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p1/preprocess/Cystic_Fibrosis/code/GSE100521.py

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+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cystic_Fibrosis"
+cohort = "GSE100521"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
+in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE100521"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Cystic_Fibrosis/GSE100521.csv"
+out_gene_data_file = "./output/preprocess/1/Cystic_Fibrosis/gene_data/GSE100521.csv"
+out_clinical_data_file = "./output/preprocess/1/Cystic_Fibrosis/clinical_data/GSE100521.csv"
+json_path = "./output/preprocess/1/Cystic_Fibrosis/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 re
+import pandas as pd
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on the background info, this dataset has Illumina HumanHT-12 v4 microarray data
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Find rows for trait, age, and gender
+trait_row = 0   # row 0 contains CF vs Non CF info
+age_row = 1     # row 1 contains age info
+gender_row = 2  # row 2 contains gender info
+
+# 2.2 Define data conversion functions
+def convert_trait(value: str):
+    """
+    Convert a string describing the subject's CF status to a binary value:
+    0 for Non-CF subject, 1 for CF patient.
+    Unknown values => None
+    """
+    # Extract the part after the colon
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if 'non cf subject' in val:
+        return 0
+    elif 'cf patient' in val:
+        return 1
+    else:
+        return None
+
+def convert_age(value: str):
+    """
+    Convert a string describing the age to a continuous (float) value.
+    Unknown values => None
+    """
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip()
+    # Attempt to convert to float
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    """
+    Convert a string describing gender to a binary value:
+    female => 0, male => 1
+    Unknown values => None
+    """
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == 'female':
+        return 0
+    elif val == 'male':
+        return 1
+    else:
+        return None
+
+# We assume the variable "clinical_data" is available in this environment,
+# containing the sample characteristics as a DataFrame.
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if trait_row is not None)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview
+    preview_output = preview_df(selected_clinical_df)
+    print("Preview of selected clinical features:", preview_output)
+
+    # Save to CSV
+    selected_clinical_df.to_csv(out_clinical_data_file, index=True)
+# 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 biomedical expertise, 'ILMN_xxxxx' identifiers are Illumina probe IDs and 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. Identify the columns in the gene_annotation dataframe that match the probe identifiers in gene_data (ILMN_xxx) 
+#    and those that represent gene symbols. From the annotation preview, 'ID' matches 'ILMN_xxx' and 'Symbol' is the gene symbol.
+
+# 2. Create a gene mapping dataframe using the identified columns
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
+
+# 3. Convert probe-level measurements to gene-level expression data by applying the mapping
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (Optional) Quick check - display the shape or a small preview
+print("Mapped gene_data shape:", gene_data.shape)
+print("Mapped gene_data head:\n", gene_data.head(5))
+import pandas as pd
+
+# STEP7
+
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Check trait availability
+is_trait_available = True
+
+if not is_trait_available:
+    # If trait is unavailable, skip further processing
+    empty_df = pd.DataFrame()
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=True,
+        df=empty_df,
+        note="Trait data not available; skipping further steps."
+    )
+else:
+    # Read the previously saved clinical data with index_col=0
+    selected_clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
+
+    # 3. Link the clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+    # 4. Handle missing values in the linked data
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 5. Determine whether the trait and demographic features are severely biased
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 6. Final quality check and record the dataset info
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note="Final check after linking and missing-value handling."
+    )
+
+    # 7. If usable, save the final linked data
+    if is_usable:
+        unbiased_linked_data.to_csv(out_data_file)

p1/preprocess/Cystic_Fibrosis/code/GSE107846.py

@@ -0,0 +1,184 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cystic_Fibrosis"
+cohort = "GSE107846"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
+in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE107846"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Cystic_Fibrosis/GSE107846.csv"
+out_gene_data_file = "./output/preprocess/1/Cystic_Fibrosis/gene_data/GSE107846.csv"
+out_clinical_data_file = "./output/preprocess/1/Cystic_Fibrosis/clinical_data/GSE107846.csv"
+json_path = "./output/preprocess/1/Cystic_Fibrosis/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 GEO entry, we assume it's a gene expression dataset.
+
+# 2. Variable Availability
+trait_row = 5   # "state: CF" or "state: Healthy"
+age_row = 1     # "age: ..."
+gender_row = 2  # "Sex: F" / "Sex: M"
+
+# 2.2 Data Type Conversions
+def convert_trait(value: str):
+    # Extract the text after the first colon
+    val = value.split(":", 1)[1].strip() if ":" in value else value.strip()
+    # Convert to binary: CF -> 1, Healthy -> 0
+    if val.upper() == "CF":
+        return 1
+    elif val.upper() == "HEALTHY":
+        return 0
+    return None
+
+def convert_age(value: str):
+    # Extract the text after the first colon
+    val = value.split(":", 1)[1].strip() if ":" in value else value.strip()
+    # Convert to float if possible
+    try:
+        return float(val)
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    # Extract the text after the first colon
+    val = value.split(":", 1)[1].strip() if ":" in value else value.strip()
+    # Convert to binary: F -> 0, M -> 1
+    if val.upper() == "F":
+        return 0
+    elif val.upper() == "M":
+        return 1
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction if trait data is available
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data, 
+        trait=trait, 
+        trait_row=trait_row, 
+        convert_trait=convert_trait,
+        age_row=age_row, 
+        convert_age=convert_age,
+        gender_row=gender_row, 
+        convert_gender=convert_gender
+    )
+    preview = preview_df(selected_clinical_df)
+    print("Preview 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])
+# Based on the gene identifiers (ILMN_XXXXXX), these appear to be Illumina probe IDs.
+# Therefore, they require mapping to standard gene symbols.
+
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Decide which key in the gene annotation corresponds to the same identifier type as in the gene expression data
+#    and which key corresponds to the gene symbols.
+#    From observation, 'ID' matches ILMN probe identifiers (e.g., "ILMN_1245321") and 'SYMBOL' stores gene symbols.
+
+# 2. Get a gene mapping dataframe.
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="SYMBOL")
+
+# 3. Convert probe-level measurements to gene expression data by applying the gene mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# (gene_data now contains gene expression values indexed by gene symbols)
+print("Mapped gene_data shape:", gene_data.shape)
+print("First few rows of mapped gene expression data:\n", gene_data.head())
+import pandas as pd
+
+# STEP7
+
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Based on Step 2, we concluded trait_row=5 (thus trait data is available).
+is_trait_available = True
+
+if not is_trait_available:
+    # 2-4: Skip linking, missing value handling, and bias checks because trait is unavailable.
+    empty_df = pd.DataFrame()
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=True,
+        df=empty_df,
+        note="Trait data not available; skipping further steps."
+    )
+else:
+    # 2. Load the clinical data. Since the CSV was saved with index=False, we first read the file,
+    # then manually set the row index to ["Cystic_Fibrosis","Age","Gender"].
+    selected_clinical_data = pd.read_csv(out_clinical_data_file, header=0)
+    selected_clinical_data.index = [trait, "Age", "Gender"]
+
+    # 3. Link the clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+    # 4. Handle missing values in the linked data
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 5. Determine whether the trait and demographic features are severely biased
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 6. Conduct final quality validation and save the cohort information
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note="Final check after linking and missing-value handling."
+    )
+
+    # 7. If the dataset is usable, save it as CSV
+    if is_usable:
+        unbiased_linked_data.to_csv(out_data_file)

