Datasets:

Liu-Hy
/

GenoTEX

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+p1/preprocess/Von_Willebrand_Disease/gene_data/TCGA.csv filter=lfs diff=lfs merge=lfs -text

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p1/preprocess/Arrhythmia/code/GSE136992.py ADDED Viewed

	@@ -0,0 +1,173 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Arrhythmia"
+cohort = "GSE136992"
+# Input paths
+in_trait_dir = "../DATA/GEO/Arrhythmia"
+in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE136992"
+# Output paths
+out_data_file = "./output/preprocess/1/Arrhythmia/GSE136992.csv"
+out_gene_data_file = "./output/preprocess/1/Arrhythmia/gene_data/GSE136992.csv"
+out_clinical_data_file = "./output/preprocess/1/Arrhythmia/clinical_data/GSE136992.csv"
+json_path = "./output/preprocess/1/Arrhythmia/cohort_info.json"
+# STEP 1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    background_prefixes,
+    clinical_prefixes
+)
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Determine gene expression data availability
+is_gene_available = True  # This dataset includes Illumina whole genome gene expression data
+# 2.1 Determine data availability (keys for trait, age, gender)
+trait_row = None  # No row found for "Arrhythmia" in the sample characteristics
+age_row = 2       # Row 2 contains multiple distinct 'age' values
+gender_row = 3    # Row 3 contains multiple distinct 'gender' values
+# 2.2 Define data type conversions
+def convert_trait(value: str):
+    # Trait data is not available; return None
+    return None
+def convert_age(value: str):
+    """Convert 'age: XX weeks' to a numeric type."""
+    try:
+        # Extract the substring after 'age:' and strip spaces
+        raw = value.split(':', 1)[1].strip()
+        # Remove the word 'weeks' if present
+        raw = raw.lower().replace('weeks', '').strip()
+        return float(raw)
+    except:
+        return None
+def convert_gender(value: str):
+    """Convert 'gender: male/female' to binary (0=female, 1=male)."""
+    try:
+        raw = value.split(':', 1)[1].strip().lower()
+        if raw == 'male':
+            return 1
+        elif raw == 'female':
+            return 0
+        else:
+            return None
+    except:
+        return None
+# 3. Conduct initial filtering and save metadata
+is_trait_available = (trait_row is not None)
+validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+# 4. Skip clinical feature extraction, since 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 identifiers, starting with "ILMN_", are Illumina probe IDs rather than standard gene symbols.
+# Therefore, they need to be mapped to the corresponding human gene symbols.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+# 1. Decide which columns hold the probe IDs (same as gene_data.index) and the gene symbols.
+#    From the annotation preview, "ID" matches the probe IDs, and "Symbol" contains the gene symbols.
+# 2. Get a gene mapping dataframe (probe ID -> gene symbol).
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Symbol")
+# 3. Convert the probe-level expression data to gene-level data using the mapping.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP 7: Data Normalization and Linking
+# First, check if trait data is available.
+# From our previous steps, we know 'trait_row' was None, so trait data is not available.
+# Hence, we skip linking, missing-value handling, and bias checks,
+# but we still need to do final validation to mark it unusable.
+if not is_trait_available:
+    import pandas as pd
+    print("Trait data is not available. Skipping link, missing-value handling, and bias checks.")
+    # Provide a boolean for is_biased to avoid the ValueError in final validation.
+    # The dataset is not usable because the trait is missing, so we can set is_biased=True.
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,     # gene data is available,
+        is_trait_available=False,   # but trait data is missing
+        is_biased=True,            # no valid trait -> not usable
+        df=pd.DataFrame(),          # an empty DataFrame suffices here
+        note="Trait data not found; dataset is not usable."
+    )
+    print("Dataset was not deemed usable due to missing trait data; final linked data not saved.")
+else:
+    # 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)
+    print(f"Saved normalized gene data to {out_gene_data_file}")
+    # 2. Link the clinical and genetic data on sample IDs (requires clinical data from step 2)
+    linked_data = geo_link_clinical_genetic_data(selected_clinical_data, normalized_gene_data)
+    # 3. Handle missing values in linked data
+    linked_data = handle_missing_values(linked_data, trait_col=trait)
+    # 4. Determine bias
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait=trait)
+    # 5. 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=True,
+        is_biased=trait_biased,
+        df=linked_data,
+        note="Trait data and gene data successfully linked."
+    )
+    # 6. Save final linked data if usable
+    if is_usable:
+        linked_data.to_csv(out_data_file)
+        print(f"Saved final linked data to {out_data_file}")
+    else:
+        print("Dataset was not deemed usable; final linked data not saved.")

p1/preprocess/Arrhythmia/code/GSE143924.py ADDED Viewed

	@@ -0,0 +1,132 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Arrhythmia"
+cohort = "GSE143924"
+# Input paths
+in_trait_dir = "../DATA/GEO/Arrhythmia"
+in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE143924"
+# Output paths
+out_data_file = "./output/preprocess/1/Arrhythmia/GSE143924.csv"
+out_gene_data_file = "./output/preprocess/1/Arrhythmia/gene_data/GSE143924.csv"
+out_clinical_data_file = "./output/preprocess/1/Arrhythmia/clinical_data/GSE143924.csv"
+json_path = "./output/preprocess/1/Arrhythmia/cohort_info.json"
+# STEP 1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    background_prefixes,
+    clinical_prefixes
+)
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Assess Gene Expression Data Availability
+is_gene_available = True  # "Transcriptome analysis" indicates gene expression data is available.
+# 2) Identify Variable Availability (trait, age, gender) and Define Converters
+trait_row = 1
+age_row = None
+gender_row = None
+def convert_trait(value: str):
+    parts = value.split(':')
+    val = parts[1].strip() if len(parts) > 1 else parts[0].strip()
+    # Map sinus rhythm => 0, atrial fibrillation => 1, otherwise None
+    if 'sinus rhythm' in val.lower():
+        return 0
+    elif 'atrial fibrillation' in val.lower():
+        return 1
+    return None
+def convert_age(value: str):
+    return None  # No age data available
+def convert_gender(value: str):
+    return None  # No gender data available
+# 3) Initial Filtering 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 and save
+if trait_row is not None:
+    selected_clinical_data = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    print("Preview of Selected Clinical Features:")
+    print(preview_df(selected_clinical_data, n=5, max_items=200))
+    selected_clinical_data.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the observed gene identifiers, they appear to be recognized human gene symbols or their aliases.
+requires_gene_mapping = False
+# STEP 5: Data Normalization and Linking
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print(f"Saved normalized gene data to {out_gene_data_file}")
+# 2. Link the clinical and genetic data on sample IDs
+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_col=trait)
+# 4. Determine whether the trait/demographic features are severely biased
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait=trait)
+# 5. Conduct final quality validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Trait data and gene data successfully linked."
+)
+# 6. If the dataset is deemed usable, save the final linked data as a CSV file
+if is_usable:
+    linked_data.to_csv(out_data_file)
+    print(f"Saved final linked data to {out_data_file}")
+else:
+    print("Dataset was not deemed usable; final linked data not saved.")

p1/preprocess/Arrhythmia/code/GSE182600.py ADDED Viewed

	@@ -0,0 +1,179 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Arrhythmia"
+cohort = "GSE182600"
+# Input paths
+in_trait_dir = "../DATA/GEO/Arrhythmia"
+in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE182600"
+# Output paths
+out_data_file = "./output/preprocess/1/Arrhythmia/GSE182600.csv"
+out_gene_data_file = "./output/preprocess/1/Arrhythmia/gene_data/GSE182600.csv"
+out_clinical_data_file = "./output/preprocess/1/Arrhythmia/clinical_data/GSE182600.csv"
+json_path = "./output/preprocess/1/Arrhythmia/cohort_info.json"
+# STEP 1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    background_prefixes,
+    clinical_prefixes
+)
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Determine if gene expression data is available
+is_gene_available = True  # Based on the background info describing "genome-wide gene expression"
+# Step 2: Identify trait/age/gender rows and define data conversion functions
+trait_row = 0    # Row containing disease states, including "Arrhythmia"
+age_row = 1      # Row containing age
+gender_row = 2   # Row containing gender
+def convert_trait(value: str) -> int:
+    """
+    Convert the 'disease state' string to a binary value.
+    1 if it indicates 'Arrhythmia', else 0.
+    """
+    # Extract the part after "disease state:"
+    parts = value.split(":")
+    if len(parts) < 2:
+        return None
+    disease_str = parts[1].strip().lower()
+    return 1 if disease_str == "arrhythmia" else 0
+def convert_age(value: str) -> float:
+    """
+    Convert the 'age' string to a float.
+    Return None if conversion fails.
+    """
+    parts = value.split(":")
+    if len(parts) < 2:
+        return None
+    try:
+        return float(parts[1].strip())
+    except ValueError:
+        return None
+def convert_gender(value: str) -> int:
+    """
+    Convert the 'gender' string to a binary value.
+    Female -> 0, Male -> 1, None if unknown.
+    """
+    parts = value.split(":")
+    if len(parts) < 2:
+        return None
+    gender_str = parts[1].strip().lower()
+    if gender_str == "f":
+        return 0
+    elif gender_str == "m":
+        return 1
+    else:
+        return None
+# Step 3: Determine if trait data is available
+is_trait_available = (trait_row is not None)
+# Perform initial filtering and save metadata
+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: If trait is available, extract clinical features and save
+if trait_row is not None:
+    # Assume 'clinical_data' is already loaded as a DataFrame in the environment
+    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 dataframe:", 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 listed identifiers (e.g., "ILMN_...") are Illumina probe IDs, not standard human gene symbols.
+# Therefore, they require mapping to gene symbols.
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+# 1. Identify which columns from the gene_annotation match the gene expression IDs and the gene symbols
+prob_col = "ID"       # column in gene_annotation matching the probe ID (e.g., "ILMN_...")
+symbol_col = "Symbol" # column in gene_annotation storing the gene symbol
+# 2. Generate a mapping dataframe using the library function
+mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=symbol_col)
+# 3. Apply the mapping to convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP 7: Data Normalization and Linking
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print(f"Saved normalized gene data to {out_gene_data_file}")
+# 2. Link the clinical and genetic data on sample IDs
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+# 3. Handle missing values in the linked data
+linked_data = handle_missing_values(linked_data, trait_col="Trait")
+# 4. Determine whether the trait/demographic features are severely biased
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait="Trait")
+# 5. Conduct final quality validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Trait data and gene data successfully linked."
+)
+# 6. If the dataset is deemed usable, save the final linked data as a CSV file
+if is_usable:
+    linked_data.to_csv(out_data_file)
+    print(f"Saved final linked data to {out_data_file}")
+else:
+    print("Dataset was not deemed usable; final linked data not saved.")

