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input/GEO/Sjögrens_Syndrome/GSE66795/GSE66795_series_matrix.txt.gz

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input/GEO/Vitamin_D_Levels/GSE123993/GSE123993_series_matrix.txt.gz

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p1/preprocess/Adrenocortical_Cancer/clinical_data/GSE68950.csv

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p1/preprocess/Adrenocortical_Cancer/clinical_data/GSE75415.csv

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+GSM1954726,GSM1954727,GSM1954728,GSM1954729,GSM1954730,GSM1954731,GSM1954732,GSM1954733,GSM1954734,GSM1954735,GSM1954736,GSM1954737,GSM1954738,GSM1954739,GSM1954740,GSM1954741,GSM1954742,GSM1954743,GSM1954744,GSM1954745,GSM1954746,GSM1954747,GSM1954748,GSM1954749,GSM1954750,GSM1954751,GSM1954752,GSM1954753,GSM1954754,GSM1954755,GSM1954756
+0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,,,,,,,,

p1/preprocess/Adrenocortical_Cancer/clinical_data/GSE90713.csv

@@ -0,0 +1,2 @@
+GSM2411058,GSM2411059,GSM2411060,GSM2411061,GSM2411062,GSM2411063,GSM2411064,GSM2411065,GSM2411066,GSM2411067,GSM2411068,GSM2411069,GSM2411070,GSM2411071,GSM2411072,GSM2411073,GSM2411074,GSM2411075,GSM2411076,GSM2411077,GSM2411078,GSM2411079,GSM2411080,GSM2411081,GSM2411082,GSM2411083,GSM2411084,GSM2411085,GSM2411086,GSM2411087,GSM2411088,GSM2411089,GSM2411090,GSM2411091,GSM2411092,GSM2411093,GSM2411094,GSM2411095,GSM2411096,GSM2411097,GSM2411098,GSM2411099,GSM2411100,GSM2411101,GSM2411102,GSM2411103,GSM2411104,GSM2411105,GSM2411106,GSM2411107,GSM2411108,GSM2411109,GSM2411110,GSM2411111,GSM2411112,GSM2411113,GSM2411114,GSM2411115,GSM2411116,GSM2411117,GSM2411118,GSM2411119,GSM2411120
+1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0

p1/preprocess/Adrenocortical_Cancer/clinical_data/TCGA.csv

@@ -0,0 +1,93 @@
+sampleID,Adrenocortical_Cancer,Age
+TCGA-OR-A5J1-01,1,58
+TCGA-OR-A5J2-01,1,44
+TCGA-OR-A5J3-01,1,23
+TCGA-OR-A5J4-01,1,23
+TCGA-OR-A5J5-01,1,30
+TCGA-OR-A5J6-01,1,29
+TCGA-OR-A5J7-01,1,30
+TCGA-OR-A5J8-01,1,66
+TCGA-OR-A5J9-01,1,22
+TCGA-OR-A5JA-01,1,53
+TCGA-OR-A5JB-01,1,52
+TCGA-OR-A5JC-01,1,37
+TCGA-OR-A5JD-01,1,57
+TCGA-OR-A5JE-01,1,17
+TCGA-OR-A5JF-01,1,69
+TCGA-OR-A5JG-01,1,61
+TCGA-OR-A5JH-01,1,32
+TCGA-OR-A5JI-01,1,22
+TCGA-OR-A5JJ-01,1,65
+TCGA-OR-A5JK-01,1,49
+TCGA-OR-A5JL-01,1,36
+TCGA-OR-A5JM-01,1,25
+TCGA-OR-A5JO-01,1,26
+TCGA-OR-A5JP-01,1,40
+TCGA-OR-A5JQ-01,1,26
+TCGA-OR-A5JR-01,1,45
+TCGA-OR-A5JS-01,1,65
+TCGA-OR-A5JT-01,1,65
+TCGA-OR-A5JU-01,1,58
+TCGA-OR-A5JV-01,1,55
+TCGA-OR-A5JW-01,1,47
+TCGA-OR-A5JX-01,1,50
+TCGA-OR-A5JY-01,1,68
+TCGA-OR-A5JZ-01,1,60
+TCGA-OR-A5K0-01,1,69
+TCGA-OR-A5K1-01,1,48
+TCGA-OR-A5K2-01,1,32
+TCGA-OR-A5K3-01,1,53
+TCGA-OR-A5K4-01,1,64
+TCGA-OR-A5K5-01,1,59
+TCGA-OR-A5K6-01,1,56
+TCGA-OR-A5K8-01,1,39
+TCGA-OR-A5K9-01,1,61
+TCGA-OR-A5KB-01,1,61
+TCGA-OR-A5KO-01,1,39
+TCGA-OR-A5KP-01,1,45
+TCGA-OR-A5KQ-01,1,20
+TCGA-OR-A5KS-01,1,72
+TCGA-OR-A5KT-01,1,44
+TCGA-OR-A5KU-01,1,37
+TCGA-OR-A5KV-01,1,17
+TCGA-OR-A5KW-01,1,55
+TCGA-OR-A5KX-01,1,25
+TCGA-OR-A5KY-01,1,23
+TCGA-OR-A5KZ-01,1,42
+TCGA-OR-A5L1-01,1,37
+TCGA-OR-A5L2-01,1,83
+TCGA-OR-A5L3-01,1,67
+TCGA-OR-A5L4-01,1,48
+TCGA-OR-A5L5-01,1,77
+TCGA-OR-A5L6-01,1,60
+TCGA-OR-A5L8-01,1,36
+TCGA-OR-A5L9-01,1,53
+TCGA-OR-A5LA-01,1,52
+TCGA-OR-A5LB-01,1,59
+TCGA-OR-A5LC-01,1,71
+TCGA-OR-A5LD-01,1,52
+TCGA-OR-A5LE-01,1,14
+TCGA-OR-A5LF-01,1,74
+TCGA-OR-A5LG-01,1,46
+TCGA-OR-A5LH-01,1,36
+TCGA-OR-A5LI-01,1,42
+TCGA-OR-A5LJ-01,1,54
+TCGA-OR-A5LK-01,1,62
+TCGA-OR-A5LL-01,1,75
+TCGA-OR-A5LM-01,1,23
+TCGA-OR-A5LN-01,1,31
+TCGA-OR-A5LO-01,1,61
+TCGA-OR-A5LP-01,1,37
+TCGA-OR-A5LR-01,1,30
+TCGA-OR-A5LS-01,1,34
+TCGA-OR-A5LT-01,1,57
+TCGA-OU-A5PI-01,1,53
+TCGA-P6-A5OF-01,1,55
+TCGA-P6-A5OG-01,1,45
+TCGA-P6-A5OH-01,1,59
+TCGA-PA-A5YG-01,1,51
+TCGA-PK-A5H8-01,1,42
+TCGA-PK-A5H9-01,1,27
+TCGA-PK-A5HA-01,1,63
+TCGA-PK-A5HB-01,1,63
+TCGA-PK-A5HC-01,1,44

p1/preprocess/Adrenocortical_Cancer/code/GSE108088.py

@@ -0,0 +1,149 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE108088"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE108088"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Adrenocortical_Cancer/GSE108088.csv"
+out_gene_data_file = "./output/preprocess/1/Adrenocortical_Cancer/gene_data/GSE108088.csv"
+out_clinical_data_file = "./output/preprocess/1/Adrenocortical_Cancer/clinical_data/GSE108088.csv"
+json_path = "./output/preprocess/1/Adrenocortical_Cancer/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+import pandas as pd
+import numpy as np
+
+# 1. Determine gene expression availability
+# Based on the background info "comprehensive molecular profiling," we assume it includes gene expression data.
+is_gene_available = True
+
+# 2. Identify the keys for trait, age, and gender
+# After examining the sample characteristics dictionary, there's no direct or inferred "Adrenocortical_Cancer," 
+# no age info, and no gender info. Hence, we set them all to None.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.1 and 2.2: Data type conversion functions
+def convert_trait(raw_value: str):
+    # This function would parse the raw_value and return 0 or 1 if the trait is binary,
+    # or a float if continuous. Here, we have no trait data, so it's a placeholder.
+    # If used, ensure to handle unknown or malformed entries by returning None.
+    # We split by 'colon' if needed, but since trait_row is None, we won't use it.
+    return None
+
+def convert_age(raw_value: str):
+    # Sample placeholder function. No age data is found, so it returns None.
+    return None
+
+def convert_gender(raw_value: str):
+    # Sample placeholder function. No gender data is found, so it returns None.
+    return None
+
+# 3. Conduct initial filtering on dataset usability, saving relevant metadata
+# Trait data availability is determined by whether trait_row is None.
+is_trait_available = (trait_row is not None)
+
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical feature extraction
+# We only proceed if trait_row is not None.
+# Since trait_row is None, we skip this substep.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the given identifiers (e.g., "1007_s_at", "1053_at", etc.), these are Affymetrix probe IDs,
+# not human gene symbols. Therefore, further mapping is required to convert them into gene symbols.
+
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP6: Gene Identifier Mapping
+
+# 1. Identify which columns in the annotation dataframe correspond to the probe IDs and gene symbols.
+prob_col = "ID"
+gene_col = "Gene Symbol"
+
+# 2. Obtain the gene mapping dataframe.
+mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)
+
+# 3. Convert probe-level expression data to gene-level expression data by applying the mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Let's inspect the shape and a quick preview of the mapped gene expression data.
+print("Gene data shape after mapping:", gene_data.shape)
+print("Gene data preview:")
+print(preview_df(gene_data, n=5))
+# STEP 7: Data Normalization and Linking
+
+# In this dataset, we determined in Step 2 that trait data is not available (trait_row = None).
+# Therefore, we cannot link clinical and genetic data or perform trait-based processing.
+# Nonetheless, we can still normalize probe-level data to standard gene symbols and finalize validation.
+
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+
+# 2. Since trait data is missing, skip linking clinical and genetic data, 
+#    skip missing-value handling and bias detection for the trait.
+
+# 3. Conduct final validation and record info.
+#    Since trait data is unavailable, set is_trait_available=False, 
+#    pass a dummy/empty DataFrame and is_biased=False (it won't be used).
+dummy_df = pd.DataFrame()
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=False,
+    df=dummy_df,
+    note="No trait data found; skipped clinical-linking steps."
+)
+
+# 4. If the dataset were usable, save. In this scenario, it's not usable due to missing trait data.
+if is_usable:
+    dummy_df.to_csv(out_data_file, index=True)

p1/preprocess/Adrenocortical_Cancer/code/GSE143383.py

@@ -0,0 +1,165 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE143383"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE143383"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Adrenocortical_Cancer/GSE143383.csv"
+out_gene_data_file = "./output/preprocess/1/Adrenocortical_Cancer/gene_data/GSE143383.csv"
+out_clinical_data_file = "./output/preprocess/1/Adrenocortical_Cancer/clinical_data/GSE143383.csv"
+json_path = "./output/preprocess/1/Adrenocortical_Cancer/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on "gene expression analysis" and Affymetrix platform info.
+
+# 2. Variable Availability and Data Type Conversion
+#    2.1 Identify rows for trait, age, and gender
+trait_row = None  # No variable in the dictionary indicates a differing trait (likely constant or not listed).
+age_row = None    # No row found for age in the sample characteristics.
+gender_row = 0    # Row 0 contains 'gender: X'.
+
+#    2.2 Define the conversion functions
+def convert_trait(x: str) -> Optional[float]:
+    """Not applicable here because trait_row is None. This is a placeholder."""
+    return None
+
+def convert_age(x: str) -> Optional[float]:
+    """Not applicable here because age_row is None. This is a placeholder."""
+    return None
+
+def convert_gender(x: str) -> Optional[int]:
+    """
+    Convert 'gender: X' to binary.
+    'F' -> 0, 'M' -> 1, anything else -> None.
+    """
+    parts = x.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == 'f':
+        return 0
+    elif val == 'm':
+        return 1
+    else:
+        return None
+
+# 3. Save Metadata - initial filtering
+#    Trait data availability depends on whether trait_row is None.
+is_trait_available = (trait_row is not None)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+#    Skip if trait_row is None.
+if trait_row is not None:
+    # Assuming `clinical_data` is the dataframe for sample characteristics
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,  # 'Adrenocortical_Cancer'
+        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
+    print("Clinical features preview:", preview_df(selected_clinical_df, n=5))
+    selected_clinical_df.to_csv(out_clinical_data_file)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the listed identifiers (e.g., "11715100_at"), they appear to be Affymetrix probe set IDs, not human gene symbols.
+# Hence, gene mapping is required.
+
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns in the gene_annotation dataframe that correspond to the probe IDs and gene symbols.
+#    From the preview, "ID" matches the probe identifiers in gene_data, and "Gene Symbol" contains the gene symbols.
+
+# 2. Create a gene mapping dataframe using the relevant columns.
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Gene Symbol")
+
+# 3. Convert probe-level measurements in gene_data to gene-level data.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Print a quick preview of the resulting gene_data
+print("Mapped gene_data preview:")
+print(gene_data.head(5))
+# STEP 7: Data Normalization and Linking
+
+# In this dataset, we determined in Step 2 that trait data is not available (trait_row = None).
+# Therefore, we cannot link clinical and genetic data or perform trait-based processing.
+# Nonetheless, we can still normalize probe-level data to standard gene symbols and finalize validation.
+
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+
+# 2. Since trait data is missing, skip linking clinical and genetic data, 
+#    skip missing-value handling and bias detection for the trait.
+
+# 3. Conduct final validation and record info.
+#    Since trait data is unavailable, set is_trait_available=False, 
+#    pass a dummy/empty DataFrame and is_biased=False (it won't be used).
+dummy_df = pd.DataFrame()
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=False,
+    df=dummy_df,
+    note="No trait data found; skipped clinical-linking steps."
+)
+
+# 4. If the dataset were usable, save. In this scenario, it's not usable due to missing trait data.
+if is_usable:
+    dummy_df.to_csv(out_data_file, index=True)

