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+title = "A new plotting backend for Scanpy: HoloViews"
+date = 2026-08-27T00:00:05+01:00
+description = "An opt-in HoloViews plotting backend for Scanpy, in preview."
+author = "Muskan Hashim, Philipp Angerer"
+draft = false
++++
+
+
+
+Scanpy's plotting API has served the single-cell community well for years.
+But datasets have outgrown static images.
+
+We routinely work with hundreds of thousands, sometimes millions, of cells, and the questions we ask are increasingly interactive: which cells are those, what's the expression of this gene here, how do these two embeddings line up?
+Answering them, and more, with a static PNG means re-running a cell, tweaking a parameter, and re-running again.
+
+So we've been rebuilding `sc.pl` on a new foundation: [HoloViews](https://holoviews.org/).
+It's available today as an opt-in preview, and we'd love your feedback while it's still taking shape.
+
+## What is HoloViews?
+
+[HoloViews](https://holoviews.org/) is part of the [HoloViz](https://holoviz.org/) family of open-source Python visualization tools, alongside [Panel](https://panel.holoviz.org/), [Datashader](https://datashader.org/), and [hvPlot](https://hvplot.holoviz.org/).
+Its central design principle is that the declaration of your data is independent from how it's plotted.
+
+A HoloViews object is an annotated data container that knows how to render itself.
+You describe what to show (the x and y dimensions, the value to color by), and HoloViews handles how to draw it.
+Because the object carries its data, you can inspect it, slice it, compose it, and switch rendering backends without touching your analysis code.
+
+The same object renders through three backends, which means you can write the plot once and pick the one that best fits the moment:
+
+- [Bokeh](https://bokeh.org/) for interactive plots in the browser: pan, zoom, hover, and select.
+- [Matplotlib](https://matplotlib.org/) for publication-quality static figures.
+- [Plotly](https://plotly.com/python/) for interactive 3D.
+
+## How it fits Scanpy and scverse
+
+HoloViews can be taught to speak [AnnData](https://anndata.readthedocs.io/) natively.
+A small companion package, [hv-anndata](https://hv-anndata.readthedocs.io/en/latest/), gives HoloViews a first-class AnnData interface and the `A` accessor for pointing at any part of your object.
+
+Most of the time you use the `sc.pl` shortcuts:
+
+```python
+import scanpy as sc
+
+sc.settings.preset = sc.Preset.ScanpyV2Preview
+A = sc.pl.hv_init("bokeh")
+
+adata = sc.datasets.pbmc68k_reduced()
+sc.pl.umap(adata, color=A.obs["bulk_labels"])
+```
+
+That one line expands to plain HoloViews, where full control lives:
+
+```python
+import holoviews as hv
+
+hv.Scatter(
+ adata,
+ A.obsm["umap"][0],
+ [A.obsm["umap"][1], A.obs["bulk_labels"]],
+).opts(color=A.obs["bulk_labels"], aspect="square", legend_position="right")
+```
+
+You can drop to this level whenever the shortcut doesn't expose what you need.
+`.opts()` passes straight through to the backend: there is no bespoke styling buried inside a scanpy function.
+
+There's no copying columns into a tidy DataFrame, reindexing, or bookkeeping to keep colors aligned with cells.
+You reference `A.obsm["umap"]`, `A.obs["bulk_labels"]`, or `A.X[:, "GENE"]` directly, and hv-anndata handles the plumbing.
+The AnnData object stays the single source of truth, in line with the scverse philosophy of organizing workflows around a shared, interoperable data structure.
+
+On top of the interface, hv-anndata ships single-cell-aware components: a ManifoldMap for embeddings, a Dotmap, and a ClusterMap.
+The `sc.pl` plotting functions (scatter, umap, heatmap, violin, stacked_violin, matrixplot, tracksplot, and more) are in Scanpy from 1.13.0a1 onward, available once you set the `ScanpyV2Preview` preset; each function's [docs page](https://scanpy.scverse.org/en/latest/api/generated/scanpy.pl.scatter.html) has New and Legacy tabs for both backends.
+
+Because scanpy's own tools return tidy data, they compose straight into HoloViews.
+`sc.pl.dotplot`, for example, aggregates once with `sc.get.aggregate` and maps the result onto a HoloViews `Points` element, with mean expression as color and the fraction of expressing cells as dot size:
+
+```python
+markers = ["C1QA", "PSAP", "CD79A", "CD79B", "CST3", "LYZ"]
+sc.pl.dotplot(adata[:, markers], A.obs["bulk_labels"])
+```
+
+The whole plot type collapses to an aggregation feeding a data-first element, with no bespoke drawing code: the [implementation](https://github.com/scverse/scanpy/blob/9cdf9e600c045dca512adfdee59ce6e292d2bc9d/src/scanpy/plotting/_v2/_core.py#L631-L650) is only around twenty lines, where the matplotlib version ran to hundreds of lines across several files.
