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.activate()#

Activate conversion for supported objects.

This includes AnnData objects as well as Array objects and pandas.DataFrames via rpy2.robjects.numpy2ri and rpy2.robjects.pandas2ri.

Does nothing if this is the active converter.

Return type

None

anndata2ri.deactivate()#

Deactivate the conversion described above if it is active.

Return type

None

anndata2ri.py2rpy(obj)#

Convert Python objects to R interface objects.

Supports: :rtype: Sexp

anndata2ri.rpy2py(obj)#

Convert R interface objects to Python objects.

Supports: :rtype: AnnData | DataFrame

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

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.activate()#

Activate conversion between sparse matrices from Scipy and R’s Matrix package.

Does nothing if this is the active conversion.

Return type

None

anndata2ri.scipy2ri.deactivate()#

Deactivate the conversion described above if it is active.

Return type

None

anndata2ri.scipy2ri.py2rpy(obj)#

Convert scipy sparse matrices objects to R sparse matrices.

Supports:

Return type

Sexp

csc_matrix (dtype in {float32, float64, bool}) →

dgCMatrix or lgCMatrix

csr_matrix (dtype in {float32, float64, bool}) →

dgRMatrix or lgRMatrix

coo_matrix (dtype in {float32, float64, bool}) →

dgTMatrix or lgTMatrix

dia_matrix (dtype in {float32, float64, bool}) →

ddiMatrix or ldiMatrix

anndata2ri.scipy2ri.rpy2py(obj)#

Convert R sparse matrices to scipy sparse matrices.

Supports:

Return type

spmatrix

dgCMatrix, lgCMatrix, or ngCMatrix →

csc_matrix (dtype float64 or bool)

dgRMatrix, lgRMatrix, or ngRMatrix →

csr_matrix (dtype float64 or bool)

dgTMatrix, lgTMatrix, or ngTMatrix →

coo_matrix (dtype float64 or bool)

ddiMatrix or ldiMatrix →

dia_matrix (dtype float64 or bool)

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

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

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_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']] | {‘d’, ‘l’, ‘n’}Union[Iterable[Literal['d', 'l', 'n']], Literal['d', 'l', 'n']] (default: frozenset({'l', 'd', 'n'}))

Data type character(s) from supported_r_matrix_types

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

Storage mode(s) from supported_r_matrix_storage

Return type

frozenset[str]

Returns

All supported classes with those characters