p1/preprocess/Cystic_Fibrosis/code/GSE129168.py

@@ -0,0 +1,169 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cystic_Fibrosis"
+cohort = "GSE129168"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
+in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE129168"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Cystic_Fibrosis/GSE129168.csv"
+out_gene_data_file = "./output/preprocess/1/Cystic_Fibrosis/gene_data/GSE129168.csv"
+out_clinical_data_file = "./output/preprocess/1/Cystic_Fibrosis/clinical_data/GSE129168.csv"
+json_path = "./output/preprocess/1/Cystic_Fibrosis/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Gene expression data availability
+is_gene_available = True  # This dataset provides transcriptome data for CF iPSCs, so we consider it as gene expression data.
+
+# 2) Variable Availability
+# Observing the sample characteristics dictionary, row=2 contains genotype info
+# indicating CF vs non-CF lines (p.Phe508del vs gene-corrected/WT).
+# No suitable age or gender info is present.
+trait_row = 2
+age_row = None
+gender_row = None
+
+# 2) Data Type Conversion
+def convert_trait(value):
+    if not value or pd.isnull(value):
+        return None
+    val = value.split(':')[-1].strip().lower()
+    # Mark p.Phe508del (but not gene-corrected) as CF
+    if 'p.phe508del' in val and 'gene corrected' not in val:
+        return 1
+    return 0
+
+def convert_age(value):
+    return None  # Not available
+
+def convert_gender(value):
+    return None  # Not 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
+# Proceed only if the trait data is available
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview
+    preview_result = preview_df(selected_clinical_df)
+    print("Preview of selected clinical features:", preview_result)
+    # Save to CSV
+    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 index names like "A_23_P100001", these are array probe IDs rather than standard human gene symbols.
+# Therefore, they need to be mapped to gene symbols.
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+# 1) Identify the columns in gene_annotation that correspond to the probe ID and gene symbol
+probe_col = "ID"
+symbol_col = "GENE_SYMBOL"
+
+# 2) Obtain the mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, probe_col, symbol_col)
+
+# 3) Convert probe-level measurements to gene expression data by applying the mapping
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+import pandas as pd
+
+# STEP7
+
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Based on Step 2, we concluded trait_row = 2 (thus trait data is available).
+is_trait_available = True
+
+if not is_trait_available:
+    # 2-4: Skip linking, missing value handling, and bias checks because trait is unavailable.
+    empty_df = pd.DataFrame()
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False,
+        is_biased=True,
+        df=empty_df,
+        note="Trait data not available; skipping further steps."
+    )
+else:
+    # 2. Load the clinical data from the previous step and set its index to the trait name
+    selected_clinical_data = pd.read_csv(out_clinical_data_file)
+    selected_clinical_data.index = [trait]
+
+    # Link the clinical and genetic data
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Determine whether the trait and demographic features are severely biased
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Conduct final quality validation and save the cohort information
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data,
+        note="Final check after linking and missing-value handling."
+    )
+
+    # 6. If the dataset is usable, save it as CSV
+    if is_usable:
+        unbiased_linked_data.to_csv(out_data_file)

p1/preprocess/Cystic_Fibrosis/code/GSE139038.py

@@ -0,0 +1,192 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cystic_Fibrosis"
+cohort = "GSE139038"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
+in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE139038"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Cystic_Fibrosis/GSE139038.csv"
+out_gene_data_file = "./output/preprocess/1/Cystic_Fibrosis/gene_data/GSE139038.csv"
+out_clinical_data_file = "./output/preprocess/1/Cystic_Fibrosis/clinical_data/GSE139038.csv"
+json_path = "./output/preprocess/1/Cystic_Fibrosis/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 series info indicating a gene expression study
+
+# 2. Variable Availability and Data Type Conversion
+
+# Looking at the sample characteristics dictionary,
+# - For "trait" (Cystic_Fibrosis), no matching or inferable data was found. So trait_row = None.
+# - For "age", it appears in row 0 and has multiple unique values. So age_row = 0.
+# - For "gender", row 1 has only "Female" (constant). Hence, it's not useful for association. gender_row = None.
+
+trait_row = None
+age_row = 0
+gender_row = None
+
+# Define the data conversion functions.
+def convert_trait(val: str):
+    """
+    Convert trait data to binary (1/0). Return None if unknown.
+    """
+    parts = val.split(':', 1)
+    if len(parts) < 2:
+        return None
+    raw = parts[1].strip().lower()
+    # Example placeholder logic:
+    # If the variable explicitly indicated "cystic fibrosis," return 1; 
+    # if it indicated "normal"/"control," return 0; else None.
+    if raw == "cystic fibrosis":
+        return 1
+    elif raw in ["normal", "control", "no"]:
+        return 0
+    return None
+
+def convert_age(val: str):
+    """
+    Convert age data to continuous (float). Return None if unknown.
+    """
+    parts = val.split(':', 1)
+    if len(parts) < 2:
+        return None
+    raw = parts[1].strip()
+    try:
+        return float(raw)
+    except ValueError:
+        return None
+
+def convert_gender(val: str):
+    """
+    Convert gender data to binary (female=0, male=1). Return None if unknown.
+    """
+    parts = val.split(':', 1)
+    if len(parts) < 2:
+        return None
+    raw = parts[1].strip().lower()
+    if raw == "female":
+        return 0
+    elif raw == "male":
+        return 1
+    return None
+
+# 3. Save Metadata - Initial Filtering
+# Trait data availability depends on whether trait_row is None.
+is_trait_available = (trait_row is not None)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Since trait_row is None, we skip extracting clinical features.
+# STEP3
+# 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., "10_10_1", "10_10_10") are not standard human gene symbols.
+# They appear to be platform-specific probe references, so a mapping to human gene symbols is needed.
+
+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 have determined that the gene annotation column "ID" matches the identifier in the gene expression data index,
+#    and "Gene_Symbol" provides the corresponding gene symbols.
+
+# 2. Create a gene mapping dataframe from the annotation dataframe using the appropriate columns.
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene_Symbol')
+
+# 3. Apply the mapping to convert probe-level data to gene-level data.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# Optionally print shape or a small preview to verify results
+print("Mapped gene_data shape:", gene_data.shape)
+print(gene_data.head())
+import pandas as pd
+
+# STEP7
+
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# Check if trait data was actually available from previous steps
+# (In previous steps, we set is_trait_available = (trait_row is not None).)
+# We'll assume here it's accessible in the environment, or re-derive it:
+is_trait_available = False  # Reflecting the outcome from prior steps
+
+if not is_trait_available:
+    # 2-4: Skip linking, missing value handling, and bias checks because trait is unavailable.
+    # 5. Conduct final validation with an empty DataFrame, forcing the dataset to be marked not usable.
+    empty_df = pd.DataFrame()
+    validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,    # The expression data exists
+        is_trait_available=False,  # Trait data is not available
+        is_biased=True,            # Force as biased so the dataset is not usable
+        df=empty_df,
+        note="Trait data not available; skipping further steps."
+    )
+else:
+    # 2. Define a placeholder for selected_clinical_data (if we actually had trait data).
+    #    In this dataset, trait_row was None, so this part won't run.
+    selected_clinical_data = pd.DataFrame()  # Placeholder if trait were available
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+    # 3. Handle missing values in the linked data
+    linked_data = handle_missing_values(linked_data, trait)
+
+    # 4. Determine whether the trait and demographic features are severely biased
+    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+    # 5. Conduct final quality validation and save the cohort information
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=is_trait_biased,
+        df=unbiased_linked_data
+    )
+
+    # 6. If the dataset is usable, save it as CSV
+    if is_usable:
+        unbiased_linked_data.to_csv(out_data_file)

p1/preprocess/Cystic_Fibrosis/code/GSE142610.py

@@ -0,0 +1,100 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cystic_Fibrosis"
+cohort = "GSE142610"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
+in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE142610"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Cystic_Fibrosis/GSE142610.csv"
+out_gene_data_file = "./output/preprocess/1/Cystic_Fibrosis/gene_data/GSE142610.csv"
+out_clinical_data_file = "./output/preprocess/1/Cystic_Fibrosis/clinical_data/GSE142610.csv"
+json_path = "./output/preprocess/1/Cystic_Fibrosis/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 likely available
+is_gene_available = True  # Based on the series summary describing transcriptomic (gene expression) analysis
+
+# 2. Determine data availability (trait, age, gender)
+#    We see that all samples are from a CF cell line without variation. Hence, trait is constant.
+#    No age or gender information is provided. Therefore:
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2 Define conversion functions.
+#     Since trait_row, age_row, and gender_row are None, these functions will not be used here,
+#     but we provide them for completeness.
+
+def convert_trait(raw_value: str):
+    return None
+
+def convert_age(raw_value: str):
+    return None
+
+def convert_gender(raw_value: str):
+    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 (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])
+# Based on inspection, some identifiers (e.g., "7A5", "A2BP1") appear to be synonyms or outdated symbols
+# rather than standard HGNC gene symbols. Therefore, they may require mapping to unify them into
+# current official human gene symbols.
+
+print("Some gene identifiers are synonyms or aliases rather than current official 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 key in the gene annotation DataFrame matches the gene expression data IDs
+#    and which key contains the gene symbols. 
+#    From the preview, both "ID" and "ORF" columns appear to match the probe IDs in the expression data,
+#    but "ORF" likely corresponds to the gene symbol we want.
+
+# 2. Get a gene mapping DataFrame using the library function get_gene_mapping
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='ORF')
+
+# 3. Convert (probe-level) gene expression data to (gene-level) data
+gene_data = apply_gene_mapping(gene_data, mapping_df)

