p1/preprocess/Bipolar_disorder/code/GSE46449.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bipolar_disorder"
cohort = "GSE46449"

# Input paths
in_trait_dir = "../DATA/GEO/Bipolar_disorder"
in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE46449"

# Output paths
out_data_file = "./output/preprocess/1/Bipolar_disorder/GSE46449.csv"
out_gene_data_file = "./output/preprocess/1/Bipolar_disorder/gene_data/GSE46449.csv"
out_clinical_data_file = "./output/preprocess/1/Bipolar_disorder/clinical_data/GSE46449.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 gene expression data availability:
is_gene_available = True  # Based on the description, this dataset uses microarray technology for gene expression.

# 2. Identify rows for trait, age, and gender; define conversion functions:
trait_row = 1  # 'genotype: bipolar patient' / 'genotype: control subject'
age_row = 2    # 'age: [numeric value]'
gender_row = None  # Only 'gender: male' is observed, which is a single-value field, so consider it unavailable.

def convert_trait(value: str) -> Optional[int]:
    parts = value.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    if val == 'bipolar patient':
        return 1
    elif val == 'control subject':
        return 0
    return None

def convert_age(value: str) -> Optional[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]:
    parts = value.split(':', 1)
    if len(parts) < 2:
        return None
    val = parts[1].strip().lower()
    if val == 'female':
        return 0
    elif val == 'male':
        return 1
    return None

# 3. Initial filtering and metadata saving:
is_trait_available = (trait_row is not None)
is_usable = validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=is_trait_available
)

# 4. Clinical feature extraction if trait is available:
if trait_row is not None:
    selected_clinical_df = geo_select_clinical_features(
        clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait,
        age_row=age_row,
        convert_age=convert_age,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    preview = preview_df(selected_clinical_df)
    print("Preview of clinical features:", preview)
    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
# STEP3
# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
gene_data = get_genetic_data(matrix_file)

# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
print(gene_data.index[:20])
# Based on the observed identifiers (e.g., '1007_s_at'), these are Affymetrix probe set IDs, not human gene symbols.
# Therefore, they will need to be mapped to gene symbols.
print("requires_gene_mapping = True")
# STEP5
# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
gene_annotation = get_gene_annotation(soft_file)

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

# 1. Determine the relevant columns in the gene annotation dataframe:
#    "ID" corresponds to the probe identifiers used in the gene expression index,
#    while "Gene Symbol" corresponds to the gene symbols to map onto.
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Gene Symbol")

# 2. Convert probe-level measurements to gene expression data
gene_data = apply_gene_mapping(gene_data, 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(selected_clinical_df, 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)