p1/preprocess/Arrhythmia/code/GSE235307.py ADDED Viewed

	@@ -0,0 +1,199 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Arrhythmia"
+cohort = "GSE235307"
+# Input paths
+in_trait_dir = "../DATA/GEO/Arrhythmia"
+in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE235307"
+# Output paths
+out_data_file = "./output/preprocess/1/Arrhythmia/GSE235307.csv"
+out_gene_data_file = "./output/preprocess/1/Arrhythmia/gene_data/GSE235307.csv"
+out_clinical_data_file = "./output/preprocess/1/Arrhythmia/clinical_data/GSE235307.csv"
+json_path = "./output/preprocess/1/Arrhythmia/cohort_info.json"
+# STEP 1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    background_prefixes,
+    clinical_prefixes
+)
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+# Based on the series summary stating “Gene expression ...”, we set is_gene_available=True.
+is_gene_available = True
+# 2. Variable Availability and Data Type Conversion
+# 2.1 Assign row keys if data is available and non-constant.
+# Observing the sample characteristics, we identify:
+#  - trait_row: 5 (where we see "Atrial fibrillation" vs "Sinus rhythm")
+#  - age_row: 2 (ages vary)
+#  - gender_row: 1 (male/female are present)
+trait_row = 5
+age_row = 2
+gender_row = 1
+# 2.2 Define the conversion functions
+def convert_trait(value: str) -> Optional[int]:
+    """Convert 'cardiac rhythm after 1 year follow-up' to binary (0 or 1)."""
+    # Extract the substring after colon
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()  # e.g. 'sinus rhythm', 'atrial fibrillation'
+    if val == 'sinus rhythm':
+        return 0
+    elif val == 'atrial fibrillation':
+        return 1
+    else:
+        return None
+def convert_age(value: str) -> Optional[float]:
+    """Convert the age string to float."""
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip()
+    try:
+        return float(val)
+    except ValueError:
+        return None
+def convert_gender(value: str) -> Optional[int]:
+    """Convert gender to binary (0 for Female, 1 for Male)."""
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == 'male':
+        return 1
+    elif val == 'female':
+        return 0
+    else:
+        return None
+# 3. Save Metadata using 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_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Preview the selected clinical features
+    preview_result = preview_df(selected_clinical_df)
+    print("Preview of selected clinical features:", preview_result)
+    # Save the clinical features 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])
+# Observing the given identifiers (e.g., '4', '5', '6', etc.), they do not match typical human gene symbols.
+# Therefore, they likely need to be mapped to recognized gene symbols.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+# 1. Identify columns in the gene_annotation dataframe corresponding to the probe IDs (matching gene_data.index)
+#    and the gene symbols.
+probe_id_column = "ID"
+gene_symbol_column = "GENE_SYMBOL"
+# 2. Get a gene mapping dataframe from the gene annotation
+mapping_df = get_gene_mapping(
+    gene_annotation,
+    prob_col=probe_id_column,
+    gene_col=gene_symbol_column
+)
+# 3. Convert probe-level measurements to gene-level expression data using the mapping
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+import pandas as pd
+# STEP 7: Data Normalization and Linking
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print(f"Saved normalized gene data to {out_gene_data_file}")
+# 2. Read the clinical DataFrame in a way that preserves the three rows (Arrhythmia, Age, Gender)
+#    and interprets the first CSV row as the sample ID columns.
+clinical_df = pd.read_csv(out_clinical_data_file, header=0)
+# We know there are exactly 3 rows of data: [0]: Arrhythmia, [1]: Age, [2]: Gender
+clinical_df.index = [trait, "Age", "Gender"]
+# 3. Link the clinical and genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)
+# 4. Handle missing values
+linked_data = handle_missing_values(linked_data, trait)
+# 5. Check for bias in the trait and remove any biased demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+# 6. Perform final validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Trait data is available; completed linking and preprocessing."
+)
+# 7. If the dataset is usable, save the final linked data
+if is_usable:
+    linked_data.to_csv(out_data_file, index=True)
+    print(f"Saved linked data to {out_data_file}")
+else:
+    print("The dataset is not usable; skipping final data output.")

p1/preprocess/Arrhythmia/code/GSE55231.py ADDED Viewed

	@@ -0,0 +1,172 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Arrhythmia"
+cohort = "GSE55231"
+# Input paths
+in_trait_dir = "../DATA/GEO/Arrhythmia"
+in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE55231"
+# Output paths
+out_data_file = "./output/preprocess/1/Arrhythmia/GSE55231.csv"
+out_gene_data_file = "./output/preprocess/1/Arrhythmia/gene_data/GSE55231.csv"
+out_clinical_data_file = "./output/preprocess/1/Arrhythmia/clinical_data/GSE55231.csv"
+json_path = "./output/preprocess/1/Arrhythmia/cohort_info.json"
+# STEP 1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    background_prefixes,
+    clinical_prefixes
+)
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Determine if gene expression data is available
+is_gene_available = True  # Based on study description (eQTL analysis, transcription profiling)
+# 2. Identify variable availability
+# Trait "Arrhythmia" is not listed in the sample characteristics, so treat it as not available.
+trait_row = None
+# Age is provided under key 2
+age_row = 2
+# Gender is provided under key 0
+gender_row = 0
+# 2.2 Define conversion functions
+def convert_trait(value: str):
+    # Trait data is not available. Return None for all inputs.
+    return None
+def convert_age(value: str):
+    # Parse the string after colon and convert to float if possible
+    parts = value.split(':', 1)
+    raw = parts[1].strip() if len(parts) > 1 else parts[0].strip()
+    try:
+        return float(raw)
+    except ValueError:
+        return None
+def convert_gender(value: str):
+    # Parse the string after colon and convert to binary (female=0, male=1)
+    parts = value.split(':', 1)
+    raw = parts[1].strip().lower() if len(parts) > 1 else parts[0].strip().lower()
+    if raw == 'female':
+        return 0
+    elif raw == 'male':
+        return 1
+    return None
+# 3. Initial usability filtering and metadata saving
+is_trait_available = (trait_row is not None)
+cohort_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, 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 observation, the "ILMN_" prefix indicates Illumina probe IDs, not standard human gene symbols.
+# Therefore, they require mapping to gene symbols.
+print("These identifiers are Illumina probe IDs.\nrequires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+# 1. Identify the columns in gene_annotation that match the probe ID and gene symbol
+probe_col = 'ID'
+gene_symbol_col = 'Symbol'
+# 2. Create the gene mapping dataframe
+gene_mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+# 3. Convert probe-level measurements to gene-level expression data
+gene_data = apply_gene_mapping(gene_data, gene_mapping_df)
+# Just for a brief preview, let's check the resulting shape
+print("Mapped gene_data shape:", gene_data.shape)
+# STEP 7: Data Normalization and Linking
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print(f"Saved normalized gene data to {out_gene_data_file}")
+# 2. Check if we have a clinical dataframe called 'selected_clinical_df' (which only exists if trait_row was not None)
+if 'selected_clinical_df' in globals():
+    # We have trait data, so we can link and proceed with the final steps.
+    selected_clinical = selected_clinical_df
+    # 3. Link the clinical and genetic data on sample IDs
+    linked_data = geo_link_clinical_genetic_data(selected_clinical, normalized_gene_data)
+    # 4. Handle missing values, removing or imputing as instructed
+    linked_data = handle_missing_values(linked_data, trait)
+    # 5. Determine whether the trait is severely biased.
+    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+    # 6. Conduct final quality validation and save metadata
+    is_usable = validate_and_save_cohort_info(
+        is_final=True,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=True,
+        is_biased=trait_biased,
+        df=linked_data,
+        note="Cohort data successfully processed with trait-based analysis."
+    )
+    # 7. If the dataset is usable, save the final linked data
+    if is_usable:
+        linked_data.to_csv(out_data_file, index=True)
+        print(f"Saved final linked data to {out_data_file}")
+    else:
+        print("The dataset is not usable for trait-based association. Skipping final output.")
+else:
+    # Trait data was not extracted in Step 2 (trait_row was None), so no clinical linking or bias checks.
+    print("No trait data found. Skipping linking, missing value handling, and trait bias analysis.")
+    # Perform an initial metadata save (not final) since we lack a trait.
+    is_usable = validate_and_save_cohort_info(
+        is_final=False,
+        cohort=cohort,
+        info_path=json_path,
+        is_gene_available=True,
+        is_trait_available=False
+    )
+    # Without trait data, this dataset won't move forward to final association analysis
+    print("No final output generated due to missing trait data.")

