col: {gender_col}")
+
+# Step 4: Feature Engineering and Validation
+import os
+
+# 1) Extract and standardize clinical features (trait, optional age, gender)
+selected_clinical_df = tcga_select_clinical_features(
+    clinical_df=clinical_df,
+    trait=trait,
+    age_col=age_col,
+    gender_col=gender_col
+)
+
+# 2) Normalize gene symbols and save normalized gene expression data
+gene_df_norm = normalize_gene_symbols_in_index(genetic_df.copy())
+os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
+gene_df_norm.to_csv(out_gene_data_file)
+
+# 3) Link clinical and genetic data on sample IDs
+common_samples = selected_clinical_df.index.intersection(gene_df_norm.columns)
+linked_data = pd.concat(
+    [
+        selected_clinical_df.loc[common_samples],
+        gene_df_norm.loc[:, common_samples].T
+    ],
+    axis=1
+)
+
+# 4) Handle missing values in the linked data
+processed_df = handle_missing_values(linked_data, trait_col=trait)
+
+# 5) Determine whether trait/demographics are severely biased; drop biased demographics
+is_biased, processed_df = judge_and_remove_biased_features(processed_df, trait=trait)
+
+# 6) Final validation and save cohort info
+is_gene_available = gene_df_norm.shape[0] > 0
+is_trait_available = not selected_clinical_df[trait].isna().all()
+note = "INFO: TCGA-PCPG cohort selected for MEN2-related phenotype due to strong association with pheochromocytoma/paraganglioma."
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort="TCGA",
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available,
+    is_biased=is_biased,
+    df=processed_df,
+    note=note
+)
+
+# 7) Save linked data only if usable
+if is_usable:
+    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+    processed_df.to_csv(out_data_file)

output/preprocess/Multiple_Endocrine_Neoplasia_Type_2/cohort_info.json

@@ -1,22 +1 @@
-{
-  "GSE19987": {
-    "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
-  }
-}
+{"GSE19987": {"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": 75, "note": "INFO: Trait inferred from 'genetic class' (MEN2 vs others); Age/Gender not provided in series matrix."}, "TCGA": {"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": 187, "note": "INFO: TCGA-PCPG cohort selected for MEN2-related phenotype due to strong association with pheochromocytoma/paraganglioma."}}

output/preprocess/Multiple_sclerosis/clinical_data/GSE131282.csv

@@ -1,4 +1,4 @@
 ,GSM3768507,GSM3768508,GSM3768509,GSM3768510,GSM3768511,GSM3768512,GSM3768513,GSM3768514,GSM3768515,GSM3768516,GSM3768517,GSM3768518,GSM3768519,GSM3768520,GSM3768521,GSM3768522,GSM3768523,GSM3768524,GSM3768525,GSM3768526,GSM3768527,GSM3768528,GSM3768529,GSM3768530,GSM3768531,GSM3768532,GSM3768533,GSM3768534,GSM3768535,GSM3768536,GSM3768537,GSM3768538,GSM3768539,GSM3768540,GSM3768541,GSM3768542,GSM3768543,GSM3768544,GSM3768545,GSM3768546,GSM3768547,GSM3768548,GSM3768549,GSM3768550,GSM3768551,GSM3768552,GSM3768553,GSM3768554,GSM3768555,GSM3768556,GSM3768557,GSM3768558,GSM3768559,GSM3768560,GSM3768561,GSM3768562,GSM3768563,GSM3768564,GSM3768565,GSM3768566,GSM3768567,GSM3768568,GSM3768569,GSM3768570,GSM3768571,GSM3768572,GSM3768573,GSM3768574,GSM3768575,GSM3768576,GSM3768577,GSM3768578,GSM3768579,GSM3768580,GSM3768581,GSM3768582,GSM3768583,GSM3768584,GSM3768613,GSM3768614,GSM3768616,GSM3768617,GSM3768619,GSM3768620,GSM3768621,GSM3768623,GSM3768624,GSM3768625,GSM3768626,GSM3768627,GSM3768628,GSM3768629,GSM3768630,GSM3768631,GSM3768632,GSM3768633,GSM3768634,GSM3768635,GSM3768636,GSM3768637,GSM3768638,GSM3768639,GSM3768640,GSM3768641,GSM3768642,GSM3768643,GSM3768644,GSM3768645,GSM3768646,GSM3768647,GSM3768648,GSM3768649,GSM3768650,GSM3768651,GSM3768652,GSM3768653,GSM3768654,GSM3768655,GSM3768656,GSM3768657,GSM3768658,GSM3768659,GSM3768660,GSM3768661,GSM3768662,GSM3768663,GSM3768664,GSM3768665,GSM3768666,GSM3768667,GSM3768668,GSM3768669,GSM3768670,GSM3768671,GSM3768672,GSM3768673,GSM3768674,GSM3768675,GSM3768676,GSM3768677,GSM3768678,GSM3768679,GSM3768680,GSM3768681,GSM3768682,GSM3768683,GSM3768684,GSM3768685,GSM3768686,GSM3768687,GSM3768688,GSM3768689,GSM3768690,GSM3768691,GSM3768692,GSM3768693,GSM3768694,GSM3768695,GSM3768696,GSM3768697,GSM3768698,GSM3768699,GSM3768700,GSM3768701,GSM3768702,GSM3768703,GSM3768704,GSM3768705,GSM3768706,GSM3768707,GSM3768708,GSM3768709,GSM3768710,GSM3768711,GSM3768712,GSM3768713,GSM3768714,GSM3768715,GSM3768716,GSM3768717,GSM3768718,GSM3768719,GSM3768720,GSM3768721
-Multiple_sclerosis,1.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+Multiple_sclerosis,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,1.0
 Age,58.0,59.0,80.0,63.0,47.0,78.0,59.0,88.0,45.0,45.0,61.0,50.0,54.0,78.0,80.0,61.0,45.0,69.0,39.0,58.0,78.0,56.0,44.0,58.0,78.0,58.0,58.0,80.0,58.0,56.0,78.0,42.0,58.0,58.0,50.0,78.0,92.0,54.0,71.0,58.0,39.0,78.0,78.0,56.0,58.0,54.0,45.0,59.0,45.0,77.0,78.0,56.0,44.0,58.0,78.0,34.0,58.0,63.0,78.0,78.0,58.0,92.0,58.0,69.0,58.0,49.0,47.0,78.0,45.0,58.0,70.0,56.0,58.0,71.0,45.0,78.0,78.0,49.0,58.0,92.0,56.0,35.0,80.0,56.0,84.0,75.0,38.0,59.0,77.0,58.0,78.0,64.0,56.0,95.0,60.0,78.0,75.0,58.0,78.0,75.0,51.0,56.0,64.0,77.0,58.0,78.0,60.0,39.0,47.0,87.0,75.0,88.0,64.0,75.0,35.0,58.0,39.0,56.0,61.0,78.0,84.0,73.0,59.0,75.0,47.0,78.0,77.0,39.0,60.0,77.0,49.0,89.0,75.0,58.0,58.0,84.0,70.0,47.0,77.0,58.0,56.0,60.0,75.0,58.0,88.0,92.0,45.0,59.0,84.0,78.0,84.0,60.0,75.0,58.0,58.0,49.0,51.0,58.0,78.0,77.0,35.0,84.0,49.0,75.0,75.0,61.0,75.0,78.0,47.0,58.0,39.0,78.0,77.0,87.0,35.0,45.0,84.0,70.0,58.0,73.0,45.0,78.0,64.0,58.0
 Gender,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0

