Improving distance measures between genomic tracks with mutual proximity

Abstract

An increasing number of genomic tracks such as DNA methylation, histone modifications or transcriptomes are being produced to annotate genomes with functional states. The comparison of such high dimensional vectors obtained under various experimental conditions requires the use of a distance or dissimilarity measure. Pearson, Cosine and $L_{p}$-norm distances are commonly used for both count and binary vectors. In this article, we highlight how enhancement methods such as the contrast increasing mutual proximity' (MP) or local scaling' improve common distance measures. We present a systematic approach to evaluate the performance of such enhanced distance measures in terms of separability of groups of experimental replicates to outline their effect. We show that the MP' applied on the various distance measures drastically increases performance. Depending on the type of epigenetic experiment, MP' coupled together with Pearson, Cosine, $L_1$, Yule or Jaccard distances proves to be highly efficient in discriminating epigenomic profiles.

Keywords: Lp-norm; ChIP-seq; DNA methylation; RNA-seq; distance measure; high dimensional dataset; mutual proximity.