p1/preprocess/Adrenocortical_Cancer/code/GSE19776.py

@@ -0,0 +1,175 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE19776"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE19776"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Adrenocortical_Cancer/GSE19776.csv"
+out_gene_data_file = "./output/preprocess/1/Adrenocortical_Cancer/gene_data/GSE19776.csv"
+out_clinical_data_file = "./output/preprocess/1/Adrenocortical_Cancer/clinical_data/GSE19776.csv"
+json_path = "./output/preprocess/1/Adrenocortical_Cancer/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Decide if the dataset contains gene expression data
+# Based on the series title "Adrenocortical Carcinoma Gene Expression Profiling",
+# we conclude that it is likely to contain gene expression data.
+is_gene_available = True
+
+# Step 2: Variable Availability and Data Type Conversion
+
+# 2.1 Identify Rows
+#   - trait: We see only "tissue: adrenocortical carcinoma" under key 0. This is a single unique value,
+#     which is uninformative for association. Hence treat it as not available for the trait.
+trait_row = None
+
+#   - age: Found under key 5 (multiple distinct values, some are "age: Unknown").
+age_row = 5
+
+#   - gender: Found under key 4 (M/F). Multiple values, not constant.
+gender_row = 4
+
+# 2.2 Define Conversion Functions
+def convert_trait(x: str) -> int:
+    """
+    Returns None because trait is not available (single unique value in dataset).
+    This function is a placeholder to adhere to the required interface.
+    """
+    return None
+
+def convert_age(x: str) -> float:
+    """
+    Convert the substring after 'age:' to float if possible.
+    If it's 'Unknown' or non-parsable, return None.
+    """
+    val = x.split(':')[-1].strip()
+    if val.lower() == "unknown":
+        return None
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(x: str) -> int:
+    """
+    Convert 'gender: F' -> 0, 'gender: M' -> 1.
+    If the value is unknown or doesn't match, return None.
+    """
+    val = x.split(':')[-1].strip().upper()
+    if val == 'F':
+        return 0
+    elif val == 'M':
+        return 1
+    return None
+
+# Step 3: Save initial filtering metadata
+# Trait data is not available if trait_row is None
+is_trait_available = (trait_row is not None)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 4: Extract clinical features only if trait_row is not None
+# Since trait_row = None, we skip clinical feature extraction.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# The provided gene identifiers are all numeric, which are not standard human gene symbols.
+# They likely refer to probe IDs or some other numeric format.
+# Therefore, gene mapping to human gene symbols is required.
+
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP6: Gene Identifier Mapping
+
+# Reviewer feedback indicates a mismatch between the numeric row IDs in the gene expression dataframe
+# (e.g., "3", "4", "5") and the probe IDs in the annotation file (e.g., "1007_s_at", "1053_at").
+# Because there is no overlap, a direct mapping is not possible with the provided annotation.
+# We'll demonstrate a fallback approach: we attempt to match, but if no overlap is found, we skip mapping.
+
+# 1. Decide which columns in the annotation *would* store the probe IDs and gene symbols if they matched.
+probe_col = "ID"
+gene_col = "Gene Symbol"
+
+# 2. Extract the potential mapping dataframe.
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_col)
+
+# 3. Check for any intersection in identifiers before applying the mapping.
+common_ids = set(gene_data.index).intersection(mapping_df['ID'])
+if len(common_ids) == 0:
+    print("No matching identifiers found between gene expression data and annotation. Skipping gene mapping.")
+else:
+    gene_data = apply_gene_mapping(gene_data, mapping_df)
+    print("Gene mapping applied successfully.")
+# STEP 7: Data Normalization and Linking
+
+# In this dataset, we determined in Step 2 that trait data is not available (trait_row = None).
+# Therefore, we cannot link clinical and genetic data or perform trait-based processing.
+# Nonetheless, we can still normalize probe-level data to standard gene symbols and finalize validation.
+
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+
+# 2. Since trait data is missing, skip linking clinical and genetic data, 
+#    skip missing-value handling and bias detection for the trait.
+
+# 3. Conduct final validation and record info.
+#    Since trait data is unavailable, set is_trait_available=False, 
+#    pass a dummy/empty DataFrame and is_biased=False (it won't be used).
+dummy_df = pd.DataFrame()
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=False,
+    df=dummy_df,
+    note="No trait data found; skipped clinical-linking steps."
+)
+
+# 4. If the dataset were usable, save. In this scenario, it's not usable due to missing trait data.
+if is_usable:
+    dummy_df.to_csv(out_data_file, index=True)

p1/preprocess/Adrenocortical_Cancer/code/GSE49278.py

@@ -0,0 +1,152 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE49278"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE49278"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Adrenocortical_Cancer/GSE49278.csv"
+out_gene_data_file = "./output/preprocess/1/Adrenocortical_Cancer/gene_data/GSE49278.csv"
+out_clinical_data_file = "./output/preprocess/1/Adrenocortical_Cancer/clinical_data/GSE49278.csv"
+json_path = "./output/preprocess/1/Adrenocortical_Cancer/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on the background info: "Expression profiling by array ..."
+
+# 2. Variable Availability and Data Type Conversion
+# Observing the sample characteristics, key=2 has only one unique value (Adrenocortical carcinoma),
+# so that is constant and not useful for association analyses, thus trait_row = None.
+trait_row = None
+
+# key=0 shows multiple age values => available
+age_row = 0
+
+# key=1 shows two gender values => available
+gender_row = 1
+
+# Define conversion functions
+def convert_trait(value: str):
+    # Since trait data is effectively not available (constant),
+    # this function returns None
+    return None
+
+def convert_age(value: str):
+    # Typical format: "age (years): 70"
+    # Convert the part after the colon to a numeric type
+    try:
+        val_str = value.split(':', 1)[1].strip()
+        return float(val_str)
+    except:
+        return None
+
+def convert_gender(value: str):
+    # Typical format: "gender: F" or "gender: M"
+    # Convert F -> 0, M -> 1
+    try:
+        val_str = value.split(':', 1)[1].strip().upper()
+        if val_str == 'F':
+            return 0
+        elif val_str == 'M':
+            return 1
+        else:
+            return None
+    except:
+        return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+_ = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Skip this step because trait_row is None (no trait data available).
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP6: Gene Identifier Mapping
+
+# After reviewing the annotation DataFrame columns:
+#   ['ID', 'RANGE_STRAND', 'RANGE_START', 'RANGE_END', 'total_probes', 'GB_ACC', 'SPOT_ID', 'RANGE_GB']
+# we see that 'GB_ACC' usually contains "NR_" transcripts and 'SPOT_ID' has genomic coordinates. Neither appear to provide
+# valid gene symbols recognizable by extract_human_gene_symbols (which filters out NR_, XR_, LOC, etc.). 
+# Therefore, mapping to standard gene symbols is not possible here. 
+# We'll retain the original probe-level data without attempting gene-level aggregation.
+
+print("No suitable gene symbol column found. Proceeding with probe-level data only.")
+# The 'gene_data' DataFrame remains as probe-level data.
+# No further action is required for mapping in this dataset.
+# STEP 7: Data Normalization and Linking
+
+# In this dataset, we determined in Step 2 that trait data is not available (trait_row = None).
+# Therefore, we cannot link clinical and genetic data or perform trait-based processing.
+# Nonetheless, we can still normalize probe-level data to standard gene symbols and finalize validation.
+
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+
+# 2. Since trait data is missing, skip linking clinical and genetic data, 
+#    skip missing-value handling and bias detection for the trait.
+
+# 3. Conduct final validation and record info.
+#    Since trait data is unavailable, set is_trait_available=False, 
+#    pass a dummy/empty DataFrame and is_biased=False (it won't be used).
+dummy_df = pd.DataFrame()
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=False,
+    df=dummy_df,
+    note="No trait data found; skipped clinical-linking steps."
+)
+
+# 4. If the dataset were usable, save. In this scenario, it's not usable due to missing trait data.
+if is_usable:
+    dummy_df.to_csv(out_data_file, index=True)

p1/preprocess/Adrenocortical_Cancer/code/GSE67766.py

@@ -0,0 +1,132 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE67766"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE67766"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Adrenocortical_Cancer/GSE67766.csv"
+out_gene_data_file = "./output/preprocess/1/Adrenocortical_Cancer/gene_data/GSE67766.csv"
+out_clinical_data_file = "./output/preprocess/1/Adrenocortical_Cancer/clinical_data/GSE67766.csv"
+json_path = "./output/preprocess/1/Adrenocortical_Cancer/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Determine if gene expression data is available
+is_gene_available = True  # Based on background context, we assume gene expression data is present
+
+# 2. Determine availability for trait, age, and gender from the sample characteristics dictionary
+#    Given the dictionary: {0: ['cell line: SW-13']}, there is no variation or explicit mention
+#    of trait, age, or gender. Hence, they are all considered unavailable.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2 Define data type conversion functions
+def convert_trait(x: str):
+    # No trait data available. Return None for any input.
+    return None
+
+def convert_age(x: str):
+    # No age data available. Return None for any input.
+    return None
+
+def convert_gender(x: str):
+    # No gender data available. Return None for any input.
+    return None
+
+# 3. Save Metadata (initial filtering)
+#    'is_trait_available' is False because 'trait_row' is None
+is_trait_available = (trait_row is not None)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+#    Since 'trait_row' is None, we skip this step (no clinical data to extract).
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These gene identifiers ('ILMN_...') are Illumina probe IDs rather than standard human gene symbols.
+# Hence, gene mapping to official symbols is required.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1) Identify the columns for gene identifier and gene symbol based on the annotation preview.
+probe_col = "ID"
+symbol_col = "Symbol"
+
+# 2) Build the gene mapping dataframe from the annotation dataframe.
+mapping_df = get_gene_mapping(gene_annotation, probe_col, symbol_col)
+
+# 3) Apply the 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
+
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+
+# Since trait data is unavailable (trait_row = None), we cannot link or analyze trait/demographic features.
+# We must finalize this dataset as unusable for downstream analysis.
+
+# Provide a dummy dataframe and a boolean for is_biased to satisfy the library requirements.
+import pandas as pd
+empty_df = pd.DataFrame()
+
+# 5. Perform final quality validation and save cohort info. 
+#    We set is_biased=False to fulfill the function parameters; it will still result in is_usable=False 
+#    because is_trait_available=False.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=False,
+    df=empty_df,
+    note="No trait data available for this cohort."
+)
+
+# 6. Since no trait data is available, is_usable must be False, so we skip saving the final linked data.

