Quantifying Cell-type-specific Differences of Single-cell Datasets using UMAP and SHAP

With the 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. Here we present a computational pipeline that quantifies the cell-type-specific differences and identifies genes responsible for the differences. We quantify differences observed in a low-dimensional UMAP space as a proxy for the difference present in the high-dimensional space and use SHAP to quantify genes driving the differences. Here we applied our algorithm to the Iris flower dataset, scRNA-seq dataset, and mass cytometry dataset, and demonstrate that it can robustly quantify the cell-type-specific differences and it can also identify genes that are responsible for the differences.