+
+## Features we're excited about
+
+- **Full control through `.opts()`.**
+ You're no longer limited to the parameters we chose to expose; the full Bokeh, Matplotlib, and Plotly option sets are available, with styling kept out of scanpy's internals.
+ You declare what to plot through `A` and set appearance in `.opts()`, so the two are easy to tell apart.
+- **Interactivity for free.**
+ With Bokeh, mouse over a cell in a UMAP to read its cluster, gene expression, or any `obs` field with no re-running.
+- **Scales to millions of cells.**
+ [Datashader](https://datashader.org/) rasterizes the points into a faithful image and re-renders as you zoom, and turning it on can be as simple as `datashade=True`.
+- **Composable.**
+ Overlay with `*`, lay out side by side with `+`, and add a marginal histogram with `.hist()`, so `scatter * centroids + expression_violin` replaces a page of axes management.
+- **Existing plots are covered.**
+ There's an equivalent for most of the matplotlib functions you rely on.
+
+## Linked brushing
+
+Select a group of cells in a UMAP and watch them highlight in a dotplot or a second embedding; the plots share one selection.
+hv-anndata wires this up between its ManifoldMap and Dotmap using HoloViews' `link_selections`:
+
+```python
+import holoviews as hv
+import panel as pn
+import hv_anndata
+
+ls = hv.link_selections.instance()
+mm = hv_anndata.ManifoldMap(adata=adata, reduction="X_umap", ls=ls)
+
+marker_genes = {"B cells": ["CD79A", "CD79B"], "Monocytes": ["LYZ", "CST3"]}
+dm = hv_anndata.dotmap_from_manifoldmap(mm, marker_genes=marker_genes, groupby="bulk_labels")
+
+pn.Column(mm, dm)
+
+# Pull the selected cells back into your workflow. Two documented HoloViews routes:
+# - ls.filter() filters the source data down to the selection.
+# - Selection1D(source=).index gives the selected row indices, then adata[index] is the subset.
+```
+
+## Try it
+
+The new backend is an opt-in preview.
+The legacy matplotlib path and the new HoloViews one live behind the same `sc.pl` functions, so one setting chooses which is active:
+
+```python
+import scanpy as sc
+
+sc.settings.preset = sc.Preset.ScanpyV2Preview # opt in; default stays on matplotlib
+A = sc.pl.hv_init("bokeh")
+
+adata = sc.datasets.pbmc68k_reduced()
+sc.pl.scatter(adata, A.X[:, ["PSAP", "C1QA"]], color=A.obs["bulk_labels"]).opts(
+ cmap="tab10", show_legend=False
+)
+```
+
+By default the preset stays on matplotlib so nothing changes for existing code until you opt in.
+Matplotlib also stays available indefinitely through the new API, which has multiple backends.
+This is an early preview and the API will keep changing so the 2.0 version will differ from what you see today.
+Report bugs, run it on your own data, and tell us what's missing!
+
+Find us on [GitHub](https://github.com/scverse/scanpy), [Zulip](https://scverse.zulipchat.com/), and [Discourse](https://discourse.scverse.org/).
+Issues, ideas, and pull requests are all welcome.
+
+*— Muskan Hashim, Philipp Angerer, and the scverse team.*
diff --git a/static/img/blog/scanpy_holoviews_overview.svg b/static/img/blog/scanpy_holoviews_overview.svg
new file mode 100644
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@@ -0,0 +1,73 @@
+
\ No newline at end of file
+
+Scanpy's plotting API has served the single-cell community well for years.
+But datasets have outgrown static images.
+
+We routinely work with hundreds of thousands, sometimes millions, of cells, and the questions we ask are increasingly interactive: which cells are those, what's the expression of this gene here, how do these two embeddings line up?
+Answering them, and more, with a static PNG means re-running a cell, tweaking a parameter, and re-running again.
+
+So we've been rebuilding `sc.pl` on a new foundation: [HoloViews](https://holoviews.org/).
+It's available today as an opt-in preview, and we'd love your feedback while it's still taking shape.
+
+## What is HoloViews?
+
+[HoloViews](https://holoviews.org/) is part of the [HoloViz](https://holoviz.org/) family of open-source Python visualization tools, alongside [Panel](https://panel.holoviz.org/), [Datashader](https://datashader.org/), and [hvPlot](https://hvplot.holoviz.org/).
+Its central design principle is that the declaration of your data is independent from how it's plotted.
+
+A HoloViews object is an annotated data container that knows how to render itself.
+You describe what to show (the x and y dimensions, the value to color by), and HoloViews handles how to draw it.
+Because the object carries its data, you can inspect it, slice it, compose it, and switch rendering backends without touching your analysis code.
+
+The same object renders through three backends, which means you can write the plot once and pick the one that best fits the moment:
+
+- [Bokeh](https://bokeh.org/) for interactive plots in the browser: pan, zoom, hover, and select.
+- [Matplotlib](https://matplotlib.org/) for publication-quality static figures.
