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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Thyroid_Cancer"
cohort = "GSE58689"
# Input paths
in_trait_dir = "../DATA/GEO/Thyroid_Cancer"
in_cohort_dir = "../DATA/GEO/Thyroid_Cancer/GSE58689"
# Output paths
out_data_file = "./output/preprocess/3/Thyroid_Cancer/GSE58689.csv"
out_gene_data_file = "./output/preprocess/3/Thyroid_Cancer/gene_data/GSE58689.csv"
out_clinical_data_file = "./output/preprocess/3/Thyroid_Cancer/clinical_data/GSE58689.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 check
# From the background info, this is gene expression data related to thyroid cancer
is_gene_available = True
# 2.1 Data availability check
# Trait data available in row 0 (normal vs PTC)
trait_row = 0
# Gender data available in row 1 (under "Sex:")
gender_row = 1
# Age data available in row 2
age_row = 2
# 2.2 Data type conversion functions
def convert_trait(value: str) -> Optional[int]:
if pd.isna(value):
return None
value = value.split(': ')[1].lower()
if 'normal' in value:
return 0
elif 'papillary thyroid carcinoma' in value:
return 1
return None
def convert_age(value: str) -> Optional[float]:
if pd.isna(value):
return None
try:
return float(value.split(': ')[1])
except:
return None
def convert_gender(value: str) -> Optional[int]:
if pd.isna(value):
return None
value = value.split(': ')[1].lower()
if 'female' in value:
return 0
elif 'male' in value:
return 1
return None
# 3. Save metadata using 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. Extract clinical features since trait_row is not None
clinical_df = geo_select_clinical_features(clinical_data, 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 clinical features
preview_result = preview_df(clinical_df)
print("Preview of clinical features:")
print(preview_result)
# 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)
# These appear to be probe IDs (like "1007_s_at") from an Affymetrix microarray,
# not standard human gene symbols, so they will need to be mapped
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)
# Extract gene mapping information
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')
# Apply gene mapping to get gene-level expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)
# Preview results
print("\nGene mapping preview:")
print(mapping_df.head())
print("\nGene expression data preview:")
print(gene_data.head())
print("\nShape:", gene_data.shape)
# 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=is_trait_available,
is_biased=trait_biased,
df=linked_data,
note="Dataset contains gene expression data comparing normal thyroid tissue (18 samples) with papillary thyroid carcinoma (27 samples)"
)
# 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) |