output/preprocess/Multiple_sclerosis/code/GSE131279.py

@@ -0,0 +1,177 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Multiple_sclerosis"
+cohort = "GSE131279"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Multiple_sclerosis"
+in_cohort_dir = "../DATA/GEO/Multiple_sclerosis/GSE131279"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Multiple_sclerosis/GSE131279.csv"
+out_gene_data_file = "./output/z5/preprocess/Multiple_sclerosis/gene_data/GSE131279.csv"
+out_clinical_data_file = "./output/z5/preprocess/Multiple_sclerosis/clinical_data/GSE131279.csv"
+json_path = "./output/z5/preprocess/Multiple_sclerosis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+# Step 1: Determine gene expression data availability
+is_gene_available = True  # Based on series summary indicating differential gene expression analysis (not miRNA/methylation)
+
+# Step 2: Variable availability and conversion functions
+
+# Trait (Multiple_sclerosis): All samples are MS cases (no controls), thus not available for association
+trait_row = None
+
+# Age: Available at key 2 ("age at death")
+age_row = 2
+
+# Gender: Available at key 1 ("Sex")
+gender_row = 1
+
+# Conversion functions
+def convert_trait(x: str):
+    # Not used because trait_row is None
+    return None
+
+def convert_age(x: str):
+    # Expect formats like "age at death: 58"
+    if x is None:
+        return None
+    try:
+        val = x.split(":", 1)[1].strip()
+    except Exception:
+        val = str(x).strip()
+    if val in {"?", "", "NA", "N/A", "nan", "None"}:
+        return None
+    # Remove possible units or stray characters and convert to float
+    try:
+        return float(''.join(ch for ch in val if (ch.isdigit() or ch == ".")))
+    except Exception:
+        return None
+
+def convert_gender(x: str):
+    # Expect formats like "Sex: F" or "Sex: M"
+    if x is None:
+        return None
+    try:
+        val = x.split(":", 1)[1].strip().lower()
+    except Exception:
+        val = str(x).strip().lower()
+    if val in {"f", "female"}:
+        return 0
+    if val in {"m", "male"}:
+        return 1
+    return None
+
+# Step 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
+)
+
+# 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
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Determine appropriate columns for probe ID and gene symbol based on annotation preview:
+# Probe identifiers: 'ID' (e.g., ILMN_1343048)
+# Gene symbols: 'Symbol'
+
+# 1-2. Build mapping dataframe from annotation
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# 3. Apply mapping to convert probe-level data to gene-level expression
+gene_data = apply_gene_mapping(gene_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)
+
+# 2-6. Handle linking and validation depending on trait availability
+if ('trait_row' in globals()) and (trait_row is not None) and ('selected_clinical_data' in globals()):
+    # 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 evaluation and potential covariate removal
+    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(
+        True, cohort, json_path, True, True, is_trait_biased, unbiased_linked_data
+    )
+
+    # Save linked data only if usable
+    if is_usable:
+        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
+        unbiased_linked_data.to_csv(out_data_file)
+else:
+    # Trait not available; skip linking and mark as not available in final validation
+    note = "WARNING: Trait not available (all samples are MS cases); no linked data saved."
+    df_placeholder = normalized_gene_data.T
+    _ = validate_and_save_cohort_info(
+        True, cohort, json_path, True, False, False, df_placeholder, note
+    )

