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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Pancreatic_Cancer"
cohort = "GSE236951"
# Input paths
in_trait_dir = "../DATA/GEO/Pancreatic_Cancer"
in_cohort_dir = "../DATA/GEO/Pancreatic_Cancer/GSE236951"
# Output paths
out_data_file = "./output/preprocess/3/Pancreatic_Cancer/GSE236951.csv"
out_gene_data_file = "./output/preprocess/3/Pancreatic_Cancer/gene_data/GSE236951.csv"
out_clinical_data_file = "./output/preprocess/3/Pancreatic_Cancer/clinical_data/GSE236951.csv"
json_path = "./output/preprocess/3/Pancreatic_Cancer/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
# The series summary indicates nanostring gene expression analysis of ~700 immune related genes
is_gene_available = True
# 2.1 Data Availability
# Disease status in row 0, gender in row 2, age in row 3
trait_row = 0
gender_row = 2
age_row = 3
# 2.2 Data Type Conversion Functions
def convert_trait(x: str) -> Optional[int]:
if not isinstance(x, str):
return None
x = x.lower().split(': ')[-1]
if 'pancreatic' in x:
return 1
elif 'colon' in x or 'benign' in x:
return 0
return None
def convert_gender(x: str) -> Optional[int]:
if not isinstance(x, str):
return None
x = x.lower().split(': ')[-1]
if 'female' in x:
return 0
elif 'male' in x:
return 1
return None
def convert_age(x: str) -> Optional[float]:
if not isinstance(x, str):
return None
try:
return float(x.split(': ')[-1].split()[0])
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
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
)
print("Preview of extracted clinical features:")
print(preview_df(clinical_features))
clinical_features.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)
# The gene identifiers are already human gene symbols (like A2M, ABCB1, ABL1, etc.)
# No mapping is needed
requires_gene_mapping = False
# 1. Normalize gene symbols and save normalized gene data
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)
# 2. Link clinical and genetic data and trait
# First get selected clinical features using the extraction function from previous step
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,
gender_row=gender_row,
convert_gender=convert_gender
)
linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)
# 3. Handle missing values systematically
linked_data = handle_missing_values(linked_data, trait)
# 4. Check for biased features and remove them if needed
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Validate data quality and save metadata
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="Gene expression data comparing cervical carcinoma vs normal tissue samples"
)
# 6. Save linked data if usable
if is_usable:
linked_data.to_csv(out_data_file) |