friends.test: rank-based method for feature selection in interaction matrices
Abstract
The analysis of the interaction matrix between two distinct sets is essential across diverse fields, from pharmacovigilance to transcriptomics. Not all interactions are equally informative: a marker gene associated with a few specific biological processes is more informative than a highly expressed non-specific gene associated with most observed processes. Identifying these interactions is challenging due to background connections. Furthermore, data heterogeneity across sources precludes universal identification criteria. To address this challenge, we introduce \textsf{friends.test}, a method for identifying specificity by detecting structural breaks in entity interactions. Rank-based representation of the interaction matrix ensures invariance to heterogeneous data and allows for integrating data from diverse sources. To automatically locate the boundary between specific interactions and background activity, we employ model fitting. We demonstrate the applicability of \textsf{friends.test} on the GSE112026 -- transnational data from head and neck cancer. A computationally efficient \textsf{R} implementation is available at https://github.com/favorov/friends.test.
Keywords
Cite
@article{arxiv.2512.24843,
title = {friends.test: rank-based method for feature selection in interaction matrices},
author = {Alexandra Suvorikova and Alexey Kroshnin and Dmirijs Lvovs and Vera Mukhina and Andrey Mironov and Elana J. Fertig and Ludmila Danilova and Alexander Favorov},
journal= {arXiv preprint arXiv:2512.24843},
year = {2026}
}
Comments
12 pages, 3 figures. The first two listed authors contributed equally to this work