output/preprocess/Asthma/code/GSE184382.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Asthma"
cohort = "GSE184382"

# Input paths
in_trait_dir = "../DATA/GEO/Asthma"
in_cohort_dir = "../DATA/GEO/Asthma/GSE184382"

# Output paths
out_data_file = "./output/z1/preprocess/Asthma/GSE184382.csv"
out_gene_data_file = "./output/z1/preprocess/Asthma/gene_data/GSE184382.csv"
out_clinical_data_file = "./output/z1/preprocess/Asthma/clinical_data/GSE184382.csv"
json_path = "./output/z1/preprocess/Asthma/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

# 1. Gene Expression Data Availability
# Background indicates both miR microarray and transcriptome microarray were performed.
is_gene_available = True

# 2. Variable Availability and Data Type Conversion

# Based on the provided Sample Characteristics Dictionary:
# {0: ['season: in season'], 1: ['ait treatment: no', 'ait treatment: yes']}
# There is no explicit or inferable asthma status, age, or gender field.
trait_row = None
age_row = None
gender_row = None

def _after_colon(x):
    if x is None:
        return None
    s = str(x)
    parts = s.split(":", 1)
    val = parts[1] if len(parts) > 1 else parts[0]
    return val.strip()

def convert_trait(x):
    # Binary: 1 for asthma, 0 for non-asthma; unknown -> None
    val = _after_colon(x)
    if val is None or val == "":
        return None
    v = val.lower()

    # Common indicators
    positives = [
        "asthma", "asthmatic", "aa", "with asthma", "asthma: yes", "diagnosis: asthma"
    ]
    negatives = [
        "non-asthma", "no asthma", "without asthma", "control", "hc", "healthy",
        "ar", "allergic rhinitis", "asthma: no"
    ]

    # Heuristic mapping
    if any(tok in v for tok in positives):
        return 1
    if any(tok in v for tok in negatives):
        return 0
    return None

def convert_age(x):
    # Continuous age in years; unknown -> None
    val = _after_colon(x)
    if val is None or val == "":
        return None
    m = re.search(r'(\d+(?:\.\d+)?)', val)
    if not m:
        return None
    try:
        return float(m.group(1))
    except Exception:
        return None

def convert_gender(x):
    # Binary: female -> 0, male -> 1; unknown -> None
    val = _after_colon(x)
    if val is None or val == "":
        return None
    v = val.strip().lower()
    if v in ["male", "m", "man", "boy"]:
        return 1
    if v in ["female", "f", "woman", "girl"]:
        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 (skip because trait_row is None)
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 = preview_df(selected_clinical_df)
    print(preview)
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical_df.to_csv(out_clinical_data_file)

# 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 provided identifiers include Agilent-style probe IDs (e.g., "A_19_P00315452") and other non-gene-symbol entries.
# These are not standard human gene symbols and require mapping to 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
# 1-2. Determine the appropriate columns for probe IDs and gene symbols from gene_annotation
probe_col = 'ID'            # Matches the probe identifiers in the gene expression matrix
gene_symbol_col = 'GENE_SYMBOL'  # Contains human gene symbols

# Extract mapping dataframe
gene_mapping = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_symbol_col)

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

# Step 7: Data Normalization and Linking
# 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. Handle presence/absence of clinical data (trait). In this cohort, trait was unavailable in Step 2.
clinical_df = None
has_clinical = False

# Try to use in-memory clinical data if it exists; otherwise try to load from disk if any
if 'selected_clinical_data' in locals():
    clinical_df = selected_clinical_data
    has_clinical = True
elif os.path.exists(out_clinical_data_file):
    clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
    has_clinical = True

# Determine if trait is available in clinical data
trait_available = bool(has_clinical and (trait in clinical_df.index))

if trait_available:
    # Link clinical and genetic data
    linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)

    # Handle missing values
    linked_data = handle_missing_values(linked_data, trait)

    # Bias checks (remove biased covariates; record trait bias)
    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)

    # Final validation and save cohort info
    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=is_trait_biased,
        df=unbiased_linked_data,
        note="INFO: Clinical trait available; completed linking and preprocessing."
    )

    # Save linked data if usable
    if is_usable:
        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
        unbiased_linked_data.to_csv(out_data_file)

else:
    # Trait/clinical data unavailable: finalize metadata without linking
    _ = 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(),  # empty df to indicate no linked data available
        note="WARNING: Trait/clinical data unavailable for this series; linking and bias analysis skipped. Only normalized gene data saved."
    )