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© 2021, International Association of Scientists in the Interdisciplinary Areas. Cell type determination based on transcriptome profiles is a key application of single-cell RNA sequencing (scRNA-seq). It is usually achieved through unsupervised clustering. Good feature selection is capable of improving the clustering accuracy and is a crucial component of single-cell clustering pipelines. However, most current single-cell feature selection methods are univariable filter methods ignoring gene dependency. Even the multivariable filter methods developed in recent years only consider “one-to-many” relationship between genes. In this paper, a novel single-cell feature selection method based on convex analysis of mixtures (FSCAM) is proposed, which takes into account “many-to-many” relationship. Compared to the previous “one-to-many” methods, FSCAM selects genes with a combination of relevancy, redundancy and completeness. Pertinent benchmarking is conducted on the real datasets to validate the superiority of FSCAM. Through plugging into the framework of partition around medoids (PAM) clustering, a single-cell clustering algorithm based on FSCAM method (SCC_FSCAM) is further developed. Comparing SCC_FSCAM with existing advanced clustering algorithms, the results show that our algorithm has advantages in both internal criteria (clustering number) and external criteria (adjusted Rand index) and has a good stability. Graphical Abstract: [Figure not available: see fulltext.]
Author(s): Wang Y, Gao J, Xuan C, Guan T, Wang Y, Zhou G, Ding T
Publication type: Article
Publication status: Published
Journal: Interdisciplinary Sciences: Computational Life Sciences
Year: 2022
Volume: 14
Pages: 394–408
Print publication date: 01/06/2022
Online publication date: 14/01/2022
Acceptance date: 23/11/2021
ISSN (print): 1913-2751
ISSN (electronic): 1867-1462
Publisher: Springer Science and Business Media Deutschland GmbH
URL: https://doi.org/10.1007/s12539-021-00495-8
DOI: 10.1007/s12539-021-00495-8
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