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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "HIV_Resistance"
cohort = "GSE46599"
# Input paths
in_trait_dir = "../DATA/GEO/HIV_Resistance"
in_cohort_dir = "../DATA/GEO/HIV_Resistance/GSE46599"
# Output paths
out_data_file = "./output/preprocess/3/HIV_Resistance/GSE46599.csv"
out_gene_data_file = "./output/preprocess/3/HIV_Resistance/gene_data/GSE46599.csv"
out_clinical_data_file = "./output/preprocess/3/HIV_Resistance/clinical_data/GSE46599.csv"
json_path = "./output/preprocess/3/HIV_Resistance/cohort_info.json"
# Get relevant file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
# Extract background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
# Get dictionary of unique values per row in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)
# Print background info
print("Background Information:")
print("-" * 50)
print(background_info)
print("\n")
# Print clinical data unique values
print("Sample Characteristics:")
print("-" * 50)
for row, values in unique_values_dict.items():
print(f"{row}:")
print(f" {values}")
print()
# 1. Gene Expression Data Availability
is_gene_available = True # Yes, this is gene expression data studying ISGs, not miRNA/methylation
# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
trait_row = 4 # HIV resistance status in row 4
age_row = None # Age not available
gender_row = None # Gender not available
# 2.2 Data Type Conversion Functions
def convert_trait(x):
if not isinstance(x, str):
return None
val = x.split(': ')[-1].lower()
if 'resistant' == val:
return 1
elif 'partially resistant' == val:
return 0.5
elif 'permissive' == val:
return 0
elif 'untreated' == val:
return None
return None
def convert_age(x):
return None # Not used
def convert_gender(x):
return None # Not used
# 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 extracted features
preview = preview_df(clinical_features)
print("Preview of clinical features:")
print(preview)
# Save to CSV
clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data
genetic_data = get_genetic_data(matrix_file_path)
# Print first 20 probe IDs
print("First 20 probe IDs:")
print(genetic_data.index[:20])
# These are Illumina BeadArray probe IDs (starting with ILMN_), not gene symbols
requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)
# Preview column names and first few values
preview_dict = preview_df(gene_annotation)
print("Column names and preview values:")
for col, values in preview_dict.items():
print(f"\n{col}:")
print(values)
# Get gene mapping from annotation data
# 'ID' column contains probe IDs (ILMN_*) matching gene expression data
# 'Symbol' column contains gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')
# Apply gene mapping to convert probe-level data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)
# Preview first few genes
print("\nFirst few genes after mapping:")
print(gene_data.head().index)
# 1. Normalize gene symbols and save normalized 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)
# Read the processed clinical data file
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
# Link clinical and genetic data using the normalized gene data
linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)
# Handle missing values systematically
linked_data = handle_missing_values(linked_data, trait)
# Detect bias in trait and demographic features, remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# Validate data quality and save cohort info
note = "Gene expression data from glucocorticoid sensitivity study."
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=is_biased,
df=linked_data,
note=note
)
# 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(f"Dataset {cohort} did not pass quality validation and will not be saved.") |