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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Ovarian_Cancer"
cohort = "GSE130402"
# Input paths
in_trait_dir = "../DATA/GEO/Ovarian_Cancer"
in_cohort_dir = "../DATA/GEO/Ovarian_Cancer/GSE130402"
# Output paths
out_data_file = "./output/preprocess/3/Ovarian_Cancer/GSE130402.csv"
out_gene_data_file = "./output/preprocess/3/Ovarian_Cancer/gene_data/GSE130402.csv"
out_clinical_data_file = "./output/preprocess/3/Ovarian_Cancer/clinical_data/GSE130402.csv"
json_path = "./output/preprocess/3/Ovarian_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
# Based on background information, this is about miRNA transfection effects on gene expression
is_gene_available = True
# 2. Data Availability and Type Conversion
# Trait: We can infer cell type as Ovarian_Cancer from cell line info
trait_row = 0
def convert_trait(x):
value = x.split(': ')[1].strip()
if 'HEY' in value or 'SKOV3' in value:
return 1 # Ovarian cancer cell lines
elif 'PC3' in value:
return 0 # Prostate cancer cell line
return None
# Age and Gender are not available for cell line data
age_row = None
gender_row = None
def convert_age(x):
return None
def convert_gender(x):
return None
# 3. Save initial 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. Extract clinical features
if trait_row is not None:
selected_clinical = 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 data
preview = preview_df(selected_clinical)
print("Preview of selected clinical features:")
print(preview)
# Save to file
selected_clinical.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)
requires_gene_mapping = True # These appear to be Affymetrix probe IDs rather than standard gene symbols
# 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_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')
# Apply gene mapping to expression data
gene_data = apply_gene_mapping(genetic_data, mapping_data)
# Preview transformed data
print("Gene Expression Data Preview:")
print("\nShape:", gene_data.shape)
print("\nFirst 5 rows:")
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="Gene expression data comparing ovarian cancer cell lines (HEY, SKOV3) with prostate cancer cell line (PC3), examining miRNA effects on MET."
)
# 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) |