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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Lung_Cancer"
cohort = "GSE244123"
# Input paths
in_trait_dir = "../DATA/GEO/Lung_Cancer"
in_cohort_dir = "../DATA/GEO/Lung_Cancer/GSE244123"
# Output paths
out_data_file = "./output/preprocess/3/Lung_Cancer/GSE244123.csv"
out_gene_data_file = "./output/preprocess/3/Lung_Cancer/gene_data/GSE244123.csv"
out_clinical_data_file = "./output/preprocess/3/Lung_Cancer/clinical_data/GSE244123.csv"
json_path = "./output/preprocess/3/Lung_Cancer/cohort_info.json"
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Extract background info and clinical data using specified prefixes
background_info, clinical_data = get_background_and_clinical_data(
matrix_file,
prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1']
)
# 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
is_gene_available = True # Title indicates gene expression data from lung cancer
# 2.1 Data Availability
trait_row = 1 # Can use grade as indicator of lung cancer status, normal vs grades II-IV
age_row = 5 # Age data is available
gender_row = 4 # Gender data is available as Sex
# 2.2 Data Type Conversion Functions
def convert_trait(x):
if pd.isna(x):
return None
val = x.split(': ')[1].strip()
if val == 'normal':
return 0
elif val in ['II', 'III', 'IV']:
return 1
return None
def convert_age(x):
if pd.isna(x):
return None
try:
return float(x.split(': ')[1])
except:
return None
def convert_gender(x):
if pd.isna(x):
return None
val = x.split(': ')[1].strip()
if val == 'F':
return 0
elif val == 'M':
return 1
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
print("Preview of clinical features:")
print(preview_df(clinical_features))
# Save to CSV
clinical_features.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)
# Looking at the IDs like A1BG, A1CF, A2M, etc.
# These are standard HGNC gene symbols based on nomenclature from HUGO Gene Nomenclature Committee (HGNC)
# No mapping needed as they are already standard human gene symbols
requires_gene_mapping = False
# 1. Normalize gene symbols using NCBI Gene database synonyms
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
normalized_gene_data.to_csv(out_gene_data_file)
# Load clinical data from previous steps
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
# 3. Handle missing values systematically
linked_data = handle_missing_values(linked_data, trait)
# 4. Evaluate bias in features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Record cohort information
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="Contains normalized gene expression data and clinical data."
)
# 6. Save data if usable
if is_usable:
linked_data.to_csv(out_data_file) |