4.7 Review

Accurate feature selection improves single-cell RNA-seq cell clustering

Journal

BRIEFINGS IN BIOINFORMATICS
Volume 22, Issue 5, Pages -

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/bib/bbab034

Keywords

single-cell RNA sequencing; cell clustering; feature selection

Funding

  1. Shenzhen Research Institute of Big Data (SRIBD) [R01GM122083, R01GM124061, P50AG025688]

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Cell clustering is a crucial task in single-cell RNA sequencing (scRNA-seq) data analysis, with feature selection playing a key role in improving clustering accuracy. The study evaluates the impact of feature selection on cell clustering accuracy and introduces a new algorithm named FEAture SelecTion (FEAST) for selecting more representative features. Applying FEAST to 12 public scRNA-seq datasets demonstrates a significant improvement in clustering accuracy when combined with existing clustering tools.
Cell clustering is one of the most important and commonly performed tasks in single-cell RNA sequencing (scRNA-seq) data analysis. An important step in cell clustering is to select a subset of genes (referred to as 'features'), whose expression patterns will then be used for downstream clustering. A good set of features should include the ones that distinguish different cell types, and the quality of such set could have a significant impact on the clustering accuracy. All existing scRNA-seq clustering tools include a feature selection step relying on some simple unsupervised feature selection methods, mostly based on the statistical moments of gene-wise expression distributions. In this work, we carefully evaluate the impact of feature selection on cell clustering accuracy. In addition, we develop a feature selection algorithm named FEAture SelecTion (FEAST), which provides more representative features. We apply the method on 12 public scRNA-seq datasets and demonstrate that using features selected by FEAST with existing clustering tools significantly improve the clustering accuracy.

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