p1/preprocess/Cystic_Fibrosis/code/GSE53543.py

@@ -0,0 +1,185 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cystic_Fibrosis"
+cohort = "GSE53543"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
+in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE53543"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Cystic_Fibrosis/GSE53543.csv"
+out_gene_data_file = "./output/preprocess/1/Cystic_Fibrosis/gene_data/GSE53543.csv"
+out_clinical_data_file = "./output/preprocess/1/Cystic_Fibrosis/clinical_data/GSE53543.csv"
+json_path = "./output/preprocess/1/Cystic_Fibrosis/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, this dataset contains gene expression data
+
+# 2. Variable Availability
+#   After examining the sample characteristics dictionary, we see:
+#   - There is no row containing Cystic Fibrosis information, so trait_row = None
+#   - There is no row containing age information, so age_row = None
+#   - Row 1 contains gender information with two distinct values (Female, Male), so gender_row = 1
+trait_row = None
+age_row = None
+gender_row = 1
+
+# 2.2 Data Type Conversion Functions
+def convert_trait(x: str):
+    """
+    Convert string to an appropriate trait value.
+    Since we have no trait data, the function returns None for any input.
+    """
+    return None
+
+def convert_age(x: str):
+    """
+    Convert string to a continuous value for age.
+    Since age data is not available in this dataset, the function returns None for any input.
+    """
+    return None
+
+def convert_gender(x: str):
+    """
+    Convert string to a binary value for gender: female -> 0, male -> 1.
+    Any unknown token returns None.
+    """
+    if not x:
+        return None
+    val = x.split(":", 1)[-1].strip().lower()
+    if val == "female":
+        return 0
+    elif val == "male":
+        return 1
+    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)
+
+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
+#    This step is skipped because 'trait_row' is None 
+#    (no trait data available 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])
+# Observed gene identifiers (e.g., "ILMN_1651229") are Illumina probe IDs, not standard human gene symbols.
+# They require mapping to official gene symbols.
+
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+
+# 1. Identify the columns in the annotation dataframe that match the gene_expression data's probe IDs and gene symbols
+probe_col = "ID"     # This column in 'gene_annotation' matches the probe IDs in 'gene_data'
+symbol_col = "Symbol"  # This column in 'gene_annotation' holds the gene symbols
+
+# 2. Extract the mapping info from the annotation dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=symbol_col)
+
+# 3. Apply the gene mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP7
+# Since trait_row = None, we have no trait data. Therefore, we cannot remove samples for a nonexistent trait,
+# nor perform trait-based bias checks. We will still normalize and link the available clinical data (gender only)
+# with gene data. Finally, we record that the dataset lacks trait data and is thus not fully usable.
+
+# 1. Define the selected clinical data (which has gender info but no trait info).
+selected_clinical_data = clinical_data
+
+# 2. Normalize the obtained gene expression data using synonym information and save to CSV.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 3. Link the (gender-only) clinical data and gene data.
+linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+# 4. We skip handle_missing_values() and bias checks because there's no trait column at all.
+#    Attempting them would cause a KeyError on "Cystic_Fibrosis".
+
+# 5. Perform final validation, marking that no trait data is available. This dataset is not fully usable for
+#    trait-based analyses, but we still record its metadata in the JSON file.
+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=None,
+    df=None,
+    note="No trait data available. Only gender is present."
+)
+
+# 6. Because the dataset lacks trait data, it won't be marked as fully usable, so we do not save any final linked CSV.
+# STEP8
+# As determined, this dataset lacks a valid trait column ("Cystic_Fibrosis"), so we cannot run
+# trait-based missing value checks or bias assessments. We will still normalize and link
+# the data, then record that the dataset is not fully usable (no trait data).
+
+# 1. Normalize the obtained gene data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical (gender-only) and genetic data
+#    Assuming 'selected_clinical_data' is simply 'clinical_data' from our previous steps.
+selected_clinical_data = clinical_data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+
+# 3. Because there is no 'Cystic_Fibrosis' column, we skip handle_missing_values() and bias checks.
+
+# 4. Conduct the final quality validation and record metadata.
+#    The trait is not available, so we pass `is_trait_available=False`. The function requires is_biased to be boolean.
+#    We set it to False to fulfill the function's requirements and note that the dataset lacks trait-based analysis.
+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,  # Manually setting to False; no trait data => no trait bias check
+    df=linked_data,
+    note="No trait column found; cannot perform trait-based analysis. Only gender is present."
+)
+
+# 5. If the dataset were usable for trait-based analysis, we would save the final linked CSV.
+#    But since it's not, we skip saving to `out_data_file`.
+if is_usable:
+    linked_data.to_csv(out_data_file)

p1/preprocess/Cystic_Fibrosis/code/GSE60690.py

@@ -0,0 +1,165 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cystic_Fibrosis"
+cohort = "GSE60690"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
+in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE60690"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Cystic_Fibrosis/GSE60690.csv"
+out_gene_data_file = "./output/preprocess/1/Cystic_Fibrosis/gene_data/GSE60690.csv"
+out_clinical_data_file = "./output/preprocess/1/Cystic_Fibrosis/clinical_data/GSE60690.csv"
+json_path = "./output/preprocess/1/Cystic_Fibrosis/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
+#    From the background info ("global gene expression was measured in RNA from LCLs"),
+#    it is clear that the dataset contains gene expression data.
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+#    2.1 Data Availability
+#    - The "trait" here is "Cystic_Fibrosis", but the background indicates
+#      this dataset is entirely CF patients (no variation). Hence treat as not available.
+trait_row = None
+
+#    - Age is found in row 2 ("age of enrollment: ...").
+age_row = 2
+
+#    - Gender is found in row 0 ("Sex: Male", "Sex: Female").
+gender_row = 0
+
+#    2.2 Data Type Conversion
+import re
+
+def convert_trait(value: str) -> int:
+    """
+    Although trait_row is None (trait not available),
+    define a function for completeness.
+    Returns None if called, as there's no variation here.
+    """
+    return None
+
+def convert_age(value: str) -> float:
+    """
+    Convert 'age of enrollment: 38.2' -> 38.2 (float).
+    If 'NA', return None.
+    """
+    # Extract the portion after the colon
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    age_str = parts[1].strip()
+    if age_str.upper() == 'NA':
+        return None
+    try:
+        return float(age_str)
+    except ValueError:
+        return None
+
+def convert_gender(value: str) -> int:
+    """
+    Convert 'Sex: Male' -> 1
+            'Sex: Female' -> 0
+    Otherwise return None.
+    """
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    gender_str = parts[1].strip().lower()
+    if gender_str == 'male':
+        return 1
+    elif gender_str == 'female':
+        return 0
+    return None
+
+# 3. Save Metadata
+#    trait data availability depends on trait_row.
+is_trait_available = trait_row is not None
+
+usable_status = 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 variation).
+#    Thus, we do not call geo_select_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])
+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 the columns in 'gene_annotation' that match the probe IDs in 'gene_data'
+#    and the column that stores gene symbols. Based on inspection, "ID" aligns with
+#    the probe identifiers in the expression data, and "gene_assignment" stores gene symbols.
+probe_col = "ID"
+gene_symbol_col = "gene_assignment"
+
+# 2. Extract a mapping dataframe containing these two columns
+mapping_df = get_gene_mapping(gene_annotation, probe_col, gene_symbol_col)
+
+# 3. Apply the mapping to convert the probe-level expression data to gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP 7
+
+# 1) Normalize the 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)
+
+# The dataset lacks trait variation (only CF patients), so no clinical data can be used for association.
+# We skip linking to clinical data and skip further steps requiring trait info.
+
+# 2) Final validation and saving metadata
+#    The library requires non-None boolean for 'is_biased' when is_final=True.
+#    Since there's no trait variation, we consider it "biased" for association.
+is_biased = True
+
+_ = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,
+    is_biased=is_biased,
+    df=pd.DataFrame(),  # Passing an empty DataFrame as the dataset for final validation
+    note="All samples are CF patients; no variation in the trait."
+)
+
+# 3) Because the trait is unavailable for association, the dataset is not usable.
+#    We therefore do not create or save any linked data CSV file.

