Sparse Orthogonal Nonnegative Matrix Factorization for Identifying Differentially Expressed Genes and Clustering Tumor Samples

Ling Yun Dai; Jin Xing Liu; Rong Zhu; Xiang Zhen Kong; Mi Xiao Hou; Sha Sha Yuan

doi:10.1109/BIBM.2018.8621438

Sparse Orthogonal Nonnegative Matrix Factorization for Identifying Differentially Expressed Genes and Clustering Tumor Samples

Ling Yun Dai
, Jin Xing Liu
, Rong Zhu
, Xiang Zhen Kong
, Mi Xiao Hou
, Sha Sha Yuan

Qufu Normal University

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

3 Scopus citations

Abstract

Gene expression data are critical for disease diagnoses and classification. However gene expression data usually are high-dimensional and high-noisy. Currently, many matrix factorization methods have been widely used for dimensionality reduction and data preprocessing in bioinformatics. Particularly, nonnegative matrix factorization (NMF) has the outstanding interpretability in analyzing gene expression data due to the nonnegative constraints. In this paper, a new nonnegative matrix factorization algorithm named sparse orthogonal nonnegative matrix factorization (SONMF) is proposed and applied to identify differentially expressed genes and cluster tumor samples, in which the L1-norm regularization and the orthogonal constraint are incorporated into the traditional NMF model to get more powerful data analysis tool. An iterative algorithm is proposed to optimize the new objective function. In order to prove the efficiency of the algorithm, SONMF is tested on four public gene expression datasets and compared with the other four NMF methods. The experimental results on the four real tumor datasets confirm the efficiency of SONMF for identifying differentially expressed genes and clustering tumor samples.

Original language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Editors	Harald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1332-1337
Number of pages	6
ISBN (Electronic)	9781538654880
DOIs	https://doi.org/10.1109/BIBM.2018.8621438
State	Published - 21 Jan 2019
Externally published	Yes
Event	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain Duration: 3 Dec 2018 → 6 Dec 2018

Publication series

Name	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Country/Territory	Spain
City	Madrid
Period	3/12/18 → 6/12/18

Keywords

L1-norm
clustering
differentially expressed genes
nonnegative matrix factorization
orthogonal constraint

Access to Document

10.1109/BIBM.2018.8621438

Cite this

Dai, L. Y., Liu, J. X., Zhu, R., Kong, X. Z., Hou, M. X., & Yuan, S. S. (2019). Sparse Orthogonal Nonnegative Matrix Factorization for Identifying Differentially Expressed Genes and Clustering Tumor Samples. In H. Schmidt, D. Griol, H. Wang, J. Baumbach, H. Zheng, Z. Callejas, X. Hu, J. Dickerson, & L. Zhang (Eds.), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 (pp. 1332-1337). Article 8621438 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2018.8621438

Dai, Ling Yun ; Liu, Jin Xing ; Zhu, Rong et al. / Sparse Orthogonal Nonnegative Matrix Factorization for Identifying Differentially Expressed Genes and Clustering Tumor Samples. Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. editor / Harald Schmidt ; David Griol ; Haiying Wang ; Jan Baumbach ; Huiru Zheng ; Zoraida Callejas ; Xiaohua Hu ; Julie Dickerson ; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1332-1337 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

@inproceedings{251cb56527154344bafd3f6556950af1,

title = "Sparse Orthogonal Nonnegative Matrix Factorization for Identifying Differentially Expressed Genes and Clustering Tumor Samples",

abstract = "Gene expression data are critical for disease diagnoses and classification. However gene expression data usually are high-dimensional and high-noisy. Currently, many matrix factorization methods have been widely used for dimensionality reduction and data preprocessing in bioinformatics. Particularly, nonnegative matrix factorization (NMF) has the outstanding interpretability in analyzing gene expression data due to the nonnegative constraints. In this paper, a new nonnegative matrix factorization algorithm named sparse orthogonal nonnegative matrix factorization (SONMF) is proposed and applied to identify differentially expressed genes and cluster tumor samples, in which the L1-norm regularization and the orthogonal constraint are incorporated into the traditional NMF model to get more powerful data analysis tool. An iterative algorithm is proposed to optimize the new objective function. In order to prove the efficiency of the algorithm, SONMF is tested on four public gene expression datasets and compared with the other four NMF methods. The experimental results on the four real tumor datasets confirm the efficiency of SONMF for identifying differentially expressed genes and clustering tumor samples.",

keywords = "L1-norm, clustering, differentially expressed genes, nonnegative matrix factorization, orthogonal constraint",

author = "Dai, \{Ling Yun\} and Liu, \{Jin Xing\} and Rong Zhu and Kong, \{Xiang Zhen\} and Hou, \{Mi Xiao\} and Yuan, \{Sha Sha\}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 ; Conference date: 03-12-2018 Through 06-12-2018",

