Hobotnica: exploring molecular signature quality

Abstract

A Molecular Features Set (MFS), is a result of vast diversity of bioinformatics pipelines. In case when MFS is used for further analysis to distinguish between phenotypes, it is often referred to as a signature. Lack of the "gold standard" for most experimental data modalities makes it hard to provide valid estimation for a particular MFSs quality. Yet, this goal can partially be achieved by analyzing inner-sample Distance Matrix (DM) and their power to distinguish between phenotypes. The quality of a DM can be assessed by summarizing its power to quantify the differences of inner-phenotype and outer-phenotype distances. This estimation of the DM quality can be construed as a measure of the MFSs quality. Here we propose Hobotnica, an approach to estimate MFSs quality by their ability to stratify data, and assign them significance scores, that allows for collating various signatures and comparing their quality for contrasting groups.