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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Height"
cohort = "GSE106800"
# Input paths
in_trait_dir = "../DATA/GEO/Height"
in_cohort_dir = "../DATA/GEO/Height/GSE106800"
# Output paths
out_data_file = "./output/preprocess/3/Height/GSE106800.csv"
out_gene_data_file = "./output/preprocess/3/Height/gene_data/GSE106800.csv"
out_clinical_data_file = "./output/preprocess/3/Height/clinical_data/GSE106800.csv"
json_path = "./output/preprocess/3/Height/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 Data Availability
# From background info: "Microarray analysis was performed on skeletal muscle biopsies"
# This indicates gene expression data is available
is_gene_available = True
# 2. Variable Availability and Data Type Conversion
# Height data is available in row 3
trait_row = 3
def convert_trait(x):
try:
# Extract numeric value after colon and space
return float(x.split(': ')[1])
except:
return None
# Age data is available in row 2
age_row = 2
def convert_age(x):
try:
return float(x.split(': ')[1])
except:
return None
# Gender data is available in row 0 but only one value (male)
gender_row = None # Constant features are not useful
def convert_gender(x):
try:
val = x.split(': ')[1].lower()
if val == 'male':
return 1
elif val == 'female':
return 0
return None
except:
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
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
preview = preview_df(clinical_features)
print("Preview of clinical features:")
print(preview)
# Save clinical features
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())
# Review the gene identifiers
# The identifiers are numeric codes like '16650001', '16650003' etc.
# These are not standard gene symbols (like BRCA1, TNF etc.)
# They appear to be probe IDs that need to be mapped to gene symbols
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())
print("\nPreview of first few rows:")
print(json.dumps(preview_df(gene_metadata), indent=2))
# Get gene mapping dataframe from gene annotation
# 'ID' column in gene_metadata contains probe IDs that match gene expression data
# 'gene_assignment' contains gene symbols in a messy format
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment')
# Apply the mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(genetic_data, mapping_df)
# Save the gene expression data
gene_data.to_csv(out_gene_data_file)
# 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)
# 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 skeletal muscle biopsies and height measurements from subjects"
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) |