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.
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Kernel which computes a transition matrix based on RNA velocity. |
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Kernel which computes transition probabilities based on similarities among cells. |
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Kernel which computes directed transition probabilities based on a kNN graph and pseudotime. |
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Kernel which computes directed transition probabilities based on a KNN graph and the CytoTRACE score [Gulati et al., 2020]. |
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Kernel which is initialized based on a precomputed transition matrix. |
External Kernels#
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Stationary optimal transport (OT) kernel from [Zhang et al., 2021]. |
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Waddington optimal transport (WOT) kernel from [Schiebinger et al., 2019]. |