The SpatialExperiment class is an R/Bioconductor S4 class for storing
data from spatial -omics experiments. The class
extends the SingleCellExperiment class for single-cell data to support
storage and retrieval of additional information from spot-based and
molecule-based platforms, including spatial coordinates, images, and
image metadata. A specialized constructor function is included for data
from the 10x Genomics Visium platform.
The following schematic illustrates the SpatialExperiment class
structure.
Figure 1: Overview of the SpatialExperiment class structure
As shown, an object consists of: (i) assays containing expression counts,
(ii) rowData containing information on features, i.e. genes, (iii)
colData containing information on spots or cells, including nonspatial
and spatial metadata, (iv) spatialCoords containing spatial coordinates,
and (v) imgData containing image data. For spot-based ST data (e.g. 10x
Genomics Visium), a single assay named counts is used. For molecule-based
ST data (e.g. seqFISH), two assays named counts and molecules can be used.
Additional details on the class structure are provided in our preprint.
For demonstration of the general class structure, we load an example
SpatialExperiment (abbreviated as SPE) object (variable spe):
library(SpatialExperiment)
example(SpatialExperiment, echo = FALSE)
spe
## class: SpatialExperiment
## dim: 50 99
## metadata(4): resources spatialList resources spatialList
## assays(1): counts
## rownames(50): ENSMUSG00000051951 ENSMUSG00000089699 ...
## ENSMUSG00000005886 ENSMUSG00000101476
## rowData names(3): ID Symbol Type
## colnames(99): AAACAACGAATAGTTC-1 AAACAAGTATCTCCCA-1 ...
## AAAGTCGACCCTCAGT-1 AAAGTGCCATCAATTA-1
## colData names(4): in_tissue array_row array_col sample_id
## reducedDimNames(0):
## mainExpName: Gene Expression
## altExpNames(0):
## spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
## imgData names(4): sample_id image_id data scaleFactor
spatialCoordsIn addition to observation metadata stored inside the colData slot,
the SpatialExperiment class stores spatial coordinates as:
spatialCoords, a numeric matrix of spatial coordinates (e.g. x and y)spatialCoords are stored inside the int_colData, and are directly
accessible via the corresponding accessor:
head(spatialCoords(spe))
## pxl_col_in_fullres pxl_row_in_fullres
## AAACAACGAATAGTTC-1 2312 1252
## AAACAAGTATCTCCCA-1 8230 7237
## AAACAATCTACTAGCA-1 4170 1611
## AAACACCAATAACTGC-1 2519 8315
## AAACAGAGCGACTCCT-1 7679 2927
## AAACAGCTTTCAGAAG-1 1831 6400
The corresponding column names can be also accessed with spatialCoordsNames():
spatialCoordsNames(spe)
## [1] "pxl_col_in_fullres" "pxl_row_in_fullres"
imgDataAll image related data are stored inside the int_metadata’s
imgData field as a DataFrame of the following structure:
sample_id the image belongs toimage_id in order to accommodate multiple imagesdata (a SpatialImage object)scaleFactor that adjusts pixel positions of the original,The imgData() accessor can be used to retrieve
the image data stored within the object:
imgData(spe)
## DataFrame with 2 rows and 4 columns
## sample_id image_id data scaleFactor
## <character> <character> <list> <numeric>
## 1 section1 lowres #### 0.0510334
## 2 section2 lowres #### 0.0510334
SpatialImage classImages are stored inside the data field of the imgData as a list of
SpatialImages. Each image may be of one of the following sub-classes:
LoadedSpatialImage
raster object@image contains a raster object: a matrix
of RGB colors for each pixel in the imageStoredSpatialImage
@path specifies a local file from which to retrieve the imageRemoteSpatialImage
@url specifies where to retrieve the image fromA SpatialImage can be accessed using getImg(),
or retrieved directly from the imgData():
(spi <- getImg(spe))
## 576 x 600 (width x height) StoredSpatialImage
## imgSource():
## /private/tmp/RtmpJTGtJs/Rinst91128d4dc5d/SpatialExperiment/extdata/10xVisium/sec
## tion1/outs/spatial/tissue_lowres_image.png
identical(spi, imgData(spe)$data[[1]])
## [1] TRUE
Data available in an object of class SpatialImage may be
accessed via the imgRaster() and imgSource() accessors:
plot(imgRaster(spe))
Image entries may be added or removed from a SpatialExperiment’s
imgData DataFrame using addImg() and rmvImg(), respectively.
