matrixplot#

matrixplot(data: AnnData, keys: Sequence[str] | Mapping[str, Sequence[str]], group_by: str, *, mapping: FeatureSpec | None = None, geom: Literal['raster', 'tile'] = 'raster', scale_axis: Literal[0, 1] | None = None, dendrogram: bool = False, group_lines: bool = True, group_lines_color: str = 'black', group_lines_size: float = 1.0, dendrogram_color: str = 'black', dendrogram_size: float = 0.5, group_lines_kwargs: dict | None = None, dendrogram_kwargs: dict | None = None, key_labels: bool = True, value_column: str = 'value', variable_column: str = 'variable', color_low: str = '#0000ff', color_mid: str = '#ffffff', color_high: str = '#ff0000', mid_point: Literal['mean', 'median', 'mid'] | float = 'mid', axis: Literal[0, 1] | None = 0, observations_name: str = 'Barcode', variables_name: str = 'Variable', include_dimensions: bool | int = False, interactive: bool = False, **geom_kwargs) → PlotSpec#

Matrix plot.

Basically a heatmap with fixed aggregate=True argument.

Parameters:

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

  • keys (Sequence[str] | Mapping[str, Sequence[str]]) – Variable keys to include. When a mapping is provided, each entry maps a group label to the keys belonging to that group; the keys are placed on the x-axis in mapping order. The same key cannot appear in more than one group.

  • group_by (str) – The key to group the data by.

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

  • geom ({'raster', 'tile'}, default 'raster') – The geom to use. Use ‘raster’ for performance. Use ‘tile’ to enable tooltips.

  • scale_axis ({0, 1} | None, default None) – Whether to standardize a dimension between 0 and 1. Subtracts the minimum and divides by the maximum. If 0, standardize each variable (column). If 1, standardize each group (row).

  • dendrogram (bool, default False) – Whether to add a dendrogram for the group_by axis. Uses scanpy.tl.dendrogram if not already computed.

  • group_lines (bool, default True) – Whether to draw horizontal lines within the plot separating groups.

  • group_lines_color (str, default 'black') – Color of the group separator lines.

  • group_lines_size (float, default 1.0) – Size (thickness) of the group separator lines.

  • dendrogram_color (str, default 'black') – Color of the dendrogram segments.

  • dendrogram_size (float, default 0.5) – Size (thickness) of the dendrogram segments.

  • group_lines_kwargs (dict | None, default None) – Additional parameters to pass to the group separator lines geom_segment.

  • dendrogram_kwargs (dict | None, default None) – Additional parameters to pass to the dendrogram geom_segment.

  • key_labels (bool, default True) – Whether to draw bracket labels above the plot when keys is a mapping.

  • value_column (str, default 'value') – Name for the value column after unpivoting.

  • variable_column (str, default 'variable') – Name for the variable column after unpivoting.

  • color_low (str, default '#0000ff') – Color for low values in the gradient.

  • color_mid (str, default '#ffffff') – Color for mid values in the gradient.

  • color_high (str, default '#ff0000') – Color for high values in the gradient.

  • mid_point ({'mean', 'median', 'mid'} | float, default 'mid') – Midpoint for the color gradient.

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

  • 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 heatmap geom layer.

Returns:

PlotSpec – Matrix plot.

Examples

Matrix plot of marker expression aggregated per cell type.

import scanpy as sc
from lets_plot import *

import cellestial as cl

data = sc.read("data/pbmc3k_pped.h5ad")

markers = ["C1QA", "PSAP", "CD79A", "CD79B", "CST3", "LYZ"]

cl.matrixplot(
    data,
    group_by="cell_type_lvl1",
    keys=markers,
    dendrogram=True,
)

Standardize per-variable so each marker spans 0-1 across groups.

cl.matrixplot(
    data,
    group_by="cell_type_lvl1",
    keys=markers,
    scale_axis=0,
)
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