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For: Yang Z, Michailidis G. Quantifying heterogeneity of expression data based on principal components. Bioinformatics 2019;35:553-559. [PMID: 30060088 DOI: 10.1093/bioinformatics/bty671] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Revised: 07/05/2018] [Accepted: 07/27/2018] [Indexed: 11/12/2022] Open

For:	Yang Z, Michailidis G. Quantifying heterogeneity of expression data based on principal components. Bioinformatics 2019;35:553-559. [PMID: 30060088 DOI: 10.1093/bioinformatics/bty671] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Revised: 07/05/2018] [Accepted: 07/27/2018] [Indexed: 11/12/2022] Open

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Poythress JC, Park C, Ahn J. Dimension-wise sparse low-rank approximation of a matrix with application to variable selection in high-dimensional integrative analyzes of association. J Appl Stat 2021;49:3889-3907. [DOI: 10.1080/02664763.2021.1967892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]