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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Thyroid_Cancer"
cohort = "GSE76039"
# Input paths
in_trait_dir = "../DATA/GEO/Thyroid_Cancer"
in_cohort_dir = "../DATA/GEO/Thyroid_Cancer/GSE76039"
# Output paths
out_data_file = "./output/preprocess/3/Thyroid_Cancer/GSE76039.csv"
out_gene_data_file = "./output/preprocess/3/Thyroid_Cancer/gene_data/GSE76039.csv"
out_clinical_data_file = "./output/preprocess/3/Thyroid_Cancer/clinical_data/GSE76039.csv"
json_path = "./output/preprocess/3/Thyroid_Cancer/cohort_info.json"
# Get file paths for SOFT and matrix files
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
# Get background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
# Create dictionary of unique values for each feature
unique_values_dict = get_unique_values_by_row(clinical_data)
# Print the information
print("Dataset Background Information:")
print(background_info)
print("\nSample Characteristics:")
for feature, values in unique_values_dict.items():
print(f"\n{feature}:")
print(values)
# 1. Gene Expression Data Availability
# Series used Affymetrix array for expression profiling, so gene data is available
is_gene_available = True
# 2.1 Data Availability
# For trait: Can be inferred from tumor type field
trait_row = 2
# Age not available
age_row = None
# Gender available in row 0
gender_row = 0
# 2.2 Data Type Conversion Functions
def convert_trait(value):
# Binary: 0 for non-ATC, 1 for ATC
if not isinstance(value, str):
return None
try:
value = value.split(": ")[1].lower()
if 'anaplastic' in value:
return 1
elif value: # Any other tumor type is non-ATC
return 0
return None
except:
return None
def convert_gender(value):
if not isinstance(value, str):
return None
try:
value = value.split(": ")[1].lower()
if value == 'female':
return 0
elif value == 'male':
return 1
return None
except:
return None
convert_age = None # Not needed since age data unavailable
# 3. Save initial filtering metadata
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
if trait_row is not None:
clinical_df = geo_select_clinical_features(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_df)
print("Clinical feature preview:", preview)
# Save clinical data
clinical_df.to_csv(out_clinical_data_file)
# Extract genetic data matrix
genetic_data = get_genetic_data(matrix_file_path)
# Print first few rows with column names to examine data structure
print("Data preview:")
print("\nColumn names:")
print(list(genetic_data.columns)[:5])
print("\nFirst 5 rows:")
print(genetic_data.head())
print("\nShape:", genetic_data.shape)
# Verify this is gene expression data and check identifiers
is_gene_available = True
# Save updated 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)
)
# Save gene expression data
genetic_data.to_csv(out_gene_data_file)
# The gene identifiers appear to be Affymetrix probe IDs (e.g. 1007_s_at, 1053_at)
# which need to be mapped to official gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file_path)
# Preview column names and first few values
preview = preview_df(gene_metadata)
print("\nGene annotation columns and sample values:")
print(preview)
# Get mapping between probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')
# Apply the mapping to convert probe data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)
# Preview transformed data
print("\nGene expression data preview:")
print(f"Shape: {gene_data.shape}")
print("\nFirst 5 genes:")
print(gene_data.head())
# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols and save gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)
# 2. Link clinical and genetic data
clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)
# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# 4. Judge bias in features and remove biased ones
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Final validation and save metadata
is_usable = validate_and_save_cohort_info(
is_final=True,
cohort=cohort,
info_path=json_path,
is_gene_available=is_gene_available,
is_trait_available=True,
is_biased=trait_biased,
df=linked_data,
note="Dataset contains gene expression data comparing follicular thyroid cancers with follicular thyroid adenomas."
)
# 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)
else:
print("Dataset marked as unusable due to biased trait distribution.") |