p1/preprocess/Adrenocortical_Cancer/code/GSE68606.py

@@ -0,0 +1,149 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE68606"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE68606"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Adrenocortical_Cancer/GSE68606.csv"
+out_gene_data_file = "./output/preprocess/1/Adrenocortical_Cancer/gene_data/GSE68606.csv"
+out_clinical_data_file = "./output/preprocess/1/Adrenocortical_Cancer/clinical_data/GSE68606.csv"
+json_path = "./output/preprocess/1/Adrenocortical_Cancer/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Gene Expression Data Availability
+# Based on the "Assay Type: Gene Expression" and "Affymetrix Human Genome U133A arrays" in the metadata,
+# we conclude that this dataset likely contains gene expression data.
+is_gene_available = True
+
+# 2) Variable Availability and Data Type Conversion
+
+# 2.1 Identify availability of 'trait', 'age', and 'gender' by looking at the Sample Characteristics Dictionary
+# We did not find "Adrenocortical_Cancer" or an equivalent entry in any row,
+# so trait data is considered not available.
+trait_row = None
+
+# Age data is present in row 6 with multiple unique numeric values.
+age_row = 6
+
+# Gender data is present in row 5 (female/male).
+gender_row = 5
+
+# 2.2 Define conversion functions for each variable
+
+def convert_trait(x: str):
+    # Trait data is not available in this dataset, return None for all inputs.
+    return None
+
+def convert_age(x: str):
+    # Extract the substring after the colon and strip whitespace
+    val = x.split(":", 1)[-1].strip()
+    # Convert to integer if possible, otherwise None
+    return int(val) if val.isdigit() else None
+
+def convert_gender(x: str):
+    # Extract the substring after the colon and strip whitespace
+    val = x.split(":", 1)[-1].strip().lower()
+    if val == "female":
+        return 0
+    elif val == "male":
+        return 1
+    else:
+        return None
+
+# 3) Save Metadata (Initial Filtering)
+
+is_trait_available = (trait_row is not None)  # False in this case
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction
+# Skip this step because trait_row is None (no trait data available).
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These identifiers (e.g., '1007_s_at', '1053_at') are Affymetrix probe set IDs, not human gene symbols.
+# Therefore, they require mapping to gene symbols.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1) The key for the probe identifiers in the gene annotation is "ID",
+#    and the key for the gene symbols is "Gene Symbol".
+
+# 2) Build a gene mapping dataframe using those two columns.
+gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# 3) Apply the mapping to convert probe-level measurements to gene expression data.
+gene_data = apply_gene_mapping(gene_data, gene_mapping)
+# STEP 7: Data Normalization and Linking
+
+# Even though we lack trait data, it's still valuable to finalize gene-level data.
+# 1. Normalize gene symbols and save the normalized gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+
+# Since trait_row = None, there's no trait data to link or analyze.
+# We cannot produce a linked dataset or evaluate trait bias in a meaningful way.
+# However, the task instructions request a "final" validation.
+
+import pandas as pd
+
+# Provide a dummy DataFrame and set is_biased to False
+# so that validate_and_save_cohort_info can finalize and mark this dataset as unusable for trait analysis.
+empty_df = pd.DataFrame()
+is_biased = False
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,      # We do have gene data
+    is_trait_available=False,    # But no trait data
+    is_biased=is_biased,         # Arbitrarily set to False since no trait is present
+    df=empty_df,                 # An empty DataFrame to satisfy the function's requirements
+    note="No trait data available, so no final linked dataset can be produced."
+)
+
+# 6. Because the dataset is not usable for trait-based analysis, we do not save a final linked dataset.

p1/preprocess/Adrenocortical_Cancer/code/GSE68950.py

@@ -0,0 +1,153 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Adrenocortical_Cancer"
+cohort = "GSE68950"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
+in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE68950"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Adrenocortical_Cancer/GSE68950.csv"
+out_gene_data_file = "./output/preprocess/1/Adrenocortical_Cancer/gene_data/GSE68950.csv"
+out_clinical_data_file = "./output/preprocess/1/Adrenocortical_Cancer/clinical_data/GSE68950.csv"
+json_path = "./output/preprocess/1/Adrenocortical_Cancer/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # "Assay Type: Gene Expression" indicates gene expression data.
+
+# 2.1 Variable Availability
+# The term "adrenal cortical carcinoma" is present in the "disease state" field (row 1),
+# matching our trait "Adrenocortical_Cancer." Hence, trait_row = 1.
+trait_row = 1
+age_row = None
+gender_row = None
+
+# 2.2 Data Type Conversions
+def convert_trait(value: str):
+    """
+    Convert 'disease state' to a binary trait:
+    1 for 'adrenal cortical carcinoma',
+    0 for anything else.
+    """
+    label = value.split(":", 1)[-1].strip().lower()
+    if "adrenal cortical carcinoma" in label:
+        return 1
+    else:
+        return 0
+
+def convert_age(value: str):
+    return None  # Age data not available
+
+def convert_gender(value: str):
+    return None  # Gender data not available
+
+# 3. Save Metadata with initial filtering
+is_trait_available = (trait_row is not None)
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if trait_row is not None)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,
+        trait=trait,  # "Adrenocortical_Cancer"
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview the selected clinical features
+    print(preview_df(selected_clinical_df))
+    # Save the extracted clinical data
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# The gene identifiers shown (e.g., "1007_s_at", "1053_at") are Affymetrix probe set IDs
+# rather than standard human gene symbols, so they require mapping.
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns for gene identifier and gene symbol in the annotation dataframe
+probe_col = "ID"
+symbol_col = "Gene Symbol"
+
+# 2. Get the mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=symbol_col)
+
+# 3. Map probe-level expression to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP 7: Data Normalization and Linking
+
+# 1. Normalize gene symbols and save the normalized gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+
+# 2. Link clinical and genetic data on sample IDs
+#    "selected_clinical_df" was defined in a previous step, so we can use it directly.
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values systematically
+processed_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine whether the trait or demographic features are severely biased
+trait_biased, processed_data = judge_and_remove_biased_features(processed_data, trait)
+
+# 5. Final quality 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=trait_biased,
+    df=processed_data,
+    note="Trait data present and mapped from step 2."
+)
+
+# 6. Save the final linked data only if usable
+if is_usable:
+    processed_data.to_csv(out_data_file, index=True)

p1/preprocess/Age-Related_Macular_Degeneration/clinical_data/GSE29801.csv

@@ -0,0 +1,4 @@
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p1/preprocess/Age-Related_Macular_Degeneration/code/GSE29801.py

@@ -0,0 +1,168 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Age-Related_Macular_Degeneration"
+cohort = "GSE29801"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Age-Related_Macular_Degeneration"
+in_cohort_dir = "../DATA/GEO/Age-Related_Macular_Degeneration/GSE29801"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Age-Related_Macular_Degeneration/GSE29801.csv"
+out_gene_data_file = "./output/preprocess/1/Age-Related_Macular_Degeneration/gene_data/GSE29801.csv"
+out_clinical_data_file = "./output/preprocess/1/Age-Related_Macular_Degeneration/clinical_data/GSE29801.csv"
+json_path = "./output/preprocess/1/Age-Related_Macular_Degeneration/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on transcriptome analysis in the series description
+
+# 2. Variable Availability and Data Type Conversion
+trait_row = 3   # Using "ocular disease: normal/AMD" as our binary trait indicator
+age_row = 2     # "age (years): ..." entries
+gender_row = 1  # "gender: male/female" entries
+
+def convert_trait(value: str):
+    parts = value.split(":", 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == "normal":
+        return 0
+    elif val == "amd":
+        return 1
+    return None
+
+def convert_age(value: str):
+    parts = value.split(":", 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip()
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    parts = value.split(":", 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == "female":
+        return 0
+    elif val == "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 if trait data is available
+if trait_row is not None:
+    df_clinical = geo_select_clinical_features(
+        clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    print("Clinical Data Preview:", preview_df(df_clinical))
+    df_clinical.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Observing the provided gene identifiers, they appear to be numeric (e.g., "12", "13", ... ), 
+# which are not standard human gene symbols. Therefore, these IDs would need to be mapped.
+
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. From earlier previews, the gene expression data indexes match the "ID" column in the annotation,
+#    and the gene symbols are in the "GENE_SYMBOL" column.
+probe_id_col = "ID"
+gene_symbol_col = "GENE_SYMBOL"
+
+# 2. Build the gene mapping dataframe using these columns.
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_id_col, gene_col=gene_symbol_col)
+
+# 3. Convert probe-level data to gene-level expression using the mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional) Print resulting shape for a quick check.
+print("Mapped gene_data shape:", gene_data.shape)
+
+# STEP 7: Data Normalization and Linking
+
+# In this dataset, we determined in Step 2 that trait data is not available (trait_row = None).
+# Therefore, we cannot link clinical and genetic data or perform trait-based processing.
+# Nonetheless, we can still normalize probe-level data to standard gene symbols and finalize validation.
+
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+
+# 2. Since trait data is missing, skip linking clinical and genetic data, 
+#    skip missing-value handling and bias detection for the trait.
+
+# 3. Conduct final validation and record info.
+#    Since trait data is unavailable, set is_trait_available=False, 
+#    pass a dummy/empty DataFrame and is_biased=False (it won't be used).
+dummy_df = pd.DataFrame()
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=False,
+    df=dummy_df,
+    note="No trait data found; skipped clinical-linking steps."
+)
+
+# 4. If the dataset were usable, save. In this scenario, it's not usable due to missing trait data.
+if is_usable:
+    dummy_df.to_csv(out_data_file, index=True)

p1/preprocess/Age-Related_Macular_Degeneration/code/GSE38662.py

@@ -0,0 +1,152 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Age-Related_Macular_Degeneration"
+cohort = "GSE38662"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Age-Related_Macular_Degeneration"
+in_cohort_dir = "../DATA/GEO/Age-Related_Macular_Degeneration/GSE38662"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Age-Related_Macular_Degeneration/GSE38662.csv"
+out_gene_data_file = "./output/preprocess/1/Age-Related_Macular_Degeneration/gene_data/GSE38662.csv"
+out_clinical_data_file = "./output/preprocess/1/Age-Related_Macular_Degeneration/clinical_data/GSE38662.csv"
+json_path = "./output/preprocess/1/Age-Related_Macular_Degeneration/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+# Based on the background info ("hESCs were extracted ... hybridization on Affymetrix arrays"), 
+# we conclude it is likely gene expression data:
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# The sample characteristics do not mention "Age-Related_Macular_Degeneration" or any disease status,
+# so there's no row with trait data. There's also no age information.
+# Gender data is given in row 3 as "gender: 46,XY" or "gender: 46,XX".
+
+trait_row = None   # trait not found
+age_row = None     # age not found
+gender_row = 3     # gender found
+
+# Since trait and age are unavailable, we'll define placeholders for their conversion functions
+# but they won't be used. We do need a working convert_gender function.
+
+def convert_trait(x: str):
+    return None  # trait is unavailable, no actual conversion
+
+def convert_age(x: str):
+    return None  # age is unavailable, no actual conversion
+
+def convert_gender(x: str):
+    """
+    Convert string like 'gender: 46,XY' to binary form (female=0, male=1).
+    Unknowns return None.
+    """
+    # Split by colon and take the value portion
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip()  # e.g. "46,XY"
+
+    # Convert based on XX or XY
+    if "XX" in val:
+        return 0
+    elif "XY" in val:
+        return 1
+    else:
+        return None
+
+# 3. Save Metadata (initial filtering)
+# Trait data availability depends on `trait_row` being not None. Here it is None, so is_trait_available=False.
+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
+# Since trait_row is None, we skip extracting clinical features.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These identifiers (e.g., "1007_s_at", "1053_at") appear to be Affymetrix probe set IDs rather than standard human gene symbols.
+# Therefore, they require mapping to gene symbols.
+
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# 1. Identify the columns in the annotation DataFrame that match the probe IDs and the gene symbols.
+#    From the preview, "ID" matches the probe identifiers (e.g., "1007_s_at"), and "Gene Symbol" holds the gene symbols.
+mapping_df = get_gene_mapping(gene_annotation, "ID", "Gene Symbol")
+
+# 2. Apply the gene mapping to convert probe-level data to gene-level data.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional) Print the resulting dataframe's shape to confirm mapping
+print("Mapped gene_data shape:", gene_data.shape)
+# STEP 7: Data Normalization and Linking
+
+# In this dataset, we determined in Step 2 that trait data is not available (trait_row = None).
+# Therefore, we cannot link clinical and genetic data or perform trait-based processing.
+# Nonetheless, we can still normalize probe-level data to standard gene symbols and finalize validation.
+
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+
+# 2. Since trait data is missing, skip linking clinical and genetic data, 
+#    skip missing-value handling and bias detection for the trait.
+
+# 3. Conduct final validation and record info.
+#    Since trait data is unavailable, set is_trait_available=False, 
+#    pass a dummy/empty DataFrame and is_biased=False (it won't be used).
+dummy_df = pd.DataFrame()
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=False,
+    df=dummy_df,
+    note="No trait data found; skipped clinical-linking steps."
+)
+
+# 4. If the dataset were usable, save. In this scenario, it's not usable due to missing trait data.
+if is_usable:
+    dummy_df.to_csv(out_data_file, index=True)

