Discovery of pharmaceutically-targetable pathways and prediction of survivorship for pneumonia and sepsis patients from the view point of ensemble gene noise

Tristan de Jong; Victor Guryev; Yury M. Moshkin

Both the mean and variance relate to population (inter-individual) statistics reflecting distinct aspects of gene regulation. Changes in means fit the classical DGE view on gene response to a pathology and other biological processes, while changes in variances yield a view on heterogeneity of gene response. However, as we noted before ( Figure 1 and S1), statistical inference of these changes is biased towards higher a significance for genes with a high [28] . For CAP and sepsis patients, we assumed that a condition of the deceased patients was worse than that of the survived. We considered that healthy < survived < deceased can also be represented as ordinal disease state variable. Circumstantially, this is supported by distinct blood gene expression endotypes [8] and an increased gene expression heterogeneity ( Figure 1A ). Kendall rank correlation identified a number of pathways and protein complexes for which ensemble gene noise was positively and significantly associated with the disease state in H1N1 (FDR ≤ 0.05), and CAP and sepsis patients (Bonferroni-adjusted p ≤ 0.05) ( Figure 2A ). None of the pathways or gene complexes were negatively associated with the disease state at the specified significance thresholds. We used different p value adjustment procedures (FDR -less conservative, and Bonferroni -more conservative) for H1N1, CAP and sepsis patents due to the large differences in sample sizes (number of patients) between these data sets.