p1/preprocess/Arrhythmia/code/GSE93101.py ADDED Viewed

	@@ -0,0 +1,181 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Arrhythmia"
+cohort = "GSE93101"
+# Input paths
+in_trait_dir = "../DATA/GEO/Arrhythmia"
+in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE93101"
+# Output paths
+out_data_file = "./output/preprocess/1/Arrhythmia/GSE93101.csv"
+out_gene_data_file = "./output/preprocess/1/Arrhythmia/gene_data/GSE93101.csv"
+out_clinical_data_file = "./output/preprocess/1/Arrhythmia/clinical_data/GSE93101.csv"
+json_path = "./output/preprocess/1/Arrhythmia/cohort_info.json"
+# STEP 1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    background_prefixes,
+    clinical_prefixes
+)
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Decide whether gene expression data is available
+# From the background information, this submission represents transcriptome data.
+is_gene_available = True
+# 2. Identify rows for trait, age, and gender, and define conversion functions.
+#   - trait_row, age_row, gender_row
+trait_row = 0  # "course:" with multiple diseases listed, including "Arrhythmia"
+age_row = 1    # "age:"
+gender_row = 2 # "gender:"
+def convert_trait(value: str) -> Optional[int]:
+    """Convert the 'course' field to a binary variable: 1 if Arrhythmia, 0 otherwise."""
+    try:
+        # Example: "course: Arrhythmia"
+        val = value.split(":")[1].strip().lower()
+        return 1 if val == "arrhythmia" else 0
+    except IndexError:
+        return None
+def convert_age(value: str) -> Optional[float]:
+    """Convert the 'age' field to a float."""
+    try:
+        # Example: "age: 55.8"
+        val = value.split(":")[1].strip()
+        return float(val)
+    except (IndexError, ValueError):
+        return None
+def convert_gender(value: str) -> Optional[int]:
+    """Convert the 'gender' field to 0 (Female) or 1 (Male)."""
+    try:
+        # Example: "gender: F"
+        val = value.split(":")[1].strip().lower()
+        if val == "f":
+            return 0
+        elif val == "m":
+            return 1
+        else:
+            return None
+    except IndexError:
+        return None
+# 3. Perform initial filtering and save metadata
+#    Trait data availability is inferred from 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. If the trait data is available, extract and preview clinical features
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_data,          # Assume 'clinical_data' is our previously obtained pandas DataFrame
+        trait,                  # Global variable: "Arrhythmia"
+        trait_row,
+        convert_trait,
+        age_row,
+        convert_age,
+        gender_row,
+        convert_gender
+    )
+    # Preview the extracted clinical features
+    preview = preview_df(selected_clinical_df, n=5)
+    print(preview)
+    # Save the clinical features to file
+    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 provided identifiers (e.g., "ILMN_1651209", "ILMN_1651228"), they appear to be Illumina probe IDs, not standard human gene symbols.
+# Therefore, mapping to gene symbols is needed.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+# 1. Decide which columns correspond to the probe ID and the gene symbol.
+#    From the preview, the 'ID' column in 'gene_annotation' matches the expression data's row index,
+#    and the 'Symbol' column appears to store the gene symbol.
+probe_id_col = "ID"
+gene_symbol_col = "Symbol"
+# 2. Get the gene mapping dataframe from the annotation.
+mapping_df = get_gene_mapping(gene_annotation, probe_id_col, gene_symbol_col)
+# 3. Convert probe-level measurements to gene-level expression data.
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP 7: Data Normalization and Linking
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print(f"Saved normalized gene data to {out_gene_data_file}")
+# 2. Use the 'selected_clinical_df' variable from step 2 to link clinical and genetic data
+selected_clinical = selected_clinical_df
+# 3. Link the clinical and genetic data on sample IDs
+linked_data = geo_link_clinical_genetic_data(selected_clinical, normalized_gene_data)
+# 4. Handle missing values, removing or imputing as instructed
+linked_data = handle_missing_values(linked_data, trait)
+# 5. Determine whether the trait (and potentially other features) is severely biased.
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+# 6. Conduct final quality validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,  # We do have a trait column
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Cohort data successfully processed with trait-based analysis."
+)
+# 7. If the dataset is usable, save the final linked data
+if is_usable:
+    linked_data.to_csv(out_data_file, index=True)
+    print(f"Saved final linked data to {out_data_file}")
+else:
+    print("The dataset is not usable for trait-based association. Skipping final output.")

p1/preprocess/Arrhythmia/code/TCGA.py ADDED Viewed

	@@ -0,0 +1,60 @@

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

p1/preprocess/Arrhythmia/gene_data/GSE115574.csv ADDED Viewed

	@@ -0,0 +1 @@

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

p1/preprocess/Arrhythmia/gene_data/GSE136992.csv ADDED Viewed

	@@ -0,0 +1 @@

+ Gene,GSM4064970,GSM4064971,GSM4064972,GSM4064973,GSM4064974,GSM4064975,GSM4064976,GSM4064977,GSM4064978,GSM4064979,GSM4064980,GSM4064981,GSM4064982,GSM4064983,GSM4064984,GSM4064985,GSM4064986,GSM4064987,GSM4064988,GSM4064989,GSM4064990,GSM4064991,GSM4064992,GSM4064993,GSM4064994,GSM4064995,GSM4064996,GSM4064997,GSM4064998,GSM4064999,GSM4065000,GSM4065001,GSM4065002,GSM4065003,GSM4065004,GSM4065005,GSM4065006,GSM4065007,GSM4065008,GSM4065009,GSM4065010,GSM4065011,GSM4065012,GSM4065013,GSM4065014,GSM4065015,GSM4065016,GSM4065017,GSM4065018,GSM4065019,GSM4065020,GSM4065021,GSM4065022,GSM4065023,GSM4065024,GSM4065025,GSM4065026,GSM4065027,GSM4065028,GSM4065029

p1/preprocess/Arrhythmia/gene_data/GSE143924.csv ADDED Viewed

	@@ -0,0 +1 @@


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

p1/preprocess/Arrhythmia/gene_data/GSE41177.csv ADDED Viewed

	@@ -0,0 +1 @@

+ Gene,GSM1005418,GSM1005419,GSM1005420,GSM1005421,GSM1005422,GSM1005423,GSM1005424,GSM1005425,GSM1005426,GSM1005427,GSM1005428,GSM1005429,GSM1005430,GSM1005431,GSM1005432,GSM1005433,GSM1005434,GSM1005435,GSM1005436,GSM1005437,GSM1005438,GSM1005439,GSM1005440,GSM1005441,GSM1005442,GSM1005443,GSM1005444,GSM1005445,GSM1006245,GSM1006246,GSM1006247,GSM1006248,GSM1006249,GSM1006250,GSM1006251,GSM1006252,GSM1006253,GSM1006254

p1/preprocess/Arrhythmia/gene_data/GSE53622.csv ADDED Viewed

	@@ -0,0 +1 @@

+ Gene,GSM1296956,GSM1296957,GSM1296958,GSM1296959,GSM1296960,GSM1296961,GSM1296962,GSM1296963,GSM1296964,GSM1296965,GSM1296966,GSM1296967,GSM1296968,GSM1296969,GSM1296970,GSM1296971,GSM1296972,GSM1296973,GSM1296974,GSM1296975,GSM1296976,GSM1296977,GSM1296978,GSM1296979,GSM1296980,GSM1296981,GSM1296982,GSM1296983,GSM1296984,GSM1296985,GSM1296986,GSM1296987,GSM1296988,GSM1296989,GSM1296990,GSM1296991,GSM1296992,GSM1296993,GSM1296994,GSM1296995,GSM1296996,GSM1296997,GSM1296998,GSM1296999,GSM1297000,GSM1297001,GSM1297002,GSM1297003,GSM1297004,GSM1297005,GSM1297006,GSM1297007,GSM1297008,GSM1297009,GSM1297010,GSM1297011,GSM1297012,GSM1297013,GSM1297014,GSM1297015,GSM1297016,GSM1297017,GSM1297018,GSM1297019,GSM1297020,GSM1297021,GSM1297022,GSM1297023,GSM1297024,GSM1297025,GSM1297026,GSM1297027,GSM1297028,GSM1297029,GSM1297030,GSM1297031,GSM1297032,GSM1297033,GSM1297034,GSM1297035,GSM1297036,GSM1297037,GSM1297038,GSM1297039,GSM1297040,GSM1297041,GSM1297042,GSM1297043,GSM1297044,GSM1297045,GSM1297046,GSM1297047,GSM1297048,GSM1297049,GSM1297050,GSM1297051,GSM1297052,GSM1297053,GSM1297054,GSM1297055,GSM1297056,GSM1297057,GSM1297058,GSM1297059,GSM1297060,GSM1297061,GSM1297062,GSM1297063,GSM1297064,GSM1297065,GSM1297066,GSM1297067,GSM1297068,GSM1297069,GSM1297070,GSM1297071,GSM1297072,GSM1297073,GSM1297074,GSM1297075

p1/preprocess/Arrhythmia/gene_data/GSE55231.csv ADDED Viewed

	@@ -0,0 +1 @@

+ Gene,GSM1332057,GSM1332058,GSM1332059,GSM1332060,GSM1332061,GSM1332062,GSM1332063,GSM1332064,GSM1332065,GSM1332066,GSM1332067,GSM1332068,GSM1332069,GSM1332070,GSM1332071,GSM1332072,GSM1332073,GSM1332074,GSM1332075,GSM1332076,GSM1332077,GSM1332078,GSM1332079,GSM1332080,GSM1332081,GSM1332082,GSM1332083,GSM1332084,GSM1332085,GSM1332086,GSM1332087,GSM1332088,GSM1332089,GSM1332090,GSM1332091,GSM1332092,GSM1332093,GSM1332094,GSM1332095,GSM1332096,GSM1332097,GSM1332098,GSM1332099,GSM1332100,GSM1332101,GSM1332102,GSM1332103,GSM1332104,GSM1332105,GSM1332106,GSM1332107,GSM1332108,GSM1332109,GSM1332110,GSM1332111,GSM1332112,GSM1332113,GSM1332114,GSM1332115,GSM1332116,GSM1332117,GSM1332118,GSM1332119,GSM1332120,GSM1332121,GSM1332122,GSM1332123,GSM1332124,GSM1332125,GSM1332126,GSM1332127,GSM1332128,GSM1332129,GSM1332130,GSM1332131,GSM1332132,GSM1332133,GSM1332134,GSM1332135,GSM1332136,GSM1332137,GSM1332138,GSM1332139,GSM1332140,GSM1332141,GSM1332142,GSM1332143,GSM1332144,GSM1332145,GSM1332146,GSM1332147,GSM1332148,GSM1332149,GSM1332150,GSM1332151,GSM1332152,GSM1332153,GSM1332154,GSM1332155,GSM1332156,GSM1332157,GSM1332158,GSM1332159,GSM1332160,GSM1332161,GSM1332162,GSM1332163,GSM1332164,GSM1332165,GSM1332166,GSM1332167,GSM1332168,GSM1332169,GSM1332170,GSM1332171,GSM1332172,GSM1332173,GSM1332174,GSM1332175,GSM1332176,GSM1332177,GSM1332178,GSM1332179,GSM1332180,GSM1332181,GSM1332182,GSM1332183,GSM1332184,GSM1332185

p1/preprocess/Arrhythmia/gene_data/GSE93101.csv ADDED Viewed

	@@ -0,0 +1 @@


1	+ Gene,GSM2443799,GSM2443800,GSM2443801,GSM2443802,GSM2443803,GSM2443804,GSM2443805,GSM2443806,GSM2443807,GSM2443808,GSM2443809,GSM2443810,GSM2443811,GSM2443812,GSM2443813,GSM2443814,GSM2443815,GSM2443816,GSM2443817,GSM2443818,GSM2443819,GSM2443820,GSM2443821,GSM2443822,GSM2443823,GSM2443824,GSM2443825,GSM2443826,GSM2443827,GSM2443828,GSM2443829,GSM2443830,GSM2443831

p1/preprocess/Asthma/clinical_data/GSE123086.csv ADDED Viewed

	@@ -0,0 +1,3 @@

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p1/preprocess/Asthma/clinical_data/GSE182798.csv ADDED Viewed

