hv_anndata.plotting.scanpy.heatmap¶

hv_anndata.plotting.scanpy.heatmap(adata, base=A.X, /, vdims=(), *, transpose=False, add_dendrogram=False)¶

Shortcut for a heatmap.

Basically just

>>> hv.HeatMap(adata, [A.obs.index, A.var.index], [base[:, :], *vdims]).opts(...)

Set base to e.g. A or A.layers[key], and transpose=True to switch the order of the dims.

If add_dendrogram is True, the dendrogram is added. Call it directly to customize the dendrogram:

>>> hv.operation.dendrogram(heatmap, adjoint_dims=..., main_dim=base[:, :])

Parameters:

adata AnnData: The AnnData object.
base LayerAcc | GraphAcc, default: A.X: The base layer/graph of the heatmap.
vdims Collection[AdDim], default: (): The value dimensions.
transpose bool, default: False: Whether to transpose the dims.
add_dendrogram bool | Literal['obs', 'var'], default: False: Where to add dendrograms to the heatmap: True for both, "obs"/"var" for one, and False for none.

Return type:

HeatMap

Returns:

A heatmap object

Examples

bokeh

import hv_anndata.plotting.scanpy as hv_sc
from hv_anndata import data, register, A

register()

adata = data.pbmc68k_processed()
markers = ["C1QA", "PSAP", "CD79A", "CD79B", "CST3", "LYZ"]
hv_sc.heatmap(
    adata[:, markers], A.X, [A.obs["n_counts"]]
).opts(hv.opts.HeatMap(xticks=0, aspect=2))

/home/docs/.local/share/hatch/env/virtual/hv-anndata/STk7F69l/docs/lib/python3.13/site-packages/numba/cpython/hashing.py:477: UserWarning: FNV hashing is not implemented in Numba. See PEP 456 https://www.python.org/dev/peps/pep-0456/ for rationale over not using FNV. Numba will continue to work, but hashes for built in types will be computed using siphash24. This will permit e.g. dictionaries to continue to behave as expected, however anything relying on the value of the hash opposed to hash as a derived property is likely to not work as expected.
  warnings.warn(msg)

matplotlib

import hv_anndata.plotting.scanpy as hv_sc
from hv_anndata import data, register, A

register()

adata = data.pbmc68k_processed()
markers = ["C1QA", "PSAP", "CD79A", "CD79B", "CST3", "LYZ"]
hv_sc.heatmap(
    adata[:, markers], A.X, [A.obs["n_counts"]]
).opts(hv.opts.HeatMap(xticks=0, aspect=2))

plotly

import hv_anndata.plotting.scanpy as hv_sc
from hv_anndata import data, register, A

register()

adata = data.pbmc68k_processed()
markers = ["C1QA", "PSAP", "CD79A", "CD79B", "CST3", "LYZ"]
hv_sc.heatmap(
    adata[:, markers], A.X, [A.obs["n_counts"]]
).opts(hv.opts.HeatMap(xticks=0, aspect=2))

With dendrogram:

bokeh

hv_sc.heatmap(
    adata[:, markers], A.X, [A.obs["n_counts"]], add_dendrogram="obs"
).opts(hv.opts.HeatMap(xticks=0, aspect=2))

matplotlib

hv_sc.heatmap(
    adata[:, markers], A.X, [A.obs["n_counts"]], add_dendrogram="obs"
).opts(hv.opts.HeatMap(xticks=0, aspect=2))

plotly

hv_sc.heatmap(
    adata[:, markers], A.X, [A.obs["n_counts"]], add_dendrogram="obs"
).opts(hv.opts.HeatMap(xticks=0, aspect=2))

WARNING:param.LayoutPlot56284: Plotly plotting class for Empty type not found, object will not be rendered.
WARNING:param.LayoutPlot56284: Plotly plotting class for Dendrogram type not found, object will not be rendered.