Besides a path or URL to source the image from and a numeric scale factor,
addImg() requires specification of the sample_id the new image belongs to,
and an image_id that is not yet in use for that sample:
url <- "https://i.redd.it/3pw5uah7xo041.jpg"
spe <- addImg(spe,
sample_id = "section1",
image_id = "pomeranian",
imageSource = url,
scaleFactor = NA_real_,
load = TRUE)
img <- imgRaster(spe,
sample_id = "section1",
image_id = "pomeranian")
plot(img)
The rmvImg() function is more flexible in the specification
of the sample_id and image_id arguments. Specifically:
TRUE is equivalent to all, e.g.sample_id = "<sample>", image_id = TRUENULL defaults to the first entry available, e.g.sample_id = "<sample>", image_id = NULLFor example, sample_id = TRUE, image_id = TRUE will specify all images;
sample_id = NULL, image_id = NULL corresponds to the first image entry in the imgData;
sample_id = TRUE, image_id = NULL equals the first image for all samples; and
sample_id = NULL, image_id = TRUE matches all images for the first sample.
Here, we remove section1’s pomeranian image added in the previous
code chunk; the image is now completely gone from the imgData:
imgData(spe <- rmvImg(spe, "section1", "pomeranian"))
## DataFrame with 2 rows and 4 columns
## sample_id image_id data scaleFactor
## <character> <character> <list> <numeric>
## 1 section1 lowres #### 0.0510334
## 2 section2 lowres #### 0.0510334
The SpatialExperiment constructor provides several arguments
to give maximum flexibility to the user.
In particular, these include:
spatialCoords, a numeric matrix containing spatial coordinatesspatialCoordsNames, a character vector specifying whichcolData fields correspond to spatial coordinatesspatialCoords can be supplied via colData
by specifying the column names that correspond to spatial coordinates
with spatialCoordsNames:
n <- length(z <- letters)
y <- matrix(nrow = n, ncol = n)
cd <- DataFrame(x = seq(n), y = seq(n), z)
spe1 <- SpatialExperiment(
assay = y,
colData = cd,
spatialCoordsNames = c("x", "y"))
Alternatively, spatialCoords may be supplied separately
as a matrix, e.g.:
xy <- as.matrix(cd[, c("x", "y")])
spe2 <- SpatialExperiment(
assay = y,
colData = cd["z"],
spatialCoords = xy)
Importantly, both of the above SpatialExperiment() function calls
lead to construction of the exact same object:
identical(spe1, spe2)
## [1] TRUE
Finally, spatialCoords(Names) are optional, i.e.,
we can construct a SPE using only a subset of the above arguments:
spe <- SpatialExperiment(
assays = y)
isEmpty(spatialCoords(spe))
## [1] TRUE
In general, spatialCoordsNames takes precedence over spatialCoords,
i.e., if both are supplied, the latter will be ignored. In other words,
spatialCoords are preferentially extracted from the DataFrame
provided via colData. E.g., in the following function call,
spatialCoords will be ignored, and xy-coordinates are instead extracted
from the input colData according to the specified spatialCoordsNames.
In this case, a message is also provided:
n <- 10; m <- 20
y <- matrix(nrow = n, ncol = m)
cd <- DataFrame(x = seq(m), y = seq(m))
xy <- matrix(nrow = m, ncol = 2)
colnames(xy) <- c("x", "y")
SpatialExperiment(
assay = y,
colData = cd,
spatialCoordsNames = c("x", "y"),
spatialCoords = xy)
## Both 'spatialCoords' and 'spatialCoordsNames' have been supplied;
## using 'spatialCoords'. Set either to NULL to suppress this message.
When working with spot-based ST data, such as 10x Genomics Visium or other
platforms providing images, it is possible to store the image information
in the dedicated imgData structure.
Also, the SpatialExperiment class stores a sample_id value in the
colData structure, which is possible to set with the sample_id
argument (default is “sample_01”).
Here we show how to load the default Space Ranger data files from a
10x Genomics Visium experiment, and build a SpatialExperiment object.
In particular, the readImgData() function is used to build an imgData
DataFrame to be passed to the SpatialExperiment constructor.
