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# Path Configuration
from tools.preprocess import *
# Processing context
trait = "Intellectual_Disability"
cohort = "GSE273850"
# Input paths
in_trait_dir = "../DATA/GEO/Intellectual_Disability"
in_cohort_dir = "../DATA/GEO/Intellectual_Disability/GSE273850"
# Output paths
out_data_file = "./output/preprocess/3/Intellectual_Disability/GSE273850.csv"
out_gene_data_file = "./output/preprocess/3/Intellectual_Disability/gene_data/GSE273850.csv"
out_clinical_data_file = "./output/preprocess/3/Intellectual_Disability/clinical_data/GSE273850.csv"
json_path = "./output/preprocess/3/Intellectual_Disability/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
# Affymetrix array data indicates gene expression data is available
is_gene_available = True
# 2. Variable and Data Type Analysis
# 2.1 Row identifiers
trait_row = 0 # Genotype info in row 0 indicates T21 vs control
gender_row = 1 # Sex info in row 1
age_row = None # Age not available
# 2.2 Conversion functions
def convert_trait(value: str) -> int:
"""Convert T21 status to binary: 1 for T21, 0 for control"""
if not value or ':' not in value:
return None
value = value.split(':')[1].strip().lower()
if 't21' in value:
return 1
elif 'euploid' in value:
return 0
return None
def convert_gender(value: str) -> int:
"""Convert gender to binary: 0 for female, 1 for male"""
if not value or ':' not in value:
return None
value = value.split(':')[1].strip().lower()
if 'female' in value:
return 0
elif 'male' in value:
return 1
return None
def convert_age(value: str) -> float:
"""Placeholder function since age is not available"""
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. 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,
gender_row=gender_row,
convert_gender=convert_gender
)
# Preview the extracted features
preview = preview_df(clinical_features)
print("Clinical features preview:", preview)
# Save to CSV
clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from the matrix file
genetic_data = get_genetic_data(matrix_file_path)
# Print first 20 row IDs
print("First 20 row IDs:")
print(genetic_data.index[:20].tolist())
# The transcript IDs are in the format "TCxxxxxxxx.hg.1" which are not standard human gene symbols
# They appear to be transcript cluster identifiers that need mapping to 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())
# Look at general data statistics
print("\nData shape:", gene_metadata.shape)
# Preview the first few rows
print("\nPreview of the annotation data:")
print(json.dumps(preview_df(gene_metadata), indent=2))
# 1. Identify columns for gene mapping
# Based on observation:
# - 'ID' column in gene annotation contains probe IDs like 'TC0100006437.hg.1'
# - Gene symbols are contained in 'SPOT_ID.1' column within RefSeq/ENSEMBL descriptions
# 2. Create gene mapping dataframe from the annotation data
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='SPOT_ID.1')
# 3. Apply gene mapping to convert probe-level data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)
# Preview the gene data
print("\nShape of gene expression data:", gene_data.shape)
print("\nFirst few rows of gene expression data:")
print(preview_df(gene_data))
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)
# Get clinical features
clinical_features = geo_select_clinical_features(
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
)
# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)
# Early exit if trait values are all NaN
if linked_data[trait].isna().all():
is_biased = True
linked_data = None
else:
# 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 pediatric AML samples, focusing on Down syndrome cases versus other AML types."
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) |