p1/preprocess/Asthma/code/GSE188424.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Asthma"
cohort = "GSE188424"

# Input paths
in_trait_dir = "../DATA/GEO/Asthma"
in_cohort_dir = "../DATA/GEO/Asthma/GSE188424"

# Output paths
out_data_file = "./output/preprocess/1/Asthma/GSE188424.csv"
out_gene_data_file = "./output/preprocess/1/Asthma/gene_data/GSE188424.csv"
out_clinical_data_file = "./output/preprocess/1/Asthma/clinical_data/GSE188424.csv"
json_path = "./output/preprocess/1/Asthma/cohort_info.json"

# STEP 1

# 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("\nSample Characteristics Dictionary:")
print(sample_characteristics_dict)
# Step 1: Determine gene expression data availability
is_gene_available = True  # Transcriptome data indicated in the series description

# Step 2.1: Determine availability of trait, age, and gender data
# From the dictionary {0: ['gender: female', 'gender: male']},
# only gender data is found under key=0. No separate entries for trait or age are available.
trait_row = None
age_row = None
gender_row = 0

# Step 2.2: Define data conversion functions
def convert_trait(value: str):
    # No trait data row is available; return None.
    return None

def convert_age(value: str):
    # No age data row is available; return None.
    return None

def convert_gender(value: str):
    """
    Convert the gender string to 0 or 1:
      - female -> 0
      - male -> 1
      - others/unknown -> None
    """
    parts = value.split(':', 1)
    if len(parts) < 2:
        return None
    gender_str = parts[1].strip().lower()
    if gender_str == 'female':
        return 0
    elif gender_str == 'male':
        return 1
    return None

# Step 3: Save metadata via initial filtering
# Trait data availability is determined by whether trait_row is None.
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: Since trait_row is None, we skip clinical feature extraction.
# 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 identifiers (e.g., ILMN_1651199), these are Illumina probe IDs 
# rather than human gene symbols and require mapping.
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 & 2. Identify the correct columns in gene_annotation corresponding to the Illumina probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

# 3. Convert probe-level measurements to gene expression data by applying the gene mapping
gene_data = apply_gene_mapping(gene_data, mapping_df)
# STEP 7: Data Normalization and Linking

# 1. Normalize gene symbols in the obtained gene expression data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)
print(f"Saved normalized gene data to {out_gene_data_file}")

# Since 'trait_row' was None, no clinical feature extraction occurred and trait data is unavailable.
# We must skip linking and final data prep steps and directly do final validation to record that this dataset is unusable for trait-based analysis.

empty_df = pd.DataFrame()  # Placeholder, as df must be provided to the validation function
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,  # No trait data was found
    is_biased=True,            # Arbitrary True to pass validation, making the dataset not usable
    df=empty_df,
    note="Trait data is unavailable; skipping linking and final data steps."
)

print("Trait data unavailable. Skipping linking and final data output.")