scPrisma

scPrisma infers, filters, and enhances periodic signals in single-cell RNA sequencing (scRNA-seq) data using spectral template matching and topological priors.

Key Features:

• Spectral Computational Methodology: Employs spectral template matching combined with topological priors to decouple, enhance, and filter biological processes encoded in scRNA-seq data.
• Signal Decoupling and Enhancement: Distinguishes and separates different classes of signals, including periodic and linear signals, and decouples overlapping biological signals.
• Topologically Informed Analysis: Incorporates topological priors to identify biologically relevant signals and to infer topologically informative genes.
• Flexibility and Adaptability: Incorporates prior knowledge and generalizes across diverse templates and biological systems.

Scientific Applications:

• Cell Cycle Analysis: Applied to dissect cell-cycle–related periodic signals in HeLa cells.
• Circadian Rhythm Studies: Used to analyze circadian rhythms in liver lobules and the suprachiasmatic nucleus.
• Diurnal Cycle Investigation: Applied to study diurnal cycles in Chlamydomonas.
• Cell Population Distinction: Distinguishes mixed cellular populations and aids identification of regulatory networks and cell–cell interactions pertinent to predefined signals such as circadian rhythms.

Methodology:

Applies spectral template matching to single-cell RNA sequencing data while utilizing topological priors to enhance signal clarity and decouple overlapping biological signals.