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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Acute_Myeloid_Leukemia"
cohort = "GSE249638"
# Input paths
in_trait_dir = "../DATA/GEO/Acute_Myeloid_Leukemia"
in_cohort_dir = "../DATA/GEO/Acute_Myeloid_Leukemia/GSE249638"
# Output paths
out_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/GSE249638.csv"
out_gene_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/gene_data/GSE249638.csv"
out_clinical_data_file = "./output/preprocess/3/Acute_Myeloid_Leukemia/clinical_data/GSE249638.csv"
json_path = "./output/preprocess/3/Acute_Myeloid_Leukemia/cohort_info.json"
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Extract background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file)
# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)
# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")
# Print sample characteristics
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
print(f"Feature: {feature}")
print(f"Values: {values}\n")
# 1. Gene Expression Data Availability
# Yes - this is a transcriptomic profiling study of CD4+ T cells
is_gene_available = True
# 2.1 Data Availability & 2.2 Data Type Conversion
# Trait (AML status) is available in Feature 1, using binary type
trait_row = 1
def convert_trait(x):
if not x or ':' not in x:
return None
value = x.split(':')[1].strip().lower()
if 'acute myeloid leukemia' in value:
return 1
elif 'healthy control' in value:
return 0
return None
# Age not available
age_row = None
convert_age = None
# Gender not available
gender_row = None
convert_gender = 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
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
print("Preview of clinical features:")
print(preview_df(clinical_features))
# Save to CSV
clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)
# Print first 20 row IDs
print("First 20 gene/probe identifiers:")
print(gene_data.index[:20])
# The identifiers like '2824546_st' are probe IDs from Affymetrix microarray platform, not human gene symbols
requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file)
# Preview gene annotation data
print("Gene annotation columns and example values:")
print(preview_df(gene_annotation))
# 2. Get mapping between probe IDs and gene symbols
gene_annotation = gene_annotation.drop('ID', axis=1) # Drop the original ID column
gene_annotation = gene_annotation.rename(columns={'probeset_id': 'ID'})
mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
# 3. Apply the mapping to convert probe-level measurements to gene expression data
gene_data = apply_gene_mapping(gene_data, mapping)
# Preview first few genes and their expression values
print("\nPreview of mapped gene expression data:")
print(preview_df(gene_data))
# 1. Normalize gene symbols and save normalized gene data
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)
# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
# 3. Handle missing values systematically
linked_data = handle_missing_values(linked_data, trait)
# 4. Check for biased features and remove them if needed
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Validate data quality and save metadata
# Note: Dataset contains gene expression data from AML cell lines. The trait "Acute_Myeloid_Leukemia" is defined
# based on cell subtypes (AMKL vs non-AMKL).
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="Gene expression data from AML cell lines. Trait defined as AMKL vs non-AMKL subtypes."
)
# 6. Save linked data if usable
if is_usable:
linked_data.to_csv(out_data_file) |