p1/preprocess/Bipolar_disorder/code/GSE120342.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bipolar_disorder"
cohort = "GSE120342"

# Input paths
in_trait_dir = "../DATA/GEO/Bipolar_disorder"
in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE120342"

# Output paths
out_data_file = "./output/preprocess/1/Bipolar_disorder/GSE120342.csv"
out_gene_data_file = "./output/preprocess/1/Bipolar_disorder/gene_data/GSE120342.csv"
out_clinical_data_file = "./output/preprocess/1/Bipolar_disorder/clinical_data/GSE120342.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) Determine if this dataset likely contains suitable gene expression data
is_gene_available = True  # Based on "Aberrant transcriptomes..." mention

# 2) Identify data availability and create conversion functions

# From the sample characteristics:
# {
#   0: ['disease state: control', 'disease state: SCZ', 'disease state: BD(-)', 'disease state: BD(+)'],
#   1: ['laterality: left', 'laterality: right']
# }
# There is only information about disease state and laterality. No explicit age or gender metadata.
# For trait: we can use row 0, as it contains BD-, BD+, SCZ, control.
# For age and gender information: None (not available).

trait_row = 0
age_row = None
gender_row = None

def convert_trait(value: str):
    parts = value.split(":", 1)
    val = parts[1].strip() if len(parts) > 1 else value.strip()
    # Convert BD to 1, others (SCZ, control) to 0
    if val in ["control", "SCZ"]:
        return 0
    elif val in ["BD(-)", "BD(+)"]:
        return 1
    else:
        return None

# Since age and gender rows are not available, we set their convert functions to None
convert_age = None
convert_gender = None

# 3) Conduct 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) Clinical feature extraction 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
    )
    print(preview_df(df_clinical, n=5, max_items=200))
    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])
# The listed identifiers (e.g., "cg00000292") appear to be CpG probe IDs rather than standard human gene symbols.
# Therefore, mapping is needed to associate each probe with corresponding gene information.

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 on the columns to use as probe ID and gene symbol
probe_col = "ID"      # Matches the gene expression dataframe index (e.g., 'cg00000292')
symbol_col = "Symbol" # Contains the gene symbols from the annotation

# 2. Get a gene mapping dataframe
gene_mapping_df = get_gene_mapping(gene_annotation, probe_col, symbol_col)

# 3. Convert probe-level measurements to gene expression data
gene_data = apply_gene_mapping(gene_data, gene_mapping_df)
# 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
linked_data = geo_link_clinical_genetic_data(df_clinical, normalized_gene_data)

# 3. Handle missing values systematically using the actual column name (stored in variable trait)
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)