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p1/preprocess/Asthma/clinical_data/GSE185658.csv ADDED Viewed

	@@ -0,0 +1,2 @@


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p1/preprocess/Asthma/clinical_data/GSE270312.csv ADDED Viewed

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p1/preprocess/Asthma/code/GSE123086.py ADDED Viewed

	@@ -0,0 +1,228 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Asthma"
+cohort = "GSE123086"
+# Input paths
+in_trait_dir = "../DATA/GEO/Asthma"
+in_cohort_dir = "../DATA/GEO/Asthma/GSE123086"
+# Output paths
+out_data_file = "./output/preprocess/1/Asthma/GSE123086.csv"
+out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/GSE123086.csv"
+out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/GSE123086.csv"
+json_path = "./output/preprocess/1/Asthma/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 likely contains gene expression data
+#    Based on the microarray-based gene expression description, set this to True.
+is_gene_available = True
+# 2. Identify availability of "trait", "age", and "gender" from the sample characteristics
+#    After examining each row in the sample characteristics dictionary:
+#    - The primary diagnosis row is key 1, which includes "primary diagnosis: ASTHMA" among others.
+#      That will serve as our trait row, since it's not constant and contains "ASTHMA".
+#    - Age values appear predominantly in row 3 (and some in row 4). We'll select row 3 for age.
+#    - Gender data is scattered (partly in row 2, partly in row 3) and not presented in a single row,
+#      so we set gender_row to None.
+trait_row = 1
+age_row = 3
+gender_row = None
+# 2.2. Define data conversion functions
+def convert_trait(x: str) -> Optional[int]:
+    """
+    Convert trait data into a binary variable, 1 for ASTHMA, 0 otherwise.
+    If not parsable, return None.
+    """
+    parts = x.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().upper()
+    return 1 if val == "ASTHMA" else 0
+def convert_age(x: str) -> Optional[float]:
+    """
+    Convert age data into a continuous float. If the string does not
+    contain 'age:' or cannot be parsed, return None.
+    """
+    parts = x.split(':')
+    if len(parts) < 2:
+        return None
+    if "age" in parts[0].lower():
+        try:
+            return float(parts[1].strip())
+        except ValueError:
+            return None
+    return None
+def convert_gender(x: str) -> Optional[int]:
+    """
+    Convert gender data to 0 (female) or 1 (male). If not parsable, return None.
+    """
+    parts = x.split(':')
+    if len(parts) < 2:
+        return None
+    if "sex" in parts[0].lower():
+        val = parts[1].strip().lower()
+        if val == "female":
+            return 0
+        elif val == "male":
+            return 1
+    return None
+# 3. Save metadata (initial filtering)
+#    Trait availability is True if trait_row is not None, otherwise False.
+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 and save them
+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 resulting DataFrame
+    preview_clin = preview_df(selected_clinical_df)
+    print("Preview of selected clinical features:", preview_clin)
+    # Save the clinical data 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])
+# Observing the identifiers: they appear to be numeric and not standard human gene symbols.
+# Therefore, they 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 6: Gene Identifier Mapping (Revised Debugged Code)
+def apply_gene_mapping_entrez(expression_df: pd.DataFrame, mapping_df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Convert measured data about gene probes (indexed by numeric 'ID') into gene-level data
+    (using numeric Entrez IDs). Handles one-to-many or many-to-one mappings by splitting
+    probe expression values equally among mapped genes, and summing where multiple probes
+    map to the same gene.
+    """
+    # Remove any duplicate probe entries in the mapping
+    mapping_df = mapping_df.drop_duplicates(subset=['ID', 'Gene'])
+    mapping_df = mapping_df.dropna(subset=['ID', 'Gene'])
+    # Also ensure expression_df has a unique index
+    expression_df = expression_df[~expression_df.index.duplicated(keep='first')]
+    # Make sure mapping DataFrame is indexed by probe ID
+    mapping_df.set_index('ID', inplace=True)
+    # Some platforms may have multiple Entrez IDs joined by a delimiter. Split safely if needed.
+    mapping_df['Gene'] = mapping_df['Gene'].astype(str)
+    mapping_df['Gene'] = mapping_df['Gene'].apply(
+        lambda x: x.split('//') if '//' in x else x.split(';') if ';' in x else [x]
+    )
+    # Count the number of genes each probe maps to
+    mapping_df['num_genes'] = mapping_df['Gene'].apply(len)
+    # Expand to one row per (probe, gene) pair
+    mapping_df = mapping_df.explode('Gene').dropna(subset=['Gene'])
+    # Join expression values (probe-level) onto the mapping table
+    merged_df = mapping_df.join(expression_df, how='inner')  # inner join to keep only matched probes
+    # Identify the columns containing actual expression values (the sample columns)
+    # We'll exclude 'Gene' and 'num_genes'
+    expr_cols = [c for c in merged_df.columns if c not in ['Gene', 'num_genes']]
+    # Divide each probe's expression by the number of genes it maps to
+    merged_df[expr_cols] = merged_df[expr_cols].div(merged_df['num_genes'].replace(0, 1), axis=0)
+    # Finally, sum over genes to get gene-level expression data
+    gene_expression_df = merged_df.groupby('Gene')[expr_cols].sum()
+    return gene_expression_df
+# 1. Identify the columns in the annotation that match our needs
+probe_col = "ID"
+gene_col = "ENTREZ_GENE_ID"
+# 2. Build a mapping DataFrame
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_col)
+# 3. Convert probe-level data to gene-level data
+#    Using the debugged function that preserves numeric Entrez IDs
+gene_data = apply_gene_mapping_entrez(gene_data, mapping_df)
+# Check resulting shape and index
+print("Mapped gene_data shape:", gene_data.shape)
+print("First 10 gene identifiers in mapped data:", gene_data.index[:10].tolist())
+# 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 with the 'geo_link_clinical_genetic_data' function from the library.
+#    Replace 'df_clinical' with the correct clinical DataFrame variable 'selected_clinical_df'.
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+# 3. Handle missing values in the linked data
+linked_data = handle_missing_values(linked_data, trait)
+# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+# 5. Conduct quality check and save the cohort information, passing the final unbiased data.
+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 linked data is usable, save it as a CSV file to 'out_data_file'.
+if is_usable:
+    unbiased_linked_data.to_csv(out_data_file)

p1/preprocess/Asthma/code/GSE123088.py ADDED Viewed

	@@ -0,0 +1,181 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Asthma"
+cohort = "GSE123088"
+# Input paths
+in_trait_dir = "../DATA/GEO/Asthma"
+in_cohort_dir = "../DATA/GEO/Asthma/GSE123088"
+# Output paths
+out_data_file = "./output/preprocess/1/Asthma/GSE123088.csv"
+out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/GSE123088.csv"
+out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/GSE123088.csv"
+json_path = "./output/preprocess/1/Asthma/cohort_info.json"
+# STEP1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("Sample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1. Decide if gene expression data is available:
+is_gene_available = True  # Based on the background info, we assume it's a gene expression dataset.
+# Step 2. Identify keys and define conversion functions.
+# 2.1 Find the rows that hold the trait (Asthma), age, and gender data.
+trait_row = 1   # multiple diagnoses found here, including 'ASTHMA'
+age_row = 3     # row with various ages
+gender_row = 2  # row containing both 'Sex: Male' and 'Sex: Female'
+# 2.2 Data type conversion functions
+def convert_trait(x: str):
+    """
+    Convert trait to binary:
+    1 -> Asthma
+    0 -> Non-Asthma
+    If cannot parse, return None.
+    """
+    parts = x.split(":")
+    if len(parts) < 2:
+        return None
+    value = parts[1].strip().lower()
+    # If the word "asthma" appears, treat it as 1; otherwise 0.
+    return 1 if "asthma" in value else 0
+def convert_age(x: str):
+    """
+    Convert age to a float (continuous).
+    Unknown or unparsable -> None
+    """
+    parts = x.split(":")
+    if len(parts) < 2:
+        return None
+    value = parts[1].strip()
+    try:
+        return float(value)
+    except:
+        return None
+def convert_gender(x: str):
+    """
+    Convert gender to binary:
+    0 -> Female
+    1 -> Male
+    Unknown -> None
+    """
+    parts = x.split(":")
+    if len(parts) < 2:
+        return None
+    value = parts[1].strip().lower()
+    if value == "male":
+        return 1
+    elif value == "female":
+        return 0
+    else:
+        return None
+# Step 3. Save basic 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
+)
+# Step 4. Clinical feature extraction (only if trait data is available).
+if trait_row is not None:
+    df_clinical = geo_select_clinical_features(
+        clinical_df=clinical_data,
+        trait=trait,
+        trait_row=trait_row,
+        convert_trait=convert_trait,
+        age_row=age_row,
+        convert_age=convert_age,
+        gender_row=gender_row,
+        convert_gender=convert_gender
+    )
+    # Observe the output
+    preview_result = preview_df(df_clinical)
+    print("Preview of extracted clinical features:\n", preview_result)
+    # Save the clinical features
+    df_clinical.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the numeric IDs observed (e.g., '1', '2', '3'), these are not standard human gene symbols.
+# They appear to be Entrez IDs or some other numeric identifiers. Therefore, gene mapping is required.
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+# 1. Identify which columns correspond to the gene expression IDs and the gene symbols:
+#    From the preview, the "ID" column matches the numeric identifiers in the gene expression DataFrame,
+#    and "ENTREZ_GENE_ID" represents the gene symbol (though it's also numeric, it's the only available gene label).
+mapping_df = get_gene_mapping(
+    annotation=gene_annotation,
+    prob_col="ID",           # The probe/ID column that matches the expression data index
+    gene_col="ENTREZ_GENE_ID"  # The column we treat as the 'Gene' symbol
+)
+# 2. Convert probe-level measurements to gene-level expression
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# gene_data now contains aggregated expression by gene.
+# 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 with the 'geo_link_clinical_genetic_data' function from the library.
+linked_data = geo_link_clinical_genetic_data(df_clinical, normalized_gene_data)
+# 3. Handle missing values in the linked data
+linked_data = handle_missing_values(linked_data, trait)
+# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.
+is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)
+# 5. Conduct quality check and save the cohort information, passing the final unbiased data.
+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 linked data is usable, save it as a CSV file to 'out_data_file'.
+if is_usable:
+    unbiased_linked_data.to_csv(out_data_file)

