Enumerating the gene sets in breast cancer a direct alternative to hierarchical clustering

摘要

BackgroundTwo-way hierarchical clustering, with results visualized as heatmaps, has served as the method of choice for exploring structure in large matrices of expression data since the advent of microarrays. While it has delivered important insights, including a typology of breast cancer subtypes, it suffers from instability in the face of gene or sample selection, and an inability to detect small sets that may be dominated by larger sets such as the estrogen-related genes in breast cancer. The rank-based partitioning algorithm introduced in this paper addresses several of these limitations. It delivers results comparable to two-way hierarchical clustering, and much more. Applied systematically across a range of parameter settings, it enumerates all the partition-inducing gene sets in a matrix of expression values.