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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Head_and_Neck_Cancer"
cohort = "GSE184944"
# Input paths
in_trait_dir = "../DATA/GEO/Head_and_Neck_Cancer"
in_cohort_dir = "../DATA/GEO/Head_and_Neck_Cancer/GSE184944"
# Output paths
out_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/GSE184944.csv"
out_gene_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/gene_data/GSE184944.csv"
out_clinical_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/clinical_data/GSE184944.csv"
json_path = "./output/preprocess/3/Head_and_Neck_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
# Yes - based on background info this is a gene expression dataset using NanoString technology
is_gene_available = True
# 2.1 Data Availability
# Row 0 contains leukoplakia type info which maps to clinical phenotype:
# PL (proliferative leukoplakia) has higher malignant transformation risk vs LL (localized leukoplakia)
trait_row = 0
# Age not recorded in characteristics
age_row = None
# Row 2 contains gender info
gender_row = 2
# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
"""Convert leukoplakia type to binary:
1 for PL/PEL (proliferative forms with higher risk)
0 for LL/EL (localized forms with lower risk)
"""
val = value.split(': ')[1].strip().upper()
if val in ['PL', 'PEL']:
return 1
elif val in ['LL', 'EL']:
return 0
return None
def convert_age(value: str) -> float:
# Not used since age data unavailable
return None
def convert_gender(value: str) -> int:
"""Convert gender to binary: 0 for Female, 1 for Male"""
val = value.split(': ')[1].strip().upper()
if val == 'F':
return 0
elif val == 'M':
return 1
return None
# 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
if trait_row is not None:
clinical_features = geo_select_clinical_features(
clinical_df=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
)
print("Preview of extracted clinical features:")
print(preview_df(clinical_features))
clinical_features.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 standard human gene symbols (e.g. A2M, ABCB1, ABL1, etc.)
# so no mapping is needed
requires_gene_mapping = False
# 1. Normalize gene symbols and save normalized gene data
normalized_gene_data = normalize_gene_symbols_in_index(genetic_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)
# Read the processed clinical data file
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
# Link clinical and genetic data using the normalized gene data
linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_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 = "Gene expression data from oral leukoplakia study comparing proliferative vs localized types."
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:
os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
linked_data.to_csv(out_data_file)
else:
print(f"Dataset {cohort} did not pass quality validation and will not be saved.") |