Convert an AnnData to a Seurat

Convert an AnnData object to a Seurat object

Usage

as_Seurat(
  adata,
  assay_name = "RNA",
  x_mapping = NULL,
  layers_mapping = TRUE,
  object_metadata_mapping = TRUE,
  assay_metadata_mapping = TRUE,
  reduction_mapping = TRUE,
  graph_mapping = TRUE,
  misc_mapping = TRUE
)

Arguments

adata

The AnnData object to convert.

assay_name

Name of the assay to be created in the new Seurat object

x_mapping

A string specifying the name of the layer in the resulting Seurat object where the data in the X slot of adata will be mapped to

layers_mapping

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

object_metadata_mapping

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

assay_metadata_mapping

A named vector where names are variable metadata columns in the assay of the resulting Seurat object and values are columns of var in adata. See below for details.

reduction_mapping

A named vector where names are names of Embeddings in the resulting Seurat object and values are keys of obsm in adata. Alternatively, a named list where names are names of Embeddings in the resulting Seurat object and values are vectors with the items "key", "embeddings" and (optionally) "loadings". See below for details.

graph_mapping

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

misc_mapping

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

Value

A Seurat 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 Seurat 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 table below. In most cases, the default is to copy all items using the same names except where the correspondence between objects is unclear. The reduction_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. Empt 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(seurat_item = "adata_item") will copy adata_item from the slot in adata to seurat_item in the corresponding slot of the new Seurat 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 Seurat	Example mapping argument	Default if NULL
adata$X	Layers(seurat)	x_mapping = "counts" OR layers_mapping = c(counts = NA)	The data in adata$X is copied to a layer named X
adata$layers	Layers(seurat)	layers_mapping = c(counts = "counts")	All items are copied by name
adata$obs	seurat[[]]	object_metadata_mapping = c(n_counts = "n_counts", cell_type = "CellType")	All columns are copied by name
adata$var	seurat[[assay_name]][[]]	assay_metadata_mapping = c(n_cells = "n_cells", pct_zero = "PctZero")	All columns are copied by name
adata$obsm	Embeddings(sce)	reduction_mapping = c(pca = "X_pca") OR reduction_mapping = list(pca = c(key = "PC_", obsm = "X_pca", varm = "PCs"))	All items that can be coerced to a numeric matrix are copied by name without loadings except for "X_pca" for which loadings are added from "PCs"
adata$obsp	Graphs(seurat)	graph_mapping = c(nn = "connectivities")	All items are copied by name
adata$varp	NA	NA	There is no corresponding slot for varp
adata$uns	Misc(seurat)	misc_mapping = c(project_metadata = "metadata")	All items are copied by name

The reduction_mapping argument

For the simpler named vector format, the names should be the names of Embeddings in the resulting Seurat object, and the values should be the keys of obsm in adata. A key will created from the obsm key.

For more advanced mapping, use the list format where each item is a vector with the following names defining arguments to SeuratObject::CreateDimReducObject():

• key: the key of the resulting SeuratObject::DimReduc object, passed to the key argument after processing with SeuratObject::Key()

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

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

The x_mapping and layers_mapping arguments

In order to specify where the data in adata$X will be stored in the Layers(seurat) slot of the resulting object, you can use either the x_mapping argument or the layers_mapping argument. If you use x_mapping, it should be a string specifying the name of the layer in Layers(seurat) where the data in adata$X will be stored. If you use layers_mapping, it should be a named vector where names are names of Layers(seurat) 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 layers_mapping. You must provide a layer named counts or data in either x_mapping or layers_mapping.

See also

Other object converters: as_AnnData(), as_HDF5AnnData(), as_InMemoryAnnData(), as_SingleCellExperiment()

Examples

  ad <- AnnData(
    X = matrix(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
  seurat <- ad$as_Seurat(
    assay_name = "RNA",
    x_mapping = "counts",
    layers_mapping = TRUE,
    object_metadata_mapping = TRUE,
    assay_metadata_mapping = TRUE,
    reduction_mapping = TRUE,
    graph_mapping = TRUE,
    misc_mapping = TRUE
  )
#> Warning: Data is of class matrix. Coercing to dgCMatrix.