cellestial 0.48.2

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Cellestial

The Grammar of Graphics for single-cell omics.

Introduction

Cellestial is an interactive and a highly customizable Single-Cell & Spatial omics data visualization library. Built on top Lets-Plot, it offers a ggplot-like layered and modular approach offering high customizability and publication-ready figures.

Cellestial is highly integrated with scverse's AnnData with room for integration with any upcoming single-cell omics data type in the Python single-cell omics ecosystem.

Cellestial leverages the performance of Polars ensuring speed and scalability.

Installation

pip install cellestial

uv add cellestial

poetry add cellestial

Quickstart

Cellestial accepts a standard AnnData directly. The same function plots a categorical column or a gene, and everything composes through + like ggplot.

from lets_plot import *
import cellestial as cl

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

cl.umap(
    data,
    key="cell_type_lvl1",
    axis_type="arrow",
    legend_ondata=True,
) + scale_color_hue()

Examples

Spatial omics

Overlay categorical labels or gene expression on tissue coordinates.

Show code

import squidpy as sq

lymph_node = cl.datasets.human_lymph_node(cache_directory="data")
hne = sq.datasets.visium_hne_adata()

gggrid(
    [
        cl.spatial(lymph_node, key="clusters"),
        cl.spatial(hne, key="leiden"),
    ],
    ncol=2,
) + ggsize(1000, 400)

Dimensionality reduction with layers

Cellestial ships single-cell-specific layers (cluster_outlines, stream, arrow_axis, ondata_legend) that compose with + like any geom.

Show code

velocity_data = cl.datasets.pancreas(cache_directory="data")

outlined = cl.umap(
    data,
    key="cell_type_lvl1",
    axis_type="arrow",
    size=1.5,
    legend_ondata=True,
) + scale_color_hue() + cl.cluster_outlines(groups=["Lymphocytes", "B Cells"])

streamed = cl.umap(
    velocity_data,
    key="clusters_coarse",
    axis_type="arrow",
    size=4,
    alpha=0.4,
    legend_ondata=True,
    ondata_color="black",
) + cl.stream()

gggrid([outlined, streamed])

Marker genes

Heatmaps, dotplots, matrixplots and stacked violins share the same call shape and ship with built-in dendrograms and group bars.

Show code

markers = [
    "PSAP", "LYZ", "CST3",          # Monocytes
    "CD79A", "CD79B",                # B cells
    "IL7R", "CD3D", "CD3E", "CD4",   # T cells (CD4+)
    "CD8A", "CD8B",                  # T cells (CD8+)
    "NKG7", "GNLY", "KLRD1",         # NK cells
    "HLA-DRA", "FCER1A",             # Dendritic cells
]

cl.heatmap(
    data,
    group_by="cell_type_lvl1",
    keys=markers,
    geom="raster",
    group_lines_size=0.5,
    group_lines_color="white",
    group_bars=True,
    group_bars_labels=True,
    dendrogram=True,
    dendrogram_size=1,
) + scale_fill_viridis()

Statistical comparisons

cl.bracket runs pairwise tests on a cl.boxplot or cl.violin and draws annotated significance brackets.

Show code

cl.boxplot(
    data,
    key="CD3D",
    fill="cell_type_lvl1",
    threshold=0.1,
) + scale_fill_hue() + cl.bracket(
    y_padding=0.2,
    label="pvalue",
    prefix="p",
    prefix_style="<",
    comparisons=[
        ("Lymphocytes", "Monocytes"),
        ("Monocytes", "Erythroid"),
        ("Monocytes", "B Cells"),
    ],
)

Ridge plots

Quick exploratory views of expression distributions across groups.

Show code

cl.ridge(
    data,
    key="B2M",
    alpha=0.6,
    group_by="cell_type_lvl1",
) + scale_fill_hue()

Migrating from Scanpy

Cellestial mirrors most of scanpy.pl with a few naming shifts:

• color= becomes key=.
• Multi-panel calls use a plural function (cl.umaps, cl.violins, ...).
• groupby becomes group_by, var_names becomes keys.
• Saving is a separate cl.save(plot, "umap.png") call.

See the migration guide for a side-by-side mapping.

Documentation

• Quickstart — guided tour of the API.
• Features — tooltips, layers, grids, colors, faceting.
• API reference — every function.
• Migrating from Scanpy.

License

Apache 2.0. See LICENSE.