Graph regularized robust non-negative matrix factorization for clustering and selecting differentially expressed genes

Na Yu; Jin Xing Liu; Ying Lian Gao; Chun Hou Zheng; Juan Wang; Ming Juan Wu

doi:10.1109/BIBM.2017.8217925

Graph regularized robust non-negative matrix factorization for clustering and selecting differentially expressed genes

Na Yu
, Jin Xing Liu
, Ying Lian Gao
, Chun Hou Zheng
, Juan Wang
, Ming Juan Wu

Qufu Normal University

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

6 Scopus citations

Abstract

Non-negative Matrix Factorization (NMF) is widely used as a data dimensionality reduction tool. However, the assumption of most conventional NMF-based methods is that the gene expression data are only destroyed by Gaussian noise. In practice, the gene expression data are unavoidably destroyed by sparse noise. Although Sparsity-Regularized Robust NMF by using L_1/2 constraint (L_1/2-RNMF) can achieve satisfactory results when the sparse noise exists, it does not consider the intrinsic geometric structure in data. Hence, we introduce graph regularization into L_1/2-RNMF. In this paper, we developed a novel NMF method named Graph regularized Robust Nonnegative Matrix Factorization (GrRNMF), which mainly consists of two aspects: Firstly, the Gaussian noise and sparse noise are modeled, respectively. Secondly, it can reveal the geometric information in data by adding graph regularization term. Extensive experimental results on The Cancer Genome Atlas (TCGA) data indicate that the GrRNMF method has higher accuracy than other state-of-the-art methods in samples clustering and the selection of differentially expressed genes.

Original language	English
Title of host publication	Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
Editors	Illhoi Yoo, Jane Huiru Zheng, Yang Gong, Xiaohua Tony Hu, Chi-Ren Shyu, Yana Bromberg, Jean Gao, Dmitry Korkin
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1752-1756
Number of pages	5
ISBN (Electronic)	9781509030491
DOIs	https://doi.org/10.1109/BIBM.2017.8217925
State	Published - 15 Dec 2017
Externally published	Yes
Event	2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 - Kansas City, United States Duration: 13 Nov 2017 → 16 Nov 2017

Publication series

Name	Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
Volume	2017-January

Conference

Conference	2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
Country/Territory	United States
City	Kansas City
Period	13/11/17 → 16/11/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Non-negative Matrix Factorization
TCGA data
differentially expressed genes
graph regularization
sparse noise

Access to Document

10.1109/BIBM.2017.8217925

Cite this

Yu, N., Liu, J. X., Gao, Y. L., Zheng, C. H., Wang, J., & Wu, M. J. (2017). Graph regularized robust non-negative matrix factorization for clustering and selecting differentially expressed genes. In I. Yoo, J. H. Zheng, Y. Gong, X. T. Hu, C.-R. Shyu, Y. Bromberg, J. Gao, & D. Korkin (Eds.), Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 (pp. 1752-1756). (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2017.8217925

Yu, Na ; Liu, Jin Xing ; Gao, Ying Lian et al. / Graph regularized robust non-negative matrix factorization for clustering and selecting differentially expressed genes. Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. editor / Illhoi Yoo ; Jane Huiru Zheng ; Yang Gong ; Xiaohua Tony Hu ; Chi-Ren Shyu ; Yana Bromberg ; Jean Gao ; Dmitry Korkin. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1752-1756 (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017).

@inproceedings{2b8a8bff6e354076ab8c11c40e9083f5,

title = "Graph regularized robust non-negative matrix factorization for clustering and selecting differentially expressed genes",

abstract = "Non-negative Matrix Factorization (NMF) is widely used as a data dimensionality reduction tool. However, the assumption of most conventional NMF-based methods is that the gene expression data are only destroyed by Gaussian noise. In practice, the gene expression data are unavoidably destroyed by sparse noise. Although Sparsity-Regularized Robust NMF by using L1/2 constraint (L1/2-RNMF) can achieve satisfactory results when the sparse noise exists, it does not consider the intrinsic geometric structure in data. Hence, we introduce graph regularization into L1/2-RNMF. In this paper, we developed a novel NMF method named Graph regularized Robust Nonnegative Matrix Factorization (GrRNMF), which mainly consists of two aspects: Firstly, the Gaussian noise and sparse noise are modeled, respectively. Secondly, it can reveal the geometric information in data by adding graph regularization term. Extensive experimental results on The Cancer Genome Atlas (TCGA) data indicate that the GrRNMF method has higher accuracy than other state-of-the-art methods in samples clustering and the selection of differentially expressed genes.",

keywords = "Non-negative Matrix Factorization, TCGA data, differentially expressed genes, graph regularization, sparse noise",

author = "Na Yu and Liu, \{Jin Xing\} and Gao, \{Ying Lian\} and Zheng, \{Chun Hou\} and Juan Wang and Wu, \{Ming Juan\}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 ; Conference date: 13-11-2017 Through 16-11-2017",

