- dynamo.vf.rank_s_divergence_genes(adata, skey='sensitivity_pca', genes=None, prefix_store='rank_s_div_gene', **kwargs)
- Rank genes based on their diagonal Sensitivity for each cell group.
Be aware that this ‘divergence’ refers to the diagonal elements of a gene-wise Sensitivity, rather than its trace, which is the common definition of the divergence.
Run .vf.sensitivity and set store_in_adata=True before using this function.
AnnData) – AnnData object that contains the reconstructed vector field in the .uns attribute.
str) – The key in .uns of the cell-wise sensitivity matrix.
str) – The prefix added to the key for storing the returned ranking info in adata.
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.
AnnData object which has the rank dictionary for diagonal sensitivity in .uns.
- Return type: