Journal Article

Hobotnica: exploring molecular signature quality version 2; peer review: 2 approved

Budkina, Anna, Marchionni, Luigi, Sizykh, Alexey, Stupnikov, Alexey, Medvedeva, Yulia, Favorov, Alexander, Afsari, Bahman, Wheelan, Sarah

2022

Overview

A Molecular Features Set (MFS), is a result of a vast diversity of bioinformatics pipelines. The lack of a \"gold standard\" for most experimental data modalities makes it difficult to provide valid estimation for a particular MFS's quality. Yet, this goal can partially be achieved by analyzing inner-sample Distance Matrices (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 MFS's quality.  Here we propose Hobotnica, an approach to estimate MFSs quality by their ability to stratify data, and assign them significance scores, that allow for collating various signatures and comparing their quality for contrasting groups.

• Publisher: Faculty of 1000 Ltd,F1000 Research Limited,F1000 Research Ltd
• Subject: Bioinformatics / Breast cancer / Case studies / Datasets / Differential Gene Expression / Distance Matrix / eng / Gene expression / Gene Signature / Hypotheses / Method / Molecular signature / Phenotypes / Prostate cancer / Rank statistics