output/preprocess/Multiple_sclerosis/code/GSE131281.py

@@ -0,0 +1,196 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Multiple_sclerosis"
+cohort = "GSE131281"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Multiple_sclerosis"
+in_cohort_dir = "../DATA/GEO/Multiple_sclerosis/GSE131281"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Multiple_sclerosis/GSE131281.csv"
+out_gene_data_file = "./output/z5/preprocess/Multiple_sclerosis/gene_data/GSE131281.csv"
+out_clinical_data_file = "./output/z5/preprocess/Multiple_sclerosis/clinical_data/GSE131281.csv"
+json_path = "./output/z5/preprocess/Multiple_sclerosis/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  # Series describes differential gene expression analysis (not miRNA-only or methylation-only)
+
+# 2) Identify rows for variables based on the provided dictionary
+trait_row = 5    # 'ms type' -> can infer MS case/control
+age_row = 2      # 'age at death'
+gender_row = 1   # 'Sex'
+
+# 2.2) Converters
+def _extract_after_colon(x: str) -> str:
+    if x is None or (isinstance(x, float) and pd.isna(x)):
+        return ''
+    s = str(x).strip()
+    if ':' in s:
+        return s.split(':', 1)[1].strip()
+    return s.strip()
+
+def convert_trait(x):
+    val = _extract_after_colon(x).strip()
+    if not val:
+        return None
+    v_up = val.upper()
+    # MS case types
+    ms_types = {'MS', 'SPMS', 'PPMS', 'PRMS', 'RRMS'}
+    if v_up in ms_types:
+        return 1
+    # Controls likely annotated as '?' for ms type
+    if v_up in {'?', 'CONTROL', 'CTL', 'NA', 'N/A', 'NONE'}:
+        return 0
+    # Fallback: if string contains 'MS' but not a known control marker, treat as case
+    if 'MS' in v_up:
+        return 1
+    return None
+
+def convert_age(x):
+    val = _extract_after_colon(x)
+    # extract the first number
+    m = re.search(r'[-+]?\d*\.?\d+', val)
+    if not m:
+        return None
+    try:
+        num = float(m.group())
+        return num
+    except Exception:
+        return None
+
+def convert_gender(x):
+    val = _extract_after_colon(x).strip().lower()
+    if val in {'f', 'female'}:
+        return 0
+    if val in {'m', 'male'}:
+        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
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+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. Decide the appropriate columns for mapping based on annotation preview:
+#    - Probe/ID column: 'ID' (matches ILMN_* probe IDs)
+#    - Gene symbol column: 'Symbol'
+probe_col = 'ID'
+symbol_col = 'Symbol'
+
+# 2. Build the mapping dataframe from annotation
+gene_mapping = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=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
+
+# Ensure clinical features are available as a DataFrame
+try:
+    selected_clinical_df
+except NameError:
+    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 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) Bias assessment and removal of 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: Probe-to-gene mapping via SOFT 'Symbol' with equal split for multi-gene probes; "
+        "gene symbols normalized using NCBI synonym table; trait from 'ms type', age from 'age at death', gender from 'Sex'.")
+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/Multiple_sclerosis/code/GSE131282.py

@@ -0,0 +1,187 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Multiple_sclerosis"
+cohort = "GSE131282"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Multiple_sclerosis"
+in_cohort_dir = "../DATA/GEO/Multiple_sclerosis/GSE131282"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Multiple_sclerosis/GSE131282.csv"
+out_gene_data_file = "./output/z5/preprocess/Multiple_sclerosis/gene_data/GSE131282.csv"
+out_clinical_data_file = "./output/z5/preprocess/Multiple_sclerosis/clinical_data/GSE131282.csv"
+json_path = "./output/z5/preprocess/Multiple_sclerosis/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
+# Presence of RIN and cortical tissue suggests mRNA expression profiling is available.
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+
+# Identify rows for variables based on the sample characteristics dictionary provided:
+trait_row = 0      # patient id encodes MS (M..) vs Control (C..)
+age_row = 2        # 'age at death'
+gender_row = 1     # 'Sex'
+
+def _after_colon(value):
+    if value is None:
+        return None
+    s = str(value)
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    return s.strip() if s.strip() != '' else None
+
+def convert_trait(value):
+    v = _after_colon(value)
+    if v is None:
+        return None
+    first = v.strip()[0].upper()
+    # Heuristic: IDs starting with 'M' = MS case, 'C' = control
+    if first == 'M':
+        return 1
+    if first == 'C':
+        return 0
+    return None
+
+def convert_age(value):
+    v = _after_colon(value)
+    if v is None:
+        return None
+    m = re.search(r'[-+]?\d*\.?\d+', v)
+    if not m:
+        return None
+    try:
+        num = float(m.group())
+        return num
+    except Exception:
+        return None
+
+def convert_gender(value):
+    v = _after_colon(value)
+    if v is None:
+        return None
+    vl = v.strip().lower()
+    if vl in {'f', 'female'}:
+        return 0
+    if vl in {'m', 'male'}:
+        return 1
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if clinical data available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        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)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Determine the appropriate columns for probe IDs and gene symbols based on the annotation preview
+prob_col = 'ID'       # Matches probe identifiers like 'ILMN_1343048'
+gene_col = 'Symbol'   # Contains gene symbols
+
+# 2. Create the 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
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# Step 7: Data Normalization and Linking
+# 1. Normalize the obtained gene data and save
+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
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# Derive availability flags based on linked data before filtering/imputation (cast to Python bool)
+covariate_cols = [trait, 'Age', 'Gender']
+gene_cols_in_linked = [c for c in linked_data.columns if c not in covariate_cols]
+is_gene_available_final = bool(len(gene_cols_in_linked) > 0)
+is_trait_available_final = bool((trait in linked_data.columns) and bool(linked_data[trait].notna().any()))
+
+# 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
+note = ("INFO: Trait inferred from patient ID prefix (M=MS case, C=control). "
+        "Platform identifiers are Illumina probes; mapped via SOFT 'Symbol' column; "
+        "gene symbols normalized using NCBI synonyms.")
+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=bool(is_trait_biased),
+    df=unbiased_linked_data,
+    note=note
+)
+
+# 6. Save linked data if usable
+if is_usable:
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Multiple_sclerosis/code/GSE135511.py

