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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Intellectual_Disability"
cohort = "GSE285666"
# Input paths
in_trait_dir = "../DATA/GEO/Intellectual_Disability"
in_cohort_dir = "../DATA/GEO/Intellectual_Disability/GSE285666"
# Output paths
out_data_file = "./output/preprocess/3/Intellectual_Disability/GSE285666.csv"
out_gene_data_file = "./output/preprocess/3/Intellectual_Disability/gene_data/GSE285666.csv"
out_clinical_data_file = "./output/preprocess/3/Intellectual_Disability/clinical_data/GSE285666.csv"
json_path = "./output/preprocess/3/Intellectual_Disability/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)
# Get unique values for each clinical feature
unique_values_dict = get_unique_values_by_row(clinical_data)
# Print background information
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene expression availability - Yes, it uses Affymetrix exon arrays
is_gene_available = True
# 2.1 Get row numbers for clinical features
trait_row = 0 # disease state row
age_row = None # age not available
gender_row = None # gender not available
# 2.2 Define conversion functions
def convert_trait(value: str) -> int:
"""Convert disease state to binary: 1 for Williams syndrome, 0 for control"""
if pd.isna(value) or value is None:
return None
if ':' in value:
value = value.split(':')[1].strip().lower()
if 'williams syndrome' in value:
return 1
elif 'unaffected' in value or 'control' in value:
return 0
return None
def convert_age(value: str) -> float:
"""Placeholder function since age is not available"""
return None
def convert_gender(value: str) -> int:
"""Placeholder function since gender is not available"""
return None
# 3. Save metadata about dataset usability
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. Extract clinical features since trait data is available
clinical_features = geo_select_clinical_features(clinical_df=clinical_data,
trait=trait,
trait_row=trait_row,
convert_trait=convert_trait)
# Preview results
preview_result = preview_df(clinical_features)
print("Preview of extracted clinical features:")
print(preview_result)
# Save clinical data
clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from the matrix file
genetic_data = get_genetic_data(matrix_file_path)
# Print first 20 row IDs
print("First 20 row IDs:")
print(genetic_data.index[:20].tolist())
# These appear to be probe IDs from a microarray platform, not standard human gene symbols
# Examining the numeric format and length pattern confirms they need mapping
requires_gene_mapping = True
# Extract gene annotation data from SOFT file
gene_metadata = get_gene_annotation(soft_file_path)
# Display information about the annotation data
print("Column names:")
print(gene_metadata.columns.tolist())
# Look at general data statistics
print("\nData shape:", gene_metadata.shape)
# Display non-NaN value counts for key gene identifier columns
print("\nNumber of non-NaN values in key columns:")
for col in ['ID', 'gene_assignment']:
print(f"{col}: {gene_metadata[col].notna().sum()}")
# Preview rows with actual gene information
print("\nPreview of rows with gene information:")
gene_rows = gene_metadata[gene_metadata['gene_assignment'].notna()].head()
print(json.dumps(preview_df(gene_rows), indent=2))
# Get gene mapping dataframe
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment')
mapping_data = mapping_data[mapping_data['Gene'] != '---']
# Extract gene symbols from gene_assignment strings
def extract_gene_symbol(text):
if pd.isna(text):
return None
parts = text.split('//')
if len(parts) >= 2:
return parts[1].strip()
return None
mapping_data['Gene'] = mapping_data['Gene'].apply(extract_gene_symbol)
mapping_data = mapping_data.dropna()
# Apply gene mapping to convert probe data to gene data
gene_data = apply_gene_mapping(genetic_data, mapping_data)
# Print info about the converted data
print("Shape of probe-level data:", genetic_data.shape)
print("Shape of gene-level data:", gene_data.shape)
print("\nFirst few genes:")
print(gene_data.index[:10].tolist())
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)
# Get clinical features
clinical_features = 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
)
# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# Early exit if trait values are all NaN
if linked_data[trait].isna().all():
is_biased = True
linked_data = None
else:
# 4. Judge whether features are biased and remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Final validation and save metadata
note = "Dataset contains gene expression data from pediatric AML samples, focusing on Down syndrome cases versus other AML types."
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
)
# 6. Save the linked data only if it's usable
if is_usable:
os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
linked_data.to_csv(out_data_file) |