p1/preprocess/Cystic_Fibrosis/code/GSE67698.py

@@ -0,0 +1,178 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cystic_Fibrosis"
+cohort = "GSE67698"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
+in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE67698"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Cystic_Fibrosis/GSE67698.csv"
+out_gene_data_file = "./output/preprocess/1/Cystic_Fibrosis/gene_data/GSE67698.csv"
+out_clinical_data_file = "./output/preprocess/1/Cystic_Fibrosis/clinical_data/GSE67698.csv"
+json_path = "./output/preprocess/1/Cystic_Fibrosis/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  # "Transcriptional profiling" implies likely RNA gene expression
+
+# 2. Variable Availability and Data Type Conversion
+#   Based on the sample characteristics dictionary, row=1 has two unique values indicating
+#   deltaF508 CFTR or wildtype CFTR, which can be mapped to the trait (Cystic_Fibrosis vs. not).
+#   Age and gender do not appear to be present.
+
+trait_row = 1
+age_row = None
+gender_row = None
+
+def convert_trait(value: str) -> Optional[int]:
+    """
+    Convert the trait (CF vs. non-CF) to a binary integer.
+    Values containing 'deltaF508' -> 1 (CF)
+    Values containing 'wildtype'  -> 0 (non-CF)
+    Otherwise -> None
+    """
+    # Attempt to split by colon, keep the part after it
+    parts = value.split(':', 1)
+    if len(parts) == 2:
+        val = parts[1].strip().lower()
+    else:
+        val = value.strip().lower()
+
+    if 'deltaf508' in val:
+        return 1
+    elif 'wildtype' in val:
+        return 0
+    return None
+
+def convert_age(value: str) -> Optional[float]:
+    # Age data not available, return None
+    return None
+
+def convert_gender(value: str) -> Optional[int]:
+    # Gender data not available, return None
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview the extracted clinical features
+    preview = preview_df(selected_clinical_df)
+    print("Preview of selected clinical features:", preview)
+
+    # Save the clinical dataframe to CSV
+    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 observation, the identifiers (e.g., "A_23_P100001") are not standard human gene symbols.
+# They appear to be array 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))
+# STEP6: Gene Identifier Mapping
+
+# 1. Identify the columns in the annotation that match the expression data's probe IDs and human gene symbols.
+#    From inspection, 'ID' matches the "A_23_P..." probe IDs, and 'GENE_SYMBOL' holds the human gene symbols.
+
+# 2. Build a gene mapping dataframe using these columns.
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation,
+    prob_col="ID",
+    gene_col="GENE_SYMBOL"
+)
+
+# 3. Convert probe-level measurements to gene-level expression data by applying the gene mapping.
+gene_data = apply_gene_mapping(
+    expression_df=gene_data,
+    mapping_df=mapping_df
+)
+
+# (Optional) Display some basic information about the newly mapped gene_data.
+print("Mapped gene_data shape:", gene_data.shape)
+print("First 5 rows of mapped gene_data:")
+print(gene_data.head())
+import os
+import pandas as pd
+
+# STEP 7 (Corrected)
+
+# 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)
+
+# 2) Instead of reloading the clinical data from CSV (which was saved without index),
+#    we directly use the in-memory DataFrame "selected_clinical_df" from earlier steps.
+#    That DataFrame already has the trait as a row label, which is required downstream.
+
+# 3) Link the clinical and genetic data on sample IDs
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 4) Handle missing values in the linked data
+linked_data = handle_missing_values(linked_data, trait)
+
+# 5) Check for biased features (including the trait)
+trait_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=trait_biased,
+    df=linked_data,
+    note="Data from GSE123086, trait is Crohn's disease."
+)
+
+# 7) If the dataset is usable, save the linked data
+if is_usable:
+    linked_data.to_csv(out_data_file)

p1/preprocess/Cystic_Fibrosis/code/GSE71799.py

@@ -0,0 +1,125 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cystic_Fibrosis"
+cohort = "GSE71799"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
+in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE71799"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Cystic_Fibrosis/GSE71799.csv"
+out_gene_data_file = "./output/preprocess/1/Cystic_Fibrosis/gene_data/GSE71799.csv"
+out_clinical_data_file = "./output/preprocess/1/Cystic_Fibrosis/clinical_data/GSE71799.csv"
+json_path = "./output/preprocess/1/Cystic_Fibrosis/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
+# According to the background summary, gene expression analysis was performed, so:
+is_gene_available = True
+
+# 2. Variable Availability and Data Type Conversion
+
+# Based on the sample characteristics dictionary:
+# {0: ['responder cells: UPN727 cells']}
+# There's a single key (0) with the same value for all samples, which does not provide trait,
+# age, or gender variability. Therefore, all three are considered not available.
+
+trait_row = None
+age_row = None
+gender_row = None
+
+# Define conversion functions (though they won't be used due to None rows).
+def convert_trait(value: str):
+    # No actual data keys to parse, return None
+    return None
+
+def convert_age(value: str):
+    # No actual data keys to parse, return None
+    return None
+
+def convert_gender(value: str):
+    # No actual data keys to parse, return None
+    return None
+
+# 3. Save metadata with initial filtering
+# Trait availability depends on whether trait_row is None.
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Since trait_row is None, we skip 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 columns in the annotation dataframe: 'ID' for probe identifiers and 'Gene Symbol' for gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Gene Symbol")
+
+# 2. Convert probe-level expression data to gene-level expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+import os
+import pandas as pd
+
+# STEP 7 (Revised with dummy DataFrame for final 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)
+
+# Because trait_row is None, there's no clinical data to link, so we skip trait-related steps.
+
+# 2) Provide a dummy DataFrame and is_biased flag for final validation
+dummy_df = pd.DataFrame()
+dummy_is_biased = False
+
+# 3) Final validation
+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=dummy_is_biased,
+    df=dummy_df,
+    note="No trait data in GSE71799, only gene expression data."
+)
+
+# 4) Since the dataset is not usable due to no trait data, do not save any linked data.

p1/preprocess/Cystic_Fibrosis/code/GSE76347.py

@@ -0,0 +1,144 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cystic_Fibrosis"
+cohort = "GSE76347"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
+in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE76347"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Cystic_Fibrosis/GSE76347.csv"
+out_gene_data_file = "./output/preprocess/1/Cystic_Fibrosis/gene_data/GSE76347.csv"
+out_clinical_data_file = "./output/preprocess/1/Cystic_Fibrosis/clinical_data/GSE76347.csv"
+json_path = "./output/preprocess/1/Cystic_Fibrosis/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  # Microarray data is mentioned in the background
+
+# 2. Determine availability of trait, age, and gender
+#    and define data conversion functions
+
+# From the sample characteristics dictionary, there is only one unique trait value ("CF"),
+# so it is effectively constant (not useful for association), thus not available.
+trait_row = None  
+
+# No information about age or gender in the dictionary, so set them to None
+age_row = None
+gender_row = None
+
+# Define the data conversion functions
+def convert_trait(x: str):
+    # Since trait_row is None, we won't actually use this, but defining for completeness
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    # If trait were variable, we'd map values accordingly, but it's constant in this dataset
+    return None
+
+def convert_age(x: str):
+    # Since age_row is None, we won't actually use this, but defining for completeness
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    # Normally, parse to a float/int if valid; otherwise None
+    return None
+
+def convert_gender(x: str):
+    # Since gender_row is None, we won't actually use this, but defining for completeness
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    # Typically, 'female' -> 0, 'male' -> 1; else None
+    return None
+
+# 3. Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+#    Only proceed if trait_row is available (not None), 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
+    )
+    print("Preview of extracted clinical features:")
+    print(preview_df(selected_clinical_df))
+    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])
+# Observing the IDs: they appear to be numeric probe identifiers (e.g., from an array platform).
+# These are not standard human gene symbols and likely 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. Identify which columns correspond to the probe IDs and gene symbols in the annotation
+#    - The "ID" column in gene_annotation matches the probe IDs in gene_data
+#    - The "gene_assignment" column contains gene symbol information
+
+# 2. Get a gene mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
+
+# 3. Convert probe-level measurements into gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+import os
+import pandas as pd
+
+# STEP 7
+
+# 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)
+
+# Since we have no trait data (trait_row was None), we skip linking clinical data,
+# missing value handling, bias checks, and final validation for this dataset.
+# The partial validation has already been done previously (is_final=False), indicating
+# that trait data is missing and thus the dataset is not usable for associative studies.

p1/preprocess/Cystic_Fibrosis/code/TCGA.py

@@ -0,0 +1,54 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Cystic_Fibrosis"
+
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Cystic_Fibrosis/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Cystic_Fibrosis/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Cystic_Fibrosis/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Cystic_Fibrosis/cohort_info.json"
+
+import os
+import pandas as pd
+
+# 1. Identify subdirectories under tcga_root_dir
+subdirectories = os.listdir(tcga_root_dir)
+
+# Attempt to locate a subdirectory related to "Cystic_Fibrosis"
+# (Looking for any name containing "cystic" or "fibrosis")
+trait_subdir = None
+for d in subdirectories:
+    lower_d = d.lower().replace('_', ' ')
+    if "cystic" in lower_d and "fibrosis" in lower_d:
+        trait_subdir = d
+        break
+
+# If none found, skip this trait
+if not trait_subdir:
+    print("No suitable subdirectory found for trait 'Cystic_Fibrosis'. Skipping...")
+    is_gene_available = False
+    is_trait_available = False
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=is_gene_available,
+        is_trait_available=is_trait_available
+    )
+else:
+    # 2. Identify paths to the clinical and genetic data files
+    full_subdir_path = os.path.join(tcga_root_dir, trait_subdir)
+    clinical_path, genetic_path = tcga_get_relevant_filepaths(full_subdir_path)
+
+    # 3. Load data into DataFrames
+    clinical_df = pd.read_csv(clinical_path, index_col=0, sep='\t')
+    genetic_df = pd.read_csv(genetic_path, index_col=0, sep='\t')
+
+    # 4. Print the column names of the clinical data for inspection
+    print("Clinical Data Columns:")
+    print(clinical_df.columns.tolist())

p1/preprocess/Cystic_Fibrosis/gene_data/GSE100521.csv

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+size 18338705

p1/preprocess/Cystic_Fibrosis/gene_data/GSE53543.csv

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p1/preprocess/Cystic_Fibrosis/gene_data/GSE67698.csv

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p1/preprocess/Cystic_Fibrosis/gene_data/GSE71799.csv

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+size 25978567

p1/preprocess/Cystic_Fibrosis/gene_data/GSE76347.csv

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p1/preprocess/Depression/clinical_data/GSE110298.csv

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p1/preprocess/Depression/clinical_data/GSE201332.csv

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p1/preprocess/Depression/clinical_data/GSE208668.csv

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p1/preprocess/Depression/clinical_data/GSE99725.csv

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p1/preprocess/Depression/code/GSE110298.py

@@ -0,0 +1,224 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Depression"
+cohort = "GSE110298"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Depression"
+in_cohort_dir = "../DATA/GEO/Depression/GSE110298"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Depression/GSE110298.csv"
+out_gene_data_file = "./output/preprocess/1/Depression/gene_data/GSE110298.csv"
+out_clinical_data_file = "./output/preprocess/1/Depression/clinical_data/GSE110298.csv"
+json_path = "./output/preprocess/1/Depression/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 re
+
+# 1. Gene Expression Data Availability
+is_gene_available = True  # Based on the background info ("microarrays" mentioned, suggesting gene expression data)
+
+# 2. Variable Availability and Data Type Conversion
+
+# 2.1 Data Availability
+# From the sample characteristics dictionary, depression data is at key=6, age at key=2, gender at key=1
+trait_row = 6
+age_row = 2
+gender_row = 1
+
+# 2.2 Data Type Conversion
+
+def convert_trait(value: str):
+    """Convert the depression value (after colon) into a float/int. Unknown -> None."""
+    parts = value.split(":")
+    if len(parts) < 2:
+        return None
+    raw = parts[-1].strip()
+    # Attempt to convert to a float or integer
+    try:
+        return float(raw)
+    except ValueError:
+        return None
+
+def convert_age(value: str):
+    """Convert the age value (after colon) into an integer. Unknown -> None."""
+    parts = value.split(":")
+    if len(parts) < 2:
+        return None
+    raw = parts[-1].strip()
+    try:
+        return int(raw)
+    except ValueError:
+        return None
+
+def convert_gender(value: str):
+    """Convert gender to 0 (female) or 1 (male). Unknown -> None."""
+    parts = value.split(":")
+    if len(parts) < 2:
+        return None
+    raw = parts[-1].strip().lower()
+    if raw == "female":
+        return 0
+    elif raw == "male":
+        return 1
+    else:
+        return None
+
+# 3. Save Metadata (Initial Filtering)
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction (only if trait_row is not None)
+if trait_row is not None:
+    # Suppose 'clinical_data' is the DataFrame we obtained from previous parsing steps
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+
+    # Preview and save
+    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])
+# We observe that the gene identifiers look like Affymetrix probe set IDs rather than
+# standard gene symbols. Therefore, they likely require mapping to gene symbols.
+
+print("The gene identifiers appear to be Affymetrix probe IDs and are not standard 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 & 2. Identify the columns in 'gene_annotation' corresponding to the probe IDs and gene symbols 
+#         and extract them into a mapping dataframe.
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation,
+    prob_col="ID",
+    gene_col="Gene Symbol"
+)
+
+# 3. Convert probe-level measurements to gene expression data using the mapping dataframe.
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+
+# For verification, let's display the new gene_data dimensions and the first few gene symbols.
+print("Gene expression dataframe shape:", gene_data.shape)
+print("First few gene symbols in the processed gene_data index:")
+print(gene_data.index.tolist()[:20])
+import pandas as pd
+
+# STEP 7 (Corrected)
+
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Load the final clinical data generated in Step 2 (it contains the "Depression" row).
+#    This file was saved without an index, so we read it with index_col=0 to restore
+#    the row index as ["Depression", "Age", "Gender"] and sample IDs as columns.
+clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
+
+# 3. Link the clinical and genetic data on sample IDs.
+linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)
+
+# 4. Handle missing values in the linked dataset, including removal and imputation.
+final_data = handle_missing_values(linked_data, trait_col=trait)
+
+# 5. Determine whether the trait and some demographic features in the dataset are severely biased.
+trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)
+
+# 6. Perform final validation and record dataset 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="Trait data is present and has undergone final preprocessing."
+)
+
+# 7. If the dataset is usable, save the final linked data; otherwise, skip.
+if is_usable:
+    final_data.to_csv(out_data_file)
+import pandas as pd
+
+# STEP 8 (Corrected)
+
+# 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)
+
+# 2. Read the final clinical data that was saved in a previous step
+#    It was saved with index=False, so we read it normally and then manually set the row index.
+temp_clinical_df = pd.read_csv(out_clinical_data_file)
+# The dataframe should have shape (3, number_of_samples).
+# Assign the row index to ["Depression", "Age", "Gender"].
+temp_clinical_df.index = ["Depression", "Age", "Gender"]
+
+# 3. Link clinical and genetic data on sample IDs.
+linked_data = geo_link_clinical_genetic_data(temp_clinical_df, normalized_gene_data)
+
+# 4. Handle missing values in the linked dataset.
+final_data = handle_missing_values(linked_data, trait_col=trait)
+
+# 5. Determine whether the trait and some demographic features are severely biased.
+trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)
+
+# 6. Perform final validation and record dataset metadata, indicating trait availability.
+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="Successfully linked and processed GSE110298 with trait='Depression'."
+)
+
+# 7. If the dataset is deemed usable, save the final linked data.
+if is_usable:
+    final_data.to_csv(out_data_file)

