Reconstruction Set Test (RESET): A computationally efficient method for single sample gene set testing based on randomized reduced rank reconstruction error

We have developed a new, and analytically novel, single sample gene set testing method called Reconstruction Set Test (RESET). RESET quantifies gene set importance based on the ability of set genes to reconstruct values for all measured genes. RESET is realized using a computationally efficient randomized reduced rank reconstruction algorithm (available via the RESET R package on CRAN) that can effectively detect patterns of differential abundance and differential correlation for self-contained and competitive scenarios. As demonstrated using real and simulated scRNA-seq data, RESET provides superior performance at a lower computational cost relative to other single sample approaches.Author summary: Gene set testing methods are widely used to analyze transcriptomic data with techniques that provide sample level scores increasingly popular given their significant analytical flexibility. For the analysis of single cell data, however, current cell-level methods have several important limitations: poor computational performance, low sensitivity to patterns of differential correlation, and limited support for competitive scenarios that compare set and non-set genes. To address these challenges, we have developed the RESET (Reconstruction Set Test) method that generates overall and cell-level gene set scores using randomized reduced rank reconstruction error. Relative to existing single sample techniques, RESET can more effectively detect patterns of differential abundance and differential correlation under both self-contained and competitive scenarios at a substantially lower computational cost.