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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Esophageal_Cancer"
cohort = "GSE156915"
# Input paths
in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE156915"
# Output paths
out_data_file = "./output/preprocess/3/Esophageal_Cancer/GSE156915.csv"
out_gene_data_file = "./output/preprocess/3/Esophageal_Cancer/gene_data/GSE156915.csv"
out_clinical_data_file = "./output/preprocess/3/Esophageal_Cancer/clinical_data/GSE156915.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
# From the background info, we can see this is a gene expression study investigating
# DNA damage immune response in colorectal cancer
is_gene_available = True
# 2.1 Data Availability
# Looking at the sample characteristics:
# - Row 0 shows DDIR status which indicates DNA damage response status
trait_row = 0
# Age and gender info not available in sample characteristics
age_row = None
gender_row = None
# 2.2 Data Type Conversion Functions
def convert_trait(x):
if pd.isna(x):
return None
# Extract value after colon and strip whitespace
val = x.split(':')[1].strip()
# DDIR NEG = control = 0, DDIR POS = case = 1
if 'NEG' in val:
return 0
elif 'POS' in val:
return 1
return None
def convert_age(x):
# Not available
return None
def convert_gender(x):
# Not available
return 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. Extract Clinical Features
if trait_row is not None:
clinical_df = 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 the extracted features
preview = preview_df(clinical_df)
print("Preview of clinical features:")
print(preview)
# Save to CSV
clinical_df.to_csv(out_clinical_data_file)
# 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 appear to be human gene symbols, with some RNA genes and pseudogenes
# The identifiers match official HGNC gene symbols and nomenclature patterns
requires_gene_mapping = False
# 1. Normalize gene symbols and save normalized gene data
normalized_gene_data = normalize_gene_symbols_in_index(genetic_data)
normalized_gene_data.to_csv(out_gene_data_file)
# Read the processed clinical and gene data files
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
gene_data = pd.read_csv(out_gene_data_file, index_col=0)
# Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
# Handle missing values systematically
linked_data = handle_missing_values(linked_data, trait)
# Detect bias in trait and demographic features, remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# Validate data quality and save cohort info
note = ("This dataset studies gene expression profiles in esophageal squamous cell carcinoma, "
"comparing tumor samples with matched nonmalignant mucosa. The sample size is moderate with paired samples.")
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.") |