Single-Cell RNA Sequencing Data Interpretation by Evolutionary Multiobjective Clustering

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

7 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)1773-1784
Journal / PublicationIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume17
Issue number5
Online published25 Mar 2019
Publication statusPublished - Sept 2020

Abstract

In recent years, single-cell RNA sequencing reveals diverse cell genetics at unprecedented resolutions. Such technological advances enable researchers to uncover the functionally distinct cell subtypes such as hematopoietic stem cell subpopulation identification. However, most of the related algorithms have been hindered by the high-dimensionality and sparse nature of single-cell RNA sequencing (RNA-seq) data. To address those problems, we propose a multiobjective evolutionary clustering based on adaptive non-negative matrix factorization (MCANMF) for multiobjective single-cell RNA-seq data clustering. First, adaptive non-negative matrix factorization is proposed to decompose data for feature extraction. After that, a multiobjective clustering algorithm based on learning vector quantization is proposed to analyze single-cell RNA-seq data. To validate the effectiveness of MCANMF, we benchmark MCANMF against 15 state-of-the-art methods including seven feature extraction methods, seven clustering methods, and the kernel-based similarity learning method on six published single-cell RNA sequencing datasets comprehensively. When compared with those 15 state-of-the-art methods, MCANMF performs better than the others on those single-cell RNA sequencing datasets according to multiple evaluation metrics. Moreover, the MCANMF component analysis, time complexity analysis, and parameter analysis are conducted to demonstrate various properties of our proposed algorithm.

Research Area(s)

  • Clustering algorithms, Sequential analysis, RNA, Linear programming, Matrix decomposition, Feature extraction, Sociology, Single-cell RNA sequencing, multiobjective algorithm, bioinformatics, computational biology, GENE-EXPRESSION DATA, SEQ DATA, HETEROGENEITY, VISUALIZATION, EMBRYOS