expression#

expression(data: AnnData, key: str, *, mapping: FeatureSpec | None = None, dimensions: Literal['umap', 'pca', 'tsne'] = 'umap', use_key: str | None = None, xy: tuple[int, int] | Sequence[int] = (1, 2), size: float | None = 0.8, variable_keys: Sequence[str] | str | None = None, tooltips: Literal['none'] | Sequence[str] | FeatureSpec | None = None, interactive: bool = False, observations_name: str = 'Barcode', color_low: str = '#e6e6e6', color_mid: str | None = None, color_high: str = '#377eb8', mid_point: Literal['mean', 'median', 'mid'] | float = 'median', axis_type: Literal['axis', 'arrow'] | None = 'axis', arrow_length: float = 0.25, arrow_size: float = 1, arrow_color: str = '#3f3f3f', arrow_angle: float = 10, legend_ondata: bool = False, ondata_size: float = 12, ondata_color: str = '#3f3f3f', ondata_fontface: str = 'bold', ondata_family: str = 'sans', ondata_alpha: float = 1, ondata_weighted: bool = True, **point_kwargs) → PlotSpec#

Dimensionality reduction plot of expression data.

Parameters:

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

  • key (str) – The key (gene names) to color the points by.

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

  • dimensions ({'umap', 'pca', 'tsne'}, default 'umap') – The dimensionality reduction method to use. e.g., ‘umap’ or ‘pca’ or ‘tsne’.

  • use_key (str, default None) – The specific key to use for the desired dimensions. e.g., ‘X_umap_2d’ or ‘X_pca_2d’. Otherwise, the function will decide on the key based on the dimensions.

  • xy (tuple[int, int] | Sequence[int], default (1, 2)) – The x and y axes to use for the plot. e.g., (1, 2) for UMAP1 and UMAP2.

  • size (float | None, default 0.8) – The size of the points.

  • variable_keys (str | Sequence[str] | None, default None) – Variable keys to add to the DataFrame. If None, no additional keys are added.

  • 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.

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

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

  • color_low (str, default '#e6e6e6') –

    The color to use for the low end of the color gradient.

    Accepts:

    • Applies to continuous (non-categorical) data.

  • color_mid (str, default None) –

    The color to use for the middle part of the color gradient.

    Accepts:

    • Applies to continuous (non-categorical) data.

  • color_high (str, default '#377EB8') –

    The color to use for the high end of the color gradient.

    Accepts:

    • Applies to continuous (non-categorical) data.

  • mid_point ({'mean', 'median', 'mid'} | float, default 'median') – The midpoint (in data value) of the color gradient. Can be ‘mean’, ‘median’ and ‘mid’ or a number (float or int). - If ‘mean’, the midpoint is the mean of the data. - If ‘median’, the midpoint is the median of the data. - If ‘mid’, the midpoint is the mean of ‘min’ and ‘max’ of the data.

  • axis_type ({'axis', 'arrow'} | None) – Whether to use regular axis or arrows as the axis.

  • arrow_length (float, default 0.25) – Length of the arrow head (px).

  • arrow_size (float, default 1) – Size of the arrow.

  • arrow_color (str, default '#3f3f3f') –

    Color of the arrows.

    Accepts:

arrow_anglefloat, default=10

Angle of the arrow head in degrees.

legend_ondata: bool, default=False

whether to show legend on data

ondata_size: float, default=12

size of the legend (text) on data.

ondata_color: str, default=’#3f3f3f’

color of the legend (text) on data

ondata_fontface: str, default=’bold’

fontface of the legend (text) on data. https://lets-plot.org/python/pages/aesthetics.html#font-face

ondata_family: str, default=’sans’

family of the legend (text) on data. https://lets-plot.org/python/pages/aesthetics.html#font-family

ondata_alpha: float, default=1

alpha (transparency) of the legend on data.

ondata_weighted: bool, default=True

whether to use weighted mean for the legend on data. If True, the weighted mean of the group means is used. If False, the arithmetic mean of the group means is used.

**point_kwargs

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

Returns:

PlotSpec – Dimensionality reduction plot.

Examples

Dimensionality reduction plot with continuous data.

import scanpy as sc
from lets_plot import *

import cellestial as cl

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

cl.expression(data,key="CD14",axis_type="arrow",color_high="red")
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