Quantifying Cell-Type-Specific Differences of Single-Cell Datasets Using Uniform Manifold Approximation and Projection for Dimension Reduction and Shapley Additive exPlanations

With rapid advances in single-cell profiling technologies, larger-scale investigations that require comparisons of multiple single-cell datasets can lead to novel findings. Specifically, quantifying cell-type-specific responses to different conditions across single-cell datasets could be useful in understanding how the difference in conditions is induced at a cellular level. In this study, we present a computational pipeline that quantifies cell-type-specific differences and identifies genes responsible for the differences. We quantify differences observed in a low-dimensional uniform manifold approximation and projection for dimension reduction space as a proxy for the difference present in the high-dimensional space and use SHapley Additive exPlanations to quantify genes driving the differences. In this study, we applied our algorithm to the Iris flower dataset, single-cell RNA sequencing dataset, and mass cytometry dataset and demonstrate that it can robustly quantify cell-type-specific differences and it can also identify genes that are responsible for the differences.