@@ -0,0 +1,169 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Multiple_sclerosis"
+cohort = "GSE135511"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Multiple_sclerosis"
+in_cohort_dir = "../DATA/GEO/Multiple_sclerosis/GSE135511"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Multiple_sclerosis/GSE135511.csv"
+out_gene_data_file = "./output/z5/preprocess/Multiple_sclerosis/gene_data/GSE135511.csv"
+out_clinical_data_file = "./output/z5/preprocess/Multiple_sclerosis/clinical_data/GSE135511.csv"
+json_path = "./output/z5/preprocess/Multiple_sclerosis/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  # Based on series summary: mRNA gene expression profiling
+
+# 2) Variable availability and conversion functions
+# From the sample characteristics dictionary:
+# 0: disease state: Multiple Sclerosis vs Healthy Control  --> trait available
+# No explicit age or gender fields                        --> not available
+trait_row = 0
+age_row = None
+gender_row = None
+
+def convert_trait(x):
+    if x is None:
+        return None
+    s = str(x)
+    # Extract value after colon, if present
+    if ':' in s:
+        s = s.split(':', 1)[1]
+    s = s.strip().lower()
+    if s in {'', 'n.a.', 'na', 'n/a', 'not available', 'unknown'}:
+        return None
+    # Map to binary: MS=1, Control=0
+    if 'multiple sclerosis' in s or s == 'ms':
+        return 1
+    if 'healthy' in s or 'control' in s:
+        return 0
+    return None
+
+# Age and gender not available; define stubs for completeness
+def convert_age(x):
+    return None
+
+def convert_gender(x):
+    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 is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    clinical_preview = preview_df(selected_clinical_df)
+    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
+# Map probe IDs to gene symbols and aggregate to gene-level expression
+
+# Backup probe-level data
+probe_data = gene_data
+
+# Choose columns based on annotation preview:
+# - Probe identifier: 'ID' (e.g., ILMN_1343291)
+# - Gene symbol: 'Symbol' (e.g., JMJD1A, NCOA3)
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# Apply mapping to convert probe-level to gene-level expression
+gene_data = apply_gene_mapping(expression_df=probe_data, mapping_df=mapping_df)
+
+# Optionally save the gene-level data
+gene_data.to_csv(out_gene_data_file)
+
+# Step 7: Data Normalization and Linking
+# 1. Normalize the obtained gene data and save
+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
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values in the linked data
+linked_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final validation and save cohort info
+covariate_cols = [trait, 'Age', 'Gender']
+gene_cols_after = [c for c in unbiased_linked_data.columns if c not in covariate_cols]
+is_gene_available_final = len(gene_cols_after) > 0
+is_trait_available_final = (trait in unbiased_linked_data.columns) and (len(unbiased_linked_data) > 0)
+
+note = "INFO: Age and Gender not provided in series characteristics; post-mortem motor cortex MS vs control study."
+
+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 the linked data only if usable
+if is_usable:
+    unbiased_linked_data.to_csv(out_data_file)

output/preprocess/Multiple_sclerosis/code/GSE141381.py

@@ -0,0 +1,162 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Multiple_sclerosis"
+cohort = "GSE141381"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Multiple_sclerosis"
+in_cohort_dir = "../DATA/GEO/Multiple_sclerosis/GSE141381"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Multiple_sclerosis/GSE141381.csv"
+out_gene_data_file = "./output/z5/preprocess/Multiple_sclerosis/gene_data/GSE141381.csv"
+out_clinical_data_file = "./output/z5/preprocess/Multiple_sclerosis/clinical_data/GSE141381.csv"
+json_path = "./output/z5/preprocess/Multiple_sclerosis/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
+import pandas as pd
+
+# 1) Assess gene expression availability
+is_gene_available = True  # Based on series context, this is a gene expression dataset (not miRNA/methylation only)
+
+# 2) Determine availability rows from the Sample Characteristics Dictionary provided
+trait_row = None  # All subjects are SPMS; no case-control variability for Multiple_sclerosis
+age_row = 1       # 'age: <number>' appears primarily under key 1
+gender_row = 0    # 'gender: male/female' appears under key 0
+
+# 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:
+        return None
+    vl = v.lower()
+    # Explicit MS/controls mapping
+    positive_terms = ['multiple sclerosis', 'ms', 'spms', 'rrms', 'ppms', 'patient', 'case']
+    negative_terms = ['control', 'healthy', 'normal', 'non-ms', 'no ms', 'hc']
+    if any(t in vl for t in positive_terms):
+        return 1
+    if any(t in vl for t in negative_terms):
+        return 0
+    # Ignore treatment/placebo/baseline fields for trait mapping
+    if any(t in vl for t in ['treated', 'placebo', 'baseline']):
+        return None
+    return None
+
+def convert_age(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    v = v.lower()
+    if v in ['na', 'n/a', 'unknown', 'missing', 'nan', 'none', '']:
+        return None
+    # Extract the first integer/float from the string
+    m = re.search(r'(\d+(\.\d+)?)', v)
+    if not m:
+        return None
+    try:
+        age_val = float(m.group(1))
+        if 0 < age_val < 120:
+            return age_val
+        return None
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    vl = v.strip().lower()
+    if vl in ['female', 'f', 'woman', 'women']:
+        return 0
+    if vl 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 if trait not 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
+    )
+    print(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
+# The observed identifiers are numeric 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
+# Determine appropriate columns for probe IDs and gene symbols from annotation preview
+probe_col = 'probeset_id' if 'probeset_id' in gene_annotation.columns else 'ID'
+gene_col = 'gene_assignment' if 'gene_assignment' in gene_annotation.columns else 'mrna_assignment'
+
+# Build mapping dataframe: columns -> ID (probe), Gene (annotation text containing symbols)
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_col)
+
+# Apply mapping to convert probe-level measurements to gene-level expression
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)

