期刊
BRIEFINGS IN BIOINFORMATICS
卷 22, 期 6, 页码 -出版社
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbab266
关键词
ChIP-seq; RNA-seq; DNA methylation; L-p-norm; mutual proximity; distance measure; high dimensional dataset
资金
- French Museum Nationale d'Histoire Naturelle (MNHN)
- Sorbonne Universite
This article discusses how enhancement methods improve the performance of common distance measures, presents a systematic approach to evaluate the separability of experimental replicates, and shows that the application of the "contrast increasing mutual proximity" significantly enhances performance across various distance measures. Depending on the type of epigenetic experiment, the MP coupled with Pearson, Cosine, or other distances proves to be highly efficient in discriminating epigenomic profiles.
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 -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, , Yule or Jaccard distances proves to be highly efficient in discriminating epigenomic profiles.
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