scanpy.pp.highly_variable_genes¶

scanpy.pp.highly_variable_genes(adata, min_disp=None, max_disp=None, min_mean=None, max_mean=None, n_top_genes=None, n_bins=20, flavor='seurat', subset=False, inplace=True, batch_key=None)¶

Annotate highly variable genes [Satija15] [Zheng17].

Expects logarithmized data.

Depending on flavor, this reproduces the R-implementations of Seurat [Satija15] and Cell Ranger [Zheng17].

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.

Parameters

adata : AnnDataAnnData: The annotated data matrix of shape n_obs × n_vars. Rows correspond to cells and columns to genes.
min_mean : float, optional (default: 0.0125): If n_top_genes unequals None, this and all other cutoffs for the means and the normalized dispersions are ignored.
max_mean : float, optional (default: 3): If n_top_genes unequals None, this and all other cutoffs for the means and the normalized dispersions are ignored.
min_disp : float, optional (default: 0.5): If n_top_genes unequals None, this and all other cutoffs for the means and the normalized dispersions are ignored.
max_disp : float, optional (default: None): If n_top_genes unequals None, this and all other cutoffs for the means and the normalized dispersions are ignored.
n_top_genes : int or None, optional (default: None): Number of highly-variable genes to keep.
n_bins : int, optional (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 : {'seurat', 'cell_ranger'}: Choose the flavor for computing normalized dispersion. In their default workflows, Seurat passes the cutoffs whereas Cell Ranger passes n_top_genes.
subset : bool, optional (default: False): Inplace subset to highly-variable genes if True otherwise merely indicate highly variable genes.
inplace : bool, optional (default: True): Whether to place calculated metrics in .var or return them.
batch_key : str, optional (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.

Return type

recarray, NoneOptional[recarray]

Returns

Depending on inplace returns calculated metrics (recarray) or updates .var with the following fields

highly_variablebool: boolean indicator of highly-variable genes
means: means per gene
dispersions: dispersions per gene
dispersions_norm: normalized dispersions per gene
highly_variable_nbatchesint: If batch_key is given, this denotes in how many batches genes are detected as HVG
highly_variable_intersectionbool: If batch_key is given, this denotes the genes that are highly variable in all batches

Notes

This function replaces filter_genes_dispersion().