p1/preprocess/Age-Related_Macular_Degeneration/code/GSE43176.py

@@ -0,0 +1,154 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Age-Related_Macular_Degeneration"
+cohort = "GSE43176"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Age-Related_Macular_Degeneration"
+in_cohort_dir = "../DATA/GEO/Age-Related_Macular_Degeneration/GSE43176"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Age-Related_Macular_Degeneration/GSE43176.csv"
+out_gene_data_file = "./output/preprocess/1/Age-Related_Macular_Degeneration/gene_data/GSE43176.csv"
+out_clinical_data_file = "./output/preprocess/1/Age-Related_Macular_Degeneration/clinical_data/GSE43176.csv"
+json_path = "./output/preprocess/1/Age-Related_Macular_Degeneration/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Determine gene expression data availability
+is_gene_available = True  # Based on the Series summary, it clearly states "Gene expression profiling was performed"
+
+# 2. Determine variable availability
+
+# 2.1 Data Availability
+# We search for trait (AMD), age, and gender in the sample characteristics.
+# None of these variables appear in the provided dictionary (all data pertains to AML subtypes, cytogenetics, etc.).
+# So, they are all not available for this dataset.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2 Data Type Conversion
+# Even though the data is not available, we still define these converters.
+
+def convert_trait(value: str):
+    """
+    Extracts the part after ':' and attempts to convert to a binary or continuous variable.
+    Since trait is not actually available in this dataset, return None for any input.
+    """
+    return None
+
+def convert_age(value: str):
+    """
+    Extracts the part after ':' and attempts to convert it to a numeric (continuous) value.
+    Since age is not available in this dataset, return None for any input.
+    """
+    return None
+
+def convert_gender(value: str):
+    """
+    Extracts the part after ':' and converts female->0, male->1.
+    Since gender is not available in this dataset, return None for any input.
+    """
+    return None
+
+# 3. Conduct initial filtering on the usability of the dataset and save metadata.
+# Trait data availability is determined by 'trait_row' (which is None).
+is_trait_available = (trait_row is not None)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Since trait_row is None, we skip this step (no clinical data to extract).
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These identifiers (e.g. '1007_s_at', '1053_at', etc.) correspond to Affymetrix probe set IDs, not standard human gene symbols.
+# Therefore, we need to map them to human gene symbols.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns in the `gene_annotation` that match the probe IDs in `gene_data`
+#    and the column with human gene symbols. Here they are:
+probe_col = "ID"
+gene_symbol_col = "Gene Symbol"
+
+# 2. Get a gene mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+
+# 3. Convert the probe-level data to gene-level data using our mapping
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Optional: print a brief preview of the mapped gene_data
+print("Mapped gene_data shape:", gene_data.shape)
+print("Mapped gene_data index (first 10 genes):", gene_data.index[:10])
+# STEP 7: Data Normalization and Linking
+
+# In this dataset, we determined in Step 2 that trait data is not available (trait_row = None).
+# Therefore, we cannot link clinical and genetic data or perform trait-based processing.
+# Nonetheless, we can still normalize probe-level data to standard gene symbols and finalize validation.
+
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+
+# 2. Since trait data is missing, skip linking clinical and genetic data, 
+#    skip missing-value handling and bias detection for the trait.
+
+# 3. Conduct final validation and record info.
+#    Since trait data is unavailable, set is_trait_available=False, 
+#    pass a dummy/empty DataFrame and is_biased=False (it won't be used).
+dummy_df = pd.DataFrame()
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=False,
+    df=dummy_df,
+    note="No trait data found; skipped clinical-linking steps."
+)
+
+# 4. If the dataset were usable, save. In this scenario, it's not usable due to missing trait data.
+if is_usable:
+    dummy_df.to_csv(out_data_file, index=True)

p1/preprocess/Age-Related_Macular_Degeneration/code/GSE45485.py

@@ -0,0 +1,79 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Age-Related_Macular_Degeneration"
+cohort = "GSE45485"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Age-Related_Macular_Degeneration"
+in_cohort_dir = "../DATA/GEO/Age-Related_Macular_Degeneration/GSE45485"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Age-Related_Macular_Degeneration/GSE45485.csv"
+out_gene_data_file = "./output/preprocess/1/Age-Related_Macular_Degeneration/gene_data/GSE45485.csv"
+out_clinical_data_file = "./output/preprocess/1/Age-Related_Macular_Degeneration/clinical_data/GSE45485.csv"
+json_path = "./output/preprocess/1/Age-Related_Macular_Degeneration/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+# Based on the provided background, it involves "Gene expression and intrinsic subset assignment ... in SSc patients"
+# This suggests gene expression data is indeed present.
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# Examining the Sample Characteristics Dictionary, there is no mention of AMD, age, or gender.
+# Hence, none of these variables can be extracted (all become None).
+trait_row = None
+age_row = None
+gender_row = None
+
+# Define the required conversion functions (although they will not be used here, we must still define them).
+
+def convert_trait(value: str):
+    # Since data for the trait is not found, return None for all inputs.
+    return None
+
+def convert_age(value: str):
+    # No age data is found. Return None for all inputs.
+    return None
+
+def convert_gender(value: str):
+    # No gender data is found. Return None for all inputs.
+    return None
+
+# 3. Save Metadata via initial filtering
+# If trait_row is None, it implies that trait data is unavailable.
+is_trait_available = (trait_row is not None)
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Skip this step because trait_row is None (no clinical data for trait).

p1/preprocess/Age-Related_Macular_Degeneration/code/GSE62224.py

@@ -0,0 +1,144 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Age-Related_Macular_Degeneration"
+cohort = "GSE62224"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Age-Related_Macular_Degeneration"
+in_cohort_dir = "../DATA/GEO/Age-Related_Macular_Degeneration/GSE62224"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Age-Related_Macular_Degeneration/GSE62224.csv"
+out_gene_data_file = "./output/preprocess/1/Age-Related_Macular_Degeneration/gene_data/GSE62224.csv"
+out_clinical_data_file = "./output/preprocess/1/Age-Related_Macular_Degeneration/clinical_data/GSE62224.csv"
+json_path = "./output/preprocess/1/Age-Related_Macular_Degeneration/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on "Agilent whole genome microarrays" note in the background
+
+# 2. Variable Availability and Data Type Conversion
+
+# After reviewing the sample characteristics, there is no indication of AMD status, age, or gender. 
+# The data rows represent donor IDs (which appear to be fetal IDs), plating densities, passage number, 
+# culture days, cultureware, and treatments, none of which provide a varying "AMD" trait, numeric age, 
+# or gender classification. Thus, all three variables are unavailable.
+
+trait_row = None
+age_row = None
+gender_row = None
+
+# Even though no conversion is needed (as data is unavailable), we define the required functions:
+
+def convert_trait(value: str) -> None:
+    """
+    Since the trait data is not available, always return None.
+    """
+    return None
+
+def convert_age(value: str) -> None:
+    """
+    Since age data is not available, always return None.
+    """
+    return None
+
+def convert_gender(value: str) -> None:
+    """
+    Since gender data is not available, always return None.
+    """
+    return None
+
+# 3. Save Metadata via initial filtering (is_final=False).
+# Trait data is not available if trait_row is None.
+is_trait_available = (trait_row is not None)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Since trait_row is None, we skip this substep (no clinical data found).
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the index, these IDs appear to be numeric, not standard human gene symbols.
+# Therefore, gene symbol mapping is required.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. We identify that the gene expression data uses the 'ID' column in the annotation 
+#    and the gene symbols are stored in the 'GENE_SYMBOL' column.
+# 2. Build a gene mapping dataframe with these columns.
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="GENE_SYMBOL")
+
+# 3. Convert probe-level measurements to gene expression data by applying the mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP 7: Data Normalization and Linking
+
+# In this dataset, we determined in Step 2 that trait data is not available (trait_row = None).
+# Therefore, we cannot link clinical and genetic data or perform trait-based processing.
+# Nonetheless, we can still normalize probe-level data to standard gene symbols and finalize validation.
+
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+
+# 2. Since trait data is missing, skip linking clinical and genetic data, 
+#    skip missing-value handling and bias detection for the trait.
+
+# 3. Conduct final validation and record info.
+#    Since trait data is unavailable, set is_trait_available=False, 
+#    pass a dummy/empty DataFrame and is_biased=False (it won't be used).
+dummy_df = pd.DataFrame()
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=False,
+    df=dummy_df,
+    note="No trait data found; skipped clinical-linking steps."
+)
+
+# 4. If the dataset were usable, save. In this scenario, it's not usable due to missing trait data.
+if is_usable:
+    dummy_df.to_csv(out_data_file, index=True)

p1/preprocess/Age-Related_Macular_Degeneration/code/GSE67899.py

@@ -0,0 +1,152 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Age-Related_Macular_Degeneration"
+cohort = "GSE67899"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Age-Related_Macular_Degeneration"
+in_cohort_dir = "../DATA/GEO/Age-Related_Macular_Degeneration/GSE67899"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Age-Related_Macular_Degeneration/GSE67899.csv"
+out_gene_data_file = "./output/preprocess/1/Age-Related_Macular_Degeneration/gene_data/GSE67899.csv"
+out_clinical_data_file = "./output/preprocess/1/Age-Related_Macular_Degeneration/clinical_data/GSE67899.csv"
+json_path = "./output/preprocess/1/Age-Related_Macular_Degeneration/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Determine if gene expression data is available
+# Based on the background info mentioning TGF-beta inhibitors and typical gene regulatory factors,
+# we assume this dataset likely contains gene expression data. Hence:
+is_gene_available = True
+
+# Step 2: Identify rows for trait, age, and gender.
+# The sample characteristics dictionary does not mention AMD status, age, or gender.
+# Therefore, we set them to None. 
+trait_row = None
+age_row = None
+gender_row = None
+
+# Define data type conversion functions.
+# Although the data is unavailable, we still provide these to
+# maintain the required function signatures.
+
+def convert_trait(value: str):
+    """
+    Convert trait (AMD) values to binary (0 or 1).
+    For this study, AMD = 1 and Non-AMD = 0.
+    But since trait data is not found in the dictionary, we will return None.
+    """
+    return None
+
+def convert_age(value: str):
+    """
+    Convert age values to continuous. Extract numerical part if possible.
+    Since age data is not found in this dataset, always return None.
+    """
+    return None
+
+def convert_gender(value: str):
+    """
+    Convert gender values to binary (female=0, male=1).
+    Since gender data is not found in this dataset, always return None.
+    """
+    return None
+
+# Step 3: Conduct initial filtering and save metadata.
+# Trait data availability is based on whether trait_row is None.
+is_trait_available = (trait_row is not None)
+
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 4: We skip clinical feature extraction because trait_row is None (no trait data available).
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on observation, these numeric IDs are not standard human gene symbols and likely require mapping.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP6: Gene Identifier Mapping
+
+# 1. Decide which columns store the consistent ID and gene symbol.
+#    From the annotation preview and the gene_data index, we identify:
+#    - "ID" as the probe identifier column
+#    - "GENE_SYMBOL" as the gene symbol column
+
+# 2. Get a gene mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="GENE_SYMBOL")
+
+# 3. Convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Optional: Print shape for verification
+print("Gene expression data shape after mapping:", gene_data.shape)
+# STEP 7: Data Normalization and Linking
+
+# In this dataset, we determined in Step 2 that trait data is not available (trait_row = None).
+# Therefore, we cannot link clinical and genetic data or perform trait-based processing.
+# Nonetheless, we can still normalize probe-level data to standard gene symbols and finalize validation.
+
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+
+# 2. Since trait data is missing, skip linking clinical and genetic data, 
+#    skip missing-value handling and bias detection for the trait.
+
+# 3. Conduct final validation and record info.
+#    Since trait data is unavailable, set is_trait_available=False, 
+#    pass a dummy/empty DataFrame and is_biased=False (it won't be used).
+dummy_df = pd.DataFrame()
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=False,
+    df=dummy_df,
+    note="No trait data found; skipped clinical-linking steps."
+)
+
+# 4. If the dataset were usable, save. In this scenario, it's not usable due to missing trait data.
+if is_usable:
+    dummy_df.to_csv(out_data_file, index=True)

