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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Breast_Cancer"
cohort = "GSE234017"
# Input paths
in_trait_dir = "../DATA/GEO/Breast_Cancer"
in_cohort_dir = "../DATA/GEO/Breast_Cancer/GSE234017"
# Output paths
out_data_file = "./output/preprocess/3/Breast_Cancer/GSE234017.csv"
out_gene_data_file = "./output/preprocess/3/Breast_Cancer/gene_data/GSE234017.csv"
out_clinical_data_file = "./output/preprocess/3/Breast_Cancer/clinical_data/GSE234017.csv"
json_path = "./output/preprocess/3/Breast_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
# Yes, this is a spatial transcriptomics dataset studying gene expression
is_gene_available = True
# 2.1 Data Availability
# From Feature 2, we can infer BRCA mutation status (trait)
trait_row = 2
# Age and gender are not available in the sample characteristics
age_row = None
gender_row = None
# 2.2 Data Type Conversion Functions
def convert_trait(value):
# Extract value after colon and strip whitespace
if ':' in value:
value = value.split(':')[1].strip()
# Convert BRCA mutation status to binary (WT=0, BRCA1/2=1)
if 'WT' in value:
return 0
elif 'BRCA1' in value or 'BRCA2' in value:
return 1
return None
def convert_age(value):
# Not needed since age data unavailable
return None
def convert_gender(value):
# Not needed since gender data unavailable
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:
# Extract clinical features
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
print("Preview of clinical features:")
print(preview_df(clinical_df))
# Save to CSV
clinical_df.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 gene identifiers (starting with "RTS"), these are not standard human gene symbols
# They appear to be probe IDs that need to be mapped to gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)
# Try searching for ID patterns in all columns
print("All column names:", gene_metadata.columns.tolist())
print("\nPreview first few rows of each column to locate numeric IDs:")
for col in gene_metadata.columns:
sample_values = gene_metadata[col].dropna().head().tolist()
print(f"\n{col}:")
print(sample_values)
# Inspect raw file to see unfiltered annotation format
import gzip
print("\nRaw SOFT file preview:")
with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
header = []
for i, line in enumerate(f):
header.append(line.strip())
if i >= 10: # Preview first 10 lines
break
print('\n'.join(header))
# Get gene ID mapping from probe IDs to gene symbols
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='ORF')
# Apply mapping to convert probe-level data to gene-level expression data
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_data)
# Print gene data dimensions and preview
print("\nGene expression data shape after mapping:", 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 = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)
# 2. Load clinical data and prepare genetic data
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
if gene_data.shape[0] > gene_data.shape[1]: # If genes are in rows
gene_data = gene_data.T
# 3. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
# 4. Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# 5. Evaluate bias in features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 6. 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="Gene expression data normalized to standard gene symbols."
)
# 7. Save linked data if usable
if is_usable:
linked_data.to_csv(out_data_file) |