p1/preprocess/Bipolar_disorder/code/GSE53987.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bipolar_disorder"
cohort = "GSE53987"

# Input paths
in_trait_dir = "../DATA/GEO/Bipolar_disorder"
in_cohort_dir = "../DATA/GEO/Bipolar_disorder/GSE53987"

# Output paths
out_data_file = "./output/preprocess/1/Bipolar_disorder/GSE53987.csv"
out_gene_data_file = "./output/preprocess/1/Bipolar_disorder/gene_data/GSE53987.csv"
out_clinical_data_file = "./output/preprocess/1/Bipolar_disorder/clinical_data/GSE53987.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)
# Step 1: Decide if gene expression data is available
is_gene_available = True  # Affymetrix microarray expression data is mentioned

# Step 2: Identify data availability and define row keys for trait, age, and gender
trait_row = 7   # "disease state: bipolar disorder" is found under key 7
age_row = 0     # "age: X" is found under key 0
gender_row = 1  # "gender: M/F" is found under key 1

# 2.2 Define data type conversion functions

def convert_trait(value: str):
    # Extract the substring after the colon
    val = value.split(':')[-1].strip().lower()
    # Convert "bipolar disorder" to 1, everything else (control, MDD, schizophrenia) to 0
    if val == "bipolar disorder":
        return 1
    elif val in ["control", "major depressive disorder", "schizophrenia"]:
        return 0
    return None

def convert_age(value: str):
    # Extract the substring after the colon and convert to float
    val = value.split(':')[-1].strip()
    try:
        return float(val)
    except:
        return None

def convert_gender(value: str):
    # Extract the substring after the colon, convert M->1, F->0
    val = value.split(':')[-1].strip().lower()
    if val == "m":
        return 1
    elif val == "f":
        return 0
    return None

# Step 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
)

# Step 4: If trait data 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,
        convert_gender=convert_gender
    )
    preview = preview_df(selected_clinical_df)
    print("Preview of selected clinical features:", preview)
    selected_clinical_df.to_csv(out_clinical_data_file, index=False)
# STEP3
# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.
gene_data = get_genetic_data(matrix_file)

# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.
print(gene_data.index[:20])
# Based on the observed gene identifiers (e.g., 1007_s_at, 1053_at, 1255_g_at), these are likely Affymetrix probe set IDs
# and not standard human gene symbols.
# Therefore, they require mapping to gene symbols.
print("requires_gene_mapping = True")
# STEP5
# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.
gene_annotation = get_gene_annotation(soft_file)

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

# 1. We observe that "ID" in the gene_annotation dataframe matches the probe identifiers (e.g., "1007_s_at"), 
#    and "Gene Symbol" contains the gene symbols.
prob_col = "ID"
gene_col = "Gene Symbol"

# 2. Get a gene mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)

# 3. Convert probe-level data to gene-level data
gene_data = apply_gene_mapping(gene_data, mapping_df)

# Check the result
print("Gene data shape after mapping:", gene_data.shape)
print("First 20 gene symbols in the mapped data:\n", gene_data.index[:20])
# 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)