Technology Category
Machine Learning: Bayesian LearningData Mining & Knowledge Management: Anomaly/Outlier DetectionReasoning under Uncertainty: Graphical Models
Detecting Batch Heterogeneity via Likelihood Clustering
🤖 AI Summary
This study addresses the challenge in genomic diagnostics where batch effects are often confounded with biological signals such as copy number variations (CNVs), leading to false positives or missed detections—particularly when batch labels are unavailable. The authors propose a novel Bayesian approach that requires no prior batch information and instead leverages model evidence to cluster samples: technical artifacts reduce model evidence, whereas genuine biological variation does not. Heterogeneity is identified via a likelihood ratio test in evidence space, calibrated using a parametric bootstrap procedure. This work represents the first application of model evidence to disentangle technical from biological signals. The method demonstrates superior clustering accuracy over correlation- and dimensionality-reduction-based approaches on synthetic data, three clinical targeted sequencing panels (liquid biopsy, BRCA, and thalassemia), and mouse electrophysiology data, while maintaining strict control over false positive rates.
Technology Category
Application Category
📝 Abstract
Batch effects represent a major confounder in genomic diagnostics. In copy number variant (CNV) detection from NGS, many algorithms compare read depth between test samples and a reference sample, assuming they are process-matched. When this assumption is violated, with causes ranging from reagent lot changes to multi-site processing, the reference becomes inappropriate, introducing false CNV calls or masking true pathogenic variants. Detecting such heterogeneity before downstream analysis is critical for reliable clinical interpretation. Existing batch effect detection methods either cluster samples based on raw features, risking conflation of biological signal with technical variation, or require known batch labels that are frequently unavailable. We introduce a method that addresses both limitations by clustering samples according to their Bayesian model evidence. The central insight is that evidence quantifies compatibility between data and model assumptions, technical artifacts violate assumptions and reduce evidence, whereas biological variation, including CNV status, is anticipated by the model and yields high evidence. This asymmetry provides a discriminative signal that separates batch effects from biology. We formalize heterogeneity detection as a likelihood ratio test for mixture structure in evidence space, using parametric bootstrap calibration to ensure conservative false positive rates. We validate our approach on synthetic data demonstrating proper Type I error control, three clinical targeted sequencing panels (liquid biopsy, BRCA, and thalassemia) exhibiting distinct batch effect mechanisms, and mouse electrophysiology recordings demonstrating cross-modality generalization. Our method achieves superior clustering accuracy compared to standard correlation-based and dimensionality-reduction approaches while maintaining the conservativeness required for clinical usage.
Problem
Research questions and friction points this paper is trying to address.
batch effect
copy number variant
genomic diagnostics
heterogeneity detection
NGS
Innovation
Methods, ideas, or system contributions that make the work stand out.
Bayesian model evidence
batch effect detection
likelihood ratio test
copy number variant
parametric bootstrap
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