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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Mesothelioma"
cohort = "GSE112154"
# Input paths
in_trait_dir = "../DATA/GEO/Mesothelioma"
in_cohort_dir = "../DATA/GEO/Mesothelioma/GSE112154"
# Output paths
out_data_file = "./output/preprocess/3/Mesothelioma/GSE112154.csv"
out_gene_data_file = "./output/preprocess/3/Mesothelioma/gene_data/GSE112154.csv"
out_clinical_data_file = "./output/preprocess/3/Mesothelioma/clinical_data/GSE112154.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("\nClinical Data Structure:")
print(f"Shape: {clinical_data.shape}")
print("\nFirst few rows:")
print(clinical_data.head())
print("\nSample Characteristics:")
# Set pandas to display all rows
pd.set_option('display.max_rows', None)
# 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)
# Reset display options
pd.reset_option('display.max_rows')
# 1. Gene Expression Data Availability
is_gene_available = True # Based on series title and design, this is gene expression profiling data
# 2.1 Data Availability
# trait can be inferred from sample type - normal vs tumor
trait_row = 0
# age and gender not available in the data
age_row = None
gender_row = None
# 2.2 Data Type Conversion Functions
def convert_trait(value):
if not isinstance(value, str):
return None
value = value.lower()
if 'sample type:' not in value:
return None
value = value.split('sample type:')[1].strip()
# Convert to binary: 0 for normal, 1 for disease (DMPM)
if 'normal peritoneum' in value:
return 0
elif 'dmpm' in value:
return 1
return None
def convert_age(value):
return None # No age data
def convert_gender(value):
return None # No gender data
# 3. Save initial filtering results
is_usable = 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
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
)
# Preview the extracted features
preview = preview_df(clinical_features)
print("Clinical Features Preview:", preview)
# Save to CSV
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])
# Review the gene identifiers in the gene expression data
# The identifiers start with "ILMN_" which indicates these are Illumina probe IDs
# These need to be mapped to human gene symbols for analysis
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))
# Extract mapping between probe IDs and gene symbols
gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
# Apply gene mapping to convert probe data to gene expression data
gene_data = apply_gene_mapping(genetic_data, gene_mapping)
print("\nShape after mapping:", gene_data.shape)
print("\nFirst few rows of gene expression data:")
print(gene_data.head())
# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(gene_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 healthy cells and mesothelioma cell lines, suitable for case-control analysis."
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) |