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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Bladder_Cancer"
cohort = "GSE145261"
# Input paths
in_trait_dir = "../DATA/GEO/Bladder_Cancer"
in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE145261"
# Output paths
out_data_file = "./output/preprocess/3/Bladder_Cancer/GSE145261.csv"
out_gene_data_file = "./output/preprocess/3/Bladder_Cancer/gene_data/GSE145261.csv"
out_clinical_data_file = "./output/preprocess/3/Bladder_Cancer/clinical_data/GSE145261.csv"
json_path = "./output/preprocess/3/Bladder_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
is_gene_available = True # Based on context, this dataset studies carcinoma with molecular analysis
# 2. Variable Availability and Data Type Conversion
# 2.1 Row identification
trait_row = 3 # "tissue type" contains SCC vs UC info
age_row = 0 # "subject age" contains age info
gender_row = 1 # "subject gender" contains gender info
# 2.2 Conversion functions
def convert_trait(x):
# Binary: SCC (1) vs UC (0)
if not isinstance(x, str):
return None
x = x.lower()
if 'scc' in x or 'small cell' in x:
return 1
elif 'uc' in x or 'urothelial' in x:
return 0
return None
def convert_age(x):
# Continuous: extract age in years
if not isinstance(x, str):
return None
try:
age = int(''.join(filter(str.isdigit, x)))
if 0 <= age <= 120: # Basic age validation
return age
return None
except:
return None
def convert_gender(x):
# Binary: female (0) vs male (1)
if not isinstance(x, str):
return None
x = x.lower()
if 'female' in x:
return 0
elif 'male' in x:
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. Extract clinical features
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 extracted features
preview_data = preview_df(selected_clinical_df)
print("Preview of extracted clinical features:", preview_data)
# 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)
# These are Illumina probes (starting with ILMN_), not gene symbols
# We'll need to map them to human gene symbols for analysis
requires_gene_mapping = True
# Extract gene annotation from SOFT file using default prefixes
gene_annotation = get_gene_annotation(soft_file)
# Preview gene annotation data
print("Gene annotation shape:", gene_annotation.shape)
print("\nGene annotation columns and first few values:")
print(preview_df(gene_annotation))
# Also inspect the raw SOFT file annotation section to verify parsing
import gzip
with gzip.open(soft_file, 'rt') as f:
found_table = False
lines = []
for line in f:
if '!platform_table_begin' in line.lower():
found_table = True
lines.append(next(f)) # Get header line
for _ in range(3): # Get first 3 data lines
lines.append(next(f))
break
if found_table:
print("\nRaw annotation format in SOFT file:")
for line in lines:
print(line.strip())
# Extract gene mapping from annotation
# 'ID' column contains probe IDs (ILMN_*) matching the gene expression data
# 'Symbol' column contains gene symbols we want to map to
gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
# Apply gene mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(gene_data, gene_mapping)
# Print info about the conversion
print("Shape of mapped gene expression data:", gene_data.shape)
print("\nFirst few rows of mapped gene expression data:")
print(gene_data.head())
# 1. Normalize gene symbols and save normalized gene data
gene_data.index = gene_data.index.str.replace('-mRNA', '')
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
# 3. Handle missing values
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 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="NanoString nCounter RNA profiling data for bladder cancer recurrence study"
)
# 6. Save linked data if usable
if is_usable:
os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
linked_data.to_csv(out_data_file) |