Title: Linear programming-based prediction of immune cell composition

Authors: SeongRyeol Moon; Junbae Oh; Seungyoon Nam

Addresses: Department of Health Sciences and Technology (GAIHST), Gachon University, Incheon 21999, South Korea ' Department of Software Engineering, Ajou University, Suwon 16499, Gyeonggi-do, South Korea ' Department of Life Sciences, Gachon University, Seongnam 13120, South Korea; College of Medicine, Gachon University, Incheon 21565, South Korea

Abstract: Immune cell types play critical roles in pathogenesis of loss of tolerance, resulting in autoimmune disorders. Immune cell composition has now been recognised as a clinical tool for monitoring disease progression, as typically inferred from gene expression information of a large prior set of differentiation genes, demarcating specific immune cell types. However, given a small set of gene families, immune cell type inference has not been established. Here we used Linear Programming (LP), by a Simplex method, to infer fractions of immune cells, based on a small set of genes used in cell surface marker experiments. To evaluate the accuracy of our method, we created multiple simulated data sets, and evaluated their performance against Digital Cell Quantisation (DCQ) and ImmQuant. Finally, we applied LP method to real biological data, from multiple Systemic Lupus Erythematosus (SLE) patients, versus healthy controls, to inspect compositional changes of immune cell types, in SLE pathogenesis.

Keywords: linear programming; immune cells; immune cell type prediction.

DOI: 10.1504/IJDMB.2020.107382

International Journal of Data Mining and Bioinformatics, 2020 Vol.23 No.2, pp.176 - 187

Received: 19 Mar 2020
Accepted: 23 Mar 2020

Published online: 22 May 2020 *

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