ridges#

ridges(data: AnnData, keys: Sequence[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, layers: Sequence[FeatureSpec | LayerSpec] | FeatureSpec | LayerSpec | None = None, ncol: int | None = None, sharex: str | None = None, sharey: str | None = None, widths: list | None = None, heights: list | None = None, hspace: float | None = None, vspace: float | None = None, fit: bool | None = None, align: bool | None = None, guides: str = 'auto', **geom_kwargs) → SupPlotsSpec#

Ridge Plot.

Parameters:

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

  • keys (Sequence[str]) – The keys to get the values (numerical). e.g., [‘total_counts’, ‘pct_counts_in_top_50_genes’] or a list of gene names.

  • 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.

  • layers (Sequence[FeatureSpec | LayerSpec] | FeatureSpec | LayerSpec | None, default None) – Additional layers to add to the plot.

  • ncol (int, default None) – Number of columns in grid. If not specified, shows plots horizontally, in one row.

  • sharex (bool, default None) – Controls sharing of axis limits between subplots in the grid. all/True - share limits between all subplots. none/False - do not share limits between subplots. row - share limits between subplots in the same row. col - share limits between subplots in the same column.

  • sharey (bool, default None) – Controls sharing of axis limits between subplots in the grid. all/True - share limits between all subplots. none/False - do not share limits between subplots. row - share limits between subplots in the same row. col - share limits between subplots in the same column.

  • widths (list[float], default None) – Relative width of each column of grid, left to right.

  • heights (list[float], default None) – Relative height of each row of grid, top-down.

  • hspace (float | None, default None) – Cell horizontal spacing in px.

  • vspace (float | None, default None) – Cell vertical spacing in px.

  • fit (bool, default True) – Whether to stretch each plot to match the aspect ratio of its cell (fit=True), or to preserve the original aspect ratio of plots (fit=False).

  • align (bool, default False) – If True, align inner areas (i.e. “geom” bounds) of plots. However, cells containing other (sub)grids are not participating in the plot “inner areas” layouting.

  • guides (str, default 'auto') –

    Specifies how guides (legends and colorbars) should be treated in the layout.

    • ’collect’ collect guides from all subplots, removing duplicates.

    • ’keep’ keep guides in their original subplots; do not collect at this level.

    • ’auto’ allow guides to be collected if an upper-level layout uses guides=’collect’;

    otherwise, keep them in subplots. Duplicates are identified by comparing visual properties: For legends: title, labels, and all aesthetic values (colors, shapes, sizes, etc.). For colorbars: title, domain limits, breaks, and color gradient.

    For more information on gggrid parameters: https://lets-plot.org/python/pages/api/lets_plot.gggrid.html

  • **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:

SupPlotsSpec – Ridge plot.

Examples

import numpy as np
import scanpy as sc

import cellestial as cl

data = sc.read_h5ad("data/pbmc3k_pped.h5ad")
# get the top 8 genes with the highest mean expression
gene_means = np.asarray(data.X.mean(axis=0)).flatten()
top8_genes = data.var_names[np.argsort(gene_means)[::-1][:8]].tolist()

cl.ridges(
    data,
    keys=top8_genes,
    group_by="cell_type_lvl1",
    alpha=0.7,
    scale=2,
    ncol=2,
    color="#3f3f3f",
    guides="collect",
)
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