cellestial.bar#

bar(data: AnnData, mapping: FeatureSpec | None = None, *, axis: Literal[0, 1] | None = None, variable_keys: Sequence[str] | None = None, observations_name: str = 'Barcode', variables_name: str = 'Variable', include_dimensions: bool | int = False, interactive: bool = False, **geom_kwargs) → PlotSpec#

Bar Plot.

Parameters:

  • data (AnnData) – The AnnData object of the single cell data.

  • mapping (FeatureSpec | None, default None) – Aesthetic mappings for the plot, the result of aes().

  • axis ({0,1} | None, default None) – axis of the data, 0 for observations and 1 for variables.

  • variable_keys (str | Sequence[str] | None, default None) – Variable keys to add to the DataFrame. If None, no additional keys are added.

  • observations_name (str, default 'Barcode') – The name of the observations column.

  • variables_name (str, default 'Variable') – Name for the variables index column.

  • include_dimensions (bool | int, default False) – Whether to include dimensions in the DataFrame. Providing an integer will limit the number of dimensions to given number.

  • interactive (bool, default False) – Whether to make the plot interactive.

  • **geom_kwargs – Additional parameters for the geom_bar layer. For more information on geom_bar parameters, see: https://lets-plot.org/python/pages/api/lets_plot.geom_bar.html

Returns:

PlotSpec – Bar plot.

Examples

from lets_plot import *

import cellestial as cl
import scanpy as sc

data = sc.read_h5ad('data/pbmc3k_pped.h5ad')

p1 = (
    cl.bar(data, mapping=aes('leiden', fill='predicted_doublet'))
    + scale_fill_brewer(palette='Set2', direction=-1)
)
p1

Example 2

from lets_plot import *

import cellestial as cl
import scanpy as sc

data = sc.read_h5ad('data/pbmc3k_pped.h5ad')

p2 = (
    cl.bar(data, mapping=aes('cell_type_lvl1', fill='leiden'))
    + scale_fill_brewer(palette='Set2')
)
p2
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