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|Abstract:||A common goal in data-analysis is to sift through a large data-matrix and detect any significant submatrices (i.e., biclusters) that have a low numerical rank. We present a simple algorithm for tackling this biclustering problem. Our algorithm accumulates information about 2-by-2 submatrices (i.e., ‘loops’) within the data-matrix, and focuses on rows and columns of the data-matrix that participate in an abundance of low-rank loops. We demonstrate, through analysis and numerical-experiments, that this loop-counting method performs well in a variety of scenarios, outperforming simple spectral methods in many situations of interest. Another important feature of our method is that it can easily be modified to account for aspects of experimental design which commonly arise in practice. For example, our algorithm can be modified to correct for controls, categorical- and continuous-covariates, as well as sparsity within the data. We demonstrate these practical features with two examples; the first drawn from gene-expression analysis and the second drawn from a much larger genome-wide-association-study (GWAS).|
|Citation:||Rangan, Aaditya V., Caroline C. McGrouther, John Kelsoe, Nicholas Schork, Eli Stahl, Qian Zhu, Arjun Krishnan, Vicky Yao, Olga Troyanskaya, Seda Bilaloglu, Preeti Raghavan, Sarah Bergen, Anders Jureus, Mikael Landen, Bipolar Disorders Working Group of the Psychiatric Genomics Consortium. "A loop-counting method for covariate-corrected low-rank biclustering of gene-expression and genome-wide association study data." PLoS Computational Biology 14, no. 5 (2018): pp. e1006105. doi:10.1371/journal.pcbi.1006105|
|Type of Material:||Journal Article|
|Journal/Proceeding Title:||PLoS Computational Biology|
|Version:||Final published version. This is an open access article.|
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