scanpy.pl.ranking

scanpy.pl.ranking(adata, attr, keys, *, dictionary=None, indices=None, labels=None, color='black', n_points=30, log=False, include_lowest=False, show=None)

Plot rankings.

See, for example, how this is used in pl.pca_loadings.

Parameters:

adata AnnData

The data.

attr Literal['var', 'obs', 'uns', 'varm', 'obsm']

The attribute of AnnData that contains the score.

keys str | Sequence[str]

The scores to look up an array from the attribute of adata.

Return type:

GridSpec | None

Returns:

Returns matplotlib gridspec with access to the axes.

Examples

Show the genes with the highest loading on the first three principal components. PCA in pbmc68k_reduced() was computed on highly-variable genes only, so we subset to those genes before ranking.

import scanpy as sc
adata = sc.datasets.pbmc68k_reduced()
adata_hv = adata[:, adata.var["highly_variable"]].copy()
sc.pl.ranking(adata_hv, attr="varm", keys="PCs", indices=[0, 1, 2])

Include the lowest-loading genes alongside the highest.

sc.pl.ranking(adata_hv, attr="varm", keys="PCs", indices=[0, 1, 2], include_lowest=True)