p1/preprocess/Congestive_heart_failure/code/GSE93101.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Congestive_heart_failure"
cohort = "GSE93101"

# Input paths
in_trait_dir = "../DATA/GEO/Congestive_heart_failure"
in_cohort_dir = "../DATA/GEO/Congestive_heart_failure/GSE93101"

# Output paths
out_data_file = "./output/preprocess/1/Congestive_heart_failure/GSE93101.csv"
out_gene_data_file = "./output/preprocess/1/Congestive_heart_failure/gene_data/GSE93101.csv"
out_clinical_data_file = "./output/preprocess/1/Congestive_heart_failure/clinical_data/GSE93101.csv"
json_path = "./output/preprocess/1/Congestive_heart_failure/cohort_info.json"

# STEP1
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)
# 1. Decide gene availability based on the background information
is_gene_available = True  # The dataset mentions transcriptomic (gene expression) data

# 2. Identify variable availability and define data type conversion functions

# From the sample characteristics, we found:
#   trait_row = 0  (the "course: ..." field includes "Congestive heart failure" among multiple values)
#   age_row = 1    (the "age: ..." field has multiple values)
#   gender_row = 2 (the "gender: F/M" field has multiple values)

trait_row = 0
age_row = 1
gender_row = 2

# 2.2 Define conversion functions

def convert_trait(x: str) -> int:
    val = x.split(':')[-1].strip().lower()
    return 1 if val == "congestive heart failure" else 0

def convert_age(x: str) -> Optional[float]:
    val = x.split(':')[-1].strip()
    try:
        return float(val)
    except ValueError:
        return None

def convert_gender(x: str) -> Optional[int]:
    val = x.split(':')[-1].strip().upper()
    if val == "F":
        return 0
    elif val == "M":
        return 1
    else:
        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. If trait_row is not None, extract clinical features
if trait_row is not None:
    clinical_features = geo_select_clinical_features(
        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 and save the extracted clinical features
    print(preview_df(clinical_features))
    clinical_features.to_csv(out_clinical_data_file, index=False)
# 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 'ILMN_xxxxx' format (Illumina probe IDs), these are not HGNC gene symbols
# and thus require mapping to standard human gene symbols.
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 matching columns and create a mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col="ID", gene_col="Symbol")

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

# Let's preview the mapped gene_data
print(preview_df(gene_data))
# STEP7

# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# 2. Link the clinical and genetic data on sample IDs
linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)

# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait_col=trait)

# 4. Determine whether the trait and demographic features are severely biased
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final quality validation and saving metadata
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=trait_biased,
    df=linked_data,
    note="Processed dataset for congestive heart failure."
)

# 6. If usable, save the final linked data
if is_usable:
    linked_data.to_csv(out_data_file)