dynamo.vf.rank_divergence_genes
- dynamo.vf.rank_divergence_genes(adata, jkey='jacobian_pca', genes=None, prefix_store='rank_div_gene', **kwargs)[source]
- Rank genes based on their diagonal Jacobian for each cell group.
Be aware that this ‘divergence’ refers to the diagonal elements of a gene-wise Jacobian, rather than its trace, which is the common definition of the divergence.
Run .vf.jacobian and set store_in_adata=True before using this function.
- Parameters
adata (
AnnData) – AnnData object that contains the reconstructed vector field in the .uns attribute.jkey (str (default: 'jacobian_pca')) – The key in .uns of the cell-wise Jacobian matrix.
genes (list or None (default: None)) – A list of names for genes of interest.
prefix_store (str (default: 'rank')) – The prefix added to the key for storing the returned ranking info in adata.
kwargs –
additional keys that will be passed to the rank_genes function. It will accept the following arguments: group: str or None (default: None)
The cell group that speed ranking will be grouped-by.
- genes: list or None (default: None)
The gene list that speed will be ranked. If provided, they must overlap the dynamics genes.
- abs: bool (default: False)
When pooling the values in the array (see below), whether to take the absolute values.
- normalize: bool (default: False)
Whether normalize the array across all cells first, if the array is 2d.
- fcn_pool: callable (default: numpy.mean(x, axis=0))
The function used to pool values in the to-be-ranked array if the array is 2d.
- output_values: bool (default: False)
Whether output the values along with the rankings.
- Returns
adata – AnnData object which has the rank dictionary for diagonal jacobians in .uns.
- Return type