p1/preprocess/Asthma/code/GSE182797.py ADDED Viewed

	@@ -0,0 +1,189 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Asthma"
+cohort = "GSE182797"
+# Input paths
+in_trait_dir = "../DATA/GEO/Asthma"
+in_cohort_dir = "../DATA/GEO/Asthma/GSE182797"
+# Output paths
+out_data_file = "./output/preprocess/1/Asthma/GSE182797.csv"
+out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/GSE182797.csv"
+out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/GSE182797.csv"
+json_path = "./output/preprocess/1/Asthma/cohort_info.json"
+# STEP 1
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    background_prefixes,
+    clinical_prefixes
+)
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Gene Expression Data Availability
+is_gene_available = True  # Based on "Transcriptomic profiling" and "microarray analyses"
+# 2) Variable Availability and Data Type Conversion
+#    2.1 Identify rows
+trait_row = 0  # "diagnosis: ..." contains multiple distinct values including "adult-onset asthma"
+age_row = 2    # "age: ..." contains multiple numerical values
+gender_row = None  # Only "gender: Female" found, no variability => not available
+#    2.2 Define conversion functions
+def convert_trait(value: str):
+    """
+    Convert diagnosis data to a binary label:
+    adult-onset asthma -> 1, otherwise (healthy/IEI) -> 0, unknown -> None
+    """
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if 'adult-onset asthma' in val:
+        return 1
+    elif 'healthy' in val or 'iei' in val:
+        return 0
+    return None
+def convert_age(value: str):
+    """Convert age data to a float. Unknown or invalid entries -> None."""
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip()
+    try:
+        return float(val)
+    except ValueError:
+        return None
+def convert_gender(value: str):
+    """
+    Convert gender data to binary (female->0, male->1).
+    Not used here because gender_row is None, but defined for completeness.
+    """
+    parts = value.split(':')
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == 'female':
+        return 0
+    elif val == 'male':
+        return 1
+    return None
+# 3) Save Metadata (initial filtering)
+is_trait_available = (trait_row is not None)
+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 data is available)
+if trait_row is not None:
+    # 'clinical_data' is assumed to be the DataFrame containing sample characteristics
+    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 the selected clinical data
+    preview = preview_df(selected_clinical_df)
+    print("Preview of extracted clinical data:", 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])
+# These identifiers (e.g., 'A_19_P00315452') are microarray probe IDs
+# and do not appear to be standard human gene symbols.
+# Therefore, they 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 columns in gene_annotation for probe IDs and gene symbols
+probe_col = 'ID'
+gene_symbol_col = 'GENE_SYMBOL'
+# 2. Get the mapping of probe IDs to gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)
+# 3. Convert probe-level data to gene-level data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# (Optional) Check the shape or a small preview of the mapped gene_data
+print("Mapped gene_data shape:", gene_data.shape)
+# STEP 7: Data Normalization and Linking
+# 1) Normalize gene symbols
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print(f"Saved normalized gene data to {out_gene_data_file}")
+# 2) Read the previously saved clinical data (which should have shape (2 rows) x (80 columns))
+#    so that it aligns correctly with normalized_gene_data.
+temp_clinical = pd.read_csv(out_clinical_data_file)        # Use the first row as header
+temp_clinical.index = [trait, "Age"]
+temp_clinical.columns = normalized_gene_data.columns       # Match with the 80 sample IDs
+# Link the clinical and gene data
+linked_data = geo_link_clinical_genetic_data(temp_clinical, normalized_gene_data)
+# 3) Handle missing values
+processed_data = handle_missing_values(linked_data, trait_col=trait)
+# 4) Remove biased demographic features; check whether our trait is overly biased
+trait_biased, final_data = judge_and_remove_biased_features(processed_data, trait=trait)
+# 5) Conduct final dataset validation
+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="Final processed dataset for trait and gene expression."
+)
+# 6) If the dataset is usable, save the final linked data
+if is_usable:
+    final_data.to_csv(out_data_file)
+    print(f"Saved final linked data to {out_data_file}")
+else:
+    print("Dataset not usable. No final linked file was saved.")

p1/preprocess/Asthma/code/GSE182798.py ADDED Viewed

	@@ -0,0 +1,189 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Asthma"
+cohort = "GSE182798"
+# Input paths
+in_trait_dir = "../DATA/GEO/Asthma"
+in_cohort_dir = "../DATA/GEO/Asthma/GSE182798"
+# Output paths
+out_data_file = "./output/preprocess/1/Asthma/GSE182798.csv"
+out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/GSE182798.csv"
+out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/GSE182798.csv"
+json_path = "./output/preprocess/1/Asthma/cohort_info.json"
+# STEP 1
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    background_prefixes,
+    clinical_prefixes
+)
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+def convert_trait(x):
+    if not isinstance(x, str):
+        return None
+    # Split only once, to ensure we keep the part after the colon.
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    # Convert to a binary indicator: 1 if adult-onset asthma, else 0
+    # (other categories like IEI or healthy => 0)
+    if 'adult-onset asthma' in val:
+        return 1
+    else:
+        return 0
+def convert_age(x):
+    if not isinstance(x, str):
+        return None
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    try:
+        return float(parts[1].strip())
+    except ValueError:
+        return None
+def convert_gender(x):
+    if not isinstance(x, str):
+        return None
+    parts = x.split(':', 1)
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val in ['female', 'f']:
+        return 0
+    elif val in ['male', 'm']:
+        return 1
+    return None
+# 1. Check gene expression data availability
+is_gene_available = True  # Based on the transcriptomic profiling background
+# 2.1 Identify row indices for trait, age, and gender
+trait_row = 0   # "diagnosis: adult-onset asthma", etc. => available
+age_row = 2     # "age: 33.42", "age: 46.08", ... => available
+# Row 1 (gender) has only one unique value => treat it as not available
+gender_row = None
+# 3. Metadata: initial filtering
+#   trait_row != None => trait is available
+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 is available, extract clinical features
+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,   # None
+        convert_gender=convert_gender
+    )
+    preview_result = preview_df(selected_clinical_df)
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+    print(preview_result)
+# 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 IDs (e.g., 'A_19_P00315452') appear to be array probe identifiers rather than standard gene symbols.
+# Therefore, gene mapping is required.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+# 1) Identify the appropriate columns in the gene annotation
+#    - The probe ID column in the annotation that matches the expression data index is "ID"
+#    - The gene symbol column is "GENE_SYMBOL"
+# 2) Get a dataframe mapping probe IDs to gene symbols
+mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="GENE_SYMBOL")
+# 3) Convert probe-level expression data into gene-level expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# (Optional) Print the shape or a small preview of the resulting gene_data
+print("Gene-level expression data shape:", gene_data.shape)
+print("Gene-level expression data (head):")
+print(gene_data.head())
+# STEP 7: Data Normalization and Linking
+# 1) Normalize gene symbols in the obtained gene expression data;
+#    remove unrecognized symbols and average duplicates.
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print(f"Saved normalized gene data to {out_gene_data_file}")
+# 2) Read previously saved clinical data. Because we saved it in Step 2 with index=False and each row representing
+#    a feature (trait or age), we need to transpose it so that the samples become rows and features become columns.
+clinical_df = pd.read_csv(out_clinical_data_file, header=0)
+clinical_df = clinical_df.T
+# Rename the columns so they match the variables we want
+clinical_df.columns = [trait, "Age"]
+# 3) Link clinical with genetic data
+linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)
+# 4) Handle missing values in the linked data:
+#    remove samples with missing trait, remove genes with >20% missing,
+#    remove samples with >5% missing genes, then impute for the rest.
+linked_data = handle_missing_values(linked_data, trait)
+# 5) Check for severe bias in the trait and remove biased demographic features
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
+# 6) Conduct final quality validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Processed with trait and gene data successfully."
+)
+# 7) If the dataset is usable, save the final linked data to CSV
+if is_usable:
+    linked_data.to_csv(out_data_file)
+    print(f"Saved final linked data to {out_data_file}")
+else:
+    print("Data not usable. No final linked file was saved.")

