output/preprocess/Depression/code/GSE149980.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Depression"
cohort = "GSE149980"

# Input paths
in_trait_dir = "../DATA/GEO/Depression"
in_cohort_dir = "../DATA/GEO/Depression/GSE149980"

# Output paths
out_data_file = "./output/z2/preprocess/Depression/GSE149980.csv"
out_gene_data_file = "./output/z2/preprocess/Depression/gene_data/GSE149980.csv"
out_clinical_data_file = "./output/z2/preprocess/Depression/clinical_data/GSE149980.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 (whole gene expression profiling; not miRNA/methylation)
is_gene_available = True

# 2) Variable availability based on provided sample characteristics:
# Sample Characteristics show only:
# 0: 'response status: responder/non-responder' (not our trait "Depression")
# 1: 'tissue: LCLs'
trait_row = None      # "Depression" status is constant (all depressed) and not explicitly provided
age_row = None        # No age information present
gender_row = None     # No gender information present

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

def convert_trait(x):
    """
    Binary: depressed=1, control=0. Unknown -> None.
    Designed generally for GEO clinical strings; not used here since trait_row=None.
    """
    v = _after_colon(x)
    if v is None:
        return None
    v_low = v.lower().strip()

    positive = {
        "depression", "depressed", "mdd", "major depressive disorder",
        "unipolar depression", "patient", "case"
    }
    negative = {
        "control", "healthy", "normal", "non-depressed", "nondepressed",
        "no depression", "hc"
    }

    if v_low in positive:
        return 1
    if v_low in negative:
        return 0

    # Heuristics
    if "depress" in v_low or "mdd" in v_low:
        return 1
    if "control" in v_low or "healthy" in v_low or "normal" in v_low:
        return 0

    return None

def convert_age(x):
    """
    Continuous: age in years as float. Unknown -> None.
    """
    v = _after_colon(x)
    if v is None:
        return None
    v_low = v.lower()

    # Extract first number (integer or float)
    m = re.search(r"[-+]?\d*\.?\d+", v_low)
    if not m:
        return None
    try:
        return float(m.group())
    except Exception:
        return None

def convert_gender(x):
    """
    Binary: female=0, male=1. Unknown -> None.
    """
    v = _after_colon(x)
    if v is None:
        return None
    v_low = v.lower().strip()

    if v_low in {"male", "m", "man"}:
        return 1
    if v_low in {"female", "f", "woman"}:
        return 0

    # Heuristics
    if v_low.startswith("m "):
        return 1
    if v_low.startswith("f "):
        return 0

    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 skipped because trait_row is None

# 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
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
# Identify appropriate columns in annotation for mapping
# Probe/ID column: 'ID'; Gene symbol column: 'GENE_SYMBOL'
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')

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

# Step 7: Data Normalization and Linking
# 1. Normalize gene symbols and save gene-level expression
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# 2-6. Trait unavailable -> skip linking and downstream processing; record metadata accordingly
is_trait_available = False
note = ("INFO: Trait 'Depression' not available in clinical annotations for cohort GSE149980. "
        "All samples are depressed patients; only 'response status' is provided. "
        "Association analysis for the specified trait cannot be performed.")

# Use gene expression (transposed) to avoid abnormality override in validation
dummy_df = normalized_gene_data.T if not normalized_gene_data.empty else normalized_gene_data

is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=is_trait_available,
    is_biased=False,
    df=dummy_df,
    note=note
)

# No linked data to save since trait is unavailable