Infer, filter and enhance topological signals in single-cell data using spectral template matching

Infer, filter and enhance topological signals in single-cell data using spectral template matching

Single-cell RNA sequencing is a powerful technology that allows researchers to analyze gene expression in individual cells, providing insights into cellular processes and functions. However, analyzing this data can be challenging, as cells can simultaneously encode multiple, potentially cross-interfering, biological signals. A new computational method, scPrisma, was developed to address this challenge. scPrisma has the ability to uncover cellular spatiotemporal context and has the potential to drive further insights into cellular processes and functions, ultimately advancing our understanding of biology. You can check out the published article at: https://www.nature.com/articles/s41587-023-01663-5

Goal

Infer, filter and enhance topological signals in single-cell data using spectral template matching

Researchers

What we did

We apply scPrisma to the analysis 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. 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 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.

Results

https://www.nature.com/articles/s41587-023-01663-5

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