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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Obstructive_sleep_apnea"
cohort = "GSE75097"
# Input paths
in_trait_dir = "../DATA/GEO/Obstructive_sleep_apnea"
in_cohort_dir = "../DATA/GEO/Obstructive_sleep_apnea/GSE75097"
# Output paths
out_data_file = "./output/preprocess/3/Obstructive_sleep_apnea/GSE75097.csv"
out_gene_data_file = "./output/preprocess/3/Obstructive_sleep_apnea/gene_data/GSE75097.csv"
out_clinical_data_file = "./output/preprocess/3/Obstructive_sleep_apnea/clinical_data/GSE75097.csv"
json_path = "./output/preprocess/3/Obstructive_sleep_apnea/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
# "Microarray gene expression profiles" indicates this is gene expression data
is_gene_available = True
# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
# Row 1 contains AHI values which indicate OSA severity
trait_row = 1
# Row 3 contains age values
age_row = 3
# Row 2 contains gender values
gender_row = 2
# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
"""Convert AHI value to binary OSA status"""
if not value or ':' not in value:
return None
try:
ahi = float(value.split(': ')[1])
# AHI >= 15 indicates moderate to severe OSA
return 1 if ahi >= 15 else 0
except:
return None
def convert_age(value: str) -> float:
"""Convert age string to float"""
if not value or ':' not in value:
return None
try:
return float(value.split(': ')[1])
except:
return None
def convert_gender(value: str) -> int:
"""Convert gender to binary (0=female, 1=male)"""
if not value or ':' not in value:
return None
value = value.split(': ')[1].lower()
if value == 'female':
return 0
elif value == 'male':
return 1
return None
# 3. Save Metadata
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. Clinical Feature Extraction
if trait_row is not None:
selected_clinical_df = 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
print("Preview of selected clinical features:")
print(preview_df(selected_clinical_df))
# Save to CSV
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])
# Review gene identifiers
# Looking at the identifiers like A1BG, A1CF, A2M, etc.
# These appear to be official human gene symbols (HUGO nomenclature)
# No mapping needed as they are already in the correct format
requires_gene_mapping = False
# Reload clinical data that was processed earlier
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
# 1. Normalize gene symbols
normalized_genetic_data = normalize_gene_symbols_in_index(genetic_data)
normalized_genetic_data.to_csv(out_gene_data_file)
# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_genetic_data)
# 3. Handle missing values systematically
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 = "Gene expression profiles of peripheral blood mononuclear cells in OSA patients with clinical info including AHI, age and gender"
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
if is_usable:
os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
linked_data.to_csv(out_data_file) |