scanpy.pl.rank_genes_groups_matrixplot

scanpy.pl.rank_genes_groups_matrixplot(adata, groups=None, n_genes=None, groupby=None, values_to_plot=None, var_names=None, gene_symbols=None, min_logfoldchange=None, key=None, show=None, save=None, return_fig=False, **kwds)

Plot ranking of genes using matrixplot plot (see matrixplot())

Parameters

adata : AnnDataAnnData

Annotated data matrix.

groups : str | Sequence[str] | NoneUnion[str, Sequence[str], None] (default: None)

The groups for which to show the gene ranking.

n_genes : int | NoneOptional[int] (default: None)

Number of genes to show. This can be a negative number to show for example the down regulated genes. eg: num_genes=-10. Is ignored if gene_names is passed.

gene_symbols : str | NoneOptional[str] (default: None)

Column name in .var DataFrame that stores gene symbols. By default var_names refer to the index column of the .var DataFrame. Setting this option allows alternative names to be used.

groupby : str | NoneOptional[str] (default: None)

The key of the observation grouping to consider. By default, the groupby is chosen from the rank genes groups parameter but other groupby options can be used. It is expected that groupby is a categorical. If groupby is not a categorical observation, it would be subdivided into num_categories (see dotplot()).

min_logfoldchange : float | NoneOptional[float] (default: None)

Value to filter genes in groups if their logfoldchange is less than the min_logfoldchange

key : str | NoneOptional[str] (default: None)

Key used to store the ranking results in adata.uns.

values_to_plot : {‘scores’, ‘logfoldchanges’, ‘pvals’, ‘pvals_adj’, ‘log10_pvals’, ‘log10_pvals_adj’} | NoneOptional[Literal[‘scores’, ‘logfoldchanges’, ‘pvals’, ‘pvals_adj’, ‘log10_pvals’, ‘log10_pvals_adj’]] (default: None)

Instead of the mean gene value, plot the values computed by sc.rank_genes_groups. The options are: [‘scores’, ‘logfoldchanges’, ‘pvals’, ‘pvals_adj’, ‘log10_pvals’, ‘log10_pvals_adj’]. When plotting logfoldchanges a divergent colormap is recommended. See examples below.

var_names : Sequence[str] | Mapping | NoneUnion[Sequence[str], Mapping[str, Sequence[str]], None] (default: None)

Genes to plot. Sometimes is useful to pass a specific list of var names (e.g. genes) to check their fold changes or p-values, instead of the top/bottom genes. The var_names could be a dictionary or a list as in dotplot() or matrixplot(). See examples below.

show : bool | NoneOptional[bool] (default: None)

Show the plot, do not return axis.

save : bool | NoneOptional[bool] (default: None)

If True or a str, save the figure. A string is appended to the default filename. Infer the filetype if ending on {'.pdf', '.png', '.svg'}.

ax

A matplotlib axes object. Only works if plotting a single component.

return_fig : bool | NoneOptional[bool] (default: False)

Returns MatrixPlot object. Useful for fine-tuning the plot. Takes precedence over show=False.

**kwds

Are passed to matrixplot().

Returns

If return_fig is True, returns a MatrixPlot object, else if show is false, return axes dict

Examples

import scanpy as sc
adata = sc.datasets.pbmc68k_reduced()
sc.tl.rank_genes_groups(adata, 'bulk_labels', n_genes=adata.raw.shape[1])

Plot logfoldchanges instead of gene expression. In this case a diverging colormap like bwr or seismic works better. To center the colormap in zero, the minimum and maximum values to plot are set to -4 and 4 respectively. Also, only genes with a log fold change of 3 or more are shown.

sc.pl.rank_genes_groups_matrixplot(
    adata,
    n_genes=4,
    values_to_plot="logfoldchanges",
    cmap='bwr',
    vmin=-4,
    vmax=4,
    min_logfoldchange=3,
    colorbar_title='log fold change',
)

Also, the last genes can be plotted. This can be useful to identify genes that are lowly expressed in a group. For this n_genes=-4 is used

sc.pl.rank_genes_groups_matrixplot(
    adata,
    n_genes=-4,
    values_to_plot="logfoldchanges",
    cmap='bwr',
    vmin=-4,
    vmax=4,
    min_logfoldchange=3,
    colorbar_title='log fold change',
)

A list specific genes can be given to check their log fold change. If a dictionary, the dictionary keys will be added as labels in the plot.

var_names = {"T-cell": ['CD3D', 'CD3E', 'IL32'],
              'B-cell': ['CD79A', 'CD79B', 'MS4A1'],
              'myeloid': ['CST3', 'LYZ'] }
sc.pl.rank_genes_groups_matrixplot(
    adata,
    var_names=var_names,
    values_to_plot="logfoldchanges",
    cmap='bwr',
    vmin=-4,
    vmax=4,
    min_logfoldchange=3,
    colorbar_title='log fold change',
)