output/preprocess/Multiple_sclerosis/code/GSE141804.py

@@ -0,0 +1,221 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Multiple_sclerosis"
+cohort = "GSE141804"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Multiple_sclerosis"
+in_cohort_dir = "../DATA/GEO/Multiple_sclerosis/GSE141804"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Multiple_sclerosis/GSE141804.csv"
+out_gene_data_file = "./output/z5/preprocess/Multiple_sclerosis/gene_data/GSE141804.csv"
+out_clinical_data_file = "./output/z5/preprocess/Multiple_sclerosis/clinical_data/GSE141804.csv"
+json_path = "./output/z5/preprocess/Multiple_sclerosis/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
+# Based on "blood mononuclear cell transcriptome", treat as gene expression (mRNA) data.
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+
+# Identify rows from the Sample Characteristics Dictionary provided:
+# 0 -> gender, 1 -> age (years)
+trait_row = None  # No disease status/group key provided in the sample characteristics dictionary
+age_row = 1
+gender_row = 0
+
+# Conversion functions
+def _after_colon(value: any) -> str:
+    if pd.isna(value):
+        return ""
+    s = str(value)
+    return s.split(":", 1)[1].strip() if ":" in s else s.strip()
+
+def convert_trait(value: any) -> int or None:
+    """
+    Convert to binary Multiple_sclerosis status: 1 = MS present, 0 = MS absent.
+    This function is defined for completeness; not used since trait_row is None.
+    """
+    v = _after_colon(value).lower()
+    if not v:
+        return None
+    # Positive MS indicators
+    ms_positive = {"ms", "mso", "multiple sclerosis", "p/ms", "pms", "comorbid ms", "ms patient", "ms-only", "p/ms patient"}
+    # Negative MS indicators
+    ms_negative = {"pso", "psoriasis", "psoriasis only", "healthy", "hc", "control", "healthy control"}
+
+    # Direct exact match checks
+    if v in ms_positive:
+        return 1
+    if v in ms_negative:
+        return 0
+
+    # Heuristics
+    if "multiple sclerosis" in v or re.search(r"\bms\b", v):
+        return 1
+    if "psoriasis" in v or "healthy" in v or "control" in v:
+        return 0
+
+    return None
+
+def convert_age(value: any) -> float or None:
+    v = _after_colon(value)
+    if not v:
+        return None
+    # Extract first numeric token
+    m = re.search(r"[-+]?\d*\.?\d+", v)
+    if m:
+        try:
+            return float(m.group(0))
+        except Exception:
+            return None
+    return None
+
+def convert_gender(value: any) -> int or None:
+    v = _after_colon(value).lower()
+    if not v:
+        return None
+    if v.startswith("female") or v == "f":
+        return 0
+    if v.startswith("male") or v == "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 (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)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+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 mapping columns based on previous previews: 'ID' holds probe IDs, 'Gene Symbol' holds gene symbols
+prob_col = 'ID'
+gene_col = 'Gene Symbol'
+
+# 2. Build the probe-to-gene 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
+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-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) Decide if trait/clinical data are available (simplified logic)
+has_trait = True
+if 'trait_row' in globals() and trait_row is None:
+    has_trait = False
+else:
+    has_trait = 'selected_clinical_data' in globals() and isinstance(selected_clinical_data, pd.DataFrame)
+
+if has_trait:
+    # 3) Link clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+    # 4) Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 5) Assess bias and remove biased demographic features
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 6) Final validation and save cohort info
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note="INFO: Clinical and genetic data linked; missing values handled and demographics evaluated."
+    )
+
+    # 7) 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 skip linking
+    dummy_df = normalized_gene_data.T.head(1)  # ensure >4 columns to avoid abnormality override
+    _ = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=False,
+        df=dummy_df,
+        note="WARNING: Trait data unavailable (trait_row is None); linking skipped. Saved only normalized gene data."
+    )

output/preprocess/Multiple_sclerosis/code/GSE146383.py

@@ -0,0 +1,210 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Multiple_sclerosis"
+cohort = "GSE146383"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Multiple_sclerosis"
+in_cohort_dir = "../DATA/GEO/Multiple_sclerosis/GSE146383"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Multiple_sclerosis/GSE146383.csv"
+out_gene_data_file = "./output/z5/preprocess/Multiple_sclerosis/gene_data/GSE146383.csv"
+out_clinical_data_file = "./output/z5/preprocess/Multiple_sclerosis/clinical_data/GSE146383.csv"
+json_path = "./output/z5/preprocess/Multiple_sclerosis/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  # Transcriptome profiling indicates gene expression data is available.
+
+# 2. Variable Availability and Data Type Conversion
+
+# Determine rows from Sample Characteristics Dictionary
+trait_row = None  # Disease status/group not present in the provided characteristics dictionary.
+age_row = 1
+gender_row = 0
+
+# Conversion functions
+def _extract_value(x):
+    if x is None:
+        return None
+    s = str(x)
+    parts = s.split(":", 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    return val.strip()
+
+def convert_trait(x):
+    # Binary: MS = 1, Control = 0
+    v = _extract_value(x)
+    if v is None:
+        return None
+    vl = v.lower()
+    # Map known group labels and descriptors
+    ms_tokens = {
+        "ms", "multiple sclerosis",
+        "pdms-rec", "pdms-norec", "adms-rec", "adms-norec",
+        "patient with ms", "ms patient"
+    }
+    control_tokens = {"control", "healthy", "pdc", "adc"}
+    if any(tok == vl for tok in ms_tokens) or any(tok in vl for tok in ["pdms", "adms"]):
+        return 1
+    if vl in control_tokens:
+        return 0
+    # Additional heuristics
+    if "control" in vl or "healthy" in vl:
+        return 0
+    if "multiple sclerosis" in vl or vl == "ms":
+        return 1
+    return None
+
+def convert_age(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    m = re.search(r"-?\d+\.?\d*", v)
+    try:
+        return float(m.group()) if m else None
+    except Exception:
+        return None
+
+def convert_gender(x):
+    v = _extract_value(x)
+    if v is None:
+        return None
+    vl = v.strip().lower()
+    if vl in {"male", "m"}:
+        return 1
+    if vl in {"female", "f"}:
+        return 0
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (skip if trait_row is None)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    _ = preview_df(selected_clinical_df)
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+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 mapping columns based on annotation preview
+probe_id_col = 'ID'
+gene_symbol_col = 'Gene Symbol'
+
+# Build 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 data
+probe_level_df = gene_data
+gene_data = apply_gene_mapping(expression_df=probe_level_df, 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)
+
+# Determine if clinical features (including trait) were extracted earlier
+clinical_selected = None
+if 'selected_clinical_data' in globals():
+    clinical_selected = selected_clinical_data
+elif 'selected_clinical_df' in globals():
+    clinical_selected = selected_clinical_df
+
+has_trait = clinical_selected is not None
+
+if has_trait:
+    # 2. Link clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(clinical_selected, normalized_gene_data)
+
+    # 3. Handle missing values
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Bias assessment 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)
+else:
+    # Clinical trait unavailable: finalize metadata without linked data
+    note = "INFO: Trait/clinical labels unavailable in matrix; only gene data processed and saved."
+    _ = validate_and_save_cohort_info(
+        True,
+        cohort,
+        json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=False,
+        df=normalized_gene_data.T,  # Use transposed gene data to resemble samples x features
+        note=note
+    )
+    # Do not save out_data_file when trait is unavailable

