matrixplot#

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matrixplot(data: AnnData, keys: Sequence[str] | Mapping[str, Sequence[str]] | None = None, group_by: str | None = None, *, markers: bool | str = False, n_genes: int = 5, groups: Sequence[str] | None = None, 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 = 'white', group_lines_size: float = 0.6, dendrogram_color: str = 'black', dendrogram_size: float = 0.5, dendrogram_key: str | None = None, group_lines_kwargs: dict | None = None, dendrogram_kwargs: dict | None = None, key_labels: bool = True, key_labels_text_size: float = 1.0, key_labels_bracket_size: float = 0.6, key_labels_text_color: str = 'black', key_labels_bracket_color: str = 'black', key_labels_width: float = 0.6, 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.

Parameters:

  • data (AnnData) – The single-cell data object.

  • keys (Sequence[str] | Mapping[str, Sequence[str]] | None, default None) – Variable keys to include, placed on the x-axis. A mapping assigns keys to group labels (no key in more than one group). Must be None when markers is set.

  • group_by (str | None, default None) – The key to group the data by. Inferred from a precomputed ranking when markers is set.

  • markers (bool | str, default False) – Derive keys from a precomputed ranking. Pass True to use the default ranking key, or a string to read a custom key (e.g. “rank_genes_groups_wilcoxon”).

  • n_genes (int, default 5) – Number of top genes to take per group when markers is set.

  • groups (Sequence[str] | None, default None) – Subset of groups to include when markers is set; None keeps all groups in their stored order.

  • 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. Uses min-max scaling; constant partitions are set to 0. 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 the precomputed dendrogram, computing one if not already present.

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

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

  • group_lines_size (float, default 0.6) – 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.

  • dendrogram_key (str | None, default None) – Specific key holding the precomputed dendrogram. By default, dendrogram_{group_by} is used.

  • 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.

  • key_labels_text_size (float, default 1.0) – Scale multiplier on the auto-computed bracket label text size.

  • key_labels_bracket_size (float, default 0.6) – Size (thickness) of the bracket lines.

  • key_labels_text_color (str, default 'black') – Color of the bracket label text.

  • key_labels_bracket_color (str, default 'black') – Color of the bracket lines.

  • key_labels_width (float, default 0.6) – Bracket width (in column units) for a singleton group. Multi-key groups extend key_labels_width / 2 past the first and last key on each side.

  • 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.

Notes

Equivalent to heatmap with aggregate=True.

Examples

Matrix plot of marker expression aggregated per cell type.

import scanpy as sc
from lets_plot import *

import cellestial as cl

data = cl.datasets.pbmc3k(cache_directory="data")

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,
)

Plot the top genes from a precomputed ranking:

sc.tl.rank_genes_groups(data, groupby="cell_type_lvl1")
cl.matrixplot(data, markers=True, n_genes=5)

WARNING: It seems you use rank_genes_groups on the raw count data. Please logarithmize your data before calling rank_genes_groups.
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