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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Height"
cohort = "GSE117525"
# Input paths
in_trait_dir = "../DATA/GEO/Height"
in_cohort_dir = "../DATA/GEO/Height/GSE117525"
# Output paths
out_data_file = "./output/preprocess/3/Height/GSE117525.csv"
out_gene_data_file = "./output/preprocess/3/Height/gene_data/GSE117525.csv"
out_clinical_data_file = "./output/preprocess/3/Height/clinical_data/GSE117525.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
# Based on background info mentioning "skeletal muscle transcriptome" and "gene expression profiling"
is_gene_available = True
# 2. Variable Availability and Data Type Conversion
# 2.1 Row identifiers
trait_row = 4 # Height data in row 4
age_row = 3 # Age data in row 3
gender_row = 1 # Gender data in row 1
# 2.2 Conversion functions
def convert_trait(x):
if pd.isna(x):
return None
try:
# Extract height value after colon and convert to float
height = float(x.split(': ')[1])
return height
except:
return None
def convert_age(x):
if pd.isna(x):
return None
try:
# Extract age value and convert to float
age = float(x.split(': ')[1])
return age
except:
return None
def convert_gender(x):
if pd.isna(x):
return None
try:
# Extract gender value after colon
gender = x.split(': ')[1].strip().upper()
if gender == 'F':
return 0
elif gender == 'M':
return 1
return None
except:
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. Extract clinical features
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 to CSV
clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from the matrix file
with gzip.open(matrix_file_path, 'rt') as file:
for i, line in enumerate(file):
if "!series_matrix_table_begin" in line:
skip_rows = i + 1
break
genetic_data = pd.read_csv(matrix_file_path, compression='gzip', skiprows=skip_rows-1,
sep='\t', comment='!', header=0, index_col=0)
# Print information about the data structure
print("First few rows of the genetic data:")
print(genetic_data.head())
print("\nShape of genetic data:", genetic_data.shape)
print("\nColumn names:", genetic_data.columns.tolist())
# Looking at the identifiers like "100009676_at", "10000_at", "10001_at", etc.
# These appear to be probe IDs from a microarray platform rather than standard human gene symbols
# The "_at" suffix is characteristic of Affymetrix arrays
# These identifiers will need to be mapped to official gene symbols
requires_gene_mapping = True
# Extract gene annotation data from SOFT file
gene_metadata = get_gene_annotation(soft_file_path)
# Parse the Description column to extract gene symbols
mapping_data = pd.DataFrame({
'ID': gene_metadata['ID'],
'Gene': gene_metadata['Description'].apply(lambda x: x.split(',')[0].strip() if pd.notna(x) else None)
})
print("Preview of gene mapping data:")
print(json.dumps(preview_df(mapping_data), indent=2))
# Get gene mapping dataframe from gene annotation data
# ID column matches the probe IDs in gene expression data (e.g. "100009676_at")
# Description column contains gene names that we need to map to
mapping_df = pd.DataFrame({
'ID': gene_metadata['ID'],
'Gene': gene_metadata['Description'].apply(lambda x: extract_human_gene_symbols(x)[0] if pd.notna(x) and extract_human_gene_symbols(x) else None)
})
# Apply gene mapping to convert probe data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)
# Preview the mapped gene data
print("\nPreview of gene expression data after mapping:")
print(gene_data.head())
print("\nShape of gene expression data:", gene_data.shape)
# 1. Normalize gene symbols
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. Clean height measurements - filter out weight values that were incorrectly recorded as height
linked_data = linked_data[linked_data[trait] < 3.0] # Keep only plausible height values in meters
# 4. Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# 5. Judge whether features are biased and remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 6. Final validation and save metadata
note = "Dataset contains gene expression data from muscle tissue. Original height measurements had inconsistent units - filtered to keep only plausible meter values."
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
)
# 7. 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) |