Convert a SpatialExperiment object to a Seurat object

Given a SpatialExperiment-class object, first as.Seurat() is run, which operates on SingleCellExperiment-class objects. The remaining components (images, spatial coordinates) are added manually. The actual appearance of images are buggy for now.

Usage

as.Seurat(
  spe,
  spatial_cols = c(tissue = "in_tissue", row = "array_row", col = "array_col", imagerow =
    "pxl_row_in_fullres", imagecol = "pxl_col_in_fullres"),
  verbose = TRUE
)

Arguments

spe

A SpatialExperiment-class with colData() or spatialCoords() columns given by spatial_cols. This does not have to be a stitched spe object as this function should work with any type of spe objects.

spatial_cols

A character(5) named vector mapping which colData(spe) or spatialCoords(spe) columns contain the tissue, row, col, imagerow, and imagecol information expected by Seurat.

verbose

A logical(1) vector. If TRUE, print status update about the conversion process. This information can be useful for debugging.

Value

A Seurat object.

Details

Note that only the lowres images from imgData(spe) will be used.

Author

Nicholas J. Eagles

Examples

## Download some example data
spe_unstitched <- spatialLIBD::fetch_data(
    type = "spatialDLPFC_Visium_example_subset"
)[seq(100), seq(100)]
#> 2024-11-08 18:52:26.5334 loading file /github/home/.cache/R/BiocFileCache/785d37da3295_spatialDLPFC_spe_subset_example.rds%3Fdl%3D1

## Make the column names unique
colnames(spe_unstitched) <- spatialLIBD::add_key(spe_unstitched)$key
#> Overwriting 'spe$key'. Set 'overwrite = FALSE' if you do not want to overwrite it.

## Convert from a SpatialExperiment to a Seurat object
seur <- as.Seurat(spe_unstitched)
#> Running 'as.Seurat(spe)'...
#> Registered S3 method overwritten by 'spatstat.geom':
#>   method      from  
#>   plot.imlist imager
#> Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from pc. to pc_
#> Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from tsne. to tsne_
#> Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from umap. to umap_
#> Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from PC to PC_
#> Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP to UMAP_
#> Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from TSNE_perplexity80_ to TSNEperplexity80_
#> Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from UMAP to UMAP_
#> Warning: Key ‘UMAP_’ taken, using ‘umapharmony_’ instead
#> Adding spot coordinates and images for sample Br6432_ant...
#> Returning converted object...
seur
#> An object of class Seurat 
#> 100 features across 100 samples within 1 assay 
#> Active assay: originalexp (100 features, 0 variable features)
#>  2 layers present: counts, data
#>  8 dimensional reductions calculated: X10x_pca, X10x_tsne, X10x_umap, PCA, UMAP, TSNE_perplexity80, HARMONY, UMAP.HARMONY
#>  1 image present: Br6432_ant

## Example with an stitched SPE object
if (!exists("spe")) {
    spe <- spatialLIBD::fetch_data(type = "visiumStitched_brain_spe")
}
#> 2024-11-08 18:52:32.824552 loading file /github/home/.cache/R/BiocFileCache/6c1f1cbefd45_visiumStitched_brain_spe.rds%3Frlkey%3Dnq6a82u23xuu9hohr86oodwdi%26dl%3D1
seur_stitched <- as.Seurat(spe[seq(100), seq(100)])
#> Running 'as.Seurat(spe)'...
#> Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from PC to PC_
#> Adding spot coordinates and images for sample Br2719...
#> Returning converted object...

## Let's look at our resulting Seurat object
seur_stitched
#> An object of class Seurat 
#> 100 features across 100 samples within 1 assay 
#> Active assay: originalexp (100 features, 0 variable features)
#>  2 layers present: counts, data
#>  1 dimensional reduction calculated: PCA
#>  1 image present: Br2719