year = "2019",

month = jan,

day = "21",

doi = "10.1109/BIBM.2018.8621438",

language = "英语",

series = "Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018",

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

pages = "1332--1337",

editor = "Harald Schmidt and David Griol and Haiying Wang and Jan Baumbach and Huiru Zheng and Zoraida Callejas and Xiaohua Hu and Julie Dickerson and Le Zhang",

booktitle = "Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018",

}

Dai, LY, Liu, JX, Zhu, R, Kong, XZ, Hou, MX & Yuan, SS 2019, Sparse Orthogonal Nonnegative Matrix Factorization for Identifying Differentially Expressed Genes and Clustering Tumor Samples. in H Schmidt, D Griol, H Wang, J Baumbach, H Zheng, Z Callejas, X Hu, J Dickerson & L Zhang (eds), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018., 8621438, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1332-1337, 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Madrid, Spain, 3/12/18. https://doi.org/10.1109/BIBM.2018.8621438

Sparse Orthogonal Nonnegative Matrix Factorization for Identifying Differentially Expressed Genes and Clustering Tumor Samples. / Dai, Ling Yun; Liu, Jin Xing; Zhu, Rong et al.
Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. ed. / Harald Schmidt; David Griol; Haiying Wang; Jan Baumbach; Huiru Zheng; Zoraida Callejas; Xiaohua Hu; Julie Dickerson; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1332-1337 8621438 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

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

TY - GEN

T1 - Sparse Orthogonal Nonnegative Matrix Factorization for Identifying Differentially Expressed Genes and Clustering Tumor Samples

AU - Dai, Ling Yun

AU - Liu, Jin Xing

AU - Zhu, Rong

AU - Kong, Xiang Zhen

AU - Hou, Mi Xiao

AU - Yuan, Sha Sha

PY - 2019/1/21

Y1 - 2019/1/21

N2 - Gene expression data are critical for disease diagnoses and classification. However gene expression data usually are high-dimensional and high-noisy. Currently, many matrix factorization methods have been widely used for dimensionality reduction and data preprocessing in bioinformatics. Particularly, nonnegative matrix factorization (NMF) has the outstanding interpretability in analyzing gene expression data due to the nonnegative constraints. In this paper, a new nonnegative matrix factorization algorithm named sparse orthogonal nonnegative matrix factorization (SONMF) is proposed and applied to identify differentially expressed genes and cluster tumor samples, in which the L1-norm regularization and the orthogonal constraint are incorporated into the traditional NMF model to get more powerful data analysis tool. An iterative algorithm is proposed to optimize the new objective function. In order to prove the efficiency of the algorithm, SONMF is tested on four public gene expression datasets and compared with the other four NMF methods. The experimental results on the four real tumor datasets confirm the efficiency of SONMF for identifying differentially expressed genes and clustering tumor samples.

AB - Gene expression data are critical for disease diagnoses and classification. However gene expression data usually are high-dimensional and high-noisy. Currently, many matrix factorization methods have been widely used for dimensionality reduction and data preprocessing in bioinformatics. Particularly, nonnegative matrix factorization (NMF) has the outstanding interpretability in analyzing gene expression data due to the nonnegative constraints. In this paper, a new nonnegative matrix factorization algorithm named sparse orthogonal nonnegative matrix factorization (SONMF) is proposed and applied to identify differentially expressed genes and cluster tumor samples, in which the L1-norm regularization and the orthogonal constraint are incorporated into the traditional NMF model to get more powerful data analysis tool. An iterative algorithm is proposed to optimize the new objective function. In order to prove the efficiency of the algorithm, SONMF is tested on four public gene expression datasets and compared with the other four NMF methods. The experimental results on the four real tumor datasets confirm the efficiency of SONMF for identifying differentially expressed genes and clustering tumor samples.

KW - L1-norm

KW - clustering

KW - differentially expressed genes

KW - nonnegative matrix factorization

KW - orthogonal constraint

UR - https://www.scopus.com/pages/publications/85062482339

U2 - 10.1109/BIBM.2018.8621438

DO - 10.1109/BIBM.2018.8621438

M3 - 会议稿件

AN - SCOPUS:85062482339

T3 - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

SP - 1332

EP - 1337

BT - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

A2 - Schmidt, Harald

A2 - Griol, David

A2 - Wang, Haiying

A2 - Baumbach, Jan

A2 - Zheng, Huiru

A2 - Callejas, Zoraida

A2 - Hu, Xiaohua

A2 - Dickerson, Julie

A2 - Zhang, Le

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Y2 - 3 December 2018 through 6 December 2018

ER -

Dai LY, Liu JX, Zhu R, Kong XZ, Hou MX, Yuan SS. Sparse Orthogonal Nonnegative Matrix Factorization for Identifying Differentially Expressed Genes and Clustering Tumor Samples. In Schmidt H, Griol D, Wang H, Baumbach J, Zheng H, Callejas Z, Hu X, Dickerson J, Zhang L, editors, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1332-1337. 8621438. (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). doi: 10.1109/BIBM.2018.8621438

Sparse Orthogonal Nonnegative Matrix Factorization for Identifying Differentially Expressed Genes and Clustering Tumor Samples

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