Title: On the analysis of the human immunome via an information theoretical approach

Authors: Maciej Pietrzak; Gerard Lozanski; Michael Grever; Leslie Andritsos; James Blachly; Kerry Rogers; Michal Seweryn

Addresses: Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA ' The Ohio State University, Wexner Medical Center, Columbus, OH 43210, USA ' The Ohio State University, Wexner Medical Center, Columbus, OH 43210, USA ' The University of New Mexico, Albuquerque, NM 87131, USA ' The Ohio State University, Wexner Medical Center, Columbus, OH 43210, USA ' The Ohio State University, Wexner Medical Center, Columbus, OH 43210, USA ' Center for Medical Genomics, Jagiellonian University, Krakow, 31-026, Poland

Abstract: Deep phenotyping of the cellular components of the immune system (the immunome) enables to decompose the multilayer immune network in health and disease. Analysis of immunome data requires computational approaches that allow to detect consistent differences in the non-abundant components and relations between them. In this note we develop an algorithm that scores cell populations by quantifying the amount of information that it carries about the case/control status in the context of the entire immunome. We show that the information-based similarity measures we use are able to detect overlap between rare cell populations in the immunomes and the feature selection algorithm is at least as sensitive to signal as other machine learning tools. We also demonstrate, that we are able to identify a set of positive controls in a real-life immunome data from Hairy Cell Leukemia patients and detect other, biologically relevant cell populations in this context.

Keywords: immunome; Renyi's entropy; Shannon entropy; Renyi's divergence; contingency tables; I-Index; HCL; hairy cell leukemia.

DOI: 10.1504/IJCBDD.2020.113878

International Journal of Computational Biology and Drug Design, 2020 Vol.13 No.5/6, pp.555 - 581

Received: 28 Apr 2020
Accepted: 04 Jun 2020
Published online: 31 Mar 2021 *

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