Quantifying Pathological Progression from Single-Cell Data

Abstract

The surge in single-cell datasets and reference atlases has enabled the comparison of cell states across conditions, yet a gap persists in quantifying pathological shifts from healthy cell states. To address this gap, we introduce single-cell Pathological Shift Scoring (scPSS) which provides a statistical measure for how much a "query" cell from a diseased sample has been shifted away from a reference group of healthy cells. In scPSS, The distance of a query cell to its k-th nearest reference cell is considered as its pathological shift score. Euclidean distances in the top n principal component space of the gene expressions are used to measure distances between cells. The p-value of a query pathological shift score belonging to the null distribution of intra-reference cell shift scores provides a statistical significance measure of the query cell being in the reference cell group. This makes our method both simple and statistically rigorous. Comparative evaluations against a state-of-the-art contrastive variational inference model, modified for shift scores, demonstrate our methods accuracy and efficiency. Additionally, we have also shown that the aggregation of cell-level pathological scores from scPSS can be used to predict health conditions at the individual level.