p1/preprocess/Depression/code/GSE128387.py

@@ -0,0 +1,106 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Depression"
+cohort = "GSE128387"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Depression"
+in_cohort_dir = "../DATA/GEO/Depression/GSE128387"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Depression/GSE128387.csv"
+out_gene_data_file = "./output/preprocess/1/Depression/gene_data/GSE128387.csv"
+out_clinical_data_file = "./output/preprocess/1/Depression/clinical_data/GSE128387.csv"
+json_path = "./output/preprocess/1/Depression/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  # The dataset uses Affymetrix microarrays, indicating gene expression data.
+
+# 2. Variable Availability and Data Type Conversion
+
+# Looking at the sample characteristics dictionary, we see:
+# 0: ['tissue: Blood']
+# 1: ['illness: Major Depressive Disorder']
+# 2: ['age: 16', 'age: 13', 'age: 12', 'age: 14', 'age: 17', 'age: 15']
+# 3: ['Sex: female', 'Sex: male']
+
+# - The trait "Depression" is constant across all samples (everyone has Major Depressive Disorder).
+#   Hence there is no meaningful variation in the trait for association studies.
+trait_row = None
+
+# - Age is available at row index 2 with multiple distinct values.
+age_row = 2
+
+# - Gender is available at row index 3 with two distinct values.
+gender_row = 3
+
+# Define conversion functions as required:
+def convert_trait(x: str) -> int:
+    """
+    Since 'trait' is effectively unavailable (no variation),
+    we define a placeholder function that returns None.
+    """
+    return None
+
+def convert_age(x: str) -> float:
+    """
+    Convert 'age: XX' to a continuous float.
+    Unknown or invalid values return None.
+    """
+    parts = x.split(':')
+    if len(parts) < 2:
+        return None
+    val_str = parts[1].strip()
+    try:
+        return float(val_str)
+    except ValueError:
+        return None
+
+def convert_gender(x: str) -> int:
+    """
+    Convert 'Sex: male' to 1 and 'Sex: female' to 0.
+    Unknown or invalid values return None.
+    """
+    parts = x.split(':')
+    if len(parts) < 2:
+        return None
+    val_str = parts[1].strip().lower()
+    if val_str == 'female':
+        return 0
+    elif val_str == 'male':
+        return 1
+    else:
+        return None
+
+# 3. Save Metadata (Initial Filtering)
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical Feature Extraction
+# Skip this step because trait_row is None (the trait is not available).

p1/preprocess/Depression/code/GSE135524.py

@@ -0,0 +1,166 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Depression"
+cohort = "GSE135524"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Depression"
+in_cohort_dir = "../DATA/GEO/Depression/GSE135524"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Depression/GSE135524.csv"
+out_gene_data_file = "./output/preprocess/1/Depression/gene_data/GSE135524.csv"
+out_clinical_data_file = "./output/preprocess/1/Depression/clinical_data/GSE135524.csv"
+json_path = "./output/preprocess/1/Depression/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Determine if the dataset has gene expression data
+is_gene_available = True  # Based on the series title indicating "Gene expression"
+
+# 2. Identify data availability for trait, age, and gender
+#    The dataset seems to contain only depressed patients, so no variation in "Depression" => None
+trait_row = None
+age_row = 1
+gender_row = 2
+
+# 2.2 Create conversion functions
+
+def convert_trait(x: str) -> int:
+    """
+    Convert depression trait to some numeric or binary code if available.
+    However, we have concluded there is no variation in the depression trait for this dataset,
+    so we'll just return None for completeness.
+    """
+    return None
+
+def convert_age(x: str) -> Optional[float]:
+    """
+    Parse the age after the colon and convert to float.
+    Unknown values or malformed inputs => None
+    Example: "age: 55" -> 55.0
+    """
+    try:
+        # Extract the substring after "age:"
+        parts = x.split(":", 1)
+        if len(parts) < 2:
+            return None
+        val = parts[1].strip()
+        return float(val)
+    except:
+        return None
+
+def convert_gender(x: str) -> Optional[int]:
+    """
+    Convert gender to binary: female -> 0, male -> 1.
+    Example: "Sex: Male" -> 1, "Sex: Female" -> 0
+    Unknown or malformed -> None
+    """
+    parts = x.split(":", 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val in ["female"]:
+        return 0
+    elif val in ["male"]:
+        return 1
+    return None
+
+# Since 'trait_row' is None, trait is not available (no variation).
+is_trait_available = (trait_row is not None)
+
+# 3. Perform initial filtering and save 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. Because trait_row is None, we skip the clinical feature extraction step.
+#    No further action is needed for substep 4.
+# 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 ILMN_ identifiers are Illumina probe IDs, not human gene symbols
+# 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. Identify the columns in the gene_annotation dataframe that match the probe IDs (in gene_data.index)
+#    and the gene symbols. From the preview, 'ID' stores Illumina probe IDs, and 'Symbol' stores gene symbols.
+probe_col = "ID"
+gene_col = "Symbol"
+
+# 2. Get the gene mapping by extracting the two relevant columns.
+mapping_df = get_gene_mapping(gene_annotation, probe_col, gene_col)
+
+# 3. Convert probe-level measurements to gene-level expression data using the mapping,
+#    distributing each probe's expression equally across its mapped genes.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+import pandas as pd
+
+# STEP7
+
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data on sample IDs, even if the trait is missing.
+linked_data = geo_link_clinical_genetic_data(clinical_data, normalized_gene_data)
+
+# 3. Because the trait is unavailable (trait_row=None), ensure a placeholder column exists for it
+#    so that the missing-value handling and bias checking can still run.
+if trait not in linked_data.columns:
+    linked_data[trait] = None
+
+# 4. Handle missing values (will remove samples missing the trait, which are all samples in this case)
+final_data = handle_missing_values(linked_data, trait_col=trait)
+
+# 5. Determine whether the trait is severely biased (the function requires a trait column).
+trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)
+
+# 6. Perform final validation and record dataset metadata. Since trait is not available,
+#    the dataset will not be marked as usable for trait-based analysis.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,       # We do have gene expression data
+    is_trait_available=False,     # but the trait is absent
+    is_biased=trait_biased,
+    df=final_data,
+    note="The dataset lacks trait data, but partial preprocessing was still performed."
+)
+
+# 7. If the dataset is somehow deemed usable, save the final linked data; otherwise, skip.
+if is_usable:
+    final_data.to_csv(out_data_file)

