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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Vitamin_D_Levels"
cohort = "GSE123993"
# Input paths
in_trait_dir = "../DATA/GEO/Vitamin_D_Levels"
in_cohort_dir = "../DATA/GEO/Vitamin_D_Levels/GSE123993"
# Output paths
out_data_file = "./output/preprocess/3/Vitamin_D_Levels/GSE123993.csv"
out_gene_data_file = "./output/preprocess/3/Vitamin_D_Levels/gene_data/GSE123993.csv"
out_clinical_data_file = "./output/preprocess/3/Vitamin_D_Levels/clinical_data/GSE123993.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)
# 1. Gene Expression Data Availability
# From background info, this is microarray data using Affymetrix HuGene arrays
is_gene_available = True
# 2.1 Data Availability
# Vitamin D levels can be inferred from the intervention + time information (rows 3 and 4)
trait_row = 3
# Age is not recorded in sample characteristics
age_row = None
# Gender is in row 1 as "Sex"
gender_row = 1
# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> float:
"""Convert intervention status to vitamin D level indicator.
25(OH)D3 supplementation increases vitamin D compared to placebo"""
if not value or ":" not in value:
return None
value = value.split(":")[1].strip()
# Return 1 for supplementation group, 0 for placebo
if "25-hydroxycholecalciferol" in value:
return 1.0
elif "Placebo" in value:
return 0.0
return None
def convert_gender(value: str) -> int:
"""Convert gender to binary (0=female, 1=male)"""
if not value or ":" not in value:
return None
value = value.split(":")[1].strip().lower()
if value == "female":
return 0
elif value == "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
clinical_df = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait,
gender_row=gender_row, convert_gender=convert_gender)
print("Preview of extracted clinical features:")
print(preview_df(clinical_df))
# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
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]))
# Looking at the probe IDs like '16650001', they appear to be Illumina BeadArray probe IDs
# These are not human gene symbols and need to be mapped to gene symbols
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)
# The probe identifiers in gene_annotation['ID'] match the expression data's index
# The gene symbols are in the 'gene_assignment' column and need extraction
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
# Convert probe-level data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)
# Normalize gene symbols using NCBI synonyms database
gene_data = normalize_gene_symbols_in_index(gene_data)
# Print preview of gene data
print("Gene expression data preview:")
print(preview_df(gene_data))
# Save gene data
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
# 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) |