p1/preprocess/Age-Related_Macular_Degeneration/code/TCGA.py

@@ -0,0 +1,57 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Age-Related_Macular_Degeneration"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Age-Related_Macular_Degeneration/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Age-Related_Macular_Degeneration/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Age-Related_Macular_Degeneration/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Age-Related_Macular_Degeneration/cohort_info.json"
+
+import os
+import pandas as pd
+
+# 1. Identify the relevant subdirectory for the trait "Obesity"
+subdirectories = [
+    'CrawlData.ipynb', '.DS_Store', 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)',
+    'TCGA_Uterine_Carcinosarcoma_(UCS)', 'TCGA_Thyroid_Cancer_(THCA)', 'TCGA_Thymoma_(THYM)',
+    'TCGA_Testicular_Cancer_(TGCT)', 'TCGA_Stomach_Cancer_(STAD)', 'TCGA_Sarcoma_(SARC)',
+    'TCGA_Rectal_Cancer_(READ)', 'TCGA_Prostate_Cancer_(PRAD)', 'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)',
+    'TCGA_Pancreatic_Cancer_(PAAD)', 'TCGA_Ovarian_Cancer_(OV)', 'TCGA_Ocular_melanomas_(UVM)',
+    'TCGA_Mesothelioma_(MESO)', 'TCGA_Melanoma_(SKCM)', 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)',
+    'TCGA_Lung_Cancer_(LUNG)', 'TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lower_Grade_Glioma_(LGG)',
+    'TCGA_Liver_Cancer_(LIHC)', 'TCGA_Large_Bcell_Lymphoma_(DLBC)', 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)',
+    'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)', 'TCGA_Kidney_Chromophobe_(KICH)', 'TCGA_Head_and_Neck_Cancer_(HNSC)',
+    'TCGA_Glioblastoma_(GBM)', 'TCGA_Esophageal_Cancer_(ESCA)', 'TCGA_Endometrioid_Cancer_(UCEC)',
+    'TCGA_Colon_and_Rectal_Cancer_(COADREAD)', 'TCGA_Colon_Cancer_(COAD)', 'TCGA_Cervical_Cancer_(CESC)',
+    'TCGA_Breast_Cancer_(BRCA)', 'TCGA_Bladder_Cancer_(BLCA)', 'TCGA_Bile_Duct_Cancer_(CHOL)',
+    'TCGA_Adrenocortical_Cancer_(ACC)', 'TCGA_Acute_Myeloid_Leukemia_(LAML)'
+]
+
+trait_keyword = trait
+target_subdir = None
+
+for sd in subdirectories:
+    if trait_keyword.lower() in sd.lower():
+        target_subdir = sd
+        break
+
+if target_subdir is None:
+    # No suitable data found for this trait; mark as completed
+    print("No TCGA subdirectory found for the trait. Skipping.")
+else:
+    # 2. Locate clinical and genetic data files
+    cohort_dir = os.path.join(tcga_root_dir, target_subdir)
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # 3. Load the data
+    clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
+    genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')
+
+    # 4. Print column names of clinical data
+    print(clinical_df.columns)

p1/preprocess/Age-Related_Macular_Degeneration/cohort_info.json

@@ -0,0 +1 @@
+{"GSE67899": {"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": "No trait data found; skipped clinical-linking steps."}, "GSE62224": {"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": "No trait data found; skipped clinical-linking steps."}, "GSE45485": {"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}, "GSE43176": {"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": "No trait data found; skipped clinical-linking steps."}, "GSE38662": {"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": "No trait data found; skipped clinical-linking steps."}, "GSE29801": {"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": 293, "note": "Final processed dataset after gene normalization, missing-value handling, and bias checks."}}

p1/preprocess/Alcohol_Flush_Reaction/code/GSE133228.py

@@ -0,0 +1,152 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alcohol_Flush_Reaction"
+cohort = "GSE133228"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alcohol_Flush_Reaction"
+in_cohort_dir = "../DATA/GEO/Alcohol_Flush_Reaction/GSE133228"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Alcohol_Flush_Reaction/GSE133228.csv"
+out_gene_data_file = "./output/preprocess/1/Alcohol_Flush_Reaction/gene_data/GSE133228.csv"
+out_clinical_data_file = "./output/preprocess/1/Alcohol_Flush_Reaction/clinical_data/GSE133228.csv"
+json_path = "./output/preprocess/1/Alcohol_Flush_Reaction/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file, 
+    background_prefixes, 
+    clinical_prefixes
+)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Decide if this dataset likely contains gene expression data
+is_gene_available = True  # Based on the background info, we assume it contains gene expression
+
+# 2) Variable Availability
+#   - We see a row "gender: Male" and "gender: Female" at key = 0 (two unique values) => gender_row = 0
+#   - We see a row "age: ..." at key = 1 (multiple unique values) => age_row = 1
+#   - There's no row for "Alcohol_Flush_Reaction", so trait_row = None
+trait_row = None
+age_row = 1
+gender_row = 0
+
+# 2.2) Data Type Conversion Functions
+
+def convert_trait(value: str):
+    # No trait data is actually available, return None
+    return None
+
+def convert_age(value: str):
+    # Attempt to parse the substring after the colon as a float
+    # e.g. "age: 3" -> "3"
+    try:
+        val_str = value.split(':', 1)[1].strip()
+        return float(val_str)
+    except:
+        return None
+
+def convert_gender(value: str):
+    # Convert gender to binary: female -> 0, male -> 1
+    try:
+        val_str = value.split(':', 1)[1].strip().lower()
+        if val_str == 'female':
+            return 0
+        elif val_str == 'male':
+            return 1
+        else:
+            return None
+    except:
+        return None
+
+# 3) Save Metadata with initial filtering
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Since trait_row is None, we do not extract clinical features; skip this substep.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# After examining these IDs (e.g., '10009_at'), they appear to be microarray probe IDs rather than standard gene symbols.
+# Therefore, gene mapping is needed.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1) By observing the gene annotation preview and the gene expression data,
+#    we see that the "ID" column in 'gene_annotation' matches the probe IDs in 'gene_data'.
+#    The "Description" column appears to contain gene symbols or related information.
+
+# 2) Get the mapping dataframe
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation, 
+    prob_col='ID',      # Probe column
+    gene_col='Description'  # Gene symbol column
+)
+
+# 3) Convert probe-level measurements to gene-level expression by applying the mapping
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+# STEP 7: Data Normalization and Linking
+
+# In this dataset, we determined in Step 2 that trait data is not available (trait_row = None).
+# Therefore, we cannot link clinical and genetic data or perform trait-based processing.
+# Nonetheless, we can still normalize probe-level data to standard gene symbols and finalize validation.
+
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+
+# 2. Since trait data is missing, skip linking clinical and genetic data, 
+#    skip missing-value handling and bias detection for the trait.
+
+# 3. Conduct final validation and record info.
+#    Since trait data is unavailable, set is_trait_available=False, 
+#    pass a dummy/empty DataFrame and is_biased=False (it won't be used).
+dummy_df = pd.DataFrame()
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=False,
+    df=dummy_df,
+    note="No trait data found; skipped clinical-linking steps."
+)
+
+# 4. If the dataset were usable, save. In this scenario, it's not usable due to missing trait data.
+if is_usable:
+    dummy_df.to_csv(out_data_file, index=True)

p1/preprocess/Alcohol_Flush_Reaction/code/TCGA.py

@@ -0,0 +1,57 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alcohol_Flush_Reaction"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Alcohol_Flush_Reaction/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Alcohol_Flush_Reaction/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Alcohol_Flush_Reaction/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Alcohol_Flush_Reaction/cohort_info.json"
+
+import os
+import pandas as pd
+
+# 1. Identify the relevant subdirectory for the trait "Obesity"
+subdirectories = [
+    'CrawlData.ipynb', '.DS_Store', 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)',
+    'TCGA_Uterine_Carcinosarcoma_(UCS)', 'TCGA_Thyroid_Cancer_(THCA)', 'TCGA_Thymoma_(THYM)',
+    'TCGA_Testicular_Cancer_(TGCT)', 'TCGA_Stomach_Cancer_(STAD)', 'TCGA_Sarcoma_(SARC)',
+    'TCGA_Rectal_Cancer_(READ)', 'TCGA_Prostate_Cancer_(PRAD)', 'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)',
+    'TCGA_Pancreatic_Cancer_(PAAD)', 'TCGA_Ovarian_Cancer_(OV)', 'TCGA_Ocular_melanomas_(UVM)',
+    'TCGA_Mesothelioma_(MESO)', 'TCGA_Melanoma_(SKCM)', 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)',
+    'TCGA_Lung_Cancer_(LUNG)', 'TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lower_Grade_Glioma_(LGG)',
+    'TCGA_Liver_Cancer_(LIHC)', 'TCGA_Large_Bcell_Lymphoma_(DLBC)', 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)',
+    'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)', 'TCGA_Kidney_Chromophobe_(KICH)', 'TCGA_Head_and_Neck_Cancer_(HNSC)',
+    'TCGA_Glioblastoma_(GBM)', 'TCGA_Esophageal_Cancer_(ESCA)', 'TCGA_Endometrioid_Cancer_(UCEC)',
+    'TCGA_Colon_and_Rectal_Cancer_(COADREAD)', 'TCGA_Colon_Cancer_(COAD)', 'TCGA_Cervical_Cancer_(CESC)',
+    'TCGA_Breast_Cancer_(BRCA)', 'TCGA_Bladder_Cancer_(BLCA)', 'TCGA_Bile_Duct_Cancer_(CHOL)',
+    'TCGA_Adrenocortical_Cancer_(ACC)', 'TCGA_Acute_Myeloid_Leukemia_(LAML)'
+]
+
+trait_keyword = trait
+target_subdir = None
+
+for sd in subdirectories:
+    if trait_keyword.lower() in sd.lower():
+        target_subdir = sd
+        break
+
+if target_subdir is None:
+    # No suitable data found for this trait; mark as completed
+    print("No TCGA subdirectory found for the trait. Skipping.")
+else:
+    # 2. Locate clinical and genetic data files
+    cohort_dir = os.path.join(tcga_root_dir, target_subdir)
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # 3. Load the data
+    clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
+    genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')
+
+    # 4. Print column names of clinical data
+    print(clinical_df.columns)

p1/preprocess/Alcohol_Flush_Reaction/cohort_info.json

@@ -0,0 +1 @@
+{"GSE133228": {"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": "No trait data found; skipped clinical-linking steps."}}

p1/preprocess/Allergies/clinical_data/GSE182740.csv

@@ -0,0 +1,2 @@
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+0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0

p1/preprocess/Allergies/clinical_data/GSE185658.csv

@@ -0,0 +1,2 @@
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p1/preprocess/Allergies/clinical_data/GSE203196.csv

@@ -0,0 +1,4 @@
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p1/preprocess/Allergies/clinical_data/GSE270312.csv

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p1/preprocess/Allergies/code/GSE169149.py

@@ -0,0 +1,161 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Allergies"
+cohort = "GSE169149"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Allergies"
+in_cohort_dir = "../DATA/GEO/Allergies/GSE169149"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Allergies/GSE169149.csv"
+out_gene_data_file = "./output/preprocess/1/Allergies/gene_data/GSE169149.csv"
+out_clinical_data_file = "./output/preprocess/1/Allergies/clinical_data/GSE169149.csv"
+json_path = "./output/preprocess/1/Allergies/cohort_info.json"
+
+# STEP 1
+
+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("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Determine gene expression availability
+is_gene_available = True  # Based on the background, we assume this dataset likely contains gene expression data.
+
+# Step 2: Identify data availability for 'trait', 'age', and 'gender'
+# According to the sample characteristics dictionary, there is no mention of "Allergies," "age," or "gender."
+trait_row = None
+age_row = None
+gender_row = None
+
+# Step 2.2: Define data type conversion functions
+def convert_trait(value: str) -> Optional[int]:
+    # No actual data for 'Allergies' in this dataset
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    # No age information in this dataset
+    return None
+
+def convert_gender(value: str) -> Optional[int]:
+    # No gender information in this dataset
+    return None
+
+# Step 3: Conduct 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: If trait data is available, extract clinical features; otherwise, skip.
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the numeric nature of these identifiers, they do not appear to be conventional human gene symbols.
+# Therefore, they require mapping to known gene symbols.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Decide which columns map the same kind of IDs as the gene expression data and which store the gene symbols
+#    From the annotation preview, the "ID" column matches the expression data identifiers (1, 2, 3, ...).
+#    The "Assay" column appears to contain the gene symbols.
+
+# 2. Extract a gene mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Assay")
+
+# 3. Convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Display the first few rows of the resulting gene expression dataframe for verification
+print(gene_data.head())
+import pandas as pd
+
+# STEP 7: Data Normalization and (Conditional) Linking
+
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+print(f"Saved normalized gene data to {out_gene_data_file}")
+
+# Since trait_row was None in step 2, we have no clinical features extracted.
+# Hence 'clinical_data_selected' does not exist, and there is no trait column to link or to analyze.
+
+# We will proceed with final validation using the fact that trait data is unavailable.
+is_trait_available = False
+is_gene_available = True  # As concluded in step 2, it is a gene expression dataset
+
+if not is_trait_available:
+    # Without trait data, we cannot link or do the usual missing-value handling by trait.
+    # We still provide the normalized_gene_data to the validator (though it won't be used for trait analysis).
+    final_data = normalized_gene_data
+    is_biased = False  # We must supply a boolean; no trait data => cannot assess bias
+
+    # 5. Final quality validation
+    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_biased,
+        df=final_data,
+        note="No trait data available in this dataset."
+    )
+
+    # 6. If the dataset is usable, save final data; however, in this scenario it likely won't be.
+    if is_usable:
+        final_data.to_csv(out_data_file)
+        print(f"Saved final linked data to {out_data_file}")
+    else:
+        print("Data not usable; skipping final output.")
+else:
+    # If trait data were available, we would link, handle missing values, check bias, and finalize.
+    # This branch is skipped because 'is_trait_available' is False.
+    pass

