Score Velocity Genes
Score Velocity Genes produces likelihoods for the velocity genes found in an input Velocity Matrix (
) produced with the dynamical model. It uses groupings provided by Cell Clusters (
) or Cell Annotations (
).
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Steady-state model: It is not possible to run the tool on a matrix produced with the steady-state model. For this, use Differential Velocity for Single Cell instead. |
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Note: Score Velocity Genes is complementary to Differential Velocity for Single Cell, which performs statistical tests to report p-values. Score Velocity Genes can be used to identify the genes driving the observed dynamics, either for the entire data set or a group of cells, by ranking the genes (from largest to smallest) according to the likelihood. Some genes might be equally important for two different sets of cells, without them showing differential velocity patterns. |
It is often most natural to run the tool from a Dimensionality Reduction Plot by right-clicking on the plot, see UMAP and tSNE plot functionality for details. However, it can also be found in the Toolbox here:
Toolbox | Single Cell Analysis (
) | Gene Expression (
) | Velocity Analysis (
) | Score Velocity Genes (
)
The set set of options narrow down the focus of the tool:
- Clusters and Cell annotations. At least one of these must be supplied. Clusters accepts Cell Clusters () and Cell annotations accepts Cell Annotations ().
- Score velocity genes for a single column from the supplied Cell Clusters or Cell Annotations. Columns that only contain true/false values or numerical data are not supported. Tests will be performed between the groups of cells with different labels in this column.
- Select groups (Optional). This can be supplied to reduce the number of groups of cells considered or to control the order in which comparisons are made.
For details on how groups of cells can be defined, see Differential Expression for Single Cell.
The tool outputs the gene likelihoods obtained from the dynamical model to a table (
), both for the defined groups of cells, and the entire data set. The performed calculations closely follow those from scVelo's rank_dynamical_genes method [Bergen et al., 2020].
Subsections