Convert a Seurat object to an AnnData object

from_Seurat() converts a Seurat object to an AnnData object. Only one assay can be converted at a time. Arguments are used to configure the conversion. If NULL, the functions .from_Seurat_guess_* will be used to guess the mapping.

Usage

from_Seurat(
  seurat_obj,
  output_class = c("InMemoryAnnData", "HDF5AnnData"),
  assay_name = NULL,
  x_mapping = NULL,
  layers_mapping = NULL,
  obs_mapping = NULL,
  var_mapping = NULL,
  obsm_mapping = NULL,
  varm_mapping = NULL,
  obsp_mapping = NULL,
  varp_mapping = NULL,
  uns_mapping = NULL,
  ...
)

Arguments

seurat_obj: A Seurat object to be converted.
output_class: Name of the AnnData class. Must be one of "HDF5AnnData" or "InMemoryAnnData".
assay_name: The name of the assay to be converted. If NULL, the default assay will be used (SeuratObject::DefaultAssay()).
x_mapping: A mapping of a Seurat layer to the AnnData X slot. If NULL, no data will be copied to the X slot.
layers_mapping: A named vector mapping layer names in AnnData to the names of the layers in the Seurat object. Each name corresponds to the layer name in AnnData, and each value to the layer name in Seurat. See section "$layers mapping" for more details.
obs_mapping: A named vector or list mapping obs names in AnnData to the names of the object-level (cell level) metadata in the Seurat object. Each name corresponds to a column name in AnnData's obs, and each value to a column name in Seurat's cell metadata. See section "$obs mapping" for more details.
var_mapping: A named vector or list mapping var names in AnnData to the names of the feature-level metadata in the Seurat object. Each name corresponds to a column name in AnnData's var, and each value to a column name in Seurat's feature metadata. See section "$var mapping" for more details.
obsm_mapping: A named vector mapping obsm keys in AnnData to reduction names in the Seurat object. Each name corresponds to a key in AnnData's obsm, and each value to the name of a reduction in Seurat. Example: obsm_mapping = c(X_pca = "pca", X_umap = "umap").
varm_mapping: A named vector mapping varm keys in AnnData to reduction names in the Seurat object. Each name corresponds to a key in AnnData's varm, and each value to the name of a reduction in Seurat. Example: varm_mapping = c(PCs = "pca").
obsp_mapping: A named vector mapping obsp keys in AnnData to graph names in the Seurat object. Each name corresponds to a key in AnnData's obsp, and each value to the name of a graph in Seurat. Example: obsp_mapping = c(connectivities = "RNA_nn").
varp_mapping: A named vector mapping varp keys in AnnData to misc data names in the Seurat object. Each name corresponds to a key in AnnData's varp, and each value to the name of misc data in Seurat. Example: varp_mapping = c(foo = "foo_data").
uns_mapping: A named vector mapping uns keys in AnnData to misc data names in the Seurat object. Each name corresponds to a key in AnnData's uns, and each value to the name of misc data in Seurat. Example: uns_mapping = c(metadata = "project_meta").
...: Additional arguments passed to the generator function.

Value

An AnnData object

Details

For more information on the functionality of an AnnData object, see anndataR-package.

`$X` mapping

A mapping of a Seurat layer to the AnnData X slot. Its value must be NULL or a character vector of the Seurat layer name to copy. If NULL, no data will be copied to the X slot.

`$layers` mapping

A named list to map AnnData layers to Seurat layers. Each item in the list must be a character vector of length 1. The $X key maps to the X slot.

Example: layers_mapping = c(counts = "counts", foo = "bar").

If NULL, the internal function .from_Seurat_guess_layers will be used to guess the layer mapping as follows:

All Seurat layers are copied to AnnData layers by name.
This means that the AnnData X slot will be NULL (empty). If you want to copy data to the X slot, you must define the layer mapping explicitly.

`$obs` mapping

A named list or vector to map Seurat object-level metadata to AnnData $obs. The values of this list or vector correspond to the names of the metadata in the Seurat object, and the names correspond to the names of the metadata in the resulting $obs slot.

Example: obs_mapping = c(cellType = "cell_type").

If NULL, the internal function .from_Seurat_guess_obs will be used to guess the obs mapping as follows:

All Seurat object-level metadata is copied to AnnData $obs by name.

`$var` mapping

A named list or vector to map Seurat feature-level metadata to AnnData $var. The values of this list or vector correspond to the names of the metadata of the assay in the Seurat object, and the names correspond to the names of the metadata in the resulting $var slot.

Example: var_mapping = c(geneInfo = "gene_info").

If NULL, the internal function .from_Seurat_guess_vars will be used to guess the var mapping as follows:

All Seurat feature-level metadata is copied to AnnData $var by name.

