PT  - JOURNAL ARTICLE
AU  - Karin, Jonathan
AU  - Bornfeld, Yonathan
AU  - Nitzan, Mor
TI  - scPrisma: inference, filtering and enhancement of periodic signals in single-cell data using spectral template matching
AID  - 10.1101/2022.06.07.493867
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.06.07.493867
4099  - http://biorxiv.org/content/early/2022/06/09/2022.06.07.493867.short
4100  - http://biorxiv.org/content/early/2022/06/09/2022.06.07.493867.full
AB  - 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 scPrisma’s 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 scPrisma’s 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.Competing Interest StatementThe authors have declared no competing interest.