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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Asthma"
cohort = "GSE182797"
# Input paths
in_trait_dir = "../DATA/GEO/Asthma"
in_cohort_dir = "../DATA/GEO/Asthma/GSE182797"
# Output paths
out_data_file = "./output/preprocess/3/Asthma/GSE182797.csv"
out_gene_data_file = "./output/preprocess/3/Asthma/gene_data/GSE182797.csv"
out_clinical_data_file = "./output/preprocess/3/Asthma/clinical_data/GSE182797.csv"
json_path = "./output/preprocess/3/Asthma/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
# Based on series title and overall design, this dataset contains transcriptomic data from nasal biopsies
is_gene_available = True
# 2.1 Data Availability
# trait data is in Feature 0 (diagnosis)
trait_row = 0
# gender data is in Feature 1 but only contains females
gender_row = None # Not useful since all subjects are female
# age data is in Feature 2
age_row = 2
# 2.2 Data Type Conversion Functions
def convert_trait(x):
if not isinstance(x, str):
return None
value = x.split(': ')[-1].lower()
if 'adult-onset asthma' in value:
return 1
elif 'healthy' in value:
return 0
return None
def convert_age(x):
if not isinstance(x, str):
return None
try:
value = x.split(': ')[-1]
return float(value)
except:
return None
def convert_gender(x):
# This function won't be used but included for completeness
if not isinstance(x, str):
return None
value = x.split(': ')[-1].lower()
if 'female' in value:
return 0
elif 'male' in value:
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
# Since trait_row is not None, we need to extract clinical features
selected_clinical = 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)
# Preview the extracted features
print("Preview of extracted clinical features:")
print(preview_df(selected_clinical))
# Save clinical data
selected_clinical.to_csv(out_clinical_data_file)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)
# Print first 20 row IDs and shape of data to help debug
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])
# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
lines = []
for i, line in enumerate(f):
if "!series_matrix_table_begin" in line:
# Get the next 5 lines after the marker
for _ in range(5):
lines.append(next(f).strip())
break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
print(line)
# These are Agilent probe identifiers starting with A_19_P, NOT human gene symbols
requires_gene_mapping = True
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file)
# Preview annotation dataframe structure
print("Gene Annotation Preview:")
print("Column names:", gene_annotation.columns.tolist())
print("\nFirst few rows as dictionary:")
print(preview_df(gene_annotation))
# 1. Based on the dataframes, 'ID' column contains probe identifiers, and 'GENE_SYMBOL' contains gene symbols
prob_col = 'ID'
gene_col = 'GENE_SYMBOL'
# 2. Get gene mapping dataframe
mapping_data = get_gene_mapping(gene_annotation, prob_col, gene_col)
# 3. Convert probe-level measurements to gene expression
gene_data = apply_gene_mapping(gene_data, mapping_data)
# Print dimensions and first few rows to verify the mapping
print("Shape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few rows of mapped data:")
print(gene_data.head())
# 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
clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# 4. Evaluate bias
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Validate and save cohort info
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="Dataset contains RNA transcriptome data in human sinonasal epithelial cells."
)
# 6. Save linked data if usable
if is_usable:
linked_data.to_csv(out_data_file) |