`$obsm` mapping

A named vector mapping obsm keys in AnnData to reduction names in the Seurat object.

Each name corresponds to a key in AnnData's obsm, and each value to the name of a reduction in Seurat.

Example: obsm_mapping = c(pca = "pca", umap = "umap").

If NULL, the internal function .from_Seurat_guess_obsms will be used to guess the obsm mapping as follows:

All Seurat reductions are copied to AnnData $obsm.

`$varm` mapping

A named vector mapping varm keys in AnnData to reduction names in the Seurat object.

Each name corresponds to a key in AnnData's varm, and each value to the name of a reduction in Seurat.

Example: varm_mapping = c(PCs = "pca").

If NULL, the internal function .from_Seurat_guess_varms will be used to guess the varm mapping as follows:

The name of the PCA loadings is copied by name.

`$obsp` mapping

A named vector mapping obsp keys in AnnData to graph names in the Seurat object.

Each name corresponds to a key in AnnData's obsp, and each value to the name of a graph in Seurat.

Example: obsp_mapping = c(connectivities = "RNA_nn").

If NULL, the internal function .from_Seurat_guess_obsps will be used to guess the obsp mapping as follows:

All Seurat graphs are copied to $obsp by name.

`$varp` mapping

A named vector mapping varp keys in AnnData to misc data names in the Seurat object.

Each name corresponds to a key in AnnData's varp, and each value to the name of misc data in Seurat.

Example: varp_mapping = c(foo = "foo_data").

If NULL, the internal function .from_Seurat_guess_varps will be used to guess the varp mapping as follows:

No data is mapped to $varp.

`$uns` mapping

A named vector mapping uns keys in AnnData to misc data names in the Seurat object.

Each name corresponds to a key in AnnData's uns, and each value to the name of misc data in Seurat.

Example: uns_mapping = c(metadata = "project_meta").

If NULL, the internal function .from_Seurat_guess_uns will be used to guess the uns mapping as follows:

All Seurat miscellaneous data is copied to uns by name.

Mapping details

If an unnamed vector is provided to a mapping argument the values will be used as names

Examples

library(Seurat)
#> Loading required package: SeuratObject
#> Loading required package: sp
#> 
#> Attaching package: ‘SeuratObject’
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, t