p1/preprocess/Asthma/code/GSE184382.py ADDED Viewed

	@@ -0,0 +1,155 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Asthma"
+cohort = "GSE184382"
+# Input paths
+in_trait_dir = "../DATA/GEO/Asthma"
+in_cohort_dir = "../DATA/GEO/Asthma/GSE184382"
+# Output paths
+out_data_file = "./output/preprocess/1/Asthma/GSE184382.csv"
+out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/GSE184382.csv"
+out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/GSE184382.csv"
+json_path = "./output/preprocess/1/Asthma/cohort_info.json"
+# STEP 1
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    background_prefixes,
+    clinical_prefixes
+)
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene Expression Data Availability
+is_gene_available = True  # This dataset includes transcriptome microarray data.
+# 2. Variable Availability
+trait_row = None   # No row indicates "asthma" or similar
+age_row = None     # No row for age
+gender_row = None  # No row for gender
+# 2.2 Data Type Conversion
+def convert_trait(value: str) -> int:
+    """
+    Convert raw trait string to a binary indicator (0 or 1).
+    Since trait_row is None, this function won't be used.
+    """
+    # Placeholder implementation
+    return None
+def convert_age(value: str) -> float:
+    """
+    Convert raw age string to a float (continuous).
+    Since age_row is None, this function won't be used.
+    """
+    # Placeholder implementation
+    return None
+def convert_gender(value: str) -> int:
+    """
+    Convert raw gender string to 0 (female) or 1 (male).
+    Since gender_row is None, this function won't be used.
+    """
+    # Placeholder implementation
+    return None
+# 3. Save Metadata
+# Determine trait availability
+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 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])
+# These identifiers (e.g., "A_19_P...", "(+)E1A_r60_...", "3xSLv1") are not standard human gene symbols.
+# They appear to be array or custom IDs that require mapping to gene symbols.
+requires_gene_mapping = True
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+# 1 & 2. Identify the columns in the annotation corresponding to the gene expression IDs and the gene symbols
+#    Here, 'ID' holds probe identifiers matching those in 'gene_data'
+#    and 'GENE_SYMBOL' holds the corresponding gene symbols.
+gene_mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="GENE_SYMBOL")
+# 3. Convert probe-level measurements to gene-level data using the mapping.
+gene_data = apply_gene_mapping(gene_data, gene_mapping_df)
+# STEP 7: Data Normalization and Linking
+# We know from prior steps:
+# - Trait is NOT available (trait_row = None), so no clinical CSV was saved.
+# - We do have gene data, so we will at least normalize it.
+# 1) Normalize gene symbols
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print(f"Saved normalized gene data to {out_gene_data_file}")
+# 2) Since trait is not available, we cannot link or handle clinical data.
+#    We'll set up placeholders for final validation.
+is_trait_available = False
+trait_biased = False   # Arbitrarily set; the library requires a boolean.
+# 3) We have no clinical data to integrate; skip missing value handling.
+# 4) With no trait, we cannot check bias meaningfully. Skipped.
+# 5) Final dataset validation
+#    The library requires df and is_biased if is_final=True, so we provide an empty DataFrame.
+#    This ensures it records the dataset as not usable.
+empty_df = pd.DataFrame()
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,   # Gene data is available
+    is_trait_available=False, # Trait is not available
+    is_biased=trait_biased,
+    df=empty_df,
+    note="No trait data; final record."
+)
+# 6) If the linked data were usable, we would save it. But here, is_usable will be False.
+if is_usable:
+    # This block won't run in our scenario, but included for completeness
+    empty_df.to_csv(out_data_file)
+    print(f"Saved final linked data to {out_data_file}")
+else:
+    print("Data not usable (no trait). No final linked file was saved.")

p1/preprocess/Asthma/code/GSE185658.py ADDED Viewed

	@@ -0,0 +1,157 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Asthma"
+cohort = "GSE185658"
+# Input paths
+in_trait_dir = "../DATA/GEO/Asthma"
+in_cohort_dir = "../DATA/GEO/Asthma/GSE185658"
+# Output paths
+out_data_file = "./output/preprocess/1/Asthma/GSE185658.csv"
+out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/GSE185658.csv"
+out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/GSE185658.csv"
+json_path = "./output/preprocess/1/Asthma/cohort_info.json"
+# STEP 1
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    background_prefixes,
+    clinical_prefixes
+)
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1) Gene Expression Data Availability
+is_gene_available = True  # The background indicates Affymetrix microarrays for global gene expression
+# 2) Variable Availability and Data Type Conversion
+# Based on the sample characteristics dictionary, we only see a "group" field (row=1) that includes asthma vs healthy.
+trait_row = 1
+age_row = None
+gender_row = None
+# Define the conversion function for the trait (binary: 1 for Asthma, 0 for Healthy, None otherwise).
+def convert_trait(value):
+    parts = value.split(':')
+    label = parts[-1].strip().lower()  # Take text after ':'
+    if 'asthma' in label:
+        return 1
+    elif 'healthy' in label:
+        return 0
+    return None
+# We do not have age or gender data, so these conversion functions are not used.
+convert_age = None
+convert_gender = 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 data is available)
+if trait_row is not None:
+    selected_clinical_df = geo_select_clinical_features(
+        clinical_df=clinical_data,  # previously obtained DataFrame of sample characteristics
+        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_dict = preview_df(selected_clinical_df)
+    print("Preview of selected clinical features:", preview_dict)
+    # Save the extracted clinical features 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 numeric format (e.g., '7892501'), these are likely not standard human gene symbols.
+# Therefore, we conclude that gene mapping is required.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+# 1. Decide which columns in the gene_annotation dataframe correspond to the probe ID and the gene symbol text.
+#    From the preview, "ID" appears to match the probe identifier (same as gene_data index),
+#    and "gene_assignment" appears to contain the gene symbols (though in a messy string).
+# 2. Build a mapping dataframe using these two columns.
+mapping_df = get_gene_mapping(annotation=gene_annotation, prob_col="ID", gene_col="gene_assignment")
+# 3. Convert the probe-level measurements to gene expression data using the mapping,
+#    distributing expression when a probe maps to multiple genes and summing the contributions for each gene.
+gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)
+# STEP 7: Data Normalization and Linking
+# 1) Normalize gene symbols
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print(f"Saved normalized gene data to {out_gene_data_file}")
+# 2) Link clinical and genetic data
+#    We know from previous steps that we do have trait data in out_clinical_data_file.
+clinical_df = pd.read_csv(out_clinical_data_file, header=0)
+# The clinical CSV contains a single row with the trait values and columns as sample IDs.
+# Label that row with the trait name, so that geo_link_clinical_genetic_data can handle it properly.
+clinical_df.index = [trait]
+linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)
+# 3) Handle missing values
+linked_data = handle_missing_values(df=linked_data, trait_col=trait)
+# 4) Determine bias
+trait_biased, linked_data = judge_and_remove_biased_features(df=linked_data, trait=trait)
+# 5) Final dataset validation
+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="Completed data preprocessing and quality checks."
+)
+# 6) If usable, save the final linked data
+if is_usable:
+    linked_data.to_csv(out_data_file, index=True)
+    print(f"Saved final linked data to {out_data_file}")
+else:
+    print("Data not usable. No final file was saved.")

p1/preprocess/Asthma/code/GSE188424.py ADDED Viewed

	@@ -0,0 +1,134 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Asthma"
+cohort = "GSE188424"
+# Input paths
+in_trait_dir = "../DATA/GEO/Asthma"
+in_cohort_dir = "../DATA/GEO/Asthma/GSE188424"
+# Output paths
+out_data_file = "./output/preprocess/1/Asthma/GSE188424.csv"
+out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/GSE188424.csv"
+out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/GSE188424.csv"
+json_path = "./output/preprocess/1/Asthma/cohort_info.json"
+# STEP 1
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    background_prefixes,
+    clinical_prefixes
+)
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Determine gene expression data availability
+is_gene_available = True  # Transcriptome data indicated in the series description
+# Step 2.1: Determine availability of trait, age, and gender data
+# From the dictionary {0: ['gender: female', 'gender: male']},
+# only gender data is found under key=0. No separate entries for trait or age are available.
+trait_row = None
+age_row = None
+gender_row = 0
+# Step 2.2: Define data conversion functions
+def convert_trait(value: str):
+    # No trait data row is available; return None.
+    return None
+def convert_age(value: str):
+    # No age data row is available; return None.
+    return None
+def convert_gender(value: str):
+    """
+    Convert the gender string to 0 or 1:
+      - female -> 0
+      - male -> 1
+      - others/unknown -> None
+    """
+    parts = value.split(':', 1)
+    if len(parts) < 2:
+        return None
+    gender_str = parts[1].strip().lower()
+    if gender_str == 'female':
+        return 0
+    elif gender_str == 'male':
+        return 1
+    return None
+# Step 3: Save metadata via 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
+)
+# Step 4: Since trait_row is None, we skip clinical feature extraction.
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the observed identifiers (e.g., ILMN_1651199), these are Illumina probe IDs
+# rather than human gene symbols and require mapping.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+# 1 & 2. Identify the correct columns in gene_annotation corresponding to the Illumina probe IDs and gene symbols
+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)
+# STEP 7: Data Normalization and Linking
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print(f"Saved normalized gene data to {out_gene_data_file}")
+# Since 'trait_row' was None, no clinical feature extraction occurred and trait data is unavailable.
+# We must skip linking and final data prep steps and directly do final validation to record that this dataset is unusable for trait-based analysis.
+empty_df = pd.DataFrame()  # Placeholder, as df must be provided to the validation function
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,  # No trait data was found
+    is_biased=True,            # Arbitrary True to pass validation, making the dataset not usable
+    df=empty_df,
+    note="Trait data is unavailable; skipping linking and final data steps."
+)
+print("Trait data unavailable. Skipping linking and final data output.")

p1/preprocess/Asthma/code/GSE205151.py ADDED Viewed

	@@ -0,0 +1,96 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Asthma"
+cohort = "GSE205151"
+# Input paths
+in_trait_dir = "../DATA/GEO/Asthma"
+in_cohort_dir = "../DATA/GEO/Asthma/GSE205151"
+# Output paths
+out_data_file = "./output/preprocess/1/Asthma/GSE205151.csv"
+out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/GSE205151.csv"
+out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/GSE205151.csv"
+json_path = "./output/preprocess/1/Asthma/cohort_info.json"
+# STEP 1
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    background_prefixes,
+    clinical_prefixes
+)
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# 1. Gene expression data availability
+#    Based on the metadata: "mRNA was analyzed using a targeted Nanostring immunology array,"
+#    indicating this study involves gene expression data.
+is_gene_available = True
+# 2. Variable Availability and Conversion
+#    From the sample characteristics, only two keys (0 and 1) are available:
+#    0 -> polyic_stimulation, and 1 -> cluster
+#    There's no mention of 'Asthma' variation, age, or gender.
+#    So, all samples are asthmatic, which yields no variability in 'trait',
+#    and age/gender aren't in the dictionary.
+trait_row = None    # No variation in "Asthma" (everyone is asthmatic)
+age_row = None      # Not found
+gender_row = None   # Not found
+def convert_trait(value: str) -> int:
+    """
+    Trait data is not available/variable here,
+    so we won't actually use this function.
+    """
+    return None
+def convert_age(value: str) -> float:
+    """
+    Age data not available.
+    """
+    return None
+def convert_gender(value: str) -> int:
+    """
+    Gender data not available.
+    """
+    return None
+# 3. Save Metadata (initial filtering)
+#    Trait data is not available because there's no variability.
+is_trait_available = False
+is_usable = validate_and_save_cohort_info(
+    is_final=False,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=is_gene_available,
+    is_trait_available=is_trait_available
+)
+# 4. Clinical Feature Extraction
+#    Since trait_row is None, we skip extraction for this dataset.
+# 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, these appear to be standard human gene symbols.
+print("requires_gene_mapping = False")

