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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Substance_Use_Disorder"
cohort = "GSE94399"
# Input paths
in_trait_dir = "../DATA/GEO/Substance_Use_Disorder"
in_cohort_dir = "../DATA/GEO/Substance_Use_Disorder/GSE94399"
# Output paths
out_data_file = "./output/preprocess/3/Substance_Use_Disorder/GSE94399.csv"
out_gene_data_file = "./output/preprocess/3/Substance_Use_Disorder/gene_data/GSE94399.csv"
out_clinical_data_file = "./output/preprocess/3/Substance_Use_Disorder/clinical_data/GSE94399.csv"
json_path = "./output/preprocess/3/Substance_Use_Disorder/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("Background Information:")
print(background_info)
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
# Based on the background info mentioning NanoString gene expression data
is_gene_available = True
# 2. Data Availability and Type Conversion
# 2.1 Row identifiers
# Trait (alive vs dead/transplant at 6 months) is at row 1
trait_row = 1
# No age data available in characteristics
age_row = None
# No gender data available in characteristics
gender_row = None
# 2.2 Data type conversion functions
def convert_trait(value):
# Extract value after colon
if ':' in str(value):
value = value.split(':')[1].strip()
# Convert to binary: Alive = 0, Dead/transplant = 1
if 'Alive' in value:
return 0
elif 'Dead' in value or 'transplantation' in value:
return 1
return None
def convert_age(value):
return None
def convert_gender(value):
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. Extract and save clinical features
selected_clinical_df = geo_select_clinical_features(
clinical_df=clinical_data,
trait=trait,
trait_row=trait_row,
convert_trait=convert_trait
)
print("Preview of clinical data:")
print(preview_df(selected_clinical_df))
# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
selected_clinical_df.to_csv(out_clinical_data_file)
# Get gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)
# Examine data structure
print("Data structure and head:")
print(genetic_data.head())
print("\nShape:", genetic_data.shape)
print("\nFirst 20 row IDs (gene/probe identifiers):")
print(list(genetic_data.index)[:20])
# Get a few column names to verify sample IDs
print("\nFirst 5 column names:")
print(list(genetic_data.columns)[:5])
# Looking at identifiers like "11715100_at" and "11715101_s_at", these appear to be Affymetrix probe IDs
# rather than standard human gene symbols. They will need to be mapped to gene symbols.
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)
# Preview column names and values from annotation dataframe
print("Gene annotation DataFrame preview:")
print(preview_df(gene_annotation))
# Get gene mapping dataframe using ID and Gene Symbol columns
gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')
# Apply gene mapping to convert probe-level data to gene expression data
gene_data = apply_gene_mapping(genetic_data, gene_mapping)
# Preview results
print("Shape of gene expression data:", gene_data.shape)
print("\nPreview of gene expression data:")
print(preview_df(gene_data))
# 1. Normalize gene symbols in gene expression data
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
print("\nGene data shape (normalized gene-level):", gene_data.shape)
# Load clinical data previously processed
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
print("\nClinical data shape:", selected_clinical_df.shape)
# 2. Link clinical and genetic data using normalized gene-level data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
print("\nLinked data shape:", linked_data.shape)
# 3. Handle missing values systematically
if trait in linked_data.columns:
linked_data = handle_missing_values(linked_data, trait)
# 4. Check for bias in trait and demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Final validation and information saving
note = "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database."
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=trait_biased,
df=linked_data,
note=note
)
# 6. Save linked data only if usable and not biased
if is_usable and not trait_biased:
os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
linked_data.to_csv(out_data_file) |