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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Adrenocortical_Cancer"
cohort = "GSE68950"
# Input paths
in_trait_dir = "../DATA/GEO/Adrenocortical_Cancer"
in_cohort_dir = "../DATA/GEO/Adrenocortical_Cancer/GSE68950"
# Output paths
out_data_file = "./output/preprocess/3/Adrenocortical_Cancer/GSE68950.csv"
out_gene_data_file = "./output/preprocess/3/Adrenocortical_Cancer/gene_data/GSE68950.csv"
out_clinical_data_file = "./output/preprocess/3/Adrenocortical_Cancer/clinical_data/GSE68950.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")
# Check gene expression data availability
# Since the background info shows this is Affymetrix gene expression array data
is_gene_available = True
# Variable availability
trait_row = 3 # Use 'organism part' field, which has 'Adrenal Gland' among values
age_row = None # No age information available
gender_row = None # No gender information available
# Data type conversion functions
def convert_trait(value: str) -> int:
if value is None or ':' not in value:
return None
value = value.split(': ')[1].strip()
# Binary: 1 for Adrenal Gland samples, 0 for others
return 1 if value == 'Adrenal Gland' else 0
# Validate and save initial info
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)
# Extract clinical features since trait_row is not None
sample_characteristics = {
3: ['organism part: Leukemia', 'organism part: Urinary tract', 'organism part: Prostate',
'organism part: Stomach', 'organism part: Kidney', 'organism part: Thyroid Gland',
'organism part: Brain', 'organism part: Skin', 'organism part: Muscle',
'organism part: Head and Neck', 'organism part: Ovary', 'organism part: Lung',
'organism part: Autonomic Ganglion', 'organism part: Endometrium', 'organism part: Pancreas',
'organism part: Cervix', 'organism part: Breast', 'organism part: Colorectal',
'organism part: Liver', 'organism part: Vulva', 'organism part: Bone',
'organism part: Oesophagus', 'organism part: BiliaryTract',
'organism part: Connective and Soft Tissue', 'organism part: Lymphoma',
'organism part: Pleura', 'organism part: Testis', 'organism part: Placenta',
'organism part: Adrenal Gland', 'organism part: Unknow']
}
clinical_data = pd.DataFrame(sample_characteristics)
selected_clinical_df = geo_select_clinical_features(
clinical_data,
trait=trait,
trait_row=trait_row,
convert_trait=convert_trait,
age_row=age_row,
convert_age=None,
gender_row=gender_row,
convert_gender=None
)
# Preview the extracted clinical data
preview = preview_df(selected_clinical_df)
print(preview)
# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
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)
# From the identifiers like "1007_s_at", "117_at", etc., these appear to be probe IDs from Affymetrix microarray
# rather than human gene symbols. They will need to be mapped to official gene symbols.
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("\nFirst 5 rows:")
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 between probe IDs and gene symbols using identified columns
mapping_data = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')
# Apply gene mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(gene_data, mapping_data)
# Preview result to confirm successful mapping
print("Shape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few genes and their expression values:")
print(gene_data.head())
# 1. Load clinical data and save normalized gene data
selected_clinical = pd.read_csv(out_clinical_data_file, index_col=0)
# Check for invalid clinical data (all 0s)
if selected_clinical.shape[0] == 1 and selected_clinical.iloc[0,0] == 0:
print("Error: Clinical data contains only negative samples (all 0s). Dataset not suitable for analysis.")
_ = validate_and_save_cohort_info(
is_final=True,
cohort=cohort,
info_path=json_path,
is_gene_available=True,
is_trait_available=False,
is_biased=None,
df=None,
note="Clinical data contains only negative samples - not suitable for case-control analysis"
)
else:
# Proceed with gene data normalization and saving
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, 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="Data from Sanger cell line Affymetrix gene expression project examining cancer cell lines"
)
# 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) |