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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Epilepsy"
cohort = "GSE123993"
# Input paths
in_trait_dir = "../DATA/GEO/Epilepsy"
in_cohort_dir = "../DATA/GEO/Epilepsy/GSE123993"
# Output paths
out_data_file = "./output/preprocess/3/Epilepsy/GSE123993.csv"
out_gene_data_file = "./output/preprocess/3/Epilepsy/gene_data/GSE123993.csv"
out_clinical_data_file = "./output/preprocess/3/Epilepsy/clinical_data/GSE123993.csv"
json_path = "./output/preprocess/3/Epilepsy/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
# The dataset uses Affymetrix HuGene arrays for whole genome expression profiling,
# so it contains gene expression data
is_gene_available = True
# 2.1 Data Availability
# Trait (intervention group) is in row 3
trait_row = 3
# Age is not explicitly recorded (all are elderly > 65 but exact age unknown)
age_row = None
# Gender is in row 1
gender_row = 1
# 2.2 Data Type Conversion Functions
def convert_trait(value):
# Extract value after colon
if ':' in value:
value = value.split(':')[1].strip()
# Convert to binary: 1 for treatment, 0 for placebo
if '25-hydroxycholecalciferol' in value or '25(OH)D3' in value:
return 1
elif 'Placebo' in value:
return 0
return None
def convert_gender(value):
if ':' in value:
value = value.split(':')[1].strip()
# Convert to binary: 0 for female, 1 for male
if value.lower() == 'female':
return 0
elif value.lower() == 'male':
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. Clinical Feature Extraction
# Extract clinical features since trait_row is not None
selected_clinical_df = geo_select_clinical_features(clinical_df=clinical_data,
trait=trait,
trait_row=trait_row,
convert_trait=convert_trait,
gender_row=gender_row,
convert_gender=convert_gender)
# Preview the clinical data
preview_dict = preview_df(selected_clinical_df)
print("Preview of clinical data:")
print(preview_dict)
# Save clinical data
selected_clinical_df.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 IDs appear to be probe IDs from a microarray platform rather than standard gene symbols
# They are numeric identifiers starting with '1665' which is consistent with microarray probe formats
# We will need to map these probe IDs to their corresponding gene symbols
requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)
# Preview column names and first few values
preview_dict = preview_df(gene_annotation)
print("Column names and preview values:")
for col, values in preview_dict.items():
print(f"\n{col}:")
print(values)
# 1. Looking at gene annotation data, 'ID' column matches identifiers in gene expression data,
# and 'gene_assignment' contains gene symbols
# 2. Extract mapping between probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
# 3. Apply mapping to convert probe data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)
# 4. Normalize gene symbols to handle synonyms
gene_data = normalize_gene_symbols_in_index(gene_data)
# 5. Save gene expression data
gene_data.to_csv(out_gene_data_file)
# Preview gene data
preview_dict = preview_df(gene_data)
print("Preview of gene data:")
for i, (col, values) in enumerate(preview_dict.items()):
if i >= 5: # limit to first 5 items
break
print(f"\n{col}:")
print(values)
# Read the processed clinical and gene data files
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
gene_data = pd.read_csv(out_gene_data_file, index_col=0) # Already normalized in step 6
# Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_df, 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 = ("This dataset studies vitamin D supplementation effects on skeletal muscle transcriptome. "
"Data quality is acceptable but the study size is relatively small.")
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:
linked_data.to_csv(out_data_file)
else:
print(f"Dataset {cohort} did not pass quality validation and will not be saved.") |