year = "2017",

month = dec,

day = "15",

doi = "10.1109/BIBM.2017.8217925",

language = "英语",

series = "Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017",

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

pages = "1752--1756",

editor = "Illhoi Yoo and Zheng, \{Jane Huiru\} and Yang Gong and Hu, \{Xiaohua Tony\} and Chi-Ren Shyu and Yana Bromberg and Jean Gao and Dmitry Korkin",

booktitle = "Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017",

}

Yu, N, Liu, JX, Gao, YL, Zheng, CH, Wang, J & Wu, MJ 2017, Graph regularized robust non-negative matrix factorization for clustering and selecting differentially expressed genes. in I Yoo, JH Zheng, Y Gong, XT Hu, C-R Shyu, Y Bromberg, J Gao & D Korkin (eds), Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017, vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 1752-1756, 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017, Kansas City, United States, 13/11/17. https://doi.org/10.1109/BIBM.2017.8217925

Graph regularized robust non-negative matrix factorization for clustering and selecting differentially expressed genes. / Yu, Na; Liu, Jin Xing; Gao, Ying Lian et al.
Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. ed. / Illhoi Yoo; Jane Huiru Zheng; Yang Gong; Xiaohua Tony Hu; Chi-Ren Shyu; Yana Bromberg; Jean Gao; Dmitry Korkin. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1752-1756 (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017; Vol. 2017-January).

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

TY - GEN

T1 - Graph regularized robust non-negative matrix factorization for clustering and selecting differentially expressed genes

AU - Yu, Na

AU - Liu, Jin Xing

AU - Gao, Ying Lian

AU - Zheng, Chun Hou

AU - Wang, Juan

AU - Wu, Ming Juan

PY - 2017/12/15

Y1 - 2017/12/15

N2 - Non-negative Matrix Factorization (NMF) is widely used as a data dimensionality reduction tool. However, the assumption of most conventional NMF-based methods is that the gene expression data are only destroyed by Gaussian noise. In practice, the gene expression data are unavoidably destroyed by sparse noise. Although Sparsity-Regularized Robust NMF by using L1/2 constraint (L1/2-RNMF) can achieve satisfactory results when the sparse noise exists, it does not consider the intrinsic geometric structure in data. Hence, we introduce graph regularization into L1/2-RNMF. In this paper, we developed a novel NMF method named Graph regularized Robust Nonnegative Matrix Factorization (GrRNMF), which mainly consists of two aspects: Firstly, the Gaussian noise and sparse noise are modeled, respectively. Secondly, it can reveal the geometric information in data by adding graph regularization term. Extensive experimental results on The Cancer Genome Atlas (TCGA) data indicate that the GrRNMF method has higher accuracy than other state-of-the-art methods in samples clustering and the selection of differentially expressed genes.

AB - Non-negative Matrix Factorization (NMF) is widely used as a data dimensionality reduction tool. However, the assumption of most conventional NMF-based methods is that the gene expression data are only destroyed by Gaussian noise. In practice, the gene expression data are unavoidably destroyed by sparse noise. Although Sparsity-Regularized Robust NMF by using L1/2 constraint (L1/2-RNMF) can achieve satisfactory results when the sparse noise exists, it does not consider the intrinsic geometric structure in data. Hence, we introduce graph regularization into L1/2-RNMF. In this paper, we developed a novel NMF method named Graph regularized Robust Nonnegative Matrix Factorization (GrRNMF), which mainly consists of two aspects: Firstly, the Gaussian noise and sparse noise are modeled, respectively. Secondly, it can reveal the geometric information in data by adding graph regularization term. Extensive experimental results on The Cancer Genome Atlas (TCGA) data indicate that the GrRNMF method has higher accuracy than other state-of-the-art methods in samples clustering and the selection of differentially expressed genes.

KW - Non-negative Matrix Factorization

KW - TCGA data

KW - differentially expressed genes

KW - graph regularization

KW - sparse noise

UR - https://www.scopus.com/pages/publications/85045967394

U2 - 10.1109/BIBM.2017.8217925

DO - 10.1109/BIBM.2017.8217925

M3 - 会议稿件

AN - SCOPUS:85045967394

T3 - Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017

SP - 1752

EP - 1756

BT - Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017

A2 - Yoo, Illhoi

A2 - Zheng, Jane Huiru

A2 - Gong, Yang

A2 - Hu, Xiaohua Tony

A2 - Shyu, Chi-Ren

A2 - Bromberg, Yana

A2 - Gao, Jean

A2 - Korkin, Dmitry

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017

Y2 - 13 November 2017 through 16 November 2017

ER -

Yu N, Liu JX, Gao YL, Zheng CH, Wang J, Wu MJ. Graph regularized robust non-negative matrix factorization for clustering and selecting differentially expressed genes. In Yoo I, Zheng JH, Gong Y, Hu XT, Shyu CR, Bromberg Y, Gao J, Korkin D, editors, Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1752-1756. (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017). doi: 10.1109/BIBM.2017.8217925

Graph regularized robust non-negative matrix factorization for clustering and selecting differentially expressed genes

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this