A loop-counting method for covariate-corrected low-rank biclustering of gene-expression and genome-wide association study data
Fig 7
Illustration of bicluster found within genome-wide-association-study dataset.
In this figure we illustrate the genome-wide association-study (i.e., GWAS) data-set discussed in Example-B (see main text). This data-set involves 16577 patients, each genotyped across 276768 genetic base-pair-locations (i.e., alleles). Many of these patients have a particular psychological disorder, while the remainder do not. We use this phenotype to separate the patients into MD = 9752 cases and MX = 6825 controls. The size of this GWAS data-set is indicated in the background of this picture, and dwarfs the size of the gene-expression data-set used in Example-A (inset for comparison). At the top of the foreground we illustrate an m = 115 by n = 706 submatrix found within the case-patients. This submatrix is a low-rank bicluster, and the alleles are strongly correlated across these particular case-patients. The order of the patients and alleles within this submatrix has been chosen to emphasize this correlation. For comparison, we pull out a few other randomly-chosen case-patients and control-patients, and present their associated submatrices (defined using the same 706 alleles) further down.