A Traveling Salesman's Approach to Clustering Gene Expression Data

Given a matrix of values, rearrangement clustering involves rearranging the rows of the matrix and identifying cluster boundaries within the linear ordering of the rows. The TSP+k algorithm for rear-rangement clustering was presented in [3] and its implementation is described in this note. Using this code, we solve a 2,467-gene expression data clustering problem and identify “good” clusters that con-tain close to eight times the number of genes that were clustered by Eisen et al. (1998). Furthermore, we identify 106 functional groups that were overlooked in that paper. We make our implementation available to the general public for applications of gene expression data analysis.