Abstract
We have developed a new, and analytically novel, single sample gene set testing method called Reconstruction Set Test (RESET). RESET quantifies gene set importance at both the sample-level and for the entire dataset based on the ability of set genes to reconstruct values for all measured genes. RESET addresses four important limitations of current techniques: 1) existing single sample methods are designed to detect mean differences and struggle to identify differential correlation patterns, 2) computationally efficient techniques are self-contained methods and cannot directly detect competitive scenarios where set genes differ from non-set genes in the same sample, 3) the scores generated by current methods can only be accurately compared across samples for a single set and not between sets, and 4) the computational performance of even the fastest existing methods be significant on very large datasets. 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 accuracy at a lower computational cost relative to other single sample approaches.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Included additional results on real scRNA-seq data
