Locally Manifold Non-negative Matrix Factorization Based on Centroid for scRNA-seq Data Analysis

Chuan Yuan Wang; Ying Lian Gao; Cui Na Jiao; Jin Xing Liu; Chun Hou Zheng; Xiang Zhen Kong

doi:10.1109/BIBM49941.2020.9313380

Locally Manifold Non-negative Matrix Factorization Based on Centroid for scRNA-seq Data Analysis

Chuan Yuan Wang
, Ying Lian Gao
, Cui Na Jiao
, Jin Xing Liu
, Chun Hou Zheng
, Xiang Zhen Kong

Qufu Normal University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The rapid development of single cell RNA sequencing (scRNA-seq) has made it possible to study the association between cells and genes at molecular resolution. When the follow-up analysis is carried out, it is often difficult to extract the cell information in high-dimensional space because of the high gene dimension in single-cell sequencing, which leads to inaccurate results in the follow-up analysis. To solve the problem, we propose a method called locally manifold non-negative matrix factorization based on centroid for scRNA-seq data analysis (MNMFC). MNMFC is a similarity modeling scheme based on locally manifold, which can map cell association in high dimensional space. Through similarity learning based on locally manifold and non-negative matrix decomposition (NMF) algorithm, the data in high-dimensional space can be mapped to low-dimensional space, which provides help for downstream clustering analysis. The performance of the model was validated experimentally on 10 scRNA-seq datasets. Compared with other nine advanced single-cell clustering methods, whether it is a comprehensive analysis or an individual analysis of the dataset, MNMFC has achieved encouraging results.

Original language	English
Title of host publication	Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
Editors	Taesung Park, Young-Rae Cho, Xiaohua Tony Hu, Illhoi Yoo, Hyun Goo Woo, Jianxin Wang, Julio Facelli, Seungyoon Nam, Mingon Kang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	121-125
Number of pages	5
ISBN (Electronic)	9781728162157
DOIs	https://doi.org/10.1109/BIBM49941.2020.9313380
State	Published - 16 Dec 2020
Externally published	Yes
Event	2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 - Virtual, Seoul, Korea, Republic of Duration: 16 Dec 2020 → 19 Dec 2020

Publication series

Name	Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

Conference

Conference	2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
Country/Territory	Korea, Republic of
City	Virtual, Seoul
Period	16/12/20 → 19/12/20

Keywords

Cell similarity learning
Clustering
Non-negative Matrix Factorization
Single-cell RNA sequencing data.

Access to Document

10.1109/BIBM49941.2020.9313380

Cite this

Wang, C. Y., Gao, Y. L., Jiao, C. N., Liu, J. X., Zheng, C. H., & Kong, X. Z. (2020). Locally Manifold Non-negative Matrix Factorization Based on Centroid for scRNA-seq Data Analysis. In T. Park, Y.-R. Cho, X. T. Hu, I. Yoo, H. G. Woo, J. Wang, J. Facelli, S. Nam, & M. Kang (Eds.), Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 (pp. 121-125). Article 9313380 (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM49941.2020.9313380

Wang, Chuan Yuan ; Gao, Ying Lian ; Jiao, Cui Na et al. / Locally Manifold Non-negative Matrix Factorization Based on Centroid for scRNA-seq Data Analysis. Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020. editor / Taesung Park ; Young-Rae Cho ; Xiaohua Tony Hu ; Illhoi Yoo ; Hyun Goo Woo ; Jianxin Wang ; Julio Facelli ; Seungyoon Nam ; Mingon Kang. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 121-125 (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020).

@inproceedings{641e77b20c684718832042862da42058,

title = "Locally Manifold Non-negative Matrix Factorization Based on Centroid for scRNA-seq Data Analysis",

abstract = "The rapid development of single cell RNA sequencing (scRNA-seq) has made it possible to study the association between cells and genes at molecular resolution. When the follow-up analysis is carried out, it is often difficult to extract the cell information in high-dimensional space because of the high gene dimension in single-cell sequencing, which leads to inaccurate results in the follow-up analysis. To solve the problem, we propose a method called locally manifold non-negative matrix factorization based on centroid for scRNA-seq data analysis (MNMFC). MNMFC is a similarity modeling scheme based on locally manifold, which can map cell association in high dimensional space. Through similarity learning based on locally manifold and non-negative matrix decomposition (NMF) algorithm, the data in high-dimensional space can be mapped to low-dimensional space, which provides help for downstream clustering analysis. The performance of the model was validated experimentally on 10 scRNA-seq datasets. Compared with other nine advanced single-cell clustering methods, whether it is a comprehensive analysis or an individual analysis of the dataset, MNMFC has achieved encouraging results.",

keywords = "Cell similarity learning, Clustering, Non-negative Matrix Factorization, Single-cell RNA sequencing data.",

author = "Wang, \{Chuan Yuan\} and Gao, \{Ying Lian\} and Jiao, \{Cui Na\} and Liu, \{Jin Xing\} and Zheng, \{Chun Hou\} and Kong, \{Xiang Zhen\}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 ; Conference date: 16-12-2020 Through 19-12-2020",

