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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Height"
cohort = "GSE71994"
# Input paths
in_trait_dir = "../DATA/GEO/Height"
in_cohort_dir = "../DATA/GEO/Height/GSE71994"
# Output paths
out_data_file = "./output/preprocess/3/Height/GSE71994.csv"
out_gene_data_file = "./output/preprocess/3/Height/gene_data/GSE71994.csv"
out_clinical_data_file = "./output/preprocess/3/Height/clinical_data/GSE71994.csv"
json_path = "./output/preprocess/3/Height/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)
# Get unique values for each clinical feature
unique_values_dict = get_unique_values_by_row(clinical_data)
# Print background information
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
is_gene_available = True # Study mentions "genome-wide gene expression analysis" in PBMC
# 2.1 Data Availability
trait_row = 4 # Height data is in row 4
age_row = 3 # Age data is in row 3
gender_row = 1 # Gender data is in row 1
# 2.2 Data Type Conversion Functions
def convert_trait(x):
if not isinstance(x, str):
return None
try:
# Extract numeric value after colon
value = float(x.split(':')[1].strip())
return value # Height as continuous value in meters
except:
return None
def convert_age(x):
if not isinstance(x, str):
return None
try:
# Extract numeric value after colon
value = int(x.split(':')[1].strip())
return value # Age as continuous value in years
except:
return None
def convert_gender(x):
if not isinstance(x, str):
return None
value = x.split(':')[1].strip().lower()
if value == 'female':
return 0
elif value == 'male':
return 1
return None
# 3. Save Initial Filtering Results
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
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 results
print(preview_df(clinical_df))
# Save clinical data
clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data from the matrix file
genetic_data = get_genetic_data(matrix_file_path)
# Print information about the data structure
print("First few rows of the genetic data:")
print(genetic_data.head())
print("\nShape of genetic data:", genetic_data.shape)
print("\nColumn names:", genetic_data.columns.tolist())
# Looking at the index/ID values (e.g. 7896746), these appear to be probe/array IDs rather than human gene symbols
# These numeric IDs need to be mapped to standard gene symbols
requires_gene_mapping = True
# Extract gene annotation data from SOFT file
gene_metadata = get_gene_annotation(soft_file_path)
# Display information about the annotation data
print("Column names:")
print(gene_metadata.columns.tolist())
print("\nPreview of first few rows:")
print(json.dumps(preview_df(gene_metadata), indent=2))
# 1. Identify columns for mapping
# ID column matches probe IDs in gene expression data
# gene_assignment column contains gene symbols
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='gene_assignment')
# 2. Apply gene mapping to convert probe data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)
# 3. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# Print information about the resulting gene expression data
print("\nShape of gene expression data after mapping:", gene_data.shape)
print("\nFirst few gene symbols:", list(gene_data.index[:10]))
# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)
# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# 4. Judge whether features are biased and remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
# 5. Final validation and save metadata
note = "Dataset contains gene expression data from PBMCs and height measurements from 40 subjects"
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
)
# 6. Save the linked data only if it's usable
if is_usable:
os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
linked_data.to_csv(out_data_file) |