p1/preprocess/Asthma/code/GSE230164.py ADDED Viewed

	@@ -0,0 +1,160 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Asthma"
+cohort = "GSE230164"
+# Input paths
+in_trait_dir = "../DATA/GEO/Asthma"
+in_cohort_dir = "../DATA/GEO/Asthma/GSE230164"
+# Output paths
+out_data_file = "./output/preprocess/1/Asthma/GSE230164.csv"
+out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/GSE230164.csv"
+out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/GSE230164.csv"
+json_path = "./output/preprocess/1/Asthma/cohort_info.json"
+# STEP 1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    background_prefixes,
+    clinical_prefixes
+)
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Determine if gene expression data is likely available
+is_gene_available = True  # Based on the title "Gene expression profiling of asthma"
+# Step 2: Identify the rows for trait, age, and gender
+# From the provided sample characteristics dictionary (only key 0 with gender info),
+# we see no mention of the trait (asthma) or age, so these are not available.
+trait_row = None
+age_row = None
+gender_row = 0  # "gender: female" and "gender: male" are present
+# Data type conversion functions
+def convert_trait(value: str) -> Optional[int]:
+    """
+    Convert trait values to binary (e.g., 'asthma' -> 1, 'control' or 'healthy' -> 0).
+    Returns None if unknown.
+    """
+    # Extract the actual data after the colon if present
+    parts = value.split(':', 1)
+    val = parts[1].strip().lower() if len(parts) > 1 else value.lower()
+    # Example mapping (if we had trait data)
+    if 'asthma' in val:
+        return 1
+    if 'control' in val or 'healthy' in val:
+        return 0
+    return None
+def convert_age(value: str) -> Optional[float]:
+    """
+    Convert age values to continuous floats.
+    Returns None if parsing fails or data is unknown.
+    """
+    parts = value.split(':', 1)
+    val = parts[1].strip() if len(parts) > 1 else value
+    try:
+        return float(val)
+    except ValueError:
+        return None
+def convert_gender(value: str) -> Optional[int]:
+    """
+    Convert gender to binary (female -> 0, male -> 1).
+    Returns None if unknown.
+    """
+    parts = value.split(':', 1)
+    val = parts[1].strip().lower() if len(parts) > 1 else value.lower()
+    if 'female' in val:
+        return 0
+    if 'male' in val:
+        return 1
+    return None
+# Step 3: Initial filtering and saving of metadata
+is_trait_available = trait_row is not None
+dataset_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: Since trait_row is None, we skip substep of 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 the given identifiers (e.g., ILMN_1651199), these appear to be Illumina probe IDs
+# rather than standard human gene symbols. Therefore, gene symbol mapping is required.
+print("requires_gene_mapping = True")
+# STEP5
+# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
+gene_annotation = get_gene_annotation(soft_file)
+# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
+print("Gene annotation preview:")
+print(preview_df(gene_annotation))
+# STEP: Gene Identifier Mapping
+# 1. Identify the columns in the gene annotation dataframe
+#    - "ID" column contains Illumina probe IDs matching those in the expression data
+#    - "Symbol" column contains the gene symbols
+prob_col = 'ID'
+gene_col = 'Symbol'
+# 2. Get a gene mapping dataframe by extracting the two columns
+mapping_df = get_gene_mapping(gene_annotation, prob_col=prob_col, gene_col=gene_col)
+# 3. Convert probe-level measurements to gene expression data
+gene_data = apply_gene_mapping(gene_data, mapping_df)
+# STEP 7: Data Normalization and Linking
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print(f"Saved normalized gene data to {out_gene_data_file}")
+# Since 'trait_row' was None, no clinical feature extraction occurred and trait data is unavailable.
+# We must skip linking and final data prep steps and directly do final validation to record that this dataset is unusable for trait-based analysis.
+empty_df = pd.DataFrame()  # Placeholder, as df must be provided to the validation function
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=False,  # No trait data was found
+    is_biased=True,            # Arbitrary True to pass validation, making the dataset not usable
+    df=empty_df,
+    note="Trait data is unavailable; skipping linking and final data steps."
+)
+print("Trait data unavailable. Skipping linking and final data output.")

p1/preprocess/Asthma/code/GSE270312.py ADDED Viewed

	@@ -0,0 +1,162 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Asthma"
+cohort = "GSE270312"
+# Input paths
+in_trait_dir = "../DATA/GEO/Asthma"
+in_cohort_dir = "../DATA/GEO/Asthma/GSE270312"
+# Output paths
+out_data_file = "./output/preprocess/1/Asthma/GSE270312.csv"
+out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/GSE270312.csv"
+out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/GSE270312.csv"
+json_path = "./output/preprocess/1/Asthma/cohort_info.json"
+# STEP 1
+from tools.preprocess import *
+# 1. Identify the paths to the SOFT file and the matrix file
+soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
+# 2. Read the matrix file to obtain background information and sample characteristics data
+background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
+clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
+background_info, clinical_data = get_background_and_clinical_data(
+    matrix_file,
+    background_prefixes,
+    clinical_prefixes
+)
+# 3. Obtain the sample characteristics dictionary from the clinical dataframe
+sample_characteristics_dict = get_unique_values_by_row(clinical_data)
+# 4. Explicitly print out all the background information and the sample characteristics dictionary
+print("Background Information:")
+print(background_info)
+print("\nSample Characteristics Dictionary:")
+print(sample_characteristics_dict)
+# Step 1: Gene Expression Data Availability
+# Based on the background stating "RNA transcriptome responses" were measured, we consider it gene expression data.
+is_gene_available = True
+# Step 2: Variable Availability and Conversion
+# 2.1 Identify rows for trait, age, and gender
+# From the sample characteristics dictionary, 'asthma status' = row 3, 'gender' = row 2.
+# No age information is provided.
+trait_row = 3
+age_row = None
+gender_row = 2
+# 2.2 Define data conversion functions
+def convert_trait(value: str):
+    # Example: "asthma status: Yes"
+    # Split by colon, then strip extra spaces
+    parts = value.split(":")
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == "yes":
+        return 1
+    elif val == "no":
+        return 0
+    return None
+def convert_age(value: str):
+    # No age data available, so return None
+    return None
+def convert_gender(value: str):
+    # Example: "gender: Female"
+    parts = value.split(":")
+    if len(parts) < 2:
+        return None
+    val = parts[1].strip().lower()
+    if val == "female":
+        return 0
+    elif val == "male":
+        return 1
+    return None
+# Step 3: Save Metadata (initial filtering)
+# Trait data is considered available if we have a valid row for it
+is_trait_available = (trait_row is not None)
+filter_pass = 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_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 selected clinical features
+    preview_clinical = preview_df(selected_clinical_df)
+    # (You could print the preview or store it if needed; omitted here for brevity.)
+    # Save the clinical data
+    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
+# STEP3
+# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
+gene_data = get_genetic_data(matrix_file)
+# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
+print(gene_data.index[:20])
+# Based on the observed gene identifiers such as ABCF1, ACE, ACKR2, etc.,
+# these appear to be valid human gene symbols and do not require additional mapping.
+print("These genes are human gene symbols.")
+# Conclusion
+print("\nrequires_gene_mapping = False")
+# STEP 7: Data Normalization and Linking
+# 1. Normalize gene symbols in the obtained gene expression data
+normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
+normalized_gene_data.to_csv(out_gene_data_file)
+print(f"Saved normalized gene data to {out_gene_data_file}")
+# 2. Link the clinical and genetic data on sample IDs
+linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
+# 3. Handle missing values in the linked data
+linked_data = handle_missing_values(linked_data, trait_col=trait)
+# 4. Determine whether the trait/demographic features are severely biased
+trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait=trait)
+# 5. Conduct final quality validation and save metadata
+is_usable = validate_and_save_cohort_info(
+    is_final=True,
+    cohort=cohort,
+    info_path=json_path,
+    is_gene_available=True,
+    is_trait_available=True,
+    is_biased=trait_biased,
+    df=linked_data,
+    note="Trait data and gene data successfully linked."
+)
+# 6. If the dataset is deemed usable, save the final linked data as a CSV file
+if is_usable:
+    linked_data.to_csv(out_data_file)
+    print(f"Saved final linked data to {out_data_file}")
+else:
+    print("Dataset was not deemed usable; final linked data not saved.")

p1/preprocess/Asthma/code/TCGA.py ADDED Viewed

	@@ -0,0 +1,59 @@

+# Path Configuration
+from tools.preprocess import *
+# Processing context
+trait = "Asthma"
+# Input paths
+tcga_root_dir = "../DATA/TCGA"
+# Output paths
+out_data_file = "./output/preprocess/1/Asthma/TCGA.csv"
+out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/TCGA.csv"
+out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/TCGA.csv"
+json_path = "./output/preprocess/1/Asthma/cohort_info.json"
+import os
+# Step 1: Identify subdirectory that might relate to "Asthma"
+subdirs = [
+    'CrawlData.ipynb', '.DS_Store', 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)',
+    'TCGA_Uterine_Carcinosarcoma_(UCS)', 'TCGA_Thyroid_Cancer_(THCA)', 'TCGA_Thymoma_(THYM)',
+    'TCGA_Testicular_Cancer_(TGCT)', 'TCGA_Stomach_Cancer_(STAD)', 'TCGA_Sarcoma_(SARC)',
+    'TCGA_Rectal_Cancer_(READ)', 'TCGA_Prostate_Cancer_(PRAD)', 'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)',
+    'TCGA_Pancreatic_Cancer_(PAAD)', 'TCGA_Ovarian_Cancer_(OV)', 'TCGA_Ocular_melanomas_(UVM)',
+    'TCGA_Mesothelioma_(MESO)', 'TCGA_Melanoma_(SKCM)', 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)',
+    'TCGA_Lung_Cancer_(LUNG)', 'TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lower_Grade_Glioma_(LGG)',
+    'TCGA_Liver_Cancer_(LIHC)', 'TCGA_Large_Bcell_Lymphoma_(DLBC)', 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)',
+    'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)', 'TCGA_Kidney_Chromophobe_(KICH)', 'TCGA_Head_and_Neck_Cancer_(HNSC)',
+    'TCGA_Glioblastoma_(GBM)', 'TCGA_Esophageal_Cancer_(ESCA)', 'TCGA_Endometrioid_Cancer_(UCEC)',
+    'TCGA_Colon_and_Rectal_Cancer_(COADREAD)', 'TCGA_Colon_Cancer_(COAD)', 'TCGA_Cervical_Cancer_(CESC)',
+    'TCGA_Breast_Cancer_(BRCA)', 'TCGA_Bladder_Cancer_(BLCA)', 'TCGA_Bile_Duct_Cancer_(CHOL)',
+    'TCGA_Adrenocortical_Cancer_(ACC)', 'TCGA_Acute_Myeloid_Leukemia_(LAML)'
+]
+# Since we're looking for "Asthma" and no subdirectory name suggests an asthma-related cancer,
+# no suitable subdirectory is found.
+suitable_subdir = None
+# Confirm no matching subdirectory
+for sd in subdirs:
+    # Normally, you'd check synonyms for "Asthma" if needed.
+    if "asthma" in sd.lower():
+        suitable_subdir = sd
+        break
+# If not found, skip the trait:
+if not suitable_subdir:
+    print("No suitable subdirectory found for trait 'Asthma'. Skipping this trait.")
+    # Mark as completed but unavailable in metadata
+    validate_and_save_cohort_info(
+        is_final=False,
+        cohort="TCGA",
+        info_path=json_path,
+        is_gene_available=False,
+        is_trait_available=False
+    )
+else:
+    # (Would proceed to load data if a matching subdirectory was found.)
+    pass