p1/preprocess/Depression/code/GSE138297.py

@@ -0,0 +1,147 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Depression"
+cohort = "GSE138297"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Depression"
+in_cohort_dir = "../DATA/GEO/Depression/GSE138297"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Depression/GSE138297.csv"
+out_gene_data_file = "./output/preprocess/1/Depression/gene_data/GSE138297.csv"
+out_clinical_data_file = "./output/preprocess/1/Depression/clinical_data/GSE138297.csv"
+json_path = "./output/preprocess/1/Depression/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 microarray analysis mentioned in the summary
+
+# 2.1 Variable availability
+# No row for Depression (trait) found; multiple unique values for age (row 3) and gender (row 1)
+trait_row = None
+age_row = 3
+gender_row = 1
+
+# 2.2 Data type conversion functions
+def convert_trait(value: str):
+    # For this dataset, no trait data is available, but we define the function.
+    return None
+
+def convert_age(value: str):
+    # Example format: "age (yrs): 49"
+    try:
+        val_str = value.split(":", 1)[1].strip()
+        return float(val_str)
+    except:
+        return None
+
+def convert_gender(value: str):
+    # Example format: "sex (female=1, male=0): 1"
+    # We invert: female->0, male->1
+    try:
+        val_str = value.split(":", 1)[1].strip()
+        if val_str == "1":
+            return 0  # female
+        elif val_str == "0":
+            return 1  # male
+        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. Skip clinical feature extraction because trait_row is None
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# These numeric IDs are not standard human gene symbols and likely 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. Decide which key in 'gene_annotation' is the identifier that matches 'gene_data.index', 
+#    and which key stores the gene symbol. 
+#    Based on observation, the 'ID' column matches the probe IDs in 'gene_data.index', 
+#    and the 'gene_assignment' column contains the gene symbols.
+
+# 2. Extract the two columns to create the mapping dataframe.
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="gene_assignment")
+
+# 3. Convert the probe-level data in 'gene_data' to gene-level data using the mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+import pandas as pd
+
+# STEP7
+
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data on sample IDs, even if the trait is missing.
+linked_data = geo_link_clinical_genetic_data(clinical_data, normalized_gene_data)
+
+# 3. Because the trait is unavailable (trait_row=None), ensure a placeholder column exists for it
+#    so that the missing-value handling and bias checking can still run.
+if trait not in linked_data.columns:
+    linked_data[trait] = None
+
+# 4. Handle missing values (will remove samples missing the trait, which are all samples in this case)
+final_data = handle_missing_values(linked_data, trait_col=trait)
+
+# 5. Determine whether the trait is severely biased (the function requires a trait column).
+trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)
+
+# 6. Perform final validation and record dataset metadata. Since trait is not available,
+#    the dataset will not be marked as usable for trait-based analysis.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,       # We do have gene expression data
+    is_trait_available=False,     # but the trait is absent
+    is_biased=trait_biased,
+    df=final_data,
+    note="The dataset lacks trait data, but partial preprocessing was still performed."
+)
+
+# 7. If the dataset is somehow deemed usable, save the final linked data; otherwise, skip.
+if is_usable:
+    final_data.to_csv(out_data_file)

p1/preprocess/Depression/code/GSE149980.py

@@ -0,0 +1,125 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Depression"
+cohort = "GSE149980"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Depression"
+in_cohort_dir = "../DATA/GEO/Depression/GSE149980"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Depression/GSE149980.csv"
+out_gene_data_file = "./output/preprocess/1/Depression/gene_data/GSE149980.csv"
+out_clinical_data_file = "./output/preprocess/1/Depression/clinical_data/GSE149980.csv"
+json_path = "./output/preprocess/1/Depression/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Determine if the dataset contains suitable gene expression data
+is_gene_available = True  # The series background indicates whole gene expression profiling
+
+# 2. Identify variable availability from the sample characteristics
+#    (All participants are depressed; there's no variation for 'Depression' as a trait here)
+trait_row = None
+age_row = None
+gender_row = None
+
+# 2.2 Define data type conversion functions
+def convert_trait(value: str):
+    return None  # No usable variation for the 'Depression' trait
+
+def convert_age(value: str):
+    return None  # Age data not available
+
+def convert_gender(value: str):
+    return None  # Gender data not available
+
+# 3. Save initial metadata
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. Clinical feature extraction is skipped because `trait_row` is None
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Observing the given identifiers, they appear to be microarray probe IDs or synthetic constructs,
+# not standard human gene symbols. Therefore, they require gene symbol 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))
+# 1. Define which columns correspond to the probe IDs and which correspond to gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="GENE_SYMBOL")
+
+# 2. Convert probe-level measurements to gene-level by applying the mapping
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+import pandas as pd
+
+# STEP7
+
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Link the clinical and genetic data on sample IDs, even if the trait is missing.
+linked_data = geo_link_clinical_genetic_data(clinical_data, normalized_gene_data)
+
+# 3. Because the trait is unavailable (trait_row=None), ensure a placeholder column exists for it
+#    so that the missing-value handling and bias checking can still run.
+if trait not in linked_data.columns:
+    linked_data[trait] = None
+
+# 4. Handle missing values (will remove samples missing the trait, which are all samples in this case)
+final_data = handle_missing_values(linked_data, trait_col=trait)
+
+# 5. Determine whether the trait is severely biased (the function requires a trait column).
+trait_biased, final_data = judge_and_remove_biased_features(final_data, trait)
+
+# 6. Perform final validation and record dataset metadata. Since trait is not available,
+#    the dataset will not be marked as usable for trait-based analysis.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,       # We do have gene expression data
+    is_trait_available=False,     # but the trait is absent
+    is_biased=trait_biased,
+    df=final_data,
+    note="The dataset lacks trait data, but partial preprocessing was still performed."
+)
+
+# 7. If the dataset is somehow deemed usable, save the final linked data; otherwise, skip.
+if is_usable:
+    final_data.to_csv(out_data_file)

p1/preprocess/Depression/code/GSE201332.py

@@ -0,0 +1,241 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Depression"
+cohort = "GSE201332"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Depression"
+in_cohort_dir = "../DATA/GEO/Depression/GSE201332"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Depression/GSE201332.csv"
+out_gene_data_file = "./output/preprocess/1/Depression/gene_data/GSE201332.csv"
+out_clinical_data_file = "./output/preprocess/1/Depression/clinical_data/GSE201332.csv"
+json_path = "./output/preprocess/1/Depression/cohort_info.json"
+
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Determine if gene expression data is available
+is_gene_available = True  # Based on "Transcriptional profiling" from the series description
+
+# Step 2.1: Identify variable availability
+trait_row = 1   # "subject status: healthy controls" vs. "subject status: MDD patients"
+age_row = 3     # "age: 48y", "age: 33y", etc.
+gender_row = 2  # "gender: male", "gender: female"
+
+# Step 2.2: Define conversion functions
+def convert_trait(x: str):
+    if ":" in x:
+        val = x.split(":", 1)[1].strip().lower()
+        if "heathy" in val or "healthy" in val:
+            return 0
+        elif "mdd" in val:
+            return 1
+    return None
+
+def convert_age(x: str):
+    if ":" in x:
+        val = x.split(":", 1)[1].strip().lower().replace("y", "")
+        try:
+            return float(val)
+        except:
+            return None
+    return None
+
+def convert_gender(x: str):
+    if ":" in x:
+        val = x.split(":", 1)[1].strip().lower()
+        if "female" in val:
+            return 0
+        elif "male" in val:
+            return 1
+    return None
+
+# Step 3: Initial filtering and saving metadata
+is_trait_available = (trait_row is not None)
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# Step 4: Clinical feature extraction if trait is available
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    print("Preview of selected clinical features:")
+    print(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])
+# The provided gene identifiers ("1", "2", "3", ...) are numeric indices, which are not standard human gene symbols.
+# Hence, they must 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. From the preview, the "ID" column in the gene_annotation matches the gene expression "ID" from gene_data.
+#    The "NAME" column in gene_annotation appears to store the gene symbol (though it contains corner controls in the preview).
+
+# 2. Get the gene mapping dataframe
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='NAME')
+
+# 3. Convert probe-level measurements into gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+import pandas as pd
+
+# STEP 7
+
+# 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)
+
+# 2. Read the clinical data (which was saved with index=False) and reconstruct its original shape,
+#    i.e. row index = ['Depression','Age','Gender'] and columns = [sample IDs].
+raw_df = pd.read_csv(out_clinical_data_file, header=None)
+# Use the first row as column names
+raw_df.columns = raw_df.iloc[0]
+# Drop the first row (already used as header)
+raw_df = raw_df.drop(raw_df.index[0])
+# Now the first column contains the row labels 'Depression', 'Age', 'Gender'
+raw_df = raw_df.set_index(raw_df.columns[0])
+# Drop that column so that only sample IDs remain as columns
+selected_clinical_df = raw_df.drop(raw_df.columns[0], axis=1)
+
+# Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 3. Handle missing values (drop missing trait, remove high-missing genes/samples, impute)
+linked_data = handle_missing_values(linked_data, trait_col=trait)
+
+# 4. Determine if the trait or demographic features are severely biased
+is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 5. Final quality validation and metadata recording
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=linked_data,
+    note="All steps completed successfully."
+)
+
+# 6. If the dataset is usable, save the final linked data
+if is_usable:
+    linked_data.to_csv(out_data_file)
+import pandas as pd
+
+# STEP 8
+
+# 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)
+
+# 2. Read the clinical data that was saved with index=False, using the first column as index ('Depression','Age','Gender'),
+#    and the remaining columns as sample IDs.
+selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)  # Row index will be ['Depression','Age','Gender']
+#    The columns should match the sample IDs.
+
+# 3. Link clinical and gene expression data
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+
+# 4. Handle missing values (drop missing trait, remove high-missing genes/samples, impute)
+linked_data = handle_missing_values(linked_data, trait_col=trait)
+
+# 5. Determine if the trait or demographic features are severely biased, removing biased demographics
+is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 6. Final quality validation and metadata recording
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=linked_data,
+    note="All steps completed successfully."
+)
+
+# 7. If the dataset is deemed usable, save the final linked data
+if is_usable:
+    linked_data.to_csv(out_data_file)
+import pandas as pd
+
+# STEP 9
+
+# 1. Normalize the gene data using synonyms and save it.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Read the clinical data that was saved with index=False. 
+#    The first row in the CSV is the sample IDs, and the next rows are the values for [trait, Age, Gender].
+#    Restore the row index to [trait, "Age", "Gender"] so columns become sample IDs.
+clinical_df = pd.read_csv(out_clinical_data_file, header=0)
+clinical_df.index = [trait, "Age", "Gender"]
+
+# 3. Link clinical and gene expression data by sample (column) alignment.
+linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)
+
+# 4. Handle missing values (remove samples with missing trait, remove genes/samples with high missingness, then impute).
+linked_data = handle_missing_values(linked_data, trait_col=trait)
+
+# 5. Determine if the trait or demographic features are severely biased, dropping biased demographics if necessary.
+is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 6. Final quality validation and metadata recording.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=is_trait_biased,
+    df=linked_data,
+    note="All steps completed successfully."
+)
+
+# 7. If the dataset is deemed usable, save the final linked data.
+if is_usable:
+    linked_data.to_csv(out_data_file)

