TitleA loop-counting method for covariate-corrected low-rank biclustering of gene-expression and genome-wide association study data.
Publication TypeJournal Article
Year of Publication2018
AuthorsRangan, AV, McGrouther, CC, Kelsoe, J, Schork, N, Stahl, E, Zhu, Q, Krishnan, A, Yao, V, Troyanskaya, O, Bilaloglu, S, Raghavan, P, Bergen, S, Jureus, A, Landen, M
Corporate AuthorsBipolar Disorders Working Group of the Psychiatric Genomics Consortium
JournalPLoS Comput Biol
Date Published2018 05
KeywordsAlgorithms, Bipolar Disorder, Breast Neoplasms, Cluster Analysis, Databases, Genetic, Female, Gene Expression Profiling, Genome-Wide Association Study, Humans, Male

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).

Alternate JournalPLoS Comput. Biol.
PubMed ID29758032
PubMed Central IDPMC5997363
Grant ListU01 MH109536 / MH / NIMH NIH HHS / United States
U24 AG051129 / AG / NIA NIH HHS / United States