output/preprocess/Multiple_sclerosis/code/GSE189788.py

@@ -0,0 +1,131 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Multiple_sclerosis"
+cohort = "GSE189788"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Multiple_sclerosis"
+in_cohort_dir = "../DATA/GEO/Multiple_sclerosis/GSE189788"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Multiple_sclerosis/GSE189788.csv"
+out_gene_data_file = "./output/z5/preprocess/Multiple_sclerosis/gene_data/GSE189788.csv"
+out_clinical_data_file = "./output/z5/preprocess/Multiple_sclerosis/clinical_data/GSE189788.csv"
+json_path = "./output/z5/preprocess/Multiple_sclerosis/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
+# Affymetrix HU-133A-2 microarrays indicate mRNA expression profiling.
+is_gene_available = True
+
+# 2) Variable availability and converters
+# Sample Characteristics Dictionary review:
+# 0: ['patient diagnosis: multiple sclerosis'] -> constant; not useful for association (no controls)
+# 2: ages available and variable
+# 3: gender available and variable
+trait_row = None
+age_row = 2
+gender_row = 3
+
+def _after_colon(value: str) -> str:
+    if value is None:
+        return ""
+    s = str(value)
+    parts = s.split(":", 1)
+    return parts[1].strip() if len(parts) > 1 else s.strip()
+
+def convert_trait(x):
+    # Binary: 1 = Multiple sclerosis, 0 = control/healthy. Unknown -> None
+    v = _after_colon(x).lower()
+    if not v:
+        return None
+    # Positive MS indicators
+    ms_keys = ["multiple sclerosis", " rrms", " spms", " ppms", "ms patient", "ms case", "ms "]
+    if any(k in v for k in ms_keys) or v == "ms":
+        return 1
+    # Controls/healthy indicators
+    ctrl_keys = ["control", "healthy", "normal", "no disease", "non-disease", "donor"]
+    if any(k in v for k in ctrl_keys):
+        return 0
+    return None
+
+def convert_age(x):
+    # Continuous
+    v = _after_colon(x)
+    if not v:
+        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(x):
+    # Binary: female -> 0, male -> 1
+    v = _after_colon(x).lower()
+    if not v:
+        return None
+    # Handle common terms and letters
+    if v in {"female", "f", "woman", "women", "girl"} or "female" in v:
+        return 0
+    if v in {"male", "m", "man", "men", "boy"} or "male" in v:
+        return 1
+    return None
+
+# 3) Save metadata (initial filtering)
+is_trait_available = trait_row is not None
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction (only if trait is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    print(preview_df(selected_clinical_df, n=5))
+
+    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)

output/preprocess/Multiple_sclerosis/code/GSE193442.py

@@ -0,0 +1,133 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Multiple_sclerosis"
+cohort = "GSE193442"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Multiple_sclerosis"
+in_cohort_dir = "../DATA/GEO/Multiple_sclerosis/GSE193442"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Multiple_sclerosis/GSE193442.csv"
+out_gene_data_file = "./output/z5/preprocess/Multiple_sclerosis/gene_data/GSE193442.csv"
+out_clinical_data_file = "./output/z5/preprocess/Multiple_sclerosis/clinical_data/GSE193442.csv"
+json_path = "./output/z5/preprocess/Multiple_sclerosis/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 data availability
+is_gene_available = True  # "Transcriptional profiling" of human KIR+ CD8 T cells suggests mRNA expression data
+trait_row = None          # No disease/trait info found in sample characteristics
+age_row = None            # No age info found
+gender_row = None         # No gender info found
+
+# Step 2: Define conversion functions
+def _extract_value(x):
+    if x is None:
+        return None
+    if not isinstance(x, str):
+        x = str(x)
+    parts = x.split(":", 1)
+    val = parts[1] if len(parts) > 1 else parts[0]
+    return val.strip()
+
+def convert_trait(x):
+    val = _extract_value(x)
+    if not val:
+        return None
+    v = val.lower()
+    # Positive MS indicators
+    ms_tokens = ["multiple sclerosis", "ms", "rrms", "ppms", "spms", "cis", "clinically isolated syndrome"]
+    # Negative/control indicators
+    ctrl_tokens = ["control", "healthy", "normal", "no ms", "non-ms", "without ms"]
+    if any(tok in v for tok in ms_tokens):
+        return 1
+    if any(tok in v for tok in ctrl_tokens):
+        return 0
+    # Generic yes/no
+    if v in {"case", "patient", "disease", "yes"}:
+        return 1
+    if v in {"control", "healthy control", "no", "none"}:
+        return 0
+    return None
+
+def convert_age(x):
+    val = _extract_value(x)
+    if not val:
+        return None
+    v = val.lower().replace("years", "").replace("year", "").replace("yrs", "").replace("yr", "").strip()
+    # Keep digits and dot
+    import re
+    m = re.search(r"(\d+(\.\d+)?)", v)
+    if m:
+        try:
+            return float(m.group(1))
+        except Exception:
+            return None
+    return None
+
+def convert_gender(x):
+    val = _extract_value(x)
+    if not val:
+        return None
+    v = val.lower().strip()
+    if v in {"male", "m", "man"}:
+        return 1
+    if v in {"female", "f", "woman"}:
+        return 0
+    return None
+
+# Step 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
+)
+
+# Step 4: Clinical feature extraction (skip because trait_row is None)
+# If trait_row becomes available in future, uncomment and use:
+# if trait_row is not None:
+#     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)
+#     os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
+#     selected.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])

