A loop-counting method for covariate-corrected low-rank biclustering of gene-expression and genome-wide association study data
Fig 8
Continuous–covariate-distribution for the bicluster shown in Example-B.
As mentioned in the introduction, our algorithm proceeds iteratively, removing rows and columns from the case-matrix until there are none left. One of our goals is to ensure that, during this process, our algorithm focuses on biclusters which involve case-patients that are relatively well balanced in covariate-space. On the left we show a scatterplot illustrating the 2-dimensional distribution of covariate-components across the remaining m = 115 case-patients within the bicluster shown in Example-B (i.e., Fig 7). The horizontal and vertical lines in each subplot indicate the medians of the components of the covariate-distribution. On the right we show the same data again, except in contour form (note colorbar). The continuous-covariates remain relatively well-distributed even though relatively few case-patients are left (compare with Fig 9).