Single-cell RNA-seq interpretations using evolutionary multiobjective ensemble pruning

摘要

Motivation: In recent years, single-cell RNA sequencing enables us to discover cell types or even sub-types. Its increasing availability provides opportunities to identify cell populations from single-cell RNA-seq data. Computational methods have been employed to reveal the gene expression variations among multiple cell populations. Unfortunately, the existing ones can suffer from realistic restrictions such as experimental noises, numerical instability, high dimensionality and computational scalability.