output/preprocess/Multiple_sclerosis/code/GSE203241.py

@@ -0,0 +1,209 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Multiple_sclerosis"
+cohort = "GSE203241"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Multiple_sclerosis"
+in_cohort_dir = "../DATA/GEO/Multiple_sclerosis/GSE203241"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Multiple_sclerosis/GSE203241.csv"
+out_gene_data_file = "./output/z5/preprocess/Multiple_sclerosis/gene_data/GSE203241.csv"
+out_clinical_data_file = "./output/z5/preprocess/Multiple_sclerosis/clinical_data/GSE203241.csv"
+json_path = "./output/z5/preprocess/Multiple_sclerosis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+
+# Step 2: Dataset Analysis and Clinical Feature Extraction
+import pandas as pd
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Transcriptome profiling of blood mononuclear cells indicates gene expression data.
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Identify keys in the sample characteristics dictionary
+trait_row = None  # No explicit diagnosis/status key provided in the sample characteristics.
+age_row = 1
+gender_row = 0
+
+# 2.2 Conversion functions
+def _after_colon(value):
+    if value is None:
+        return None
+    parts = str(value).split(":", 1)
+    return parts[1].strip() if len(parts) > 1 else str(value).strip()
+
+def convert_trait(value):
+    v = _after_colon(value)
+    if v is None:
+        return None
+    vl = v.strip().lower().replace("_", " ")
+    vl_nospace = vl.replace(" ", "")
+    # Map MS patients to 1
+    ms_pos = {
+        "ms", "multiple sclerosis", "multiplesclerosis",
+        "poms", "aoms",
+        "rrms", "relapsing remitting multiple sclerosis", "relapsingremittingmultiplesclerosis"
+    }
+    # Map controls to 0
+    ms_neg = {"control", "healthy", "hc", "phc", "ahc", "normal"}
+    if vl in ms_neg or vl_nospace in ms_neg:
+        return 0
+    if vl in ms_pos or vl_nospace in ms_pos:
+        return 1
+    # Heuristics: keywords
+    if "control" in vl or "healthy" in vl:
+        return 0
+    if "sclerosis" in vl or vl == "ms":
+        return 1
+    return None
+
+def convert_age(value):
+    v = _after_colon(value)
+    if v is None or v == "":
+        return None
+    # keep only numbers, dot, minus
+    try:
+        # Remove any non-numeric trailing text (e.g., 'years')
+        num = ''.join(ch for ch in v if (ch.isdigit() or ch in ['.', '-']))
+        if num == '' or num == '-' or num == '.':
+            return None
+        return float(num)
+    except Exception:
+        return None
+
+def convert_gender(value):
+    v = _after_colon(value)
+    if v is None:
+        return None
+    vl = v.strip().lower()
+    if vl in {"female", "f", "woman", "women", "girl"}:
+        return 0
+    if vl in {"male", "m", "man", "men", "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 if 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 and save
+    _ = preview_df(selected_clinical_df, n=5)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+
+# Step 3: Gene Data Extraction
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+
+# Step 4: Gene Identifier Review
+print("requires_gene_mapping = True")
+
+# Step 5: Gene Annotation
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+
+# Step 6: Gene Identifier Mapping
+# Identify the appropriate columns for mapping based on the preview:
+# Probe ID column in annotation matches the probe IDs in gene expression data
+id_col = 'ID'
+# Gene symbol column in annotation
+gene_col = 'Gene Symbol'
+
+# Build mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=id_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)
+
+# Step 7: Data Normalization and Linking
+# 1. Normalize the obtained gene data and save
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Determine availability of clinical trait data from earlier steps
+has_selected_clinical = 'selected_clinical_data' in globals()
+
+if has_selected_clinical:
+    # 2. Link the clinical and genetic data
+    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(
+        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-genetic linking performed with available trait 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)
+else:
+    # Trait not available: perform final validation reflecting this 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,  # placeholder; ignored since is_available will be False
+        df=normalized_gene_data.T,  # use gene data (samples x genes) for shape reference
+        note=f"WARNING: Trait ({trait}) not available; clinical-genetic linking skipped."
+    )

output/preprocess/Multiple_sclerosis/code/TCGA.py

@@ -0,0 +1,64 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Multiple_sclerosis"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/z5/preprocess/Multiple_sclerosis/TCGA.csv"
+out_gene_data_file = "./output/z5/preprocess/Multiple_sclerosis/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/z5/preprocess/Multiple_sclerosis/clinical_data/TCGA.csv"
+json_path = "./output/z5/preprocess/Multiple_sclerosis/cohort_info.json"
+
+
+# Step 1: Initial Data Loading
+import os
+import pandas as pd
+
+# Step 1: Identify the most relevant TCGA cohort directory for Multiple Sclerosis (likely none in TCGA cancer cohorts)
+subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]
+
+# Define search terms for Multiple Sclerosis
+search_terms = {"multiple sclerosis", "multiple_sclerosis", "ms"}
+
+def name_tokens(name: str):
+    # Normalize and split directory name into tokens
+    base = name.lower().replace('-', ' ').replace('_', ' ').replace('(', ' ').replace(')', ' ')
+    return set(base.split())
+
+selected_dir = None
+for d in subdirs:
+    tokens = name_tokens(d)
+    # Exact phrase search in the raw lowercased name as a fallback
+    raw_name = d.lower()
+    if ("multiple sclerosis" in raw_name) or ("multiple_sclerosis" in raw_name) or (tokens & search_terms):
+        selected_dir = d
+        break
+
+clinical_df = None
+genetic_df = None
+
+if selected_dir is None:
+    print("No suitable TCGA cohort found for Multiple Sclerosis. Skipping this trait.")
+    # Record unusable cohort for TCGA in metadata and mark task as completed 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
+    )
+else:
+    # Step 2: Identify clinical and genetic file paths
+    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # Step 3: Load both files as DataFrames
+    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)
+
+    # Step 4: Print column names of the clinical data
+    print(list(clinical_df.columns))

