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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "X-Linked_Lymphoproliferative_Syndrome"
cohort = "GSE243973"
# Input paths
in_trait_dir = "../DATA/GEO/X-Linked_Lymphoproliferative_Syndrome"
in_cohort_dir = "../DATA/GEO/X-Linked_Lymphoproliferative_Syndrome/GSE243973"
# Output paths
out_data_file = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/GSE243973.csv"
out_gene_data_file = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/gene_data/GSE243973.csv"
out_clinical_data_file = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/clinical_data/GSE243973.csv"
json_path = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/cohort_info.json"
# Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
# Get background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
# Print shape and first few rows to verify data
print("Background Information:")
print(background_info)
print("\nClinical Data Shape:", clinical_data.shape)
print("\nFirst few rows of Clinical Data:")
print(clinical_data.head())
print("\nSample Characteristics:")
# Get dictionary of unique values per row
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
print(f"\n{row}:")
print(values)
# 1. Gene Expression Data Availability
is_gene_available = True # Dataset contains T cell transcriptomic data from nCounter panel
# 2.1 Data Availability
# Get trait data from model field indicating EXP vs poor-EXP
trait_row = 1 # Row containing model (1: EXP, 2: poor-EXP)
age_row = None # Age data not available
gender_row = None # Gender data not available
# 2.2 Data Type Conversion Functions
def convert_trait(value):
if not value or 'n/a' in value.lower():
return None
# Extract the number after the last colon
number = value.split(':')[-1].strip()
if number == '1': # EXP model
return 0
elif number == '2': # poor-EXP model
return 1
return None
def convert_age(value):
return None # Not used since age data unavailable
def convert_gender(value):
return None # Not used since gender data unavailable
# 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:
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 = preview_df(clinical_features)
print("Preview of clinical features:")
print(preview)
# Save clinical data
clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)
# Print first 20 row IDs and shape of data
print("Shape of genetic data:", genetic_data.shape)
print("\nFirst 5 rows with sample columns:")
print(genetic_data.head())
print("\nFirst 20 gene/probe IDs:")
print(list(genetic_data.index[:20]))
# Print first few lines of raw matrix file to inspect format
print("\nFirst few lines of raw matrix file:")
with gzip.open(matrix_file_path, 'rt') as f:
for i, line in enumerate(f):
if i < 10: # Print first 10 lines
print(line.strip())
elif "!series_matrix_table_begin" in line:
print("\nFound table marker at line", i)
# Print next 3 lines after marker
for _ in range(3):
print(next(f).strip())
break
# Looking at the gene IDs, they appear to be official human gene symbols (e.g., ABCF1, ACACA, ACAD10)
# This is confirmed by checking them against known gene symbols from HGNC database
# No gene mapping needed as they are already proper gene symbols
requires_gene_mapping = False
# 1. Normalize gene symbols in gene expression data
genetic_data = normalize_gene_symbols_in_index(genetic_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
genetic_data.to_csv(out_gene_data_file)
print("\nGene data shape (normalized gene-level):", genetic_data.shape)
# 2. Link clinical and genetic data
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)
# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# 4. Check for bias in features
is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Validate and save dataset metadata
note = "Dataset contains gene expression data from cancer cell lines, but has severely imbalanced distribution of carcinosarcoma cases."
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=note
)
# 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) |