cellestial.plot#

plot(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) → PlotSpec#

Base plot (for plots without data wrangling).

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) – Variable keys to add to the DataFrame. If None, no additional keys are added.

  • observations_name (str) – The name of the observations column, default is ‘barcode’

  • variables_name (str) – Name for the variables index column, default is ‘variable’

  • 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.

Returns:

PlotSpec – Base ggplot object.

Examples

from lets_plot import *

import cellestial as cl
import scanpy as sc

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

p1 = (
    cl.plot(data, aes(x='cell_type_lvl1', y='n_genes'))
)
p1 # plot object without layers

from lets_plot import *

import cellestial as cl
import scanpy as sc

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

p2 = (
    cl.plot(data, aes(x='cell_type_lvl1', y='n_genes'))
    + geom_violin(aes(fill='cell_type_lvl1'), scale='width')
    + geom_boxplot(width=0.2,outlier_size=0)
    + scale_fill_viridis()
)
p2
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