The sample_id used to build the imgData object must be the same one
used to build the SpatialExperiment object, otherwise an error is returned.
dir <- system.file(
file.path("extdata", "10xVisium", "section1", "outs"),
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)),
rowData = rd,
colData = DataFrame(xyz),
spatialCoordsNames = c("pxl_col_in_fullres", "pxl_row_in_fullres"),
imgData = img,
sample_id = "foo"))
## class: SpatialExperiment
## dim: 50 50
## metadata(0):
## assays(1): counts
## rownames(50): ENSMUSG00000051951 ENSMUSG00000089699 ...
## ENSMUSG00000005886 ENSMUSG00000101476
## rowData names(1): symbol
## colnames: NULL
## colData names(5): barcode in_tissue array_row array_col sample_id
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):
## spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
## imgData names(4): sample_id image_id data scaleFactor
Alternatively, VisiumIO’s TENxVisium/List()
functions facilitate the import of 10x Genomics Visium data to handle one
or more samples organized in folders reflecting the default Space Ranger
folder tree organization, as illustrated below (where “raw/filtered” refers
to either “raw” or “filtered” to match the data argument). Note that the
base directory “outs/” from Space Ranger can either be included manually in the
paths provided in the sampleFolders argument, or can be ignored; if ignored,
it will be added automatically. (Note that tissue_positions.csv was renamed
in Space Ranger v2.0.0; the tissuePattern argument can be used to specify a
pattern to match the tissue positions file.)
sample
. | — outs
· · | — raw/filtered_feature_bc_matrix.h5
· · | — raw/filtered_feature_bc_matrix
· · · | — barcodes.tsv.gz
· · · | — features.tsv.gz
· · · | — matrix.mtx.gz
· · | — spatial
· · · | — scalefactors_json.json
· · · | — tissue_lowres_image.png
· · · | — tissue_positions.csv
Using TENxVisiumList(), the above code to construct the same SPE is reduced to:
library(VisiumIO)
dir <- list.dirs(system.file(
file.path("extdata", "10xVisium"),
package = "SpatialExperiment"),
recursive = FALSE, full.names = TRUE)
(ids <- basename(dir))
## [1] "section1" "section2"
(vl <- TENxVisiumList(
sampleFolders = dir, sample_ids = ids,
processing = "raw", image = "lowres"))
## An object of class "TENxVisiumList"
## Slot "VisiumList":
## List of length 2
(spe10x <- import(vl))
## class: SpatialExperiment
## dim: 50 99
## metadata(4): resources spatialList resources spatialList
## assays(1): counts
## rownames(50): ENSMUSG00000051951 ENSMUSG00000089699 ...
## ENSMUSG00000005886 ENSMUSG00000101476
## rowData names(3): ID Symbol Type
## colnames(99): AAACAACGAATAGTTC-1 AAACAAGTATCTCCCA-1 ...
## AAAGTCGACCCTCAGT-1 AAAGTGCCATCAATTA-1
## colData names(4): in_tissue array_row array_col sample_id
## reducedDimNames(0):
## mainExpName: Gene Expression
## altExpNames(0):
## spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
## imgData names(4): sample_id image_id data scaleFactor
To demonstrate how to accommodate molecule-based ST data
(e.g. seqFISH platform) inside a SpatialExperiment object,
we generate some mock data of 1000 molecule coordinates across
50 genes and 20 cells. These should be formatted into a data.frame
where each row corresponds to a molecule, and columns specify the
xy-positions as well as which gene/cell the molecule has been assigned to:
n <- 1e3 # number of molecules
ng <- 50 # number of genes
nc <- 20 # number of cells
# sample xy-coordinates in [0, 1]
x <- runif(n)
y <- runif(n)
# assign each molecule to some gene-cell pair
gs <- paste0("gene", seq(ng))
cs <- paste0("cell", seq(nc))
gene <- sample(gs, n, TRUE)
cell <- sample(cs, n, TRUE)
# assure gene & cell are factors so that
# missing observations aren't dropped
gene <- factor(gene, gs)
cell <- factor(cell, cs)
# construct data.frame of molecule coordinates
df <- data.frame(gene, cell, x, y)
head(df)
## gene cell x y
## 1 gene4 cell7 0.60693469 0.7028649
## 2 gene43 cell6 0.34218656 0.8739626
## 3 gene13 cell19 0.85171251 0.6765979
## 4 gene1 cell5 0.69348037 0.2852427
## 5 gene43 cell11 0.72346609 0.9578094
## 6 gene9 cell9 0.09220983 0.4553273
Next, it is possible to re-shape the above table into a
BumpyMatrix using splitAsBumpyMatrix(), which takes
as input the xy-coordinates, as well as arguments specifying the row and column
index of each observation:
# construct 'BumpyMatrix'
library(BumpyMatrix)
mol <- splitAsBumpyMatrix(
df[, c("x", "y")],
row = gene, col = cell)
Finally, it is possible to construct a SpatialExperiment object with two data
slots:
counts assay stores the number of molecules per gene and cellmolecules assay holds the spatial molecule positions (xy-coordinates)Each entry in the molecules assay is a DFrame that contains the positions
of all molecules from a given gene that have been assigned to a given cell.