p1/preprocess/Allergies/code/GSE182740.py

@@ -0,0 +1,195 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Allergies"
+cohort = "GSE182740"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Allergies"
+in_cohort_dir = "../DATA/GEO/Allergies/GSE182740"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Allergies/GSE182740.csv"
+out_gene_data_file = "./output/preprocess/1/Allergies/gene_data/GSE182740.csv"
+out_clinical_data_file = "./output/preprocess/1/Allergies/clinical_data/GSE182740.csv"
+json_path = "./output/preprocess/1/Allergies/cohort_info.json"
+
+# STEP 1
+
+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("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+# Based on the background information ("Global mRNA expression" is mentioned),
+# we conclude that gene expression data is available:
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+
+# After reviewing the sample characteristics dictionary, we see that
+# key=1 contains "disease: Psoriasis", "disease: Atopic_dermatitis", "disease: Mixed", "disease: Normal_skin".
+# We can use this to infer a binary trait for "Allergies" if "Atopic_dermatitis" or "Mixed" is present, else 0.
+trait_row = 1  # because it provides disease info that we can map to 'Allergies'
+
+# No mention of age or gender in the dictionary, so these are not available:
+age_row = None
+gender_row = None
+
+# Define the conversion functions.
+def convert_trait(value: str):
+    """
+    Convert a string like "disease: Psoriasis" to a binary indicator for the trait "Allergies".
+    We parse the substring after "disease:" and map:
+    - "Atopic_dermatitis" or "Mixed" -> 1 (indicative of 'Allergies')
+    - Otherwise -> 0
+    Unknown or unexpected -> None
+    """
+    if not isinstance(value, str):
+        return None
+
+    # Typically "disease: something", split by colon
+    parts = value.split(":", 1)
+    if len(parts) < 2:
+        return None
+    disease_str = parts[1].strip().lower()  # e.g. "psoriasis", "atopic_dermatitis", "mixed", "normal_skin"
+
+    if "atopic_dermatitis" in disease_str or "mixed" in disease_str:
+        return 1
+    elif "psoriasis" in disease_str or "normal_skin" in disease_str:
+        return 0
+    else:
+        return None
+
+def convert_age(value: str):
+    """
+    Data not available; placeholder function returning None.
+    """
+    return None
+
+def convert_gender(value: str):
+    """
+    Data not available; placeholder function returning None.
+    """
+    return None
+
+# 3. Save Metadata (initial filtering)
+# Trait data is available if trait_row != None
+is_trait_available = (trait_row is not None)
+
+# Perform the initial validation and save metadata.
+# The function returns True if the dataset passes final validation,
+# but here we only do the initial filtering (is_final=False).
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Proceed only if trait_row is not None
+if trait_row is not None:
+    # Assuming "clinical_data" is the previously obtained clinical DataFrame
+    clinical_data_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 the selected clinical data
+    preview_result = preview_df(clinical_data_selected)
+    print("Clinical data preview:", preview_result)
+
+    # Save the extracted clinical features
+    clinical_data_selected.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# The given identifiers (e.g., '1007_s_at', '1053_at') appear to be Affymetrix probe IDs, not official gene symbols.
+# Hence, we need to map them to recognized gene symbols.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Decide which keys in the gene annotation store the probe IDs and gene symbols
+#    From our observation, 'ID' matches the probe IDs (e.g., '1007_s_at'), 
+#    and 'Gene Symbol' stores the gene symbols.
+
+# 2. Get a gene mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
+
+# 3. Convert probe-level measurements to gene-level measurements 
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (At this stage, 'gene_data' now holds gene-level expression data.)
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+print(f"Saved normalized gene data to {out_gene_data_file}")
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_data_selected, normalized_gene_data)
+
+# 3. Handle missing values
+cleaned_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine bias in trait and demographic features
+trait_biased, final_data = judge_and_remove_biased_features(cleaned_data, trait)
+
+# 5. Final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True, 
+    is_trait_available=True, 
+    is_biased=trait_biased,
+    df=final_data,
+    note="Processed with standard GEO pipeline."
+)
+
+# 6. If data is usable, save the final linked data
+if is_usable:
+    final_data.to_csv(out_data_file)
+    print(f"Saved final linked data to {out_data_file}")
+else:
+    print("Data not usable; skipping final output.")

p1/preprocess/Allergies/code/GSE184382.py

@@ -0,0 +1,142 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Allergies"
+cohort = "GSE184382"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Allergies"
+in_cohort_dir = "../DATA/GEO/Allergies/GSE184382"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Allergies/GSE184382.csv"
+out_gene_data_file = "./output/preprocess/1/Allergies/gene_data/GSE184382.csv"
+out_clinical_data_file = "./output/preprocess/1/Allergies/clinical_data/GSE184382.csv"
+json_path = "./output/preprocess/1/Allergies/cohort_info.json"
+
+# STEP 1
+
+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("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+# Based on the background info mentioning both miR microarray and transcriptome microarray,
+# we conclude that gene expression data is available.
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+# From the sample characteristics dictionary, we do not have any rows indicating the 'Allergies' trait,
+# age, or gender. Hence, none of these variables are available.
+trait_row = None
+age_row = None
+gender_row = None
+
+# Define conversion functions. Although the variables are not available, we still provide the requested functions.
+def convert_trait(value: str):
+    # No actual data to convert; return None
+    return None
+
+def convert_age(value: str):
+    # No actual data to convert; return None
+    return None
+
+def convert_gender(value: str):
+    # No actual data to convert; return None
+    return None
+
+# 3. Save Metadata (Initial Filtering)
+# Trait data availability is determined by whether trait_row is None.
+is_trait_available = (trait_row is not None)
+
+# We perform the initial validation (is_final=False). 
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Since trait_row is None, we skip clinical feature extraction as instructed.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the identifiers like "A_19_P00315452", these appear to be microarray probe IDs (not standard human gene symbols).
+# Therefore, they need to be mapped to human gene symbols.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Decide which annotation columns match our expression data IDs and gene symbols:
+#    - The "ID" column in the annotation file corresponds to probe identifiers (e.g., "A_21_P0014386", "A_19_P00315452").
+#    - The "GENE_SYMBOL" column stores the gene symbol.
+
+# 2. Get the gene mapping dataframe using the relevant columns from the annotation.
+gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')
+
+# 3. Convert probe-level measurements to gene expression data by applying the gene mapping.
+gene_data = apply_gene_mapping(gene_data, gene_mapping)
+import pandas as pd
+
+# STEP 5: Data Normalization and Linking
+
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+print(f"Saved normalized gene data to {out_gene_data_file}")
+
+# Since in earlier steps trait_row was None, we have no clinical data to link.
+# Hence, there's no trait column to process. We'll skip linking and further steps
+# that require the trait. However, we must still perform a final validation.
+
+# Prepare a dummy DataFrame for the final validation
+dummy_df = pd.DataFrame()
+
+# We must provide is_biased and df to the final validation.
+# Because trait data is not available, this dataset won't be usable.
+is_biased = False  # Arbitrarily set; since trait is unavailable, "is_usable" will be False anyway.
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,      # Gene data is available
+    is_trait_available=False,    # Trait data is not available
+    is_biased=is_biased,
+    df=dummy_df,
+    note="No trait data available; skipping linking."
+)
+
+# 6. If data were usable, we would save it; otherwise we do nothing
+if is_usable:
+    print("Data is unexpectedly marked usable, but trait is unavailable. Skipping save.")

p1/preprocess/Allergies/code/GSE185658.py

@@ -0,0 +1,163 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Allergies"
+cohort = "GSE185658"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Allergies"
+in_cohort_dir = "../DATA/GEO/Allergies/GSE185658"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Allergies/GSE185658.csv"
+out_gene_data_file = "./output/preprocess/1/Allergies/gene_data/GSE185658.csv"
+out_clinical_data_file = "./output/preprocess/1/Allergies/clinical_data/GSE185658.csv"
+json_path = "./output/preprocess/1/Allergies/cohort_info.json"
+
+# STEP 1
+
+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("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Check if gene expression data is available:
+is_gene_available = True  # Based on microarray mention in the background info
+
+# 2) Identify trait_row, age_row, gender_row, and define the conversion functions:
+trait_row = 1  # "group" key likely indicates allergic status (AsthmaHDM vs. others)
+age_row = None  # No age info found
+gender_row = None  # No gender info found
+
+def convert_trait(value: str):
+    # Extract the substring after the colon
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip()
+    # Interpret "AsthmaHDM" as having allergies (1) and others as no allergies (0)
+    if val == 'AsthmaHDM':
+        return 1
+    elif val in ['AsthmaHDMNeg', 'Healthy']:
+        return 0
+    return None
+
+# Not used due to unavailability:
+convert_age = None
+convert_gender = None
+
+# 3) Initial filtering and metadata saving:
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical feature extraction if trait data is available:
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,
+        trait,
+        trait_row,
+        convert_trait,
+        age_row,
+        convert_age,
+        gender_row,
+        convert_gender
+    )
+    print(preview_df(selected_clinical_df, n=5))
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the numeric indices (e.g., '7892501', '7892502') rather than standard gene symbols like 'CD69' or 'TNF',
+# these identifiers appear to be probe IDs or some other non-human-gene-symbol identifiers that would require mapping.
+
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP 6: Gene Identifier Mapping
+
+# 1. The column "ID" in gene_annotation matches the probe IDs in the expression data,
+#    and "gene_assignment" contains the relevant references for gene symbols.
+
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
+
+# 2. Convert probe-level measurements to gene-level data.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Quick check of the resulting gene_data
+print("Gene-level expression data shape:", gene_data.shape)
+print("First 20 gene symbols:", gene_data.index[:20].tolist())
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+print(f"Saved normalized gene data to {out_gene_data_file}")
+
+# 2. Read the previously saved clinical data (which contains the trait) correctly:
+#    Since we saved a single row (the trait) with multiple columns (sample IDs),
+#    we read it as a normal CSV (no index_col) and then set the row index to the trait name.
+clinical_df = pd.read_csv(out_clinical_data_file)
+# Assign the single row index to the trait; columns are sample IDs.
+clinical_df.index = [trait]
+
+# 3. Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)
+
+# 4. Handle missing values in the linked data
+linked_data = handle_missing_values(linked_data, trait_col=trait)
+
+# 5. Check and remove biased features, and see if our trait is biased
+is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 6. Final validation and saving metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_biased,
+    df=linked_data,
+    note="Processed with correct trait indexing, missing-value handling, and bias checks."
+)
+
+# 7. If the dataset is usable, save the final linked data
+if is_usable:
+    linked_data.to_csv(out_data_file, index=True)
+    print(f"Final linked data saved to {out_data_file}")
+else:
+    print("Dataset is not usable; final linked data not saved.")

