{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T03:04:37Z","timestamp":1791515077675,"version":"4.3.4"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"20","license":[{"start":{"date-parts":[[2018,5,3]],"date-time":"2018-05-03T00:00:00Z","timestamp":1525305600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11661141019"],"award-info":[{"award-number":["11661141019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61621003"],"award-info":[{"award-number":["61621003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61422309"],"award-info":[{"award-number":["61422309"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61379092"],"award-info":[{"award-number":["61379092"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDB13040600"],"award-info":[{"award-number":["XDB13040600"]}]},{"name":"Key Research Program of the Chinese Academy of Sciences","award":["KFZD-SW-219"],"award-info":[{"award-number":["KFZD-SW-219"]}]},{"name":"National Key Research and Development Program of China","award":["2017YFC0908405"],"award-info":[{"award-number":["2017YFC0908405"]}]},{"name":"CAS Frontier Science Research Key Project for Top Young Scientist","award":["QYZDB-SSW-SYS008"],"award-info":[{"award-number":["QYZDB-SSW-SYS008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,10,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>An R package of ESPCA is available at http:\/\/page.amss.ac.cn\/shihua.zhang\/<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/bty362","type":"journal-article","created":{"date-parts":[[2018,5,2]],"date-time":"2018-05-02T07:10:47Z","timestamp":1525245047000},"page":"3479-3487","source":"Crossref","is-referenced-by-count":41,"title":["Edge-group sparse PCA for network-guided high dimensional data analysis"],"prefix":"10.1093","volume":"34","author":[{"given":"Wenwen","family":"Min","sequence":"first","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shihua","family":"Zhang","sequence":"additional","affiliation":[{"name":"NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China"},{"name":"School of Mathematics Sciences, University of Chinese Academy of Sciences, Beijing, China"},{"name":"Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2018,5,3]]},"reference":[{"key":"2023012712421398900_bty362-B1","doi-asserted-by":"crossref","first-page":"1987","DOI":"10.1093\/bioinformatics\/btx097","article-title":"An approach to infer putative disease-specific mechanisms using neighboring gene networks","volume":"33","author":"Ansari","year":"2017","journal-title":"Bioinformatics"},{"key":"2023012712421398900_bty362-B2","doi-asserted-by":"crossref","first-page":"151.","DOI":"10.1186\/s13059-016-1008-y","article-title":"Gene-specific patterns of expression variation across organs and species","volume":"17","author":"Breschi","year":"2016","journal-title":"Genome Biol"},{"key":"2023012712421398900_bty362-B3","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1093\/bioinformatics\/btu674","article-title":"Statistical significance of variables driving systematic variation in high-dimensional data","volume":"31","author":"Chung","year":"2015","journal-title":"Bioinformatics"},{"key":"2023012712421398900_bty362-B4","first-page":"334","volume-title":"Conference on Neural Information Processing Systems","author":"Deshpande","year":"2014"},{"key":"2023012712421398900_bty362-B5","doi-asserted-by":"crossref","first-page":"i223","DOI":"10.1093\/bioinformatics\/btn161","article-title":"Identifying functional modules in protein\u2013protein interaction networks: an integrated exact approach","volume":"24","author":"Dittrich","year":"2008","journal-title":"Bioinformatics"},{"key":"2023012712421398900_bty362-B6","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1038\/nature11247","article-title":"An integrated encyclopedia of DNA elements in the human genome","volume":"489","author":"Dunham","year":"2012","journal-title":"Nature"},{"key":"2023012712421398900_bty362-B7","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1093\/bib\/bbv044","article-title":"Using prior knowledge from cellular pathways and molecular networks for diagnostic specimen classification","volume":"17","author":"Glaab","year":"2016","journal-title":"Brief. 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