p3/preprocess/Red_Hair/code/GSE207744.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Red_Hair"
cohort = "GSE207744"

# Input paths
in_trait_dir = "../DATA/GEO/Red_Hair"
in_cohort_dir = "../DATA/GEO/Red_Hair/GSE207744"

# Output paths
out_data_file = "./output/preprocess/3/Red_Hair/GSE207744.csv"
out_gene_data_file = "./output/preprocess/3/Red_Hair/gene_data/GSE207744.csv"
out_clinical_data_file = "./output/preprocess/3/Red_Hair/clinical_data/GSE207744.csv"
json_path = "./output/preprocess/3/Red_Hair/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)
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")

# Get dictionary of unique values per row 
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
    print(f"\n{row}:")
    print(values)
# 1. Gene Expression Data Availability
# Based on background info mentioning "transcriptomic study" and "gene expression profile analysis"
is_gene_available = True

# 2.1 Data Availability
# For trait - using lesion type (row 2)
# For age - not available in data
# For gender - not available in data  
trait_row = 2
age_row = None  
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    """Convert lesion type to binary - Non Lesion (0) vs other lesion types (1)"""
    if pd.isna(x):
        return None
    value = x.split(": ")[1] if ": " in x else x
    if value == "Non Lesion":
        return 0
    elif value in ["Actinic Keratosis", "Lesion", "Peri Lesion"]:
        return 1
    return None

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

# 3. Save initial metadata
validate_and_save_cohort_info(is_final=False, 
                             cohort=cohort,
                             info_path=json_path,
                             is_gene_available=is_gene_available, 
                             is_trait_available=(trait_row is not None))

# 4. Extract clinical features since trait_row is not None
clinical_selected = geo_select_clinical_features(clinical_df=clinical_data,
                                               trait=trait,
                                               trait_row=trait_row,
                                               convert_trait=convert_trait)

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

# Save clinical data
clinical_selected.to_csv(out_clinical_data_file)
# Get gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Examine data structure
print("Data structure and head:")
print(genetic_data.head())

print("\nShape:", genetic_data.shape)

print("\nFirst 20 row IDs (gene/probe identifiers):")
print(list(genetic_data.index)[:20])

# Get a few column names to verify sample IDs
print("\nFirst 5 column names:")
print(list(genetic_data.columns)[:5])
# Based on the identifiers observed (e.g. "(+)E1A_r60_1", "A_19_P00315452"), 
# these are probe IDs rather than standard human gene symbols
# The identifiers appear to be Agilent microarray probe IDs that need to be mapped to gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)

# Display column names and preview data
print("Column names:")
print(gene_annotation.columns)

print("\nPreview of gene annotation data:")
print(preview_df(gene_annotation))
# 1. Based on the gene identifiers and gene annotation data:
# - Gene expression data uses identifiers like "A_19_P00315452"
# - Gene annotation data has matching IDs in the "ID" column
# - Gene symbols are stored in "GENE_SYMBOL" column

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

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

# Preview the results
print("Preview of mapped gene expression data:")
print(gene_data.head())
print("\nShape after mapping:", gene_data.shape)

# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# Reload clinical data that was processed earlier
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# 1. Normalize gene symbols 
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

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

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

# 4. Check for bias in trait and demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and information saving
note = "Dataset contains gene expression data from rectal cancer patients examining chemoradiotherapy response."
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=note
)

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