p1/preprocess/Depression/code/GSE208668.py

@@ -0,0 +1,223 @@
+# Path Configuration
+from tools.preprocess import *
+
+# Processing context
+trait = "Depression"
+cohort = "GSE208668"
+
+# Input paths
+in_trait_dir = "../DATA/GEO/Depression"
+in_cohort_dir = "../DATA/GEO/Depression/GSE208668"
+
+# Output paths
+out_data_file = "./output/preprocess/1/Depression/GSE208668.csv"
+out_gene_data_file = "./output/preprocess/1/Depression/gene_data/GSE208668.csv"
+out_clinical_data_file = "./output/preprocess/1/Depression/clinical_data/GSE208668.csv"
+json_path = "./output/preprocess/1/Depression/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
+# Based on the series description ("Genome-wide transcriptional profiling results"), we treat it as gene expression data
+is_gene_available = True
+
+# 2. Identify availability of variables and define their row keys
+# Our trait of interest is "Depression", and we see "history of depression: yes/no" in row 9.
+# Hence trait_row=9. Age is in row 1. Gender is in row 2.
+trait_row = 9
+age_row = 1
+gender_row = 2
+
+# 2.2 Define data type conversion functions
+def convert_trait(value):
+    if not value or ':' not in value:
+        return None
+    val = value.split(':', 1)[1].strip().lower()
+    if val == 'yes':
+        return 1
+    elif val == 'no':
+        return 0
+    else:
+        return None
+
+def convert_age(value):
+    if not value or ':' not in value:
+        return None
+    val = value.split(':', 1)[1].strip()
+    try:
+        return float(val)
+    except:
+        return None
+
+def convert_gender(value):
+    if not value or ':' not in value:
+        return None
+    val = value.split(':', 1)[1].strip().lower()
+    if val == 'female':
+        return 0
+    elif val == 'male':
+        return 1
+    else:
+        return None
+
+# 3. Conduct initial filtering on dataset usability and save metadata
+is_trait_available = (trait_row is not None)
+
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+
+# 4. If trait data is available, extract clinical features
+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
+    )
+
+    # Preview and save clinical data
+    preview_output = preview_df(df_clinical)
+    print("Preview of extracted clinical features:")
+    print(preview_output)
+
+    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 observation, some identifiers like "7A5" appear to be alternative or outdated gene symbols.
+# To ensure consistent and up-to-date identifiers, gene mapping is needed.
+
+requires_gene_mapping = True
+print(f"requires_gene_mapping = {requires_gene_mapping}")
+# 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 match the gene identifiers in the expression data and which contain the gene symbols.
+#    From the annotation preview, "ID" matches the identifiers in gene_data's index, and "ORF" contains the gene symbols.
+
+# 2. Get a gene mapping dataframe by selecting "ID" and "ORF" from the gene annotation.
+mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='ORF')
+
+# 3. Convert probe-level measurements to gene expression data by applying the mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+
+# For verification of shape or quick sanity check, we print the shape of the resulting gene_data dataframe
+print("Mapped gene_data shape:", gene_data.shape)
+import pandas as pd
+
+# STEP7
+
+# 1. Normalize gene symbols in the gene expression data and save the result.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Read the clinical feature data. Since it was saved with index=False (causing the first row to become headers),
+#    we specify header=0 and then set "Unnamed: 0" as the index. This recovers rows like "Depression", "Age", "Gender".
+df_temp = pd.read_csv(out_clinical_data_file, header=0)
+df_temp.set_index("Unnamed: 0", inplace=True)
+df_clinical = df_temp
+
+# 3. Link the clinical data and gene data.
+linked_data = geo_link_clinical_genetic_data(df_clinical, normalized_gene_data)
+
+# 4. Handle missing values systematically (drop missing trait, drop genes >20% missing, etc.).
+linked_data = handle_missing_values(linked_data, trait)
+
+# 5. Judge and remove biased features. If the trait is biased, we record it.
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 6. Conduct final validation and save cohort info.
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="All steps completed for GSE208668."
+)
+
+# 7. If usable, save the final linked data.
+if is_usable:
+    linked_data.to_csv(out_data_file)
+import pandas as pd
+
+# STEP8
+
+# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+
+# 2. Read the clinical CSV. Since we saved it with index=False, the trait, age, and gender rows
+#    are now just 3 data rows. Hence we'll read without specifying an index, and then manually
+#    assign the correct row labels.
+df_clinical = pd.read_csv(out_clinical_data_file)
+# The DataFrame should now have shape (3, number_of_samples), with the columns being sample IDs
+# and the row indices just 0,1,2. We rename them to [trait, "Age", "Gender"].
+if df_clinical.shape[0] == 3:
+    df_clinical.index = [trait, "Age", "Gender"]
+else:
+    # If we unexpectedly have more or fewer rows, you may need to adjust this code.
+    raise ValueError(f"Unexpected shape in clinical data: {df_clinical.shape}")
+
+# 3. Link the clinical and genetic data on sample IDs.
+linked_data = geo_link_clinical_genetic_data(df_clinical, normalized_gene_data)
+
+# 4. Handle missing values systematically.
+#    This requires the trait to be a column in linked_data; with our structure,
+#    'Depression' is indeed a column after transposing in geo_link_clinical_genetic_data.
+linked_data = handle_missing_values(linked_data, trait)
+
+# 5. Determine whether the trait is severely biased; remove biased demographic covariates.
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+
+# 6. Conduct final quality validation and save cohort 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=linked_data,
+    note="All steps completed for GSE208668."
+)
+
+# 7. If the dataset is usable, save the final linked data.
+if is_usable:
+    linked_data.to_csv(out_data_file)