p3/preprocess/Esophageal_Cancer/code/GSE104958.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Esophageal_Cancer"
cohort = "GSE104958"

# Input paths
in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE104958"

# Output paths
out_data_file = "./output/preprocess/3/Esophageal_Cancer/GSE104958.csv"
out_gene_data_file = "./output/preprocess/3/Esophageal_Cancer/gene_data/GSE104958.csv"
out_clinical_data_file = "./output/preprocess/3/Esophageal_Cancer/clinical_data/GSE104958.csv"
json_path = "./output/preprocess/3/Esophageal_Cancer/cohort_info.json"

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

# Extract background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Get dictionary of unique values per row in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("Background Information:")
print("-" * 50)
print(background_info)
print("\n")

# Print clinical data unique values
print("Sample Characteristics:")
print("-" * 50)
for row, values in unique_values_dict.items():
    print(f"{row}:")
    print(f"  {values}")
    print()
# 1. Gene Expression Data Availability
# Based on the background info mentioning "DNA microarray data", this dataset contains gene expression data
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
# Trait (pCR status) is not directly available in sample characteristics,
# but needs to be inferred from RNA IDs in a later step
trait_row = None  # Not available in sample characteristics
age_row = None  # Age data not available
gender_row = None  # Gender data not available

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    # Get sample ID from string
    if not isinstance(value, str):
        return None
    try:
        # Extract RNA sample number from identifiers
        rna_id = int(''.join(filter(str.isdigit, value)))
        # Check if RNA ID is in pCR group based on background info
        pcr_samples = [1, 4, 7, 10, 12, 17, 24, 29, 35, 43]
        return 1 if rna_id in pcr_samples else 0
    except:
        return None

# Age and gender conversion functions not needed since data unavailable
convert_age = None
convert_gender = None

# 3. Save initial 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. Clinical feature extraction skipped since trait_row is None
# Extract gene expression data
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 probe IDs
print("First 20 probe IDs:")
print(genetic_data.index[:20])
# These identifiers appear to be probe IDs from a microarray/RNA-seq platform
# They are not standard human gene symbols (which would look like BRCA1, TP53, etc)
# The format A_19_P* suggests these are likely Agilent array probe IDs that need mapping
requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)

# Preview column names and first few values
preview_dict = preview_df(gene_annotation)
print("Column names and preview values:")
for col, values in preview_dict.items():
    print(f"\n{col}:")
    print(values)
# Extract probe ID and gene symbol mapping from annotation
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')

# Apply gene mapping to convert probe-level measurements to gene expression
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Print dimensions of original and mapped data
print(f"Original probe data dimensions: {genetic_data.shape}")
print(f"Mapped gene data dimensions: {gene_data.shape}")
# 1. Normalize gene symbols and save normalized gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)

# Create clinical data using sample IDs and convert_trait function
sample_ids = normalized_gene_data.columns
clinical_data = pd.DataFrame(index=['Esophageal_Cancer'])
clinical_data[sample_ids] = [convert_trait(id) for id in sample_ids]
clinical_data.to_csv(out_clinical_data_file)

# Link clinical and genetic data 
linked_data = geo_link_clinical_genetic_data(clinical_data, normalized_gene_data)

# Handle missing values systematically
linked_data = handle_missing_values(linked_data, 'Esophageal_Cancer')

# Detect bias in trait and demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, 'Esophageal_Cancer')

# Validate data quality and save cohort info
note = ("This dataset studies gene expression related to pathological complete response (pCR) " 
        "after neoadjuvant chemotherapy in esophageal cancer. The trait information was derived "
        "from RNA sample IDs mentioned in the background information.")
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
)

# Save linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)
else:
    print(f"Dataset {cohort} did not pass quality validation and will not be saved.")