# get count matrix
y <- with(df, table(gene, cell))
y <- as.matrix(unclass(y))
y[1:5, 1:5]
## cell
## gene cell1 cell2 cell3 cell4 cell5
## gene1 2 0 1 2 1
## gene2 2 0 1 2 1
## gene3 2 2 0 2 2
## gene4 1 0 2 1 0
## gene5 1 1 0 1 2
# construct SpatialExperiment
spe <- SpatialExperiment(
assays = list(
counts = y,
molecules = mol))
spe
## class: SpatialExperiment
## dim: 50 20
## metadata(0):
## assays(2): counts molecules
## rownames(50): gene1 gene2 ... gene49 gene50
## rowData names(0):
## colnames(20): cell1 cell2 ... cell19 cell20
## colData names(1): sample_id
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):
## spatialCoords names(0) :
## imgData names(0):
The BumpyMatrix of molecule locations can be accessed
using the dedicated molecules() accessor:
molecules(spe)
## 50 x 20 BumpyDataFrameMatrix
## rownames: gene1 gene2 ... gene49 gene50
## colnames: cell1 cell2 ... cell19 cell20
## preview [1,1]:
## DataFrame with 2 rows and 2 columns
## x y
## <numeric> <numeric>
## 1 0.8571569 0.543502
## 2 0.0972546 0.785036
Subsetting objects is automatically defined to synchronize across all attributes, as for any other Bioconductor Experiment class.
For example, it is possible to subset by sample_id as follows:
sub <- spe10x[, spe10x$sample_id == "section1"]
Or to retain only observations that map to tissue via:
sub <- spe10x[, colData(spe10x)$in_tissue]
sum(colData(spe10x)$in_tissue) == ncol(sub)
## [1] TRUE
To work with multiple samples, the SpatialExperiment class provides the cbind
method, which assumes unique sample_id(s) are provided for each sample.
In case the sample_id(s) are duplicated across multiple samples, the cbind
method takes care of this by appending indices to create unique sample identifiers.
spe1 <- spe2 <- spe
spe3 <- cbind(spe1, spe2)
## 'sample_id's are duplicated across 'SpatialExperiment' objects to cbind; appending sample indices.
unique(spe3$sample_id)
## [1] "sample01.1" "sample01.2"
Alternatively (and preferentially), we can create unique
sample_id(s) prior to cbinding as follows:
# make sample identifiers unique
spe1 <- spe2 <- spe
spe1$sample_id <- paste(spe1$sample_id, "A", sep = ".")
spe2$sample_id <- paste(spe2$sample_id, "B", sep = ".")
# combine into single object
spe3 <- cbind(spe1, spe2)
In particular, when trying to replace the sample_id(s) of a SpatialExperiment
object, these must map uniquely with the already existing ones, otherwise an
error is returned.
new <- spe3$sample_id
new[1] <- "section2.A"
spe3$sample_id <- new
## Error in .local(x, ..., value): Number of unique 'sample_id's is 2, but 3 were provided.
new[1] <- "third.one.of.two"