p1/preprocess/Asthma/gene_data/GSE123086.csv ADDED Viewed

	@@ -0,0 +1 @@

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

p1/preprocess/Asthma/gene_data/GSE123088.csv ADDED Viewed

	@@ -0,0 +1 @@

p1/preprocess/Asthma/gene_data/GSE182797.csv ADDED Viewed

	@@ -0,0 +1 @@

+ Gene,GSM5537157,GSM5537158,GSM5537159,GSM5537160,GSM5537161,GSM5537162,GSM5537163,GSM5537164,GSM5537165,GSM5537166,GSM5537167,GSM5537168,GSM5537169,GSM5537170,GSM5537171,GSM5537172,GSM5537173,GSM5537174,GSM5537175,GSM5537176,GSM5537177,GSM5537178,GSM5537179,GSM5537180,GSM5537181,GSM5537182,GSM5537183,GSM5537184,GSM5537185,GSM5537186,GSM5537187,GSM5537188,GSM5537189,GSM5537190,GSM5537191,GSM5537192,GSM5537193,GSM5537194,GSM5537195,GSM5537196,GSM5537197,GSM5537198,GSM5537199,GSM5537200,GSM5537201,GSM5537202,GSM5537203,GSM5537204,GSM5537205,GSM5537206,GSM5537207,GSM5537208,GSM5537209,GSM5537210,GSM5537211,GSM5537212,GSM5537213,GSM5537214,GSM5537215,GSM5537216,GSM5537217,GSM5537218,GSM5537219,GSM5537220,GSM5537221,GSM5537222,GSM5537223,GSM5537224,GSM5537225,GSM5537226,GSM5537227,GSM5537228,GSM5537229,GSM5537230,GSM5537231,GSM5537232,GSM5537233,GSM5537234,GSM5537235,GSM5537236

p1/preprocess/Asthma/gene_data/GSE182798.csv ADDED Viewed

	@@ -0,0 +1 @@

+ Gene,GSM5530417,GSM5530418,GSM5530419,GSM5530420,GSM5530421,GSM5530422,GSM5530423,GSM5530424,GSM5530425,GSM5530426,GSM5530427,GSM5530428,GSM5530429,GSM5530430,GSM5530431,GSM5530432,GSM5530433,GSM5530434,GSM5530435,GSM5530436,GSM5530437,GSM5530438,GSM5530439,GSM5530440,GSM5530441,GSM5530442,GSM5530443,GSM5530444,GSM5530445,GSM5530446,GSM5530447,GSM5530448,GSM5530449,GSM5530450,GSM5530451,GSM5530452,GSM5530453,GSM5530454,GSM5530455,GSM5530456,GSM5530457,GSM5530458,GSM5530459,GSM5530460,GSM5530461,GSM5530462,GSM5530463,GSM5530464,GSM5530465,GSM5530466,GSM5530467,GSM5530468,GSM5530469,GSM5530470,GSM5530471,GSM5530472,GSM5530473,GSM5530474,GSM5530475,GSM5530476,GSM5530477,GSM5530478,GSM5530479,GSM5530480,GSM5530481,GSM5530482,GSM5530483,GSM5530484,GSM5530485,GSM5530486,GSM5530487,GSM5530488,GSM5530489,GSM5530490,GSM5530491,GSM5530492,GSM5530493,GSM5530494,GSM5530495,GSM5530496,GSM5530497,GSM5530498,GSM5530499,GSM5530500,GSM5530501,GSM5530502,GSM5530503,GSM5530504,GSM5530505,GSM5530506,GSM5530507,GSM5530508,GSM5530509,GSM5530510,GSM5530511,GSM5530512,GSM5530513,GSM5530514,GSM5530515,GSM5530516,GSM5530517,GSM5530518,GSM5537157,GSM5537158,GSM5537159,GSM5537160,GSM5537161,GSM5537162,GSM5537163,GSM5537164,GSM5537165,GSM5537166,GSM5537167,GSM5537168,GSM5537169,GSM5537170,GSM5537171,GSM5537172,GSM5537173,GSM5537174,GSM5537175,GSM5537176,GSM5537177,GSM5537178,GSM5537179,GSM5537180,GSM5537181,GSM5537182,GSM5537183,GSM5537184,GSM5537185,GSM5537186,GSM5537187,GSM5537188,GSM5537189,GSM5537190,GSM5537191,GSM5537192,GSM5537193,GSM5537194,GSM5537195,GSM5537196,GSM5537197,GSM5537198,GSM5537199,GSM5537200,GSM5537201,GSM5537202,GSM5537203,GSM5537204,GSM5537205,GSM5537206,GSM5537207,GSM5537208,GSM5537209,GSM5537210,GSM5537211,GSM5537212,GSM5537213,GSM5537214,GSM5537215,GSM5537216,GSM5537217,GSM5537218,GSM5537219,GSM5537220,GSM5537221,GSM5537222,GSM5537223,GSM5537224,GSM5537225,GSM5537226,GSM5537227,GSM5537228,GSM5537229,GSM5537230,GSM5537231,GSM5537232,GSM5537233,GSM5537234,GSM5537235,GSM5537236

p1/preprocess/Asthma/gene_data/GSE184382.csv ADDED Viewed

	@@ -0,0 +1 @@

+ Gene,GSM5585358,GSM5585359,GSM5585360,GSM5585361,GSM5585362,GSM5585363,GSM5585364,GSM5585365,GSM5585366,GSM5585367,GSM5585368,GSM5585369,GSM5585370,GSM5585371,GSM5585372,GSM5585373,GSM5585374,GSM5585375,GSM5585376,GSM5585377,GSM5585378,GSM5585379,GSM5585380,GSM5585381,GSM5585382,GSM5585383,GSM5585384,GSM5585385,GSM5585386,GSM5585387,GSM5585388,GSM5585389,GSM5585390,GSM5585391,GSM5585392,GSM5585393,GSM5585394,GSM5585395,GSM5585396

p1/preprocess/Asthma/gene_data/GSE185658.csv ADDED Viewed

	@@ -0,0 +1 @@

+ Gene,GSM5621296,GSM5621297,GSM5621298,GSM5621299,GSM5621300,GSM5621301,GSM5621302,GSM5621303,GSM5621304,GSM5621305,GSM5621306,GSM5621307,GSM5621308,GSM5621309,GSM5621310,GSM5621311,GSM5621312,GSM5621313,GSM5621314,GSM5621315,GSM5621316,GSM5621317,GSM5621318,GSM5621319,GSM5621320,GSM5621321,GSM5621322,GSM5621323,GSM5621324,GSM5621325,GSM5621326,GSM5621327,GSM5621328,GSM5621329,GSM5621330,GSM5621331,GSM5621332,GSM5621333,GSM5621334,GSM5621335,GSM5621336,GSM5621337,GSM5621338,GSM5621339,GSM5621340,GSM5621341,GSM5621342,GSM5621343

p1/preprocess/Asthma/gene_data/GSE188424.csv ADDED Viewed

	@@ -0,0 +1 @@

+ Gene,GSM5681954,GSM5681955,GSM5681956,GSM5681957,GSM5681958,GSM5681959,GSM5681960,GSM5681961,GSM5681962,GSM5681963,GSM5681964,GSM5681965,GSM5681966,GSM5681967,GSM5681968,GSM5681969,GSM5681970,GSM5681971,GSM5681972,GSM5681973,GSM5681974,GSM5681975,GSM5681976,GSM5681977,GSM5681978,GSM5681979,GSM5681980,GSM5681981,GSM5681982,GSM5681983,GSM5681984,GSM5681985,GSM5681986,GSM5681987,GSM5681988,GSM5681989,GSM5681990,GSM5681991,GSM5681992,GSM5681993,GSM5681994,GSM5681995,GSM5681996,GSM5681997,GSM5681998,GSM5681999,GSM5682000,GSM5682001,GSM5682002,GSM5682003,GSM5682004,GSM5682005,GSM5682006,GSM5682007,GSM5682008,GSM5682009,GSM5682010,GSM5682011,GSM5682012,GSM5682013,GSM5682014,GSM5682015,GSM5682016,GSM5682017,GSM5682018,GSM5682019,GSM5682020,GSM5682021,GSM5682022,GSM5682023,GSM5682024,GSM5682025,GSM5682026,GSM5682027,GSM5682028,GSM5682029,GSM5682030,GSM5682031,GSM5682032,GSM5682033,GSM5682034,GSM5682035,GSM5682036,GSM5682037,GSM5682038,GSM5682039,GSM5682040,GSM5682041,GSM5682042,GSM5682043,GSM5682044,GSM5682045,GSM5682046,GSM5682047,GSM5682048,GSM5682049,GSM5682050,GSM5682051,GSM5682052

p1/preprocess/Asthma/gene_data/GSE230164.csv ADDED Viewed

	@@ -0,0 +1 @@

p1/preprocess/Asthma/gene_data/GSE270312.csv ADDED Viewed

	@@ -0,0 +1 @@

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

p1/preprocess/Atrial_Fibrillation/GSE143924.csv ADDED Viewed

The diff for this file is too large to render. See raw diff

p1/preprocess/Atrial_Fibrillation/clinical_data/GSE115574.csv ADDED Viewed

	@@ -0,0 +1,2 @@


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