p1/preprocess/Allergies/code/GSE192454.py

@@ -0,0 +1,152 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Allergies"
+cohort = "GSE192454"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Allergies"
+in_cohort_dir = "../DATA/GEO/Allergies/GSE192454"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Allergies/GSE192454.csv"
+out_gene_data_file = "./output/preprocess/1/Allergies/gene_data/GSE192454.csv"
+out_clinical_data_file = "./output/preprocess/1/Allergies/clinical_data/GSE192454.csv"
+json_path = "./output/preprocess/1/Allergies/cohort_info.json"
+
+# STEP 1
+
+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("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+# Based on "whole transcriptome profiling by microarray", we consider gene expression data present.
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+
+# From the sample characteristics dictionary, there is no row that indicates 'Allergies'
+# or any direct or inferred measure of atopic condition variability, so trait data is not available.
+trait_row = None
+
+# No 'age' or 'gender' information is provided. Hence, both are unavailable.
+age_row = None
+gender_row = None
+
+# Define data conversion functions as requested (they will not be used here since rows are None).
+def convert_trait(value: str):
+    # Typically extract the part after the colon
+    parts = value.split(':', 1)
+    val = parts[1].strip() if len(parts) > 1 else ''
+    # For "Allergies" we would normally map, but data is not available here
+    # Unknown or missing values go to None
+    return None
+
+def convert_age(value: str):
+    # Typically extract numeric age or None
+    parts = value.split(':', 1)
+    val = parts[1].strip() if len(parts) > 1 else ''
+    # Not available, so default to None
+    return None
+
+def convert_gender(value: str):
+    # Typically map female->0, male->1
+    parts = value.split(':', 1)
+    val = parts[1].strip() if len(parts) > 1 else ''
+    # Not available, so default to None
+    return None
+
+# 3. Save Metadata with initial filtering
+is_trait_available = (trait_row is not None)
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Since trait_row is None, no clinical feature extraction is performed.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the provided identifiers, they appear to be numeric IDs rather than human gene symbols.
+# Therefore, they likely need to be mapped to proper gene symbols.
+
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns in the gene annotation that match the gene expression data ID and the gene symbol.
+#    Here, the 'ID' column in gene_annotation matches the numeric IDs in gene_data, 
+#    and the 'GENE_SYMBOL' column stores the gene symbols.
+
+# 2. Get the gene mapping dataframe:
+mapping_df = get_gene_mapping(gene_annotation, "ID", "GENE_SYMBOL")
+
+# 3. Convert probe-level measurements to gene-level expression data:
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+import pandas as pd
+
+# STEP 5: Data Normalization and Linking
+
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+print(f"Saved normalized gene data to {out_gene_data_file}")
+
+# Since in earlier steps trait_row was None, we have no clinical data to link.
+# Hence, there's no trait column to process. We'll skip linking and further steps
+# that require the trait. However, we must still perform a final validation.
+
+# Prepare a dummy DataFrame for the final validation
+dummy_df = pd.DataFrame()
+
+# We must provide is_biased and df to the final validation.
+# Because trait data is not available, this dataset won't be usable.
+is_biased = False  # Arbitrarily set; since trait is unavailable, "is_usable" will be False anyway.
+
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,      # Gene data is available
+    is_trait_available=False,    # Trait data is not available
+    is_biased=is_biased,
+    df=dummy_df,
+    note="No trait data available; skipping linking."
+)
+
+# 6. If data were usable, we would save it; otherwise we do nothing
+if is_usable:
+    print("Data is unexpectedly marked usable, but trait is unavailable. Skipping save.")

p1/preprocess/Alopecia/clinical_data/GSE66664.csv

@@ -0,0 +1,2 @@
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p1/preprocess/Alopecia/clinical_data/GSE80342.csv

@@ -0,0 +1,4 @@
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+0.0,0.0,0.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0
+43.0,27.0,40.0,36.0,45.0,48.0,34.0,34.0,58.0,35.0,31.0,63.0,60.0,62.0,20.0,60.0,58.0,35.0,31.0,48.0,34.0,36.0,45.0,48.0,34.0,58.0,31.0,63.0,60.0,62.0,45.0
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p1/preprocess/Alopecia/clinical_data/GSE81071.csv

@@ -0,0 +1,2 @@
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p1/preprocess/Alopecia/code/GSE148346.py

@@ -0,0 +1,149 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alopecia"
+cohort = "GSE148346"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alopecia"
+in_cohort_dir = "../DATA/GEO/Alopecia/GSE148346"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Alopecia/GSE148346.csv"
+out_gene_data_file = "./output/preprocess/1/Alopecia/gene_data/GSE148346.csv"
+out_clinical_data_file = "./output/preprocess/1/Alopecia/clinical_data/GSE148346.csv"
+json_path = "./output/preprocess/1/Alopecia/cohort_info.json"
+
+# STEP 1
+
+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("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on the study context, it appears to involve gene expression data.
+
+# 2. Variable Availability
+# Examination of the sample characteristics dictionary shows no variation for the trait (all are AA cases),
+# and no entries for age or gender.
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2 Data Type Conversion
+# Even though they are not available, we define the required conversion functions for completeness.
+def convert_trait(value: str):
+    return None  # Not available; returning None
+
+def convert_age(value: str):
+    return None  # Not available; returning None
+
+def convert_gender(value: str):
+    return None  # Not available; returning None
+
+# 3. Save Metadata (Initial Filtering)
+# trait data availability is determined by whether trait_row is None
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Since trait_row is None (trait data not available), we skip this step.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns in the annotation that match the probe identifiers in the expression data and the gene symbols.
+probe_col = "ID"
+gene_col = "Gene Symbol"
+
+# 2. Get the gene mapping from the annotation dataframe.
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_col)
+
+# 3. Convert probe-level measurements to gene-level expression data.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Print a brief check of the resulting gene expression data
+print("Gene-level expression data shape:", gene_data.shape)
+print("First 20 Gene IDs (index):")
+print(gene_data.index[:20])
+# STEP 7: Data Normalization and Linking
+
+# Recall from Step 2 that trait_row was None, so we have no clinical/trait data to link.
+# Therefore, we will normalize gene_data but skip linking to clinical data or performing
+# trait-based preprocessing. We must still do final validation, indicating that the dataset
+# lacks trait data and is not usable for an association study.
+
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+print(f"Saved normalized gene data to {out_gene_data_file}")
+
+# Because trait_row is None, we have no selected_clinical_df to link.
+# We also cannot perform missing value handling or bias checks on the trait.
+# Hence, we finalize by marking the dataset as not usable for trait-based association.
+
+import pandas as pd
+
+# We provide an empty dataframe as the final dataset for validation, ensuring the function can run.
+empty_df = pd.DataFrame()
+
+# Mark trait as biased (or effectively unavailable) so that it is deemed not usable.
+trait_biased = True
+
+# 5. Final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True, 
+    is_trait_available=False,  # trait not available
+    is_biased=trait_biased,
+    df=empty_df,
+    note="No trait data available; cannot be used for association studies."
+)
+
+# 6. If the dataset were usable, we'd save it. Here, it is not usable, so we skip saving a final linked CSV.
+if is_usable:
+    # This branch will not be taken because trait is unavailable.
+    out_data_file_final = out_data_file
+    empty_df.to_csv(out_data_file_final)
+    print(f"Saved final linked data to {out_data_file_final}")
+else:
+    print("Data not usable for association; skipping final output.")

p1/preprocess/Alopecia/code/GSE18876.py

@@ -0,0 +1,158 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alopecia"
+cohort = "GSE18876"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alopecia"
+in_cohort_dir = "../DATA/GEO/Alopecia/GSE18876"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Alopecia/GSE18876.csv"
+out_gene_data_file = "./output/preprocess/1/Alopecia/gene_data/GSE18876.csv"
+out_clinical_data_file = "./output/preprocess/1/Alopecia/clinical_data/GSE18876.csv"
+json_path = "./output/preprocess/1/Alopecia/cohort_info.json"
+
+# STEP 1
+
+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("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Decide if gene expression data is available
+is_gene_available = True  # Based on the exon array info, this dataset likely contains gene expression data
+
+# 2) Determine the availability of trait, age, and gender
+trait_row = None   # No row for Alopecia in the sample characteristics
+age_row = 0        # Found "age: ..." in row 0
+gender_row = None  # All are males, so effectively constant - not useful
+
+# 2.2) Define the data type conversion functions
+def convert_trait(value: str):
+    # No trait data available, return None
+    return None
+
+def convert_age(value: str):
+    # Expected format: "age: [number]"
+    parts = value.split(":")
+    if len(parts) >= 2:
+        age_str = parts[1].strip()
+        try:
+            return float(age_str)
+        except ValueError:
+            pass
+    return None
+
+def convert_gender(value: str):
+    # No gender row; not used
+    return None
+
+# 3) Initial filtering and save metadata
+#    Trait is considered unavailable if trait_row is None.
+is_trait_available = (trait_row is not None)
+
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Because trait_row is None (trait not available), we skip clinical feature extraction.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Observing the numeric identifiers, they do not appear to match standard human gene symbols.
+# They are likely array-specific probe IDs that need to be mapped to gene symbols.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Decide which columns store matching probe IDs and gene symbols
+#    Based on the preview, 'ID' matches the probe IDs in the gene expression dataframe,
+#    and 'gene_assignment' contains gene symbol information.
+
+# 2. Create a mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
+
+# 3. Apply the gene mapping to convert probe-level measurements to gene-level expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Check the result
+print("Mapped gene_data shape:", gene_data.shape)
+print("Mapped gene_data (first 5 rows):")
+print(gene_data.head(5))
+# STEP 7: Data Normalization and Linking
+
+# Recall from Step 2 that trait_row was None, so we have no clinical/trait data to link.
+# Therefore, we will normalize gene_data but skip linking to clinical data or performing
+# trait-based preprocessing. We must still do final validation, indicating that the dataset
+# lacks trait data and is not usable for an association study.
+
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+print(f"Saved normalized gene data to {out_gene_data_file}")
+
+# Because trait_row is None, we have no selected_clinical_df to link.
+# We also cannot perform missing value handling or bias checks on the trait.
+# Hence, we finalize by marking the dataset as not usable for trait-based association.
+
+import pandas as pd
+
+# We provide an empty dataframe as the final dataset for validation, ensuring the function can run.
+empty_df = pd.DataFrame()
+
+# Mark trait as biased (or effectively unavailable) so that it is deemed not usable.
+trait_biased = True
+
+# 5. Final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True, 
+    is_trait_available=False,  # trait not available
+    is_biased=trait_biased,
+    df=empty_df,
+    note="No trait data available; cannot be used for association studies."
+)
+
+# 6. If the dataset were usable, we'd save it. Here, it is not usable, so we skip saving a final linked CSV.
+if is_usable:
+    # This branch will not be taken because trait is unavailable.
+    out_data_file_final = out_data_file
+    empty_df.to_csv(out_data_file_final)
+    print(f"Saved final linked data to {out_data_file_final}")
+else:
+    print("Data not usable for association; skipping final output.")

p1/preprocess/Alopecia/code/GSE66664.py

@@ -0,0 +1,174 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alopecia"
+cohort = "GSE66664"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alopecia"
+in_cohort_dir = "../DATA/GEO/Alopecia/GSE66664"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Alopecia/GSE66664.csv"
+out_gene_data_file = "./output/preprocess/1/Alopecia/gene_data/GSE66664.csv"
+out_clinical_data_file = "./output/preprocess/1/Alopecia/clinical_data/GSE66664.csv"
+json_path = "./output/preprocess/1/Alopecia/cohort_info.json"
+
+# STEP 1
+
+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("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Determine if the dataset is likely to contain gene expression data
+is_gene_available = True  # Based on transcriptome analysis in the series summary
+
+# 2) Variable Availability
+# Observing sample characteristics, 'BAB' = balding, 'BAN' = non-balding. These two distinct values
+# represent different states relevant to "Alopecia"; thus it can be considered as the trait variable.
+trait_row = 0
+
+# No key suggests an age variable, or it appears constant (not present). So no age data.
+age_row = None
+
+# The study states "male patients," implying no variation for gender, and there's no separate field.
+gender_row = None
+
+# 2) Data Type Conversion Functions
+def convert_trait(value: str):
+    """
+    Converts 'BAB' -> 1 (balding) and 'BAN' -> 0 (non-balding).
+    Unknown values map to None.
+    """
+    if ':' in value:
+        val = value.split(':', 1)[1].strip().upper()  # Extract after colon, e.g. 'BAB'
+        if val == 'BAB':
+            return 1
+        elif val == 'BAN':
+            return 0
+    return None
+
+def convert_age(value: str):
+    """
+    Not available in the current dataset. Return None.
+    """
+    return None
+
+def convert_gender(value: str):
+    """
+    Not available in the current dataset. Return None.
+    """
+    return None
+
+# 3) Conduct initial filtering and save metadata
+# Trait data is available if trait_row is not None
+is_trait_available = (trait_row is not None)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) Clinical Feature Extraction if trait data is available
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,              # Assume clinical_data is already in the environment
+        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, max_items=200)
+    print("Preview of selected clinical features:", preview)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# The given identifiers (e.g., ILMN_1343291) are Illumina probe IDs, not standard HGNC gene symbols.
+# Therefore, mapping to gene symbols is required.
+
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the correct columns in the annotation dataframe.
+#    The "ID" column in `gene_annotation` matches the row IDs in the gene expression data (e.g. ILMN_xxxx).
+#    The "Symbol" column in `gene_annotation` contains the gene symbols.
+
+# 2. Create a gene mapping dataframe from the annotation.
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# 3. Convert probe-level measurements to gene-level measurements.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# By now, 'gene_data' contains gene expression values indexed by actual gene symbols.
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+print(f"Saved normalized gene data to {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
+cleaned_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine bias in trait and demographic features
+trait_biased, final_data = judge_and_remove_biased_features(cleaned_data, trait)
+
+# 5. Final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True, 
+    is_trait_available=True, 
+    is_biased=trait_biased,
+    df=final_data,
+    note="Processed with standard GEO pipeline."
+)
+
+# 6. If data is usable, save the final linked data
+if is_usable:
+    final_data.to_csv(out_data_file)
+    print(f"Saved final linked data to {out_data_file}")
+else:
+    print("Data not usable; skipping final output.")

