output/preprocess/Depression/code/GSE128387.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Depression"
cohort = "GSE128387"

# Input paths
in_trait_dir = "../DATA/GEO/Depression"
in_cohort_dir = "../DATA/GEO/Depression/GSE128387"

# Output paths
out_data_file = "./output/z2/preprocess/Depression/GSE128387.csv"
out_gene_data_file = "./output/z2/preprocess/Depression/gene_data/GSE128387.csv"
out_clinical_data_file = "./output/z2/preprocess/Depression/clinical_data/GSE128387.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
import re
import pandas as pd

# 1) Gene expression data availability
is_gene_available = True  # Affymetrix microarrays; expression data from blood

# 2) Variable availability
trait_row = None  # "illness: Major Depressive Disorder" appears constant across samples
age_row = 2
gender_row = 3

# 2.2) Converters
def _extract_value(x):
    if x is None or (isinstance(x, float) and pd.isna(x)):
        return None
    s = str(x)
    parts = s.split(":", 1)
    v = parts[1] if len(parts) > 1 else parts[0]
    return v.strip()

def convert_trait(x):
    # Not used since trait_row is None, but implemented for completeness.
    v = _extract_value(x)
    if v is None:
        return None
    vl = v.lower()
    # Map depressive disorder cases to 1, healthy/control to 0
    if any(k in vl for k in ["major depressive", "mdd", "depress"]):
        return 1
    if any(k in vl for k in ["control", "healthy", "normal", "no depression", "non-depressed"]):
        return 0
    return None

def convert_age(x):
    v = _extract_value(x)
    if v is None:
        return None
    # Extract first numeric token (handles '16', '16 years', etc.)
    m = re.search(r"[-+]?\d*\.?\d+", v)
    if not m:
        return None
    try:
        age_val = float(m.group())
        # Return int if it's whole number
        return int(age_val) if age_val.is_integer() else age_val
    except Exception:
        return None

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

# 3) Save metadata (initial filtering)
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_row is None)
# If trait_row becomes available in future, uncomment the following block:
# 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)
#     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
# The observed identifiers are numeric probe-like IDs (e.g., '16657436'), not human gene symbols.
print("requires_gene_mapping = True")

# 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
# Decide columns for probe IDs and gene symbols based on annotation preview
probe_col = 'ID' if 'ID' in gene_annotation.columns else 'probeset_id'
gene_symbol_col = 'gene_assignment' if 'gene_assignment' in gene_annotation.columns else 'mrna_assignment'

# Build mapping dataframe (ID -> Gene text)
mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)

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

# Step 7: Data Normalization and Linking
import os
import pandas as pd

# 1. Normalize gene symbols and save gene expression data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

# 2-6. Trait data is unavailable for this cohort (trait_row is None), so linking is not possible.
# Perform final validation to record metadata accordingly.
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,
    is_biased=False,
    df=pd.DataFrame(),  # No linked data due to missing trait
    note="INFO: Trait not available per sample; cohort reports constant illness (MDD) without case/control labels, so no linking performed."
)

# No linked data to save
linked_data = None