Single-Cell RNA Sequencing Data Interpretation by Evolutionary Multiobjective Clustering.

Authors:
Xiangtao Li
Xiangtao Li
Northeast Normal University
Shenyang | China
Ka-Chun Wong
Ka-Chun Wong
University of Toronto
Canada

IEEE/ACM Trans Comput Biol Bioinform 2019 Mar 25. Epub 2019 Mar 25.

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. Firstly, 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.

Download full-text PDF

Source
http://dx.doi.org/10.1109/TCBB.2019.2906601DOI Listing
March 2019

Publication Analysis

Top Keywords

single-cell rna
20
rna sequencing
20
rna-seq data
12
matrix factorization
8
single-cell rna-seq
8
feature extraction
8
non-negative matrix
8
sequencing datasets
8
multiobjective clustering
8
adaptive non-negative
8
state-of-the-art methods
8
single-cell
7
data
5
mcanmf
5
sequencing
5
clustering
5
proposed analyze
4
vector quantization
4
learning vector
4
analyze single-cell
4

Similar Publications