scanpy.pp.highly_variable_genes

scanpy.pp.highly_variable_genes(adata, *, layer=None, n_top_genes=None, min_disp=0.5, max_disp=inf, min_mean=0.0125, max_mean=3, span=0.3, n_bins=20, flavor=None, subset=False, inplace=True, batch_key=None, check_values=True)

Annotate highly variable genes [Satija et al., 2015, Stuart et al., 2019, Zheng et al., 2017].

Expects logarithmized data, except when flavor='seurat_v3'/'seurat_v3_paper', in which count data is expected.

Depending on flavor, this reproduces the R-implementations of Seurat [Satija et al., 2015], Cell Ranger [Zheng et al., 2017], and Seurat v3 [Stuart et al., 2019].

'seurat_v3'/'seurat_v3_paper' requires scikit-misc package. If you plan to use this flavor, consider installing scanpy with this optional dependency: scanpy[skmisc].

For the dispersion-based methods (flavor='seurat' Satija et al. [2015] and flavor='cell_ranger' Zheng et al. [2017]), the normalized dispersion is obtained by scaling with the mean and standard deviation of the dispersions for genes falling into a given bin for mean expression of genes. This means that for each bin of mean expression, highly variable genes are selected.

For flavor='seurat_v3'/'seurat_v3_paper' [Stuart et al., 2019], a normalized variance for each gene is computed. First, the data are standardized (i.e., z-score normalization per feature) with a regularized standard deviation. Next, the normalized variance is computed as the variance of each gene after the transformation. Genes are ranked by the normalized variance. Only if batch_key is not None, the two flavors differ: For flavor='seurat_v3', genes are first sorted by the median (across batches) rank, with ties broken by the number of batches a gene is a HVG. For flavor='seurat_v3_paper', genes are first sorted by the number of batches a gene is a HVG, with ties broken by the median (across batches) rank.

The following may help when comparing to Seurat’s naming: If batch_key=None and flavor='seurat', this mimics Seurat’s FindVariableFeatures(…, method='mean.var.plot'). If batch_key=None and flavor='seurat_v3'/flavor='seurat_v3_paper', this mimics Seurat’s FindVariableFeatures(..., method='vst'). If batch_key is not None and flavor='seurat_v3_paper', this mimics Seurat’s SelectIntegrationFeatures.

See also scanpy.experimental.pp._highly_variable_genes for additional flavors (e.g. Pearson residuals).

Parameters:

adata AnnData

The annotated data matrix of shape n_obs × n_vars. Rows correspond to cells and columns to genes.

layer str | None (default: None)

If provided, use adata.layers[layer] for expression values instead of adata.X.

n_top_genes int | None (default: None)

Number of highly-variable genes to keep. Mandatory if flavor='seurat_v3'.

min_mean float (default: 0.0125)

If n_top_genes unequals None, this and all other cutoffs for the means and the normalized dispersions are ignored. Ignored if flavor='seurat_v3'.

max_mean float (default: 3)

If n_top_genes unequals None, this and all other cutoffs for the means and the normalized dispersions are ignored. Ignored if flavor='seurat_v3'.

min_disp float (default: 0.5)

If n_top_genes unequals None, this and all other cutoffs for the means and the normalized dispersions are ignored. Ignored if flavor='seurat_v3'.

max_disp float (default: inf)

If n_top_genes unequals None, this and all other cutoffs for the means and the normalized dispersions are ignored. Ignored if flavor='seurat_v3'.

span float (default: 0.3)

The fraction of the data (cells) used when estimating the variance in the loess model fit if flavor='seurat_v3'.

n_bins int (default: 20)

Number of bins for binning the mean gene expression. Normalization is done with respect to each bin. If just a single gene falls into a bin, the normalized dispersion is artificially set to 1. You’ll be informed about this if you set settings.verbosity = 4.

flavor tuple | None (default: None)

Choose the flavor for identifying highly variable genes (default depends on scanpy.settings.preset property highly_variable_genes). For the dispersion based methods in their default workflows, 'seurat' passes the cutoffs whereas 'cell_ranger' passes n_top_genes.

subset bool (default: False)

Inplace subset to highly-variable genes if True otherwise merely indicate highly variable genes.

inplace bool (default: True)

Whether to place calculated metrics in .var or return them.

batch_key str | None (default: None)

If specified, highly-variable genes are selected within each batch separately and merged. This simple process avoids the selection of batch-specific genes and acts as a lightweight batch correction method. For all flavors, except seurat_v3, genes are first sorted by how many batches they are a HVG. For dispersion-based flavors ties are broken by normalized dispersion. For flavor = 'seurat_v3_paper', ties are broken by the median (across batches) rank based on within-batch normalized variance.

check_values bool (default: True)

Check if counts in selected layer are integers. A Warning is returned if set to True. Only used if flavor='seurat_v3'/'seurat_v3_paper'.

Return type:

DataFrame | None

Returns:

Returns a pandas.DataFrame with calculated metrics if inplace=False, else returns an AnnData object where it sets the following field:

adata.var['highly_variable']pandas.Series (dtype bool)

boolean indicator of highly-variable genes

adata.var['means']pandas.Series (dtype float)

means per gene

adata.var['dispersions']pandas.Series (dtype float)

For dispersion-based flavors, dispersions per gene

adata.var['dispersions_norm']pandas.Series (dtype float)

For dispersion-based flavors, normalized dispersions per gene

adata.var['variances']pandas.Series (dtype float)

For flavor='seurat_v3'/'seurat_v3_paper', variance per gene

adata.var['variances_norm']/'seurat_v3_paper'pandas.Series (dtype float)

For flavor='seurat_v3'/'seurat_v3_paper', normalized variance per gene, averaged in the case of multiple batches

adata.var['highly_variable_rank']pandas.Series (dtype float)

For flavor='seurat_v3'/'seurat_v3_paper', rank of the gene according to normalized variance, in case of multiple batches description above

adata.var['highly_variable_nbatches']pandas.Series (dtype int)

If batch_key is given, this denotes in how many batches genes are detected as HVG

adata.var['highly_variable_intersection']pandas.Series (dtype bool)

If batch_key is given, this denotes the genes that are highly variable in all batches

Notes

This function replaces filter_genes_dispersion().