+- [Plotly](https://plotly.com/python/) for interactive 3D.
+
+## How it fits Scanpy and scverse
+
+HoloViews can be taught to speak [AnnData](https://anndata.readthedocs.io/) natively.
+A small companion package, [hv-anndata](https://hv-anndata.readthedocs.io/en/latest/), gives HoloViews a first-class AnnData interface and the `A` accessor for pointing at any part of your object.
+
+Most of the time you use the `sc.pl` shortcuts:
+
+```python
+import scanpy as sc
+
+sc.settings.preset = sc.Preset.ScanpyV2Preview
+A = sc.pl.hv_init("bokeh")
+
+adata = sc.datasets.pbmc68k_reduced()
+sc.pl.umap(adata, color=A.obs["bulk_labels"])
+```
+
+That one line expands to plain HoloViews, where full control lives:
+
+```python
+import holoviews as hv
+
+hv.Scatter(
+ adata,
+ A.obsm["umap"][0],
+ [A.obsm["umap"][1], A.obs["bulk_labels"]],
+).opts(color=A.obs["bulk_labels"], aspect="square", legend_position="right")
+```
+
+You can drop to this level whenever the shortcut doesn't expose what you need.
+`.opts()` passes straight through to the backend: there is no bespoke styling buried inside a scanpy function.
+
+There's no copying columns into a tidy DataFrame, reindexing, or bookkeeping to keep colors aligned with cells.
+You reference `A.obsm["umap"]`, `A.obs["bulk_labels"]`, or `A.X[:, "GENE"]` directly, and hv-anndata handles the plumbing.
+The AnnData object stays the single source of truth, in line with the scverse philosophy of organizing workflows around a shared, interoperable data structure.
+
+On top of the interface, hv-anndata ships single-cell-aware components: a ManifoldMap for embeddings, a Dotmap, and a ClusterMap.
+The `sc.pl` plotting functions (scatter, umap, heatmap, violin, stacked_violin, matrixplot, tracksplot, and more) are in Scanpy from 1.13.0a1 onward, available once you set the `ScanpyV2Preview` preset; each function's [docs page](https://scanpy.scverse.org/en/latest/api/generated/scanpy.pl.scatter.html) has New and Legacy tabs for both backends.
+
+Because scanpy's own tools return tidy data, they compose straight into HoloViews.
+`sc.pl.dotplot`, for example, aggregates once with `sc.get.aggregate` and maps the result onto a HoloViews `Points` element, with mean expression as color and the fraction of expressing cells as dot size:
+
+```python
+markers = ["C1QA", "PSAP", "CD79A", "CD79B", "CST3", "LYZ"]
+sc.pl.dotplot(adata[:, markers], A.obs["bulk_labels"])
+```
+
+The whole plot type collapses to an aggregation feeding a data-first element, with no bespoke drawing code: the [implementation](https://github.com/scverse/scanpy/blob/9cdf9e600c045dca512adfdee59ce6e292d2bc9d/src/scanpy/plotting/_v2/_core.py#L631-L650) is only around twenty lines, where the matplotlib version ran to hundreds of lines across several files.
+
+## Features we're excited about
+
+- **Full control through `.opts()`.**
+ You're no longer limited to the parameters we chose to expose; the full Bokeh, Matplotlib, and Plotly option sets are available, with styling kept out of scanpy's internals.
+ You declare what to plot through `A` and set appearance in `.opts()`, so the two are easy to tell apart.
+- **Interactivity for free.**
+ With Bokeh, mouse over a cell in a UMAP to read its cluster, gene expression, or any `obs` field with no re-running.
+- **Scales to millions of cells.**
+ [Datashader](https://datashader.org/) rasterizes the points into a faithful image and re-renders as you zoom, and turning it on can be as simple as `datashade=True`.
+- **Composable.**
+ Overlay with `*`, lay out side by side with `+`, and add a marginal histogram with `.hist()`, so `scatter * centroids + expression_violin` replaces a page of axes management.
+- **Existing plots are covered.**
+ There's an equivalent for most of the matplotlib functions you rely on.
+
+## Linked brushing
+
+Select a group of cells in a UMAP and watch them highlight in a dotplot or a second embedding; the plots share one selection.
+hv-anndata wires this up between its ManifoldMap and Dotmap using HoloViews' `link_selections`:
+
+```python
+import holoviews as hv
+import panel as pn
+import hv_anndata
+
+ls = hv.link_selections.instance()
+mm = hv_anndata.ManifoldMap(adata=adata, reduction="X_umap", ls=ls)
+
+marker_genes = {"B cells": ["CD79A", "CD79B"], "Monocytes": ["LYZ", "CST3"]}
+dm = hv_anndata.dotmap_from_manifoldmap(mm, marker_genes=marker_genes, groupby="bulk_labels")
+
+pn.Column(mm, dm)
+
+# Pull the selected cells back into your workflow. Two documented HoloViews routes:
+# - ls.filter(