BMC Bioinformatics | VOL. 23

Trade-off between conservation of biological variation and batch effect removal in deep generative modeling for single-cell transcriptomics

Publication Date Nov 3, 2022


BackgroundSingle-cell RNA sequencing (scRNA-seq) technology has contributed significantly to diverse research areas in biology, from cancer to development. Since scRNA-seq data is high-dimensional, a common strategy is to learn low-dimensional latent representations better to understand overall structure in the data. In this work, we build upon scVI, a powerful deep generative model which can learn biologically meaningful latent representations, but which has limited explicit control of batch effects. Rather than prioritizing batch effect removal over conservation of biological variation, or vice versa, our goal is to provide a bird’s eye view of the trade-offs between these two conflicting objectives. Specifically, using the well established concept of Pareto front from economics and engineering, we seek to learn the entire trade-off curve between conservation of biological variation and removal of batch effects.ResultsA multi-objective optimisation technique known as Pareto multi-task learning (Pareto MTL) is used to obtain the Pareto front between conservation of biological variation and batch effect removal. Our results indicate Pareto MTL can obtain a better Pareto front than the naive scalarization approach typically encountered in the literature. In addition, we propose to measure batch effect by applying a neural-network based estimator called Mutual Information Neural Estimation (MINE) and show benefits over the more standard maximum mean discrepancy measure.ConclusionThe Pareto front between conservation of biologi...


Batch Effect Removal Batch Effect Pareto Front Researchers In Computational Biology Deep Generative Model scRNA-seq Data Latent Representations Computational Biology Common Strategy Powerful Generative Model

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