p1/preprocess/Alopecia/code/GSE80342.py

@@ -0,0 +1,192 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alopecia"
+cohort = "GSE80342"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alopecia"
+in_cohort_dir = "../DATA/GEO/Alopecia/GSE80342"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Alopecia/GSE80342.csv"
+out_gene_data_file = "./output/preprocess/1/Alopecia/gene_data/GSE80342.csv"
+out_clinical_data_file = "./output/preprocess/1/Alopecia/clinical_data/GSE80342.csv"
+json_path = "./output/preprocess/1/Alopecia/cohort_info.json"
+
+# STEP 1
+
+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("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Determine if this dataset has gene expression data
+is_gene_available = True  # Based on the background info (microarray analysis assessing gene expression).
+
+# 2) Identify rows for trait, age, and gender; define type conversion functions.
+
+# From inspecting the sample characteristics, row 7 ('aatype') indicates whether 
+# a sample is a healthy control or various alopecia subtypes. We will treat 
+# "healthy_control" as 0 and all other alopecia types as 1.
+
+trait_row = 7
+age_row = 4          # row 4 has age values
+gender_row = 3       # row 3 has gender
+
+def convert_trait(raw_value: str) -> int:
+    """
+    Convert raw aatype value to a binary format: 0 if healthy_control, else 1.
+    Unknown entries become None.
+    """
+    # Example raw_value: "aatype: healthy_control"
+    parts = raw_value.split(':', maxsplit=1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == 'healthy_control':
+        return 0
+    elif val in ['persistent_patchy', 'severe_patchy', 'totalis', 'universalis']:
+        return 1
+    return None
+
+def convert_age(raw_value: str) -> float:
+    """
+    Convert raw age field (e.g., 'agebaseline: 43') to a continuous numeric format.
+    """
+    parts = raw_value.split(':', maxsplit=1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip()
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(raw_value: str) -> int:
+    """
+    Convert raw gender field to 0 for female, 1 for male, None if unknown.
+    """
+    parts = raw_value.split(':', maxsplit=1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val in ['m', 'male']:
+        return 1
+    elif val in ['f', 'female']:
+        return 0
+    return None
+
+# 3) Initialize trait availability and save preliminary metadata.
+#    If trait_row is None, the trait is not available.
+is_trait_available = (trait_row is not None)
+
+# Perform an initial validation and save relevant info.
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4) If trait_row is not None (trait data available), extract clinical features and save them.
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,  # "clinical_data" is assumed to be a DataFrame loaded from the step's context
+        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 then save
+    print("Selected Clinical Features Preview:", preview_df(selected_clinical_df))
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the observed identifiers (e.g., "1007_s_at", "1053_at"), these are Affymetrix probe set IDs, 
+# not conventional human gene symbols and they require mapping to official gene symbols.
+print("These are Affymetrix probe set IDs.\nrequires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1) We observe that the "ID" column in gene_annotation matches the probe identifiers in gene_data.index, 
+#    and the "Gene Symbol" column stores the gene symbols we need.
+
+# 2) Get the probe-to-gene mapping DataFrame.
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation,
+    prob_col="ID",            # The column storing the same IDs as in gene_data.index
+    gene_col="Gene Symbol"    # The column storing the gene symbols
+)
+
+# 3) Convert probe-level measurements to gene-level expression data by applying the mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+print(f"Saved normalized gene data to {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
+cleaned_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine bias in trait and demographic features
+trait_biased, final_data = judge_and_remove_biased_features(cleaned_data, trait)
+
+# 5. Final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True, 
+    is_trait_available=True, 
+    is_biased=trait_biased,
+    df=final_data,
+    note="Processed with standard GEO pipeline."
+)
+
+# 6. If data is usable, save the final linked data
+if is_usable:
+    final_data.to_csv(out_data_file)
+    print(f"Saved final linked data to {out_data_file}")
+else:
+    print("Data not usable; skipping final output.")

p1/preprocess/Alopecia/code/GSE81071.py

@@ -0,0 +1,189 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alopecia"
+cohort = "GSE81071"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Alopecia"
+in_cohort_dir = "../DATA/GEO/Alopecia/GSE81071"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Alopecia/GSE81071.csv"
+out_gene_data_file = "./output/preprocess/1/Alopecia/gene_data/GSE81071.csv"
+out_clinical_data_file = "./output/preprocess/1/Alopecia/clinical_data/GSE81071.csv"
+json_path = "./output/preprocess/1/Alopecia/cohort_info.json"
+
+# STEP 1
+
+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("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # This dataset contains data from Affymetrix microarrays, indicating gene expression data.
+
+# 2. Variable Availability and Data Type Conversion
+#    Based on the background info that "DLE" often leads to alopecia, we infer the trait from the row containing "disease state: DLE".
+#    Here, we choose row 0. Age and gender data are indeed not available, so keep those as None.
+trait_row = 0
+age_row = None
+gender_row = None
+
+def convert_trait(value: str):
+    """
+    Convert disease state to a binary indicator of alopecia (1 for DLE, 0 otherwise).
+    Unknown values become None.
+    """
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == 'dle':
+        return 1
+    elif val in ['normal', 'scle', 'healthy', 'skin', 'skin biopsy']:
+        return 0
+    return None
+
+def convert_age(value: str):
+    return None  # No age data available
+
+def convert_gender(value: str):
+    return None  # No gender data available
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+#    Since trait_row is not None, we extract clinical features and save the output.
+if trait_row is not None:
+    df_clinical = geo_select_clinical_features(
+        clinical_data,
+        trait,
+        trait_row,
+        convert_trait,
+        age_row,
+        convert_age,
+        gender_row,
+        convert_gender
+    )
+    print(preview_df(df_clinical))
+    df_clinical.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the example identifiers (e.g., "100009613_at"), these are Affymetrix probe IDs,
+# not standardized human gene symbols. Thus, gene symbol mapping is required.
+
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP 6: Gene Identifier Mapping
+
+# The "gene_annotation" preview shows columns "ID" and "ENTREZ_GENE_ID", 
+# but no true "Gene Symbol" column. We will therefore treat "ENTREZ_GENE_ID" 
+# as the gene identifier, skipping text-based extraction.
+
+def apply_gene_mapping_entrez(expression_df: pd.DataFrame, annotation_df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Convert probe-level expression to gene-level expression using Entrez ID.
+    Each probe is assumed to map to exactly 1 gene (ENTREZ_GENE_ID).
+    """
+    # Keep only probes that exist in the expression data
+    annotation_df = annotation_df[annotation_df['ID'].isin(expression_df.index)].copy()
+
+    # Rename "ENTREZ_GENE_ID" to "Gene" so we can group by it.
+    annotation_df.rename(columns={'ENTREZ_GENE_ID': 'Gene'}, inplace=True)
+    annotation_df['num_genes'] = 1
+    annotation_df.set_index('ID', inplace=True)
+
+    # Merge annotation with expression data on probe ID
+    merged_df = annotation_df.join(expression_df)
+    expr_cols = [col for col in merged_df.columns if col not in ['Gene', 'num_genes']]
+
+    # Distribute expression values (though here it's trivially 1-to-1)
+    merged_df[expr_cols] = merged_df[expr_cols].div(merged_df['num_genes'].replace(0, 1), axis=0)
+
+    # Sum expression values for each gene
+    gene_expression_df = merged_df.groupby('Gene')[expr_cols].sum()
+    return gene_expression_df
+
+# 1. Construct our mapping DataFrame using 'ID' -> 'ENTREZ_GENE_ID'
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='ENTREZ_GENE_ID')
+
+# 2. Apply our custom function to generate gene-level expression data
+gene_data = apply_gene_mapping_entrez(gene_data, mapping_df)
+
+# 3. Display the result for a quick check
+print("Gene expression dataframe shape:", gene_data.shape)
+print("Gene expression dataframe index preview:", gene_data.index[:20])
+import pandas as pd
+
+# STEP 7: Data Normalization and Linking
+
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file, index=True)
+print(f"Saved normalized gene data to {out_gene_data_file}")
+
+# 2. Link clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(df_clinical, normalized_gene_data)
+
+# 3. Handle missing values
+cleaned_data = handle_missing_values(linked_data, trait)
+
+# 4. Determine bias in trait and demographic features
+trait_biased, final_data = judge_and_remove_biased_features(cleaned_data, trait)
+
+# 5. Final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True, 
+    is_trait_available=True, 
+    is_biased=trait_biased,
+    df=final_data,
+    note="Processed with standard GEO pipeline."
+)
+
+# 6. If data is usable, save the final linked data
+if is_usable:
+    final_data.to_csv(out_data_file)
+    print(f"Saved final linked data to {out_data_file}")
+else:
+    print("Data not usable; skipping final output.")

p1/preprocess/Alopecia/code/TCGA.py

@@ -0,0 +1,57 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Alopecia"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Alopecia/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Alopecia/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Alopecia/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Alopecia/cohort_info.json"
+
+import os
+import pandas as pd
+
+# 1. Identify the relevant subdirectory for the trait "Obesity"
+subdirectories = [
+    'CrawlData.ipynb', '.DS_Store', 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)',
+    'TCGA_Uterine_Carcinosarcoma_(UCS)', 'TCGA_Thyroid_Cancer_(THCA)', 'TCGA_Thymoma_(THYM)',
+    'TCGA_Testicular_Cancer_(TGCT)', 'TCGA_Stomach_Cancer_(STAD)', 'TCGA_Sarcoma_(SARC)',
+    'TCGA_Rectal_Cancer_(READ)', 'TCGA_Prostate_Cancer_(PRAD)', 'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)',
+    'TCGA_Pancreatic_Cancer_(PAAD)', 'TCGA_Ovarian_Cancer_(OV)', 'TCGA_Ocular_melanomas_(UVM)',
+    'TCGA_Mesothelioma_(MESO)', 'TCGA_Melanoma_(SKCM)', 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)',
+    'TCGA_Lung_Cancer_(LUNG)', 'TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lower_Grade_Glioma_(LGG)',
+    'TCGA_Liver_Cancer_(LIHC)', 'TCGA_Large_Bcell_Lymphoma_(DLBC)', 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)',
+    'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)', 'TCGA_Kidney_Chromophobe_(KICH)', 'TCGA_Head_and_Neck_Cancer_(HNSC)',
+    'TCGA_Glioblastoma_(GBM)', 'TCGA_Esophageal_Cancer_(ESCA)', 'TCGA_Endometrioid_Cancer_(UCEC)',
+    'TCGA_Colon_and_Rectal_Cancer_(COADREAD)', 'TCGA_Colon_Cancer_(COAD)', 'TCGA_Cervical_Cancer_(CESC)',
+    'TCGA_Breast_Cancer_(BRCA)', 'TCGA_Bladder_Cancer_(BLCA)', 'TCGA_Bile_Duct_Cancer_(CHOL)',
+    'TCGA_Adrenocortical_Cancer_(ACC)', 'TCGA_Acute_Myeloid_Leukemia_(LAML)'
+]
+
+trait_keyword = trait
+target_subdir = None
+
+for sd in subdirectories:
+    if trait_keyword.lower() in sd.lower():
+        target_subdir = sd
+        break
+
+if target_subdir is None:
+    # No suitable data found for this trait; mark as completed
+    print("No TCGA subdirectory found for the trait. Skipping.")
+else:
+    # 2. Locate clinical and genetic data files
+    cohort_dir = os.path.join(tcga_root_dir, target_subdir)
+    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
+
+    # 3. Load the data
+    clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
+    genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')
+
+    # 4. Print column names of clinical data
+    print(clinical_df.columns)

p1/preprocess/Alzheimers_Disease/GSE214417.csv

@@ -0,0 +1,25 @@
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