Causal network perturbations for instance-specific analysis of single cell and disease samples

Abstract

Complex diseases involve perturbation in multiple pathways and a major challenge in clinical genomics is characterizing pathway perturbations in individual samples. This can lead to patient-specific identification of the underlying mechanism of disease thereby improving diagnosis and personalizing treatment. Existing methods rely on external databases to quantify pathway activity scores. This ignores the data dependencies and that pathways are incomplete or condition-specific.\n\nssNPA is a new approach for subtyping samples based on deregulation of their gene networks. ssNPA learns a causal graph directly from control data. Sample-specific network neighborhood deregulation is quantified via the error incurred in predicting the expression of each gene from its Markov blanket. We evaluate the performance of ssNPA on liver development single-cell RNAseq data, where the correct cell timing is recovered. In all analyses ssNPA consistently outperforms alternative methods, highlighting the advantage of network-based approaches.