scanpy.pp.filter_genes_dispersion

scanpy.pp.filter_genes_dispersion(data, flavor='seurat', min_disp=None, max_disp=None, min_mean=None, max_mean=None, n_bins=20, n_top_genes=None, log=True, subset=True, copy=False)

Extract highly variable genes [Satija15] [Zheng17].

Warning

Deprecated since version 1.3.6: Use highly_variable_genes() instead. The new function is equivalent to the present function, except that

the new function always expects logarithmized data
subset=False in the new function, it suffices to merely annotate the genes, tools like pp.pca will detect the annotation
you can now call: sc.pl.highly_variable_genes(adata)
copy is replaced by inplace

If trying out parameters, pass the data matrix instead of AnnData.

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.

Use flavor='cell_ranger' with care and in the same way as in recipe_zheng17().

Parameters:

data : AnnData: The (annotated) data matrix of shape n_obs × n_vars. Rows correspond to cells and columns to genes.
flavor : Literal['seurat', 'cell_ranger'] (default: 'seurat'): Choose the flavor for computing normalized dispersion. If choosing ‘seurat’, this expects non-logarithmized data – the logarithm of mean and dispersion is taken internally when log is at its default value True. For ‘cell_ranger’, this is usually called for logarithmized data – in this case you should set log to False. In their default workflows, Seurat passes the cutoffs whereas Cell Ranger passes n_top_genes.
min_mean : Optional[float] (default: None)
max_mean : Optional[float] (default: None)
min_disp : Optional[float] (default: None)
max_disp : Optional[float] (default: None): If n_top_genes unequals None, these cutoffs for the means and the normalized dispersions are ignored.
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.
n_top_genes : Optional[int] (default: None): Number of highly-variable genes to keep.
log : bool (default: True): Use the logarithm of the mean to variance ratio.
subset : bool (default: True): Keep highly-variable genes only (if True) else write a bool array for h ighly-variable genes while keeping all genes
copy : bool (default: False): If an AnnData is passed, determines whether a copy is returned.

Returns:

: If an AnnData adata is passed, returns or updates adata depending on copy. It filters the adata and adds the annotations

meansadata.var: Means per gene. Logarithmized when log is True.
dispersionsadata.var: Dispersions per gene. Logarithmized when log is True.
dispersions_normadata.var: Normalized dispersions per gene. Logarithmized when log is True.

If a data matrix X is passed, the annotation is returned as np.recarray with the same information stored in fields: gene_subset, means, dispersions, dispersion_norm.