ridge

ridge(data: AnnData, key: str, *, group_by: str | None = None, scale: float = 2.0, mapping: FeatureSpec | None = None, axis: Literal[0, 1] | None = None, threshold: float | None = None, add_keys: Sequence[str] | str | None = None, tooltips: Literal['none'] | Sequence[str] | FeatureSpec | None = None, observations_name: str = 'Barcode', variables_name: str = 'Variable', interactive: bool = False, **geom_kwargs) → PlotSpec

Ridge Plot.

Parameters:

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

• key (str) – The key to get the values (numerical). e.g., ‘total_counts’ or a gene name.

• group_by (str) – The key to group the ridges by (categorical). e.g., ‘cell_type’ or ‘leiden’.

• scale (float, default 2.0) – Scaling factor for the height of the ridges.

• mapping (FeatureSpec | None, default None) – Additional 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.

• threshold (float | None, default None) – If provided, filters out rows where the value column is below the threshold.

• add_keys (Sequence[str] | str | None, default None) – Additional keys to include in the dataframe.

• tooltips ({'none'} | Sequence[str] | FeatureSpec | None, default None) – Tooltips to show when hovering over the geom. Accepts Sequence[str] or result of layer_tooltips() for more complex tooltips. Use ‘none’ to disable tooltips.

• observations_name (str, default 'Barcode') – The name to give to barcode (or index) column in the dataframe.

• variables_name (str, default 'Variable') – The name to give to variable index column in the dataframe.

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

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

Returns:

PlotSpec – Ridge plot.

Examples

import cellestial as cl
import scanpy as sc

from lets_plot import *

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

ridge = (
    cl.ridge(
        data,
        key="B2M",
        group_by="cell_type_lvl1",
    )
)

ridge

Customize the geom.

ridge = (
    cl.ridge(
        data,
        key="B2M",
        group_by="cell_type_lvl1",
        alpha=0.7,
        color="#1f1f1f",
    )
)

ridge