sSNAPPY: an R/Bioconductor package for single-sample directional pathway perturbation analysis

Abstract

A common outcome of analysing RNA-Seq data is the detection of biological pathways with significantly altered activity between the conditions under investigation. Whilst many strategies test for over-representation of genes, showing changed expression within pre-defined gene-sets, these analyses typically do not account for gene-gene interactions encoded by pathway topologies, and are not able to directly predict the directional change of pathway activity. To address these issues we have developed sSNAPPY,now available as an R/Bioconductor package, which leverages pathway topology information to compute pathway perturbation scores and predict the direction of change across a set of pathways. Here, we demonstrate the use of sSNAPPY by applying the method to public scRNA-seq data, derived from ovarian cancer patient tissues collected before and after chemotherapy. Not only were we able to predict the direction of pathway perturbations discussed in the original study, but sSNAPPY was also able to detect significant changes of other biological processes, yielding far greater insight into the response to treatment. sSNAPPY represents a novel pathway analysis strategy that takes into consideration pathway topology to predict impacted biology pathways, both within related samples and across treatment groups. In addition to not relying on differentially expressed genes, the method and associated R package offers important flexibility and provides powerful visualisation tools. R version: R version 4.3.3 (2024-02-29) Bioconductor version: 3.18 Package: 1.6.1