Convert an AnnData to a SingleCellExperiment

Convert an AnnData object to a SingleCellExperiment object

Usage

as_SingleCellExperiment(
  adata,
  x_mapping = NULL,
  assays_mapping = TRUE,
  colData_mapping = TRUE,
  rowData_mapping = TRUE,
  reducedDims_mapping = TRUE,
  colPairs_mapping = TRUE,
  rowPairs_mapping = TRUE,
  metadata_mapping = TRUE
)

Arguments

adata

The AnnData object to convert

x_mapping

A string specifying the name of the assay in the resulting SingleCellExperiment where the data in the X slot of adata will be mapped to

assays_mapping

A named vector where names are names of assays in the resulting SingleCellExperiment object and values are keys of layers in adata. See below for details.

colData_mapping

A named vector where names are columns of colData in the resulting SingleCellExperiment object and values are columns of obs in adata. See below for details.

rowData_mapping

A named vector where names are columns of rowData in the resulting SingleCellExperiment object and values are columns of var in adata. See below for details.

reducedDims_mapping

A named vector where names are names of reducedDims in the resulting SingleCellExperiment object and values are keys of obsm in adata. Alternatively, a named list where names are names of reducedDims in the resulting SingleCellExperiment object and values are vectors with the items "sampleFactors" and "featureLoadings" and/or "metadata". See below for details.

colPairs_mapping

A named vector where names are names of colPairs in the resulting SingleCellExperiment object and values are keys of obsp in adata. See below for details.

rowPairs_mapping

A named vector where names are names of rowPairs in the resulting SingleCellExperiment object and values are keys of varp in adata. See below for details.

metadata_mapping

A named vector where names are names of metadata in the resulting SingleCellExperiment object and values are keys of uns in adata. See below for details.

Value

A SingleCellExperiment object containing the requested data from adata

Details

Mapping arguments

All mapping arguments expect a named character vector where names are the names of the slot in the SingleCellExperiment object and values are the keys of the corresponding slot of adata. If TRUE, the conversion function will guess which items to copy as described in the conversion tables below. In most cases, the default is to copy all items using the same names except where the correspondence between objects is unclear. The reducedDims_mapping argument can also accept a more complex list format, see below for details. To avoid copying anything to a slot, set the mapping argument to FALSE. Empty mapping arguments (NULL, c(), list()) will be treated as FALSE with a warning. If an unnamed vector is provided, the values will be used as names.

Examples:

• TRUE will guess which items to copy as described in the conversion table

• c(sce_item = "adata_item") will copy adata_item from the slot in adata to sce_item in the corresponding slot of the new SingleCellExperiment object

• FALSE will avoid copying anything to the slot

• c("adata_item") is equivalent to c(adata_item = "adata_item")

Conversion table

From AnnData	To SingleCellExperiment	Example mapping argument	Default
adata$X	assays(sce)	x_mapping = "counts"	The data in adata$X is copied to the assay named X
adata$layers	assays(sce)	assays_mapping = c(counts = "counts")	All items are copied by name
adata$obs	colData(sce)	colData_mapping = c(n_counts = "n_counts", cell_type = "CellType")	All columns are copied by name
adata$var	rowData(sce)	rowData_mapping = c(n_cells = "n_cells", pct_zero = "PctZero")	All columns are copied by name
adata$obsm	reducedDims(sce)	reducedDims_mapping = c(pca = "X_pca") OR reducedDims_mapping = list(pca = c(sampleFactors = "X_pca", featureLoadings = "PCs", metadata = "pca_metadata"))	All items are copied by name without loadings except for "X_pca" for which loadings are added from "PCs"
adata$obsp	colPairs(sce)	colPairs_mapping = c(nn = "connectivities")	All items are copied by name
adata$varp	rowPairs(sce)	rowPairs_mapping = c(gene_overlaps = "similarities")	All items are copied by name
adata$uns	metadata(sce)	uns_mapping = c(project_metadata = "metadata")	All items are copied by name

The reducedDims_mapping argument

For the simpler named vector format, the names should be the names of reducedDims in the resulting SingleCellExperiment object, and the values should be the keys of obsm in adata.

For more advanced mapping, use the list format where each item is a vector with the following names used to create a SingleCellExperiment::LinearEmbeddingMatrix (if featureLoadings or metadata is provided):

• sampleFactors: a key of the obsm slot in adata, adata$obsm[[sampleFactors]] is passed to the sampleFactors argument

• featureLoadings: a key of the varm slot in adata (optional), adata$varm[[featureLoadings]] is passed to the featureLoadings argument

• metadata: a key of the uns slot in adata (optional), adata$uns[[metadata]] is passed to the metadata argument

The x_mapping and assays_mapping arguments

In order to specify where the data in adata$X will be stored in the assays(sce) slot of the resulting object, you can use either the x_mapping argument or the assays_mapping argument. If you use x_mapping, it should be a string specifying the name of the layer in assays(sce) where the data in adata$X will be stored. If you use assays_mapping, it should be a named vector where names are names of assays(sce) and values are keys of layers in adata. In order to indicate the adata$X slot, you use NA as the value in the vector. The name you provide for x_mapping may not be a name in assays_mapping.

See also

Other object converters: as_AnnData(), as_HDF5AnnData(), as_InMemoryAnnData(), as_ReticulateAnnData(), as_Seurat(), reticulate-helpers

Examples

  ad <- AnnData(
    X = matrix(1:5, 3L, 5L),
    layers = list(A = matrix(5:1, 3L, 5L), B = matrix(letters[1:5], 3L, 5L)),
    obs = data.frame(row.names = LETTERS[1:3], cell = 1:3),
    var = data.frame(row.names = letters[1:5], gene = 1:5)
  )

  # Default usage
  sce <- ad$as_SingleCellExperiment(
    assays_mapping = TRUE,
    colData_mapping = TRUE,
    rowData_mapping = TRUE,
    reducedDims_mapping = TRUE,
    colPairs_mapping = TRUE,
    rowPairs_mapping = TRUE,
    metadata_mapping = TRUE
 )