output/preprocess/Depression/code/GSE81761.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Depression"
cohort = "GSE81761"

# Input paths
in_trait_dir = "../DATA/GEO/Depression"
in_cohort_dir = "../DATA/GEO/Depression/GSE81761"

# Output paths
out_data_file = "./output/z2/preprocess/Depression/GSE81761.csv"
out_gene_data_file = "./output/z2/preprocess/Depression/gene_data/GSE81761.csv"
out_clinical_data_file = "./output/z2/preprocess/Depression/clinical_data/GSE81761.csv"
json_path = "./output/z2/preprocess/Depression/cohort_info.json"


# Step 1: Initial Data Loading
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 2: Dataset Analysis and Clinical Feature Extraction
# 1) Gene expression availability based on platform description (Affymetrix HG-U133 Plus 2.0 => mRNA expression)
is_gene_available = True

# 2) Variable availability (rows inferred from provided Sample Characteristics Dictionary)
# Keys:
# 0: tissue
# 1: case/control (PTSD vs No PTSD)
# 2: ptsd subgroup
# 3: timepoint
# 4: Sex
# 5: age
# 6: race
# 7: ethnicity

# Trait of interest is Depression, which is not present in this dataset => not available
trait_row = None

# Age and Gender are available
age_row = 5
gender_row = 4

# 2.2 Converters
def _after_colon(x):
    if x is None:
        return None
    s = str(x)
    parts = s.split(":", 1)
    return parts[1].strip() if len(parts) == 2 else s.strip()

def convert_trait(x):
    # Depression not provided in this PTSD-focused dataset
    return None

def convert_age(x):
    val = _after_colon(x)
    if val is None or val == "":
        return None
    # Keep only digits and dot
    import re
    m = re.search(r"[-+]?\d*\.?\d+", val)
    if not m:
        return None
    try:
        return float(m.group(0))
    except Exception:
        return None

def convert_gender(x):
    val = _after_colon(x)
    if val is None:
        return None
    v = val.strip().lower()
    # Map female->0, male->1
    if v in {"female", "f", "woman", "women"}:
        return 0
    if v in {"male", "m", "man", "men"}:
        return 1
    return None

# 3) 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) Clinical Feature Extraction (skip because trait is not available)
# If trait_row becomes available in future adjustments, uncomment below:
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_df(selected_clinical_df)
    # Ensure output directory exists and save
    import os
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical_df.to_csv(out_clinical_data_file, index=True)

# Step 3: Gene Data Extraction
# 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])

# Step 4: Gene Identifier Review
# Affymetrix probe set IDs (e.g., '1007_s_at') are not gene symbols and require mapping.
requires_gene_mapping = True
print(f"requires_gene_mapping = {requires_gene_mapping}")

# Step 5: Gene Annotation
# 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
# 1-2. Determine the appropriate columns for mapping and construct the mapping dataframe
probe_col = 'ID'  # Matches probe identifiers in the expression matrix
gene_col = 'Gene Symbol'  # Column containing gene symbols (may include multiple per probe)
mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_col)

# 3. Apply mapping to convert probe-level data to gene-level expression
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)