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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Depression"
cohort = "GSE138297"
# Input paths
in_trait_dir = "../DATA/GEO/Depression"
in_cohort_dir = "../DATA/GEO/Depression/GSE138297"
# Output paths
out_data_file = "./output/preprocess/3/Depression/GSE138297.csv"
out_gene_data_file = "./output/preprocess/3/Depression/gene_data/GSE138297.csv"
out_clinical_data_file = "./output/preprocess/3/Depression/clinical_data/GSE138297.csv"
json_path = "./output/preprocess/3/Depression/cohort_info.json"
# Get paths to the SOFT and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
# Get background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file)
# Get unique values for each feature (row) in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)
# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# Based on the background, this study used microarray, so gene expression data is available
is_gene_available = True
# 2. Variable Availability and Data Type Conversion
# Trait (experimental condition) is in row 6
trait_row = 6
def convert_trait(value):
# Extract value after colon if present
if ':' in value:
value = value.split(':')[1].strip()
# Convert to binary based on FMT type
if 'Allogenic FMT' in value:
return 1
elif 'Autologous FMT' in value:
return 0
return None
# Age is in row 3
age_row = 3
def convert_age(value):
# Extract value after colon
if ':' in value:
value = value.split(':')[1].strip()
# Convert to float if possible
try:
return float(value)
except:
return None
return None
# Gender is in row 1
gender_row = 1
def convert_gender(value):
# Extract value after colon
if ':' in value:
value = value.split(':')[1].strip()
# Data is already coded as 1=female, 0=male
# But we need to reverse it to match our convention (0=female, 1=male)
try:
return 1 - int(value) # Converts 1->0 (female) and 0->1 (male)
except:
return None
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 clinical features
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
)
print("Preview of selected clinical features:")
print(preview_df(selected_clinical_df))
# Create directory if it doesn't exist
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
# Save to CSV
selected_clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_df = get_genetic_data(matrix_file)
# Print DataFrame shape and first 20 row IDs
print("DataFrame shape:", genetic_df.shape)
print("\nFirst 20 row IDs:")
print(genetic_df.index[:20])
print("\nPreview of first few rows and columns:")
print(genetic_df.head().iloc[:, :5])
# The IDs look like probe IDs from a microarray platform since they are numerical
# and have a specific format (e.g., 16650001, 16650003). These are not standard
# human gene symbols which typically use letters (e.g., GAPDH, TP53).
# The probes will need to be mapped to gene symbols.
requires_gene_mapping = True
# Extract gene annotation data, excluding control probe lines
gene_metadata = get_gene_annotation(soft_file)
# Preview filtered annotation data
print("Column names:")
print(gene_metadata.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_metadata))
# The 'ID' column in gene_metadata contains the same identifiers as in genetic_df
# The 'gene_assignment' column contains gene symbols and information
# Extract gene mapping dataframe
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment')
# Apply gene mapping to convert probe data to gene data
gene_data = apply_gene_mapping(genetic_df, mapping_df)
# Normalize gene symbols using the synonym dictionary
gene_data = normalize_gene_symbols_in_index(gene_data)
# Save to file
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
print("\nGene data shape:", gene_data.shape)
print("\nPreview of gene data:")
print(preview_df(gene_data))
# 1. Normalize gene symbols and save
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)
# 2. Link clinical and genetic data
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# 4. Check for biased features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Final validation and metadata saving
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="Study of depression in obese patients before and after bariatric surgery"
)
# 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) |