Cytokine systems approach demonstrates differences in innate and pro-inflammatory host responses between genetically distinct MERS-CoV isolates

Selinger, Christian; Tisoncik-Go, Jennifer; Menachery, Vineet D; Agnihothram, Sudhakar; Law, G Lynn; Chang, Jean; Kelly, Sara M; Sova, Pavel; Baric, Ralph S; Katze, Michael G

For normalized intensity data, the Euclidean distance matrix between samples was clustered with respect to sample-matched viral genomic RNA measurements using an algorithm described in [17] . We used a freely available MATLAB implementation of this algorithm (http://appliedtopology.org/) with the parameter setting filterSamples = 5, pverlapPct = 65, mFudge = 5. The topology induced by a distance matrix can be described by a filter function, which assigns to every sample a value (phenotypic outcome, viral load, etc.). First, the topological space is decomposed into overlapping subsets using level sets of the filter function. The set of samples falling into a particular subset are clustered, and the cluster tree is partitioned into two parts using statistical criteria of the single linkage length. Only the part of the cluster tree with small linkage lengths (dense part) is retained. If resulting filtered clusters of different subsets have at least one sample in common, then they are defined as adjacent in a global discrete cluster graph. Topological differences between MERS-CoV SA 1 and MERS-CoV Eng 1 gene expression during the course of infection was furthermore assessed using homology persistence barcodes (only 0-and 1-homology were taken into account) as described in [15] . For calculations, we used the freely available R package phom [40] and their maximum weighted bipartite graph matching [41] as a measure of difference (see R script implementation in Additional file 5). Additionally, multi-dimensional scaling (MDS) was performed on the Euclidean distance matrix between samples after differential gene expression statistical analysis [42] .