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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Hepatitis"
cohort = "GSE45032"
# Input paths
in_trait_dir = "../DATA/GEO/Hepatitis"
in_cohort_dir = "../DATA/GEO/Hepatitis/GSE45032"
# Output paths
out_data_file = "./output/preprocess/3/Hepatitis/GSE45032.csv"
out_gene_data_file = "./output/preprocess/3/Hepatitis/gene_data/GSE45032.csv"
out_clinical_data_file = "./output/preprocess/3/Hepatitis/clinical_data/GSE45032.csv"
json_path = "./output/preprocess/3/Hepatitis/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
# Based on series title and summary, this is a gene expression microarray study
is_gene_available = True
# 2. Data Type Conversion Functions
def convert_trait(value):
# Binary: 0 for CHC, 1 for HCC
if not value or ':' not in value:
return None
value = value.split(': ')[1].lower()
if 'hepatitis' in value or 'chc' in value:
return 0
elif 'carcinoma' in value or 'hcc' in value:
return 1
return None
def convert_age(value):
# Continuous
if not value or ':' not in value:
return None
try:
return float(value.split(': ')[1])
except:
return None
def convert_gender(value):
# Binary: 0 for female, 1 for male
if not value or ':' not in value:
return None
value = value.split(': ')[1].lower()
if 'female' in value:
return 0
elif 'male' in value:
return 1
return None
# 2.1 Data Row Identification
trait_row = 0 # cell type field contains trait info
age_row = 3 # age(yrs) field
gender_row = 2 # gender field
# 3. Save Initial 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:
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
preview_result = preview_df(clinical_features)
print("Preview of clinical features:")
print(preview_result)
# 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 identifiers, which are just numbers starting from 1
# These are not human gene symbols and will need to be mapped
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)
# Preview the annotation data
print("Column names:", gene_metadata.columns.tolist())
print("\nFirst few rows preview:")
print(preview_df(gene_metadata))
# Extract gene mapping - using 'ID' as identifier column and 'GeneName' as gene symbol column
mapping_data = get_gene_mapping(gene_metadata, 'ID', 'GeneName')
# Drop control probes based on GeneName being control types
control_names = ['GE_BrightCorner', 'DarkCorner']
mapping_data = mapping_data[~mapping_data['Gene'].isin(control_names)]
# Apply gene mapping to convert probe level data to gene expression
gene_data = apply_gene_mapping(gene_data, mapping_data)
# Preview the result
print("Shape of mapped gene expression data:", gene_data.shape)
print("\nFirst few rows of mapped data:")
print(gene_data.head())
print("\nFirst 20 gene symbols:")
print(gene_data.index[:20])
# 1. Since gene symbol normalization failed, we'll work with probe-level expression data
# Save the probe-level expression data
gene_data.to_csv(out_gene_data_file)
# 2. Load clinical data and link with probe-level expression data
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
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. Evaluate bias in features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Record cohort information with probe-level data note
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 numerical probe-level expression data (gene symbol normalization was skipped) and clinical data."
)
# 6. Save data if usable
if is_usable:
linked_data.to_csv(out_data_file) |