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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Adrenocortical_Cancer"
cohort = "GSE108088"
# Input paths
in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE108088"
# Output paths
out_data_file = "./output/preprocess/3/Adrenocortical_Cancer/GSE108088.csv"
out_gene_data_file = "./output/preprocess/3/Adrenocortical_Cancer/gene_data/GSE108088.csv"
out_clinical_data_file = "./output/preprocess/3/Adrenocortical_Cancer/clinical_data/GSE108088.csv"
json_path = "./output/preprocess/3/Adrenocortical_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
# Yes, this dataset appears to be gene expression data given the series summary about molecular profiling
is_gene_available = True
# 2. Variable Data Type & Conversion
# 2.1 Data Availability
# No Adrenocortical Cancer cases found in conditions
trait_row = None
age_row = None
gender_row = None
# 2.2 Data Type Conversion Functions
def convert_trait(x):
# Binary: 0 = other cancers, 1 = our target cancer
if not isinstance(x, str):
return None
value = x.split(': ')[-1].lower()
# For Adrenocortical Cancer - no matching cases found
return 0
def convert_age(x):
# Not needed since age data unavailable
return None
def convert_gender(x):
# Not needed since gender data unavailable
return None
# 3. Save Metadata - Initial Filtering
is_trait_available = trait_row is not None
validate_and_save_cohort_info(is_final=False,
cohort=cohort,
info_path=json_path,
is_gene_available=is_gene_available,
is_trait_available=is_trait_available)
# 4. Clinical Feature Extraction
# Skip since trait_row is None (no Adrenocortical Cancer cases)
# 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)
# Review gene identifiers
# The identifiers like "1007_s_at", "1053_at" are Affymetrix probe IDs from microarray data
# These need to be mapped to official gene symbols before analysis
requires_gene_mapping = True
# Get file paths using library function
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Extract gene annotation from SOFT file and get meaningful data
gene_annotation = get_gene_annotation(soft_file)
# Preview gene annotation data
print("Gene annotation shape:", gene_annotation.shape)
print("\nGene annotation preview:")
print(preview_df(gene_annotation))
print("\nNumber of non-null values in each column:")
print(gene_annotation.count())
# Print example rows showing the mapping information columns
print("\nSample mapping columns ('ID' and 'Gene Symbol'):")
print(gene_annotation[['ID', 'Gene Symbol']].head().to_string())
print("\nNote: Gene mapping will use:")
print("'ID' column: Probe identifiers")
print("'Gene Symbol' column: Contains gene symbol information")
# Get gene mapping from annotation data
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
# Apply mapping to convert probe data to gene data
gene_data = apply_gene_mapping(gene_data, mapping_data)
# Print info about the mapped gene data
print("Gene expression data shape after mapping:", gene_data.shape)
print("\nFirst few rows of mapped gene data:")
print(gene_data.head())
print("\nFirst few gene symbols:")
print(gene_data.index[:10])
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
# Save normalized gene data
gene_data.to_csv(out_gene_data_file)
# 2. Link clinical and genetic data
try:
clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# 4. Determine if features are biased
is_trait_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_trait_biased,
df=linked_data,
note="Gene expression data successfully mapped and linked with clinical features"
)
# 6. Save linked data if usable
if is_usable:
linked_data.to_csv(out_data_file)
except Exception as e:
print(f"Error in data linking and processing: {str(e)}")
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=True,
df=pd.DataFrame(),
note=f"Data processing failed: {str(e)}"
) |