Modelling Pathogen Response of the Human Immune System in a Reduced State Space

The immune system response to pathogens is organized by a network of cells communicating through expression of a variety of proteins and signaling molecules. A high number of genes are involved in encoding these communicating agents, but the relatively low number of data points is a major challenge in modelling the gene expression response. In this work we propose a feature-selection approach based on gene expression distributions at the single-cell level that improves dynamics identification at the population level. We investigate common approaches to differential expression analysis and show that Earth Mover's Distance (EMD) is a relatively robust measure for gene selection as reflected by the coefficient of variation as well as accuracy of a naive Bayes classifier based on the selected genes. We ultimately propose the bootstrap standard deviation metric as an estimate of state uncertainty and show that statistically significant signals in pathogen response can be recovered in the reduced state space constructed with the selected genes.