counts <- matrix(rbinom(20000, 1000, .001), nrow = 100)
obj <- CreateSeuratObject(counts = counts)
#> Warning: Data is of class matrix. Coercing to dgCMatrix.
obj <- NormalizeData(obj)
#> Normalizing layer: counts
obj <- FindVariableFeatures(obj)
#> Finding variable features for layer counts
obj <- ScaleData(obj)
#> Centering and scaling data matrix
obj <- RunPCA(obj, npcs = 10L)
#> PC_ 1 
#> Positive:  Feature72, Feature61, Feature35, Feature78, Feature52, Feature50, Feature66, Feature8, Feature14, Feature32 
#> 	   Feature70, Feature1, Feature56, Feature87, Feature19, Feature97, Feature7, Feature17, Feature86, Feature48 
#> 	   Feature81, Feature88, Feature43, Feature67, Feature36, Feature3, Feature26, Feature54, Feature40, Feature33 
#> Negative:  Feature9, Feature29, Feature4, Feature39, Feature15, Feature27, Feature34, Feature68, Feature62, Feature18 
#> 	   Feature22, Feature46, Feature10, Feature74, Feature49, Feature37, Feature12, Feature23, Feature95, Feature100 
#> 	   Feature64, Feature30, Feature90, Feature55, Feature47, Feature96, Feature5, Feature31, Feature60, Feature92 
#> PC_ 2 
#> Positive:  Feature41, Feature89, Feature45, Feature98, Feature33, Feature44, Feature68, Feature91, Feature18, Feature59 
#> 	   Feature61, Feature86, Feature32, Feature4, Feature51, Feature42, Feature54, Feature67, Feature56, Feature21 
#> 	   Feature53, Feature70, Feature8, Feature31, Feature22, Feature14, Feature12, Feature76, Feature62, Feature58 
#> Negative:  Feature97, Feature13, Feature48, Feature69, Feature49, Feature82, Feature7, Feature5, Feature79, Feature93 
#> 	   Feature94, Feature84, Feature23, Feature78, Feature27, Feature40, Feature26, Feature50, Feature81, Feature46 
#> 	   Feature29, Feature10, Feature71, Feature72, Feature52, Feature88, Feature87, Feature9, Feature95, Feature16 
#> PC_ 3 
#> Positive:  Feature3, Feature93, Feature2, Feature5, Feature11, Feature79, Feature66, Feature74, Feature50, Feature36 
#> 	   Feature78, Feature12, Feature45, Feature98, Feature51, Feature16, Feature83, Feature28, Feature27, Feature9 
#> 	   Feature8, Feature95, Feature33, Feature63, Feature21, Feature87, Feature40, Feature53, Feature85, Feature57 
#> Negative:  Feature58, Feature90, Feature46, Feature81, Feature44, Feature56, Feature69, Feature86, Feature37, Feature97 
#> 	   Feature92, Feature26, Feature88, Feature30, Feature96, Feature42, Feature14, Feature25, Feature71, Feature34 
#> 	   Feature70, Feature82, Feature23, Feature35, Feature13, Feature19, Feature80, Feature4, Feature29, Feature67 
#> PC_ 4 
#> Positive:  Feature96, Feature11, Feature78, Feature59, Feature31, Feature57, Feature19, Feature80, Feature41, Feature4 
#> 	   Feature55, Feature44, Feature60, Feature90, Feature32, Feature50, Feature1, Feature14, Feature9, Feature77 
#> 	   Feature85, Feature28, Feature82, Feature99, Feature25, Feature8, Feature27, Feature62, Feature64, Feature29 
#> Negative:  Feature20, Feature18, Feature81, Feature100, Feature93, Feature10, Feature15, Feature74, Feature83, Feature33 
#> 	   Feature58, Feature6, Feature72, Feature89, Feature7, Feature35, Feature53, Feature37, Feature42, Feature43 
#> 	   Feature76, Feature46, Feature98, Feature73, Feature52, Feature88, Feature63, Feature49, Feature17, Feature22 
#> PC_ 5 
#> Positive:  Feature77, Feature40, Feature9, Feature21, Feature38, Feature65, Feature80, Feature22, Feature72, Feature18 
#> 	   Feature50, Feature71, Feature1, Feature83, Feature82, Feature53, Feature67, Feature63, Feature90, Feature13 
#> 	   Feature4, Feature61, Feature56, Feature84, Feature43, Feature54, Feature19, Feature94, Feature37, Feature33 
#> Negative:  Feature30, Feature51, Feature86, Feature69, Feature45, Feature14, Feature3, Feature26, Feature57, Feature23 
#> 	   Feature99, Feature60, Feature66, Feature36, Feature15, Feature68, Feature85, Feature29, Feature78, Feature24 
#> 	   Feature16, Feature70, Feature42, Feature41, Feature48, Feature79, Feature10, Feature2, Feature49, Feature97 
obj <- FindNeighbors(obj)
#> Computing nearest neighbor graph
#> Computing SNN
obj <- RunUMAP(obj, dims = 1:10)
#> Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
#> To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
#> This message will be shown once per session
#> 09:33:47 UMAP embedding parameters a = 0.9922 b = 1.112
#> 09:33:47 Read 200 rows and found 10 numeric columns
#> 09:33:47 Using Annoy for neighbor search, n_neighbors = 30
#> 09:33:47 Building Annoy index with metric = cosine, n_trees = 50
#> 0%   10   20   30   40   50   60   70   80   90   100%
#> [----|----|----|----|----|----|----|----|----|----|
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#> |
#> 09:33:47 Writing NN index file to temp file /tmp/Rtmp8vIMpj/file1d436609c2d6
#> 09:33:47 Searching Annoy index using 1 thread, search_k = 3000
#> 09:33:47 Annoy recall = 100%
#> 09:33:47 Commencing smooth kNN distance calibration using 1 thread
#>  with target n_neighbors = 30
#> 09:33:48 Initializing from normalized Laplacian + noise (using RSpectra)
#> 09:33:48 Commencing optimization for 500 epochs, with 6160 positive edges
#> 09:33:48 Using rng type: pcg
#> 09:33:48 Optimization finished
from_Seurat(obj)
#> AnnData object with n_obs × n_vars = 200 × 100
#>     obs: 'orig.ident', 'nCount_RNA', 'nFeature_RNA'
#>     var: 'vf_vst_counts_mean', 'vf_vst_counts_variance', 'vf_vst_counts_variance.expected', 'vf_vst_counts_variance.standardized', 'vf_vst_counts_variable', 'vf_vst_counts_rank', 'var.features', 'var.features.rank'
#>     obsm: 'pca', 'umap'
#>     varm: 'pca'
#>     layers: 'counts', 'data', 'scale.data'
#>     obsp: 'nn', 'snn'

Convert a Seurat object to an AnnData object

Usage

Arguments

Value

Details

$X mapping

$layers mapping

$obs mapping

$var mapping

$obsm mapping

$varm mapping

$obsp mapping

$varp mapping

$uns mapping