Transfer SpatialExperiment object to a Seurat list object for preparation for PRECAST model fitting; see our [PRECAST package website](https://feiyoung.github.io/PRECAST/index.html) for more usage of PRECAST.
spe2seuratList(spe, batch = NULL, verbose = TRUE)a SpatialExperiment object or a list consisting of SpatialExperiment objects. If spe is a SpatialExperiment object, it must at least contain the batch(sampel) id in the colData (i.e., sample_id), the raw gene count expression matrix and spatial coordinates. And the batch must be specified (i.e., batch='sample_id'). If spe is a list consisting of multiple SpatialExperiment objects, then each object represents a data batch.
a optional argument, NULL or a string. Only if spe is a list, batch can be NULL.
an optional logical value, whether output the information.
Return a list consisting of Seurat objects, where the spatial coordinates information is saved in the metadata of Seurat, named "row" and "col".
suppressPackageStartupMessages( library(SpatialExperiment))
dir <- system.file(
file.path("extdata", "10xVisium", "section1"),
package = "SpatialExperiment")
# read in counts
fnm <- file.path(dir, "raw_feature_bc_matrix")
sce <- DropletUtils::read10xCounts(fnm)
# read in image data
img <- readImgData(
path = file.path(dir, "spatial"),
sample_id="foo")
# read in spatial coordinates
fnm <- file.path(dir, "spatial", "tissue_positions_list.csv")
xyz <- read.csv(fnm, header = FALSE,
col.names = c(
"barcode", "in_tissue", "array_row", "array_col",
"pxl_row_in_fullres", "pxl_col_in_fullres"))
# construct observation & feature metadata
rd <- S4Vectors::DataFrame(
symbol = rowData(sce)$Symbol)
# construct 'SpatialExperiment'
(spe <- SpatialExperiment(
assays = list(counts = assay(sce)),
colData = colData(sce), rowData = rd, imgData = img,
spatialData=DataFrame(xyz),
spatialCoordsNames=c("pxl_col_in_fullres", "pxl_row_in_fullres"),
sample_id="foo"))
#> Found more than one class "SpatialImage" in cache; using the first, from namespace 'SeuratObject'
#> Also defined by ‘SpatialExperiment’
#> class: SpatialExperiment
#> dim: 50 50
#> metadata(0):
#> assays(1): counts
#> rownames(50): ENSMUSG00000051951 ENSMUSG00000089699 ...
#> ENSMUSG00000005886 ENSMUSG00000101476
#> rowData names(1): symbol
#> colnames: NULL
#> colData names(3): Sample Barcode sample_id
#> Found more than one class "SpatialImage" in cache; using the first, from namespace 'SeuratObject'
#> Also defined by ‘SpatialExperiment’
#> reducedDimNames(0):
#> mainExpName: NULL
#> Found more than one class "SpatialImage" in cache; using the first, from namespace 'SeuratObject'
#> Also defined by ‘SpatialExperiment’
#> altExpNames(0):
#> spatialData names(4) : barcode in_tissue array_row array_col
#> spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
#> imgData names(4): sample_id image_id data scaleFactor
colnames(spe) <- paste0("spot", 1:ncol(spe))
## Transfer a SpatialExperiment to a seuList
colData(spe)$batch_id <- rep(c("a", "b"), each=25)
#> Found more than one class "SpatialImage" in cache; using the first, from namespace 'SeuratObject'
#> Also defined by ‘SpatialExperiment’
seuList1 <- spe2seuratList(spe, batch = 'batch_id')
#> Transfer one SpatialExperiment object by its samples(batches) to a Seurat list object for preparation for PRECAST model fitting.
#> Found more than one class "SpatialImage" in cache; using the first, from namespace 'SeuratObject'
#> Also defined by ‘SpatialExperiment’
#> Found more than one class "SpatialImage" in cache; using the first, from namespace 'SeuratObject'
#> Also defined by ‘SpatialExperiment’
#> Transfer SpatialExperiment object to a Seurat object
#> Found more than one class "SpatialImage" in cache; using the first, from namespace 'SeuratObject'
#> Also defined by ‘SpatialExperiment’
#> Found more than one class "SpatialImage" in cache; using the first, from namespace 'SeuratObject'
#> Also defined by ‘SpatialExperiment’
#> Transfer SpatialExperiment object to a Seurat object
seuList1
#> [[1]]
#> An object of class Seurat
#> 50 features across 25 samples within 1 assay
#> Active assay: RNA (50 features, 0 variable features)
#>
#> [[2]]
#> An object of class Seurat
#> 50 features across 25 samples within 1 assay
#> Active assay: RNA (50 features, 0 variable features)
#>
## Transfer a list of SpatialExperiment to a seuList
seuList2 <- spe2seuratList(list(spe, spe))
#> Transfer one SpatialExperiment object by its samples(batches) to a Seurat list object for preparation for PRECAST model fitting.
#>
| | 0 % ~calculating
#> Transfer SpatialExperiment object to a Seurat object
#>
|+++++++++++++++++++++++++ | 50% ~00s
#> Transfer SpatialExperiment object to a Seurat object
#>
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
seuList2
#> [[1]]
#> An object of class Seurat
#> 50 features across 50 samples within 1 assay
#> Active assay: RNA (50 features, 0 variable features)
#>
#> [[2]]
#> An object of class Seurat
#> 50 features across 50 samples within 1 assay
#> Active assay: RNA (50 features, 0 variable features)
#>
## Create PRECAST object
library(PRECAST)
Pobject <- CreatePRECASTObject(seuList=seuList2, selectGenesMethod = "HVGs",
premin.spots = 0, premin.features = 0,
postmin.features = 0, postmin.spots = 0, verbose = F)
#>
| | 0 % ~calculating
|+++++++++++++++++++++++++ | 50% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
#>
#>
#>
| | 0 % ~calculating
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s
#> Warning: Number of genes in one of sample is less than 2000, so set minimum number of SVGs as gene.number=31
#>
| | 0 % ~calculating
|+++++++++++++++++++++++++ | 50% ~00s
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