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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Post-Traumatic_Stress_Disorder"
cohort = "GSE44456"
# Input paths
in_trait_dir = "../DATA/GEO/Post-Traumatic_Stress_Disorder"
in_cohort_dir = "../DATA/GEO/Post-Traumatic_Stress_Disorder/GSE44456"
# Output paths
out_data_file = "./output/preprocess/3/Post-Traumatic_Stress_Disorder/GSE44456.csv"
out_gene_data_file = "./output/preprocess/3/Post-Traumatic_Stress_Disorder/gene_data/GSE44456.csv"
out_clinical_data_file = "./output/preprocess/3/Post-Traumatic_Stress_Disorder/clinical_data/GSE44456.csv"
json_path = "./output/preprocess/3/Post-Traumatic_Stress_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
is_gene_available = True # Series summary mentions gene expression data from hippocampus
# 2. Variable Availability and Data Type Conversion
trait_row = 0 # 'phenotype' row contains alcoholic vs control
age_row = 3 # 'age' row contains numeric age values
gender_row = 1 # 'gender' row contains male/female values
def convert_trait(x: str) -> Optional[int]:
"""Convert phenotype to binary (0=control, 1=alcoholic)"""
if not isinstance(x, str):
return None
x = x.split(': ')[-1].lower()
if x == 'control':
return 0
elif x == 'alcoholic':
return 1
return None
def convert_age(x: str) -> Optional[float]:
"""Convert age to float"""
if not isinstance(x, str):
return None
try:
return float(x.split(': ')[-1])
except:
return None
def convert_gender(x: str) -> Optional[int]:
"""Convert gender to binary (0=female, 1=male)"""
if not isinstance(x, str):
return None
x = x.split(': ')[-1].lower()
if x == 'female':
return 0
elif x == 'male':
return 1
return None
# 3. Save metadata for initial filtering
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
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 and save clinical data
print(preview_df(selected_clinical_df))
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])
# The identifiers appear to be probe IDs (like '7896736', '7896738', etc.)
# rather than standard human gene symbols. They are likely numeric probe IDs
# specific to the microarray platform used, which need to be mapped to gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)
# Display column names and preview data
print("Column names:")
print(gene_annotation.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_annotation))
# 1. The gene identifiers seem to be in 'ID' column, and gene symbols can be extracted from 'gene_assignment'
# Extract probe-gene mapping from gene annotation data
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')
# 2. Get gene expression data from mapping
gene_data = apply_gene_mapping(genetic_data, mapping_data)
# Preview result
print("Gene mapping results:")
print("Input probes shape:", genetic_data.shape)
print("Output genes shape:", gene_data.shape)
print("\nFirst few rows of mapped gene expression data:")
print(gene_data.head())
# Reload clinical data that was processed earlier
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(gene_data)
genetic_data.to_csv(out_gene_data_file)
# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, 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 = "Dataset contains subcutaneous adipose tissue gene expression data from PCOS patients and controls. The gender feature is biased (all female) and was removed."
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) |