p1/preprocess/Bipolar_disorder/code/GSE67311.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bipolar_disorder"
cohort = "GSE67311"

# Input paths
in_trait_dir = "../DATA/GEO/Bipolar_disorder"
in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE67311"

# Output paths
out_data_file = "./output/preprocess/1/Bipolar_disorder/GSE67311.csv"
out_gene_data_file = "./output/preprocess/1/Bipolar_disorder/gene_data/GSE67311.csv"
out_clinical_data_file = "./output/preprocess/1/Bipolar_disorder/clinical_data/GSE67311.csv"
json_path = "./output/preprocess/1/Bipolar_disorder/cohort_info.json"

# STEP1
from tools.preprocess import *
# 1. Identify the paths to the SOFT file and the matrix file
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# 2. Read the matrix file to obtain background information and sample characteristics data
background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)

# 3. Obtain the sample characteristics dictionary from the clinical dataframe
sample_characteristics_dict = get_unique_values_by_row(clinical_data)

# 4. Explicitly print out all the background information and the sample characteristics dictionary
print("Background Information:")
print(background_info)
print("Sample Characteristics Dictionary:")
print(sample_characteristics_dict)
# 1. Gene Expression Data Availability
# Based on the background info, the dataset used Affymetrix Human Gene ST arrays, so it likely contains gene expression data.
is_gene_available = True

# 2. Variable Availability and Data Type Conversion

# 2.1 Identify the row keys (trait_row, age_row, gender_row)
# From the provided sample characteristics dictionary:
#   Row 7: ['bipolar disorder: No', 'bipolar disorder: -', 'bipolar disorder: Yes']
#   No row references age or gender.
trait_row = 7
age_row = None
gender_row = None

# 2.2 Define conversion functions for each variable

def convert_trait(value: str):
    # Extract the substring after the colon.
    # Map 'Yes' -> 1, 'No' -> 0. Otherwise return None.
    # Example: "bipolar disorder: Yes" -> "Yes" -> 1
    #          "bipolar disorder: No"  -> "No"  -> 0
    #          "bipolar disorder: -"   -> None
    if not isinstance(value, str):
        return None
    parts = value.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    if val == 'yes':
        return 1
    elif val == 'no':
        return 0
    else:
        return None

# For completeness, though they won't be used since age_row and gender_row are None:
def convert_age(value: str):
    return None

def convert_gender(value: str):
    return None

# 3. Save Metadata (initial filtering) using validate_and_save_cohort_info
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,                 # "clinical_data" is assumed to be available from previous steps
        trait=trait,                   # "Bipolar_disorder"
        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 = preview_df(selected_clinical_df)
    print("Preview of extracted clinical features:", preview)
    
    # 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])
# These numeric IDs are not standard human gene symbols. They likely correspond to probe identifiers
# that require mapping to gene symbols.
print("requires_gene_mapping = True")
# STEP5
# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
gene_annotation = get_gene_annotation(soft_file)

# 2. Use the 'preview_df' function from the library to preview the data and print out the results.
print("Gene annotation preview:")
print(preview_df(gene_annotation))
# STEP6: Gene Identifier Mapping

# 1. Identify the columns in gene_annotation that match the expression data IDs and store gene symbol information.
#    From the preview, 'ID' corresponds to the same numeric probe identifiers in gene_data,
#    and 'gene_assignment' contains the gene symbol info (OR4F16, OR4F29, etc.) among other annotations.

probe_col = "ID"
symbol_col = "gene_assignment"

# 2. Get the gene mapping dataframe from the annotation dataframe.
mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=symbol_col)

# 3. Apply this mapping to convert the probe-level expression data into a gene-level expression dataframe.
gene_data = apply_gene_mapping(gene_data, mapping_df)
print("Gene mapping and aggregation complete. Final gene_data shape:", gene_data.shape)
# STEP7

# 1. Normalize the obtained gene data using the NCBI Gene synonym database
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
#    Use the correct variable name from previous steps: "selected_clinical_df"
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)

# 3. Handle missing values systematically using the actual trait name
linked_data_processed = handle_missing_values(linked_data, trait_col=trait)

# 4. Check for biased trait and remove any biased demographic features
trait_biased, linked_data_final = judge_and_remove_biased_features(linked_data_processed, trait)

# 5. Final quality validation and metadata saving
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=trait_biased,
    df=linked_data_final,
    note="Dataset processed with GEO pipeline. Checked for missing values and bias."
)

# 6. If dataset is usable, save the final linked data
if is_usable:
    linked_data_final.to_csv(out_data_file)