Kernels

Kernels compute cell-cell transition probabilities based on various input data modalities, including molecular similarity, RNA velocity [La Manno et al., 2018], experimental time points, and many more. They come with methods for high-level, qualitative visualization, including vector field- or random walk plots. For quantitative analysis of kernel-computed transition matrices, we recommend using estimators.

We don’t use the term “kernel” in the way it is used in mathematics, but rather colloquially, to refer to a class that takes in multi-view single-cell data and outputs a cell-cell transition matrix.

kernels.VelocityKernel(adata[, backward, ...])	Kernel which computes a transition matrix based on RNA velocity.
kernels.ConnectivityKernel(adata[, ...])	Kernel which computes transition probabilities based on similarities among cells.
kernels.PseudotimeKernel(adata, time_key[, ...])	Kernel which computes directed transition probabilities based on a kNN graph and pseudotime.
kernels.CytoTRACEKernel(adata[, backward])	Kernel which computes directed transition probabilities based on a KNN graph and the CytoTRACE score [Gulati et al., 2020].
kernels.PrecomputedKernel(object[, adata, ...])	Kernel which is initialized based on a precomputed transition matrix.

External Kernels

external.kernels.StationaryOTKernel(adata, g)	Stationary optimal transport (OT) kernel from [Zhang et al., 2021].
external.kernels.WOTKernel(adata, time_key)	Waddington optimal transport (WOT) kernel from [Schiebinger et al., 2019].