output/preprocess/Multiple_sclerosis/cohort_info.json

@@ -1,112 +1 @@
-{
-  "GSE203241": {
-    "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
-  },
-  "GSE193442": {
-    "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
-  },
-  "GSE189788": {
-    "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": 216
-  },
-  "GSE146383": {
-    "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
-  },
-  "GSE141804": {
-    "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
-  },
-  "GSE141381": {
-    "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": 25
-  },
-  "GSE135511": {
-    "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": 50
-  },
-  "GSE131282": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": true,
-    "has_gender": true,
-    "sample_size": 78
-  },
-  "GSE131281": {
-    "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": 106
-  },
-  "GSE131279": {
-    "is_usable": true,
-    "is_gene_available": true,
-    "is_trait_available": true,
-    "is_available": true,
-    "is_biased": false,
-    "has_age": true,
-    "has_gender": true,
-    "sample_size": 78
-  },
-  "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
-  }
-}
+{"GSE203241": {"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 (Multiple_sclerosis) not available; clinical-genetic linking skipped."}, "GSE193442": {"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}, "GSE189788": {"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}, "GSE146383": {"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 labels unavailable in matrix; only gene data processed and saved."}, "GSE141804": {"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 data unavailable (trait_row is None); linking skipped. Saved only normalized gene data."}, "GSE141381": {"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}, "GSE135511": {"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": 50, "note": "INFO: Age and Gender not provided in series characteristics; post-mortem motor cortex MS vs control study."}, "GSE131282": {"is_usable": true, "is_gene_available": true, "is_trait_available": true, "is_available": true, "is_biased": false, "has_age": true, "has_gender": true, "sample_size": 184, "note": "INFO: Trait inferred from patient ID prefix (M=MS case, C=control). Platform identifiers are Illumina probes; mapped via SOFT 'Symbol' column; gene symbols normalized using NCBI synonyms."}, "GSE131281": {"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": 106, "note": "INFO: Probe-to-gene mapping via SOFT 'Symbol' with equal split for multi-gene probes; gene symbols normalized using NCBI synonym table; trait from 'ms type', age from 'age at death', gender from 'Sex'."}, "GSE131279": {"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 (all samples are MS cases); no linked data saved."}, "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/Obesity/clinical_data/GSE123086.csv

@@ -1,4 +1,4 @@
-,Characteristic_0,Characteristic_1,Characteristic_2,Characteristic_3,Characteristic_4
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-Age,,,,51.0,49.0
-Gender,,,0.0,,
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output/preprocess/Obesity/clinical_data/GSE123088.csv

@@ -1,4 +1,4 @@
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output/preprocess/Obesity/clinical_data/GSE158237.csv

@@ -1,4 +1,4 @@
-
-
-
-
+,GSM4795701,GSM4795702,GSM4795703,GSM4795704,GSM4795705,GSM4795706,GSM4795707,GSM4795708,GSM4795709,GSM4795710,GSM4795711,GSM4795712,GSM4795713,GSM4795714,GSM4795715,GSM4795716,GSM4795717,GSM4795718,GSM4795719,GSM4795720,GSM4795721,GSM4795722,GSM4795723,GSM4795724,GSM4795725,GSM4795726,GSM4795727,GSM4795728,GSM4795729,GSM4795730,GSM4795731,GSM4795732,GSM4795733,GSM4795734,GSM4795735,GSM4795736,GSM4795737
+Obesity,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,0.0,1.0,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
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+Gender,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0

output/preprocess/Obesity/clinical_data/GSE181339.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
-0.0,1.0,0.0,,,,,,,,,,,,,,,,,
-21.0,23.0,10.0,17.0,11.0,1.0,18.0,12.0,8.0,14.0,26.0,4.0,2.0,3.0,7.0,13.0,15.0,9.0,30.0,19.0
-1.0,0.0,,,,,,,,,,,,,,,,,,
+,GSM5494930,GSM5494931,GSM5494932,GSM5494933,GSM5494934,GSM5494935,GSM5494936,GSM5494937,GSM5494938,GSM5494939,GSM5494940,GSM5494941,GSM5494942,GSM5494943,GSM5494944,GSM5494945,GSM5494946,GSM5494947,GSM5494948,GSM5494949,GSM5494950,GSM5494951,GSM5494952,GSM5494953,GSM5494954,GSM5494955,GSM5494956,GSM5494957,GSM5494958,GSM5494959,GSM5494960,GSM5494961,GSM5494962,GSM5494963,GSM5494964,GSM5494965,GSM5494966,GSM5494967,GSM5494968,GSM5494969,GSM5494970,GSM5494971,GSM5494972,GSM5494973,GSM5494974,GSM5494975,GSM5494976,GSM5494977,GSM5494978,GSM5494979,GSM5494980,GSM5494981,GSM5494982,GSM5494983,GSM5494984,GSM5494985,GSM5494986,GSM5494987,GSM5494988,GSM5494989,GSM5494990,GSM5494991,GSM5494992,GSM5494993,GSM5494994,GSM5494995,GSM5494996,GSM5494997,GSM5494998,GSM5494999,GSM5495000,GSM5495001,GSM5495002,GSM5495003,GSM5495004,GSM5495005,GSM5495006,GSM5495007
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output/preprocess/Obesity/clinical_data/GSE271700.csv

@@ -1,4 +1,3 @@
-,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17
-Obesity,1.0,0.0,,,,,,,,,,,,,,,,
-Age,51.0,43.0,46.0,41.0,29.0,33.0,36.0,44.0,48.0,40.0,49.0,50.0,35.0,47.0,31.0,28.0,37.0,39.0
-Gender,0.0,1.0,,,,,,,,,,,,,,,,
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output/preprocess/Obesity/clinical_data/GSE281144.csv

@@ -0,0 +1,3 @@
+,GSM8611649,GSM8611650,GSM8611651,GSM8611652,GSM8611653,GSM8611654,GSM8611655,GSM8611656,GSM8611657,GSM8611658,GSM8611659,GSM8611660,GSM8611661,GSM8611662,GSM8611663,GSM8611664,GSM8611665,GSM8611666,GSM8611667,GSM8611668,GSM8611669,GSM8611670,GSM8611671,GSM8611672,GSM8611673,GSM8611674,GSM8611675,GSM8611676,GSM8611677,GSM8611678,GSM8611679,GSM8611680,GSM8611681,GSM8611682
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