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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Thyroid_Cancer"
cohort = "GSE104005"
# Input paths
in_trait_dir = "../DATA/GEO/Thyroid_Cancer"
in_cohort_dir = "../DATA/GEO/Thyroid_Cancer/GSE104005"
# Output paths
out_data_file = "./output/preprocess/3/Thyroid_Cancer/GSE104005.csv"
out_gene_data_file = "./output/preprocess/3/Thyroid_Cancer/gene_data/GSE104005.csv"
out_clinical_data_file = "./output/preprocess/3/Thyroid_Cancer/clinical_data/GSE104005.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
# Yes, dataset contains gene expression data (mentioned in background info)
is_gene_available = True
# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
trait_row = 0 # From 'disease' field
age_row = 2 # From 'age' field
gender_row = 3 # From 'Sex' field
# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
"""Convert thyroid cancer status to binary"""
if not value or ':' not in value:
return None
value = value.split(':')[1].strip()
if 'Non-neoplastic' in value:
return 0
elif 'carcinoma' in value:
return 1
return None
def convert_age(value: str) -> float:
"""Convert age to continuous value"""
if not value or ':' not in value:
return None
try:
return float(value.split(':')[1].strip())
except:
return None
def convert_gender(value: str) -> int:
"""Convert gender to binary (0=F, 1=M)"""
if not value or ':' not in value:
return None
value = value.split(':')[1].strip()
if value == 'F':
return 0
elif value == 'M':
return 1
return None
# 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_result = preview_df(clinical_features)
# Save clinical features
clinical_features.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 identifiers starting with 'ILMN_' are Illumina probe IDs, not standard human gene symbols
# They need to be mapped to HGNC gene symbols for consistent analysis
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)
# 1. Identify columns storing gene identifiers and gene symbols
# 'ID' column matches the probe IDs in gene expression data
# 'Symbol' column stores gene symbols
prob_col = 'ID'
gene_col = 'Symbol'
# 2. Extract mapping information
mapping_data = get_gene_mapping(gene_metadata, prob_col, gene_col)
# 3. Convert probe-level measurements to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_data)
gene_data = normalize_gene_symbols_in_index(gene_data)
# Preview results
print("\nMapped gene expression data preview:")
print("\nFirst 5 genes:")
print(gene_data.head())
print("\nShape:", gene_data.shape)
# Save mapped 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 27 follicular thyroid cancers with 25 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) |