Feature selection revisited in the single-cell era

Pengyi Yang; Hao Huang; Chunlei Liu

doi:10.1186/s13059-021-02544-3

Feature selection revisited in the single-cell era

Genome Biol. 2021 Dec 1;22(1):321. doi: 10.1186/s13059-021-02544-3.

Authors

Pengyi Yang^{1

2

3}, Hao Huang^{4

5}, Chunlei Liu⁵

Affiliations

¹ School of Mathematics and Statistics, University of Sydney, Sydney, NSW, 2006, Australia. pengyi.yang@sydney.edu.au.
² Computational Systems Biology Group, Children's Medical Research Institute, University of Sydney, Westmead, NSW, 2145, Australia. pengyi.yang@sydney.edu.au.
³ Charles Perkins Centre, University of Sydney, Sydney, NSW, 2006, Australia. pengyi.yang@sydney.edu.au.
⁴ School of Mathematics and Statistics, University of Sydney, Sydney, NSW, 2006, Australia.
⁵ Computational Systems Biology Group, Children's Medical Research Institute, University of Sydney, Westmead, NSW, 2145, Australia.

Abstract

Recent advances in single-cell biotechnologies have resulted in high-dimensional datasets with increased complexity, making feature selection an essential technique for single-cell data analysis. Here, we revisit feature selection techniques and summarise recent developments. We review their application to a range of single-cell data types generated from traditional cytometry and imaging technologies and the latest array of single-cell omics technologies. We highlight some of the challenges and future directions and finally consider their scalability and make general recommendations on each type of feature selection method. We hope this review stimulates future research and application of feature selection in the single-cell era.

Publication types

Research Support, Non-U.S. Gov't
Review

MeSH terms

Computational Biology / methods
Epigenomics
Gene Expression Profiling / methods
High-Throughput Nucleotide Sequencing
Humans
Single-Cell Analysis / methods*
Transcriptome