Semi-supervised feature extraction for RNA-seq data analysis

Jin Xing Liu; Yong Xu; Ying Lian Gao; Dong Wang; Chun Hou Zheng; Jun Liang Shang

doi:10.1007/978-3-319-22053-6_70

Semi-supervised feature extraction for RNA-seq data analysis

Jin Xing Liu
, Yong Xu
, Ying Lian Gao
, Dong Wang
, Chun Hou Zheng
, Jun Liang Shang

Harbin Institute of Technology
Qufu Normal University
Anhui University

Research output: Contribution to journal › Conference article › peer-review

Abstract

It is of urgency to effectively identify differentially expressed genes from RNA-Seq data. In this paper, we propose a novel method, semi-supervised feature extraction, to analyze RNA-Seq data. Our scheme is shown as follows. Firstly, we construct a graph Laplacian matrix and refine it by using labeled samples. Secondly, we find semi-supervised optimal maps by solving a generalized eigenvalue problem. Thirdly, we solve an optimal problem via joint L2,1-norm constraint to obtain a projection matrix. Finally, we identify differentially expressed genes based on the projection matrix. The results on real RNA-Seq data sets demonstrate the feasibility and effectiveness of our method.

Original language	English
Pages (from-to)	679-685
Number of pages	7
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9227
DOIs	https://doi.org/10.1007/978-3-319-22053-6_70
State	Published - 2015
Externally published	Yes
Event	11th International Conference on Intelligent Computing, ICIC 2015 - Fuzhou, China Duration: 20 Aug 2015 → 23 Aug 2015

Keywords

Feature extraction
L-norm constraint
RNA-Seq data analysis
Spectral regression

Access to Document

10.1007/978-3-319-22053-6_70

Cite this

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title = "Semi-supervised feature extraction for RNA-seq data analysis",

abstract = "It is of urgency to effectively identify differentially expressed genes from RNA-Seq data. In this paper, we propose a novel method, semi-supervised feature extraction, to analyze RNA-Seq data. Our scheme is shown as follows. Firstly, we construct a graph Laplacian matrix and refine it by using labeled samples. Secondly, we find semi-supervised optimal maps by solving a generalized eigenvalue problem. Thirdly, we solve an optimal problem via joint L2,1-norm constraint to obtain a projection matrix. Finally, we identify differentially expressed genes based on the projection matrix. The results on real RNA-Seq data sets demonstrate the feasibility and effectiveness of our method.",

keywords = "Feature extraction, L-norm constraint, RNA-Seq data analysis, Spectral regression",

author = "Liu, \{Jin Xing\} and Yong Xu and Gao, \{Ying Lian\} and Dong Wang and Zheng, \{Chun Hou\} and Shang, \{Jun Liang\}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 11th International Conference on Intelligent Computing, ICIC 2015 ; Conference date: 20-08-2015 Through 23-08-2015",

year = "2015",

doi = "10.1007/978-3-319-22053-6\_70",

language = "英语",

volume = "9227",

pages = "679--685",

journal = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

issn = "0302-9743",

publisher = "Springer Science and Business Media Deutschland GmbH",

}

TY - JOUR

T1 - Semi-supervised feature extraction for RNA-seq data analysis

AU - Liu, Jin Xing

AU - Xu, Yong

AU - Gao, Ying Lian

AU - Wang, Dong

AU - Zheng, Chun Hou

AU - Shang, Jun Liang

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - It is of urgency to effectively identify differentially expressed genes from RNA-Seq data. In this paper, we propose a novel method, semi-supervised feature extraction, to analyze RNA-Seq data. Our scheme is shown as follows. Firstly, we construct a graph Laplacian matrix and refine it by using labeled samples. Secondly, we find semi-supervised optimal maps by solving a generalized eigenvalue problem. Thirdly, we solve an optimal problem via joint L2,1-norm constraint to obtain a projection matrix. Finally, we identify differentially expressed genes based on the projection matrix. The results on real RNA-Seq data sets demonstrate the feasibility and effectiveness of our method.

AB - It is of urgency to effectively identify differentially expressed genes from RNA-Seq data. In this paper, we propose a novel method, semi-supervised feature extraction, to analyze RNA-Seq data. Our scheme is shown as follows. Firstly, we construct a graph Laplacian matrix and refine it by using labeled samples. Secondly, we find semi-supervised optimal maps by solving a generalized eigenvalue problem. Thirdly, we solve an optimal problem via joint L2,1-norm constraint to obtain a projection matrix. Finally, we identify differentially expressed genes based on the projection matrix. The results on real RNA-Seq data sets demonstrate the feasibility and effectiveness of our method.

KW - Feature extraction

KW - L-norm constraint

KW - RNA-Seq data analysis

KW - Spectral regression

UR - https://www.scopus.com/pages/publications/84983681698

U2 - 10.1007/978-3-319-22053-6_70

DO - 10.1007/978-3-319-22053-6_70

M3 - 会议文章

AN - SCOPUS:84983681698

SN - 0302-9743

VL - 9227

SP - 679

EP - 685

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

T2 - 11th International Conference on Intelligent Computing, ICIC 2015

Y2 - 20 August 2015 through 23 August 2015

ER -

Semi-supervised feature extraction for RNA-seq data analysis

Abstract

Keywords

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