year = "2020",

month = dec,

day = "16",

doi = "10.1109/BIBM49941.2020.9313380",

language = "英语",

series = "Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "121--125",

editor = "Taesung Park and Young-Rae Cho and Hu, \{Xiaohua Tony\} and Illhoi Yoo and Woo, \{Hyun Goo\} and Jianxin Wang and Julio Facelli and Seungyoon Nam and Mingon Kang",

booktitle = "Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020",

}

Wang, CY, Gao, YL, Jiao, CN, Liu, JX, Zheng, CH & Kong, XZ 2020, Locally Manifold Non-negative Matrix Factorization Based on Centroid for scRNA-seq Data Analysis. in T Park, Y-R Cho, XT Hu, I Yoo, HG Woo, J Wang, J Facelli, S Nam & M Kang (eds), Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020., 9313380, Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020, Institute of Electrical and Electronics Engineers Inc., pp. 121-125, 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020, Virtual, Seoul, Korea, Republic of, 16/12/20. https://doi.org/10.1109/BIBM49941.2020.9313380

Locally Manifold Non-negative Matrix Factorization Based on Centroid for scRNA-seq Data Analysis. / Wang, Chuan Yuan; Gao, Ying Lian; Jiao, Cui Na et al.
Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020. ed. / Taesung Park; Young-Rae Cho; Xiaohua Tony Hu; Illhoi Yoo; Hyun Goo Woo; Jianxin Wang; Julio Facelli; Seungyoon Nam; Mingon Kang. Institute of Electrical and Electronics Engineers Inc., 2020. p. 121-125 9313380 (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Locally Manifold Non-negative Matrix Factorization Based on Centroid for scRNA-seq Data Analysis

AU - Wang, Chuan Yuan

AU - Gao, Ying Lian

AU - Jiao, Cui Na

AU - Liu, Jin Xing

AU - Zheng, Chun Hou

AU - Kong, Xiang Zhen

PY - 2020/12/16

Y1 - 2020/12/16

N2 - The rapid development of single cell RNA sequencing (scRNA-seq) has made it possible to study the association between cells and genes at molecular resolution. When the follow-up analysis is carried out, it is often difficult to extract the cell information in high-dimensional space because of the high gene dimension in single-cell sequencing, which leads to inaccurate results in the follow-up analysis. To solve the problem, we propose a method called locally manifold non-negative matrix factorization based on centroid for scRNA-seq data analysis (MNMFC). MNMFC is a similarity modeling scheme based on locally manifold, which can map cell association in high dimensional space. Through similarity learning based on locally manifold and non-negative matrix decomposition (NMF) algorithm, the data in high-dimensional space can be mapped to low-dimensional space, which provides help for downstream clustering analysis. The performance of the model was validated experimentally on 10 scRNA-seq datasets. Compared with other nine advanced single-cell clustering methods, whether it is a comprehensive analysis or an individual analysis of the dataset, MNMFC has achieved encouraging results.

AB - The rapid development of single cell RNA sequencing (scRNA-seq) has made it possible to study the association between cells and genes at molecular resolution. When the follow-up analysis is carried out, it is often difficult to extract the cell information in high-dimensional space because of the high gene dimension in single-cell sequencing, which leads to inaccurate results in the follow-up analysis. To solve the problem, we propose a method called locally manifold non-negative matrix factorization based on centroid for scRNA-seq data analysis (MNMFC). MNMFC is a similarity modeling scheme based on locally manifold, which can map cell association in high dimensional space. Through similarity learning based on locally manifold and non-negative matrix decomposition (NMF) algorithm, the data in high-dimensional space can be mapped to low-dimensional space, which provides help for downstream clustering analysis. The performance of the model was validated experimentally on 10 scRNA-seq datasets. Compared with other nine advanced single-cell clustering methods, whether it is a comprehensive analysis or an individual analysis of the dataset, MNMFC has achieved encouraging results.

KW - Cell similarity learning

KW - Clustering

KW - Non-negative Matrix Factorization

KW - Single-cell RNA sequencing data.

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U2 - 10.1109/BIBM49941.2020.9313380

DO - 10.1109/BIBM49941.2020.9313380

M3 - 会议稿件

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BT - Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

A2 - Park, Taesung

A2 - Cho, Young-Rae

A2 - Hu, Xiaohua Tony

A2 - Yoo, Illhoi

A2 - Woo, Hyun Goo

A2 - Wang, Jianxin

A2 - Facelli, Julio

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PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

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ER -

Wang CY, Gao YL, Jiao CN, Liu JX, Zheng CH, Kong XZ. Locally Manifold Non-negative Matrix Factorization Based on Centroid for scRNA-seq Data Analysis. In Park T, Cho YR, Hu XT, Yoo I, Woo HG, Wang J, Facelli J, Nam S, Kang M, editors, Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 121-125. 9313380. (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020). doi: 10.1109/BIBM49941.2020.9313380

Locally Manifold Non-negative Matrix Factorization Based on Centroid for scRNA-seq Data Analysis

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