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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Vitamin_D_Levels"
cohort = "GSE76324"
# Input paths
in_trait_dir = "../DATA/GEO/Vitamin_D_Levels"
in_cohort_dir = "../DATA/GEO/Vitamin_D_Levels/GSE76324"
# Output paths
out_data_file = "./output/preprocess/3/Vitamin_D_Levels/GSE76324.csv"
out_gene_data_file = "./output/preprocess/3/Vitamin_D_Levels/gene_data/GSE76324.csv"
out_clinical_data_file = "./output/preprocess/3/Vitamin_D_Levels/clinical_data/GSE76324.csv"
json_path = "./output/preprocess/3/Vitamin_D_Levels/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 shape and first few rows to verify data
print("Background Information:")
print(background_info)
print("\nClinical Data Shape:", clinical_data.shape)
print("\nFirst few rows of Clinical Data:")
print(clinical_data.head())
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)
# Check if the dataset contains gene expression data
# From background information, this is a microarray dataset for airway epithelium transcriptome analysis
is_gene_available = True
# Find row indices and define conversion functions for clinical features
trait_row = 3 # Row containing vitamin D levels
age_row = None # Age information not available
gender_row = None # Gender information not available
def convert_trait(value):
if pd.isna(value):
return None
# Extract value after colon and convert to numeric values
if 'serum 25-oh-d:' in value.lower():
if 'low' in value.lower():
return 0
elif 'mid' in value.lower():
return 1
elif 'high' in value.lower():
return 2
return None
def convert_age(value):
return None
def convert_gender(value):
return None
# Save cohort metadata
is_trait_available = trait_row is not None
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=is_trait_available)
# Extract clinical features if trait data is available
if is_trait_available:
selected_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 processed clinical data
preview = preview_df(selected_clinical_df)
print("Preview of processed clinical data:", preview)
# Save processed clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
selected_clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)
# Print first 20 row IDs
print("First 20 gene/probe IDs:")
print(list(genetic_data.index[:20]))
requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)
# Preview annotation structure
preview = preview_df(gene_annotation)
print("Gene annotation preview:")
print(preview)
# Extract probe-gene mapping from gene annotation
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
# Apply probe-to-gene mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(genetic_data, mapping_data)
# Preview the gene data
print("\nFirst 10 genes and their data shape:")
print("Shape:", gene_data.shape)
print("Gene symbols:", list(gene_data.index[:10]))
# 1. Normalize gene symbols in gene expression data
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
print("\nGene data shape (normalized gene-level):", gene_data.shape)
# 2. Link clinical and genetic data
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# 4. Check for bias in features
is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Validate and save dataset metadata
note = "Dataset contains gene expression data from cancer cell lines, but has severely imbalanced distribution of carcinosarcoma cases."
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_trait_biased,
df=linked_data,
note=note
)
# 6. 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) |