p3/preprocess/Kidney_Chromophobe/code/GSE26574.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Kidney_Chromophobe"
cohort = "GSE26574"

# Input paths
in_trait_dir = "../DATA/GEO/Kidney_Chromophobe"
in_cohort_dir = "../DATA/GEO/Kidney_Chromophobe/GSE26574"

# Output paths
out_data_file = "./output/preprocess/3/Kidney_Chromophobe/GSE26574.csv"
out_gene_data_file = "./output/preprocess/3/Kidney_Chromophobe/gene_data/GSE26574.csv"
out_clinical_data_file = "./output/preprocess/3/Kidney_Chromophobe/clinical_data/GSE26574.csv"
json_path = "./output/preprocess/3/Kidney_Chromophobe/cohort_info.json"

# Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Get unique values for each clinical feature 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background information
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
is_gene_available = True  # Based on series title and design indicating expression profiling

# 2.1 Data Availability
trait_row = 0  # Disease state contains trait info
age_row = None  # Age data not available 
gender_row = None  # Gender data not available

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # Extract value after colon and strip whitespace
    val = x.split(':')[1].strip().lower()
    # Binary coding: 1 for Chromophobe, 0 for normal/other types
    if 'chromophobe' in val:
        return 1
    elif 'normal' in val or val in ['ccrcc', 'pap_type1', 'pap_type2', 'hlrcc']:
        return 0
    return None

# Age and gender conversion functions not needed since data unavailable

# 3. Save metadata
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. Extract clinical features since trait data is available
selected_clinical = geo_select_clinical_features(clinical_df=clinical_data,
                                               trait=trait,
                                               trait_row=trait_row,
                                               convert_trait=convert_trait)

# Preview the selected clinical features
print("Preview of selected clinical features:")
print(preview_df(selected_clinical))

# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
selected_clinical.to_csv(out_clinical_data_file)
# Extract gene expression data from the matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs
print("First 20 row IDs:")
print(genetic_data.index[:20].tolist())
# The row IDs appear to be numerical indices rather than gene symbols
# This indicates we need to map these identifiers to actual gene symbols
requires_gene_mapping = True
# Extract gene annotation data from SOFT file
gene_metadata = get_gene_annotation(soft_file_path)

# Display information about the annotation data
print("Column names:")
print(gene_metadata.columns.tolist())

# Look at general data statistics 
print("\nData shape:", gene_metadata.shape)

# Preview the first few rows
print("\nPreview of the annotation data:")
print(json.dumps(preview_df(gene_metadata), indent=2))
# 1. Observe that 'ID' in annotation matches the numeric IDs in gene expression data
# and 'ORF' contains gene symbols

# 2. Extract mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='ORF')

# 3. Apply gene mapping to convert probe data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Preview the first few rows of mapped gene data
print("Preview of gene expression data after mapping:")
print(json.dumps(preview_df(gene_data), indent=2))
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)

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

# Early exit if trait values are all NaN
if linked_data[trait].isna().all():
    is_biased = True
    linked_data = None
else:
    # 4. Judge whether features are biased and remove biased demographic features
    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
note = "Dataset from a cancer gene expression study using oligonucleotide microarrays, containing samples of kidney chromophobe tumors and normal tissues."
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_biased,
    df=linked_data,
    note=note
)

# 6. Save the linked data only if it's usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)