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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Allergies"
cohort = "GSE270312"
# Input paths
in_trait_dir = "../DATA/GEO/Allergies"
in_cohort_dir = "../DATA/GEO/Allergies/GSE270312"
# Output paths
out_data_file = "./output/preprocess/3/Allergies/GSE270312.csv"
out_gene_data_file = "./output/preprocess/3/Allergies/gene_data/GSE270312.csv"
out_clinical_data_file = "./output/preprocess/3/Allergies/clinical_data/GSE270312.csv"
json_path = "./output/preprocess/3/Allergies/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")
# Analyze gene expression data availability
is_gene_available = True # RNA transcriptome responses were measured according to background info
# Define row indices for variables
trait_row = 5 # allergic rhinitis status
gender_row = 2
age_row = None # age data not available
# Define conversion functions
def convert_trait(value: str) -> int:
if not value or ':' not in value:
return None
value = value.split(':')[1].strip().lower()
if value == 'yes':
return 1
elif value == 'no':
return 0
return None
def convert_gender(value: str) -> int:
if not value or ':' not in value:
return None
value = value.split(':')[1].strip().lower()
if value == 'female':
return 0
elif value == 'male':
return 1
return None
convert_age = None # No age data
# 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)
# Extract clinical features if trait data is available
if trait_row is not None:
selected_clinical_df = 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 data
print("Preview of selected clinical features:")
print(preview_df(selected_clinical_df))
# Save to CSV
selected_clinical_df.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 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)
# Looking at the identifiers like ABCF1, ACE, ACKR2, ACKR3, ACKR4, etc.
# These appear to be standard human gene symbols following HGNC nomenclature
# No mapping needed as they are already in the correct format
requires_gene_mapping = False
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
# Save normalized gene data
gene_data.to_csv(out_gene_data_file)
# 2. Link clinical and genetic data
try:
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. Determine if features are biased
is_trait_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_trait_biased,
df=linked_data,
note="Gene expression data successfully mapped and linked with clinical features"
)
# 6. Save linked data if usable
if is_usable:
linked_data.to_csv(out_data_file)
except Exception as e:
print(f"Error in data linking and processing: {str(e)}")
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=True,
df=pd.DataFrame(),
note=f"Data processing failed: {str(e)}"
) |