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Edge-group sparse PCA for network-guided high dimensional data analysis
Min, Wenwen1; Liu, Juan1; Zhang, Shihua2,3,4
2018-10-15
Source PublicationBIOINFORMATICS
ISSN1367-4803
Volume34Issue:20Pages:3479-3487
AbstractMotivation: Principal component analysis (PCA) has been widely used to deal with high-dimensional gene expression data. In this study, we proposed an Edge-group Sparse PCA (ESPCA) model by incorporating the group structure from a prior gene network into the PCA framework for dimension reduction and feature interpretation. ESPCA enforces sparsity of principal component (PC) loadings through considering the connectivity of gene variables in the prior network. We developed an alternating iterative algorithm to solve ESPCA. The key of this algorithm is to solve a new k-edge sparse projection problem and a greedy strategy has been adapted to address it. Here we adopted ESPCA for analyzing multiple gene expression matrices simultaneously. By incorporating prior knowledge, our method can overcome the drawbacks of sparse PCA and capture some gene modules with better biological interpretations. Results: We evaluated the performance of ESPCA using a set of artificial datasets and two real biological datasets (including TCGA pan-cancer expression data and ENCODE expression data), and compared their performance with PCA and sparse PCA. The results showed that ESPCA could identify more biologically relevant genes, improve their biological interpretations and reveal distinct sample characteristics.
DOI10.1093/bioinformatics/bty362
Language英语
Funding ProjectNational Natural Science Foundation of China[11661141019] ; National Natural Science Foundation of China[61621003] ; National Natural Science Foundation of China[61422309] ; National Natural Science Foundation of China[61379092] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDB13040600] ; Key Research Program of the Chinese Academy of Sciences[KFZD-SW-219] ; National Key Research and Development Program of China[2017YFC0908405] ; CAS Frontier Science Research Key Project for Top Young Scientist[QYZDB-SSW-SYS008]
WOS Research AreaBiochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
WOS SubjectBiochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability
WOS IDWOS:000448782100008
PublisherOXFORD UNIV PRESS
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Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/31659
Collection应用数学研究所
Affiliation1.Wuhan Univ, Sch Comp Sci, Wuhan 430072, Hubei, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, NCMIS, CEMS,RCSDS, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Ctr Excellence Anim Evolut & Genet, Kunming 650223, Yunnan, Peoples R China
Recommended Citation
GB/T 7714
Min, Wenwen,Liu, Juan,Zhang, Shihua. Edge-group sparse PCA for network-guided high dimensional data analysis[J]. BIOINFORMATICS,2018,34(20):3479-3487.
APA Min, Wenwen,Liu, Juan,&Zhang, Shihua.(2018).Edge-group sparse PCA for network-guided high dimensional data analysis.BIOINFORMATICS,34(20),3479-3487.
MLA Min, Wenwen,et al."Edge-group sparse PCA for network-guided high dimensional data analysis".BIOINFORMATICS 34.20(2018):3479-3487.
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