anndata2ri

Converter between Python’s AnnData and R’s SingleCellExperiment.

SingleCellExperiment		AnnData
assay(d, 'X')	⇄	d.X
assay(d, 'counts')	⇄	d.layers['counts']
colData(d)	⇄	d.obs
rowData(d)	⇄	d.var
metadata(d)	⇄	d.uns
reducedDim(d, 'PCA')	⇄	d.obsm['X_pca']
reducedDim(d, 'DM')	⇄	d.obsm['X_diffmap']

anndata2ri.converter = <rpy2.robjects.conversion.Converter object>

A Converter for anndata.

Includes rpy2.robjects.numpy2ri, rpy2.robjects.pandas2ri, and scipy2ri.

anndata2ri.set_ipython_converter(ipython=None, converter=None)

Set the default converter for rmagic in IPython.

Parameters:

ipython InteractiveShell | None (default: None)

The IPython instance to set the converter for. If not specified, the current IPython instance is used.

converter conversion | None (default: None)

The converter to use. If not specified, converter is used.

anndata2ri.scipy2ri

Convert scipy.sparse matrices between Python and R.

For a detailed comparison between the two languages’ sparse matrix environment, see issue #8.

Here’s an overview over the matching classes (note that dtype=float32 is also supported):

R	Python
dgCMatrix	csc_matrix(dtype=float64)
lgCMatrix/ngCMatrix	csc_matrix(dtype=bool)
dgRMatrix	csr_matrix(dtype=float64)
lgRMatrix/ngRMatrix	csr_matrix(dtype=bool)
dgTMatrix	coo_matrix(dtype=float64)
lgTMatrix/ngTMatrix	coo_matrix(dtype=bool)
ddiMatrix	dia_matrix(dtype=float64)
ldiMatrix	dia_matrix(dtype=bool)

anndata2ri.scipy2ri.converter = <rpy2.robjects.conversion.Converter object>

The Converter for scipy.sparse.

Includes rpy2.robjects.numpy2ri.

anndata2ri.scipy2ri.supported_r_matrix_classes(types=frozenset({'d', 'l', 'n'}), storage=frozenset({'C', 'R', 'T', 'di'}))

Get supported classes, possibly limiting data types or storage types.

Parameters:

types Iterable[Literal['d', 'l', 'n']] | Literal['d', 'l', 'n'] (default: frozenset({'l', 'n', 'd'}))

Data type character(s) from supported_r_matrix_types

storage Iterable[Literal['C', 'R', 'T', 'di']] | Literal['C', 'R', 'T', 'di'] (default: frozenset({'di', 'T', 'C', 'R'}))

Storage mode(s) from supported_r_matrix_storage

Returns:

frozenset[str] – All supported classes with those characters

anndata2ri.scipy2ri.supported_r_matrix_storage = frozenset({'C', 'R', 'T', 'di'})

The Matrix storage types supported by this module; Column-sparse, Row-Sparse, Triplets, and DIagonal.

anndata2ri.scipy2ri.supported_r_matrix_types = frozenset({'d', 'l', 'n'})

The Matrix data types supported by this module; Double, Logical, and patterN.