We will discuss about \u201cscPrisma infers, filters and enhances topological signals in single-cell data using spectral template matching\u201d, Karin, Jonathan, Yonathan Bornfeld, and Mor Nitzan., Nature Biotechnology<\/i>\u00a0(2023): 1-10.<\/p>\n
Abstract<\/p>\n Single-cell RNA sequencing has been instrumental in uncovering cellular spatiotemporal context. This task is challenging as cells simultaneously encode multiple, potentially cross-interfering, biological signals. Here we propose scPrisma, a spectral computational method that uses topological priors to decouple, enhance and filter different classes of biological processes in single-cell data, such as periodic and linear signals. We apply scPrisma to the analysis of the cell cycle in HeLa cells, circadian rhythm and spatial zonation in liver lobules, diurnal cycle in\u00a0Chlamydomonas<\/i>\u00a0and circadian rhythm in the suprachiasmatic nucleus in the brain. scPrisma can be used to distinguish mixed cellular populations by specific characteristics such as cell type and uncover regulatory networks and cell\u2013cell interactions specific to predefined biological signals, such as the circadian rhythm. We show scPrisma\u2019s flexibility in incorporating prior knowledge, inference of topologically informative genes and generalization to additional diverse templates and systems. scPrisma can be used as a stand-alone workflow for signal analysis and as a prior step for downstream single-cell analysis.<\/p>\n<\/div>\n<\/div>\n<\/section>\n\n\n