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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Mesothelioma"
cohort = "GSE248514"
# Input paths
in_trait_dir = "../DATA/GEO/Mesothelioma"
in_cohort_dir = "../DATA/GEO/Mesothelioma/GSE248514"
# Output paths
out_data_file = "./output/preprocess/3/Mesothelioma/GSE248514.csv"
out_gene_data_file = "./output/preprocess/3/Mesothelioma/gene_data/GSE248514.csv"
out_clinical_data_file = "./output/preprocess/3/Mesothelioma/clinical_data/GSE248514.csv"
json_path = "./output/preprocess/3/Mesothelioma/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
# Yes - background info shows nanoString nCounter platform was used for gene expression analysis
is_gene_available = True
# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
trait_row = 5 # progression-free at 6 months row
age_row = None # age not available
gender_row = 3 # gender row
# 2.2 Data Type Conversion Functions
def convert_trait(x):
if pd.isna(x):
return None
val = str(x).split(': ')[-1].strip()
if val == 'Yes':
return 1
elif val == 'No':
return 0
return None
def convert_gender(x):
if pd.isna(x):
return None
val = str(x).split(': ')[-1].strip()
if val == 'Female':
return 0
elif val == 'Male':
return 1
return None
# 3. Save 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. Clinical Feature Extraction
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=None,
gender_row=gender_row,
convert_gender=convert_gender
)
# Preview the extracted features
print("Preview of clinical features:")
print(preview_df(clinical_features))
# Save clinical features
clinical_features.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])
# The gene identifiers shown in the first 20 rows are already in standard human gene symbol format
# (e.g., A2M, ABCF1, ACVR1C, etc). These are official HGNC gene symbols.
# No mapping is required.
requires_gene_mapping = False
# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(genetic_data)
genetic_data.to_csv(out_gene_data_file)
# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_features, genetic_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 mesothelioma samples, but case/control ratio is heavily imbalanced."
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) |