spe3$sample_id <- new
## Error in .local(x, ..., value): Number of unique 'sample_id's is 2, but 3 were provided.
Importantly, the sample_id colData field is protected, i.e.,
it will be retained upon attempted removal (= replacement by NULL):
# backup original sample IDs
tmp <- spe$sample_id
# try to remove sample IDs
spe$sample_id <- NULL
# sample IDs remain unchanged
identical(tmp, spe$sample_id)
## [1] TRUE
Both the SpatialImage (SpI) and SpatialExperiment (SpE) class currently support
two basic image transformations, namely, rotation (via rotateImg()) and
mirroring (via mirrorImg()). Specifically, for a SpI/E x:
rotateImg(x, degrees) expects as degrees a single numeric in +/-[0,90,…,360].mirrorImg(x, axis) expects as axis a character string specifying"h") or vertically ("v").Here, we apply various transformations using both a SpI (spi) and SpE (spe)
as input, and compare the resulting images to the original:
# extract first image
spi <- getImg(spe10x)
# apply counter-/clockwise rotation
spi1 <- rotateImg(spi, -90)
spi2 <- rotateImg(spi, +90)
# visual comparison
par(mfrow = c(1, 3))
plot(as.raster(spi))
plot(as.raster(spi1))
plot(as.raster(spi2))
# specify sample & image identifier
sid <- "section1"
iid <- "lowres"
# counter-clockwise rotation
tmp <- rotateImg(spe10x,
sample_id = sid,
image_id = iid,
degrees = -90)
# visual comparison
par(mfrow = c(1, 2))
plot(imgRaster(spe10x, sid, iid))
plot(imgRaster(tmp, sid, iid))
# extract first image
spi <- getImg(spe10x)
# mirror horizontally/vertically
spi1 <- mirrorImg(spi, "h")
spi2 <- mirrorImg(spi, "v")
# visual comparison
par(mfrow = c(1, 3))
plot(as.raster(spi))
plot(as.raster(spi1))
plot(as.raster(spi2))
# specify sample & image identifier
sid <- "section2"
iid <- "lowres"
# mirror horizontally
tmp <- mirrorImg(spe10x,
sample_id = sid,
image_id = iid,
axis = "h")
# visual comparison
par(mfrow = c(1, 2))
plot(imgRaster(spe10x, sid, iid))
plot(imgRaster(tmp, sid, iid))
sessionInfo()
## R version 4.5.0 Patched (2025-04-21 r88169)
## Platform: x86_64-apple-darwin20
## Running under: macOS Monterey 12.7.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
##
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/New_York
## tzcode source: internal
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] BumpyMatrix_1.17.0 VisiumIO_1.5.6
## [3] TENxIO_1.11.3 SpatialExperiment_1.19.1
## [5] SingleCellExperiment_1.31.0 SummarizedExperiment_1.39.0
## [7] Biobase_2.69.0 GenomicRanges_1.61.0
## [9] GenomeInfoDb_1.45.3 IRanges_2.43.0
## [11] S4Vectors_0.47.0 BiocGenerics_0.55.0
## [13] generics_0.1.3 MatrixGenerics_1.21.0
## [15] matrixStats_1.5.0 BiocStyle_2.37.0
##
## loaded via a namespace (and not attached):
## [1] rjson_0.2.23 xfun_0.52
## [3] bslib_0.9.0 rhdf5_2.53.0
## [5] lattice_0.22-7 tzdb_0.5.0
## [7] vctrs_0.6.5 rhdf5filters_1.21.0
## [9] tools_4.5.0 curl_6.2.2
## [11] parallel_4.5.0 tibble_3.2.1
## [13] pkgconfig_2.0.3 BiocBaseUtils_1.11.0
## [15] R.oo_1.27.1 Matrix_1.7-3
## [17] sparseMatrixStats_1.21.0 dqrng_0.4.1
## [19] lifecycle_1.0.4 compiler_4.5.0
## [21] tinytex_0.57 statmod_1.5.0
## [23] codetools_0.2-20 htmltools_0.5.8.1
## [25] sass_0.4.10 yaml_2.3.10
## [27] pillar_1.10.2 crayon_1.5.3
## [29] jquerylib_0.1.4 R.utils_2.13.0
## [31] BiocParallel_1.43.0 DelayedArray_0.35.1
## [33] cachem_1.1.0 limma_3.65.0
## [35] magick_2.8.6 abind_1.4-8
## [37] locfit_1.5-9.12 digest_0.6.37
## [39] bookdown_0.43 fastmap_1.2.0
## [41] grid_4.5.0 cli_3.6.5
## [43] SparseArray_1.9.0 magrittr_2.0.3
## [45] S4Arrays_1.9.0 h5mread_1.1.0
## [47] withr_3.0.2 readr_2.1.5
## [49] edgeR_4.7.2 DelayedMatrixStats_1.31.0
## [51] UCSC.utils_1.5.0 rmarkdown_2.29
## [53] XVector_0.49.0 httr_1.4.7
## [55] DropletUtils_1.29.0 hms_1.1.3
## [57] R.methodsS3_1.8.2 beachmat_2.25.0
## [59] HDF5Array_1.37.0 evaluate_1.0.3
## [61] knitr_1.50 BiocIO_1.19.0
## [63] rlang_1.1.6 Rcpp_1.0.14
## [65] glue_1.8.0 scuttle_1.19.0
## [67] formatR_1.14 BiocManager_1.30.25
## [69] jsonlite_2.0.0 R6_2.6.1
## [71] Rhdf5lib_1.31.0