scPrisma: inference, filtering and enhancement of periodic signals in single-cell data using spectral template matching
Single-cell RNA-sequencing has been instrumental in uncovering cellular spatiotemporal context. This task is however challenging due to technical and biological noise, and as the cells simultaneously encode multiple, potentially cross-interfering, biological signals. Here we propose scPrisma, a spectral computational framework that utilizes topological priors to decouple, enhance, and filter different classes of biological processes in single-cell data, such as periodic and linear signals. We demonstrate scPrismas use across diverse biological systems and tasks, including analysis and manipulation of the cell cycle in HeLa cells, circadian rhythm and spatial zonation in liver lobules, diurnal cycle in Chlamydomonas, and circadian rhythm in the suprachiasmatic nucleus in the brain. We further show how scPrisma can be used to distinguish mixed cellular populations by specific characteristics such as cell type, and uncover regulatory networks and cell-cell interactions specific to predefined biological signals, such as the circadian rhythm. We show scPrismas flexibility in utilizing diverse prior knowledge, and inference of topologically-informative genes. scPrisma can be used both as a stand-alone workflow for signal analysis, and, as it does not embed the data to lower dimensions, as a prior step for downstream single-cell analysis.