scanpy.pl.highly_variable_genes

Note

Both backends are accessible as scanpy.pl.highly_variable_genes. The active backend is chosen by scanpy.settings.preset: the default is the legacy matplotlib backend; set it to scanpy.Preset.ScanpyV2Preview to use the HoloViews backend.

New (HoloViews)

scanpy.plotting._v2.highly_variable_genes(adata)

Plot dispersions used to identify highly variable genes.

Parameters:

adata AnnData

The AnnData object.

Return type:

Layout

Returns:

A layout containing two Scatter plots, one normalized and one not.

Examples

bokeh

import scanpy as sc

sc.settings.preset = sc.Preset.ScanpyV2Preview
A = sc.pl.hv_init('bokeh')

adata = sc.datasets.pbmc68k_reduced()
sc.pp.highly_variable_genes(adata, layer="counts")  # TODO: this should be the default
sc.pl.highly_variable_genes(adata)

/tmp/ipykernel_2131/1650608756.py:7: UserWarning: `flavor='seurat_v3_paper'` expects raw count data, but non-integers were found.
  sc.pp.highly_variable_genes(adata, layer="counts")  # TODO: this should be the default

matplotlib

import scanpy as sc

sc.settings.preset = sc.Preset.ScanpyV2Preview
A = sc.pl.hv_init('matplotlib')

adata = sc.datasets.pbmc68k_reduced()
sc.pp.highly_variable_genes(adata, layer="counts")  # TODO: this should be the default
sc.pl.highly_variable_genes(adata)

/tmp/ipykernel_2131/1650608756.py:7: UserWarning: `flavor='seurat_v3_paper'` expects raw count data, but non-integers were found.
  sc.pp.highly_variable_genes(adata, layer="counts")  # TODO: this should be the default

plotly

import scanpy as sc

sc.settings.preset = sc.Preset.ScanpyV2Preview
A = sc.pl.hv_init('plotly')

adata = sc.datasets.pbmc68k_reduced()
sc.pp.highly_variable_genes(adata, layer="counts")  # TODO: this should be the default
sc.pl.highly_variable_genes(adata)

/tmp/ipykernel_2131/1650608756.py:7: UserWarning: `flavor='seurat_v3_paper'` expects raw count data, but non-integers were found.
  sc.pp.highly_variable_genes(adata, layer="counts")  # TODO: this should be the default
WARNING:param.main: Option 'legend_labels' for Scatter type not valid for selected backend ('plotly'). Option only applies to following backends: ['bokeh', 'matplotlib']
WARNING:param.main: Option 'legend_position' for Scatter type not valid for selected backend ('plotly'). Option only applies to following backends: ['bokeh', 'matplotlib']
WARNING:param.main: Option 'legend_labels' for Scatter type not valid for selected backend ('plotly'). Option only applies to following backends: ['bokeh', 'matplotlib']
WARNING:param.main: Option 'legend_position' for Scatter type not valid for selected backend ('plotly'). Option only applies to following backends: ['bokeh', 'matplotlib']

Legacy (matplotlib)

scanpy.pl.highly_variable_genes(adata_or_result, *, log=False, show=None, highly_variable_genes=True, save=None)

Plot dispersions or normalized variance versus means for genes.

Produces Supp. Fig. 5c of Zheng et al. (2017) and MeanVarPlot() and VariableFeaturePlot() of Seurat.

Parameters:

adata

Result of highly_variable_genes().

log bool (default: False)

Plot on logarithmic axes.

show bool | None (default: None)

Show the plot, do not return axis.

save bool | str | None (default: None)

If True or a str, save the figure. A string is appended to the default filename. Infer the filetype if ending on {{'.pdf', '.png', '.svg'}}.

Return type:

None

Examples

Compute and plot highly variable genes from raw PBMC data.

import scanpy as sc
sc.settings.preset = sc.Preset.ScanpyV1
adata = sc.datasets.pbmc3k()
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata, min_mean=0.0125, max_mean=3, min_disp=0.5)
sc.pl.highly_variable_genes(adata)

Plot on logarithmic axes.

sc.pl.highly_variable_genes(adata, log=True)