Introduction

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.

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.

Load data

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

spatialCoords

In 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"

imgData

All image related data are stored inside the int_metadata’s imgData field as a DataFrame of the following structure:

• each row corresponds to one image for a given sample
  and with a given unique image identifier (e.g. its resolutions)
• for each image, columns specify:
  • which sample_id the image belongs to
  • a unique image_id in order to accommodate multiple images
    for a given sample (e.g. of different resolutions)
  • the image’s data (a SpatialImage object)
  • the scaleFactor that adjusts pixel positions of the original,
    full-resolution image to pixel positions in the image

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

(spi <- getImg(spe))

## 576 x 600 (width x height) StoredSpatialImage
## imgSource():
##   /tmp/Rtmpo1cZ2h/Rinst73f217139703/SpatialExperiment/extdata/10xVisium/section1/o
##   uts/spatial/tissue_lowres_image.png

identical(spi, imgData(spe)$data[[1]])

## [1] TRUE

plot(imgRaster(spe))

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)

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

Manually

The SpatialExperiment constructor provides several arguments to give maximum flexibility to the user.

In particular, these include:

• spatialCoords, a numeric matrix containing spatial coordinates
• spatialCoordsNames, a character vector specifying which
  colData fields correspond to spatial coordinates

spatialCoords 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.

Spot-based

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

Molecule-based

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 gene17  cell8 0.22781822 0.35455740
## 2 gene49  cell7 0.49572883 0.59307591
## 3 gene17 cell18 0.42309175 0.03447203
## 4  gene9  cell3 0.53260651 0.33533213
## 5 gene19 cell18 0.01282301 0.39869615
## 6 gene25  cell5 0.62507345 0.65338405

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:

• The counts assay stores the number of molecules per gene and cell
  (equivalent to transcript counts in spot-based data)
• The molecules 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     0     0     0     1     1
##   gene2     0     1     1     0     2
##   gene3     0     2     1     1     0
##   gene4     3     1     0     2     1
##   gene5     1     3     0     1     1

# 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 0 rows and 2 columns

Subsetting

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

Combining samples

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)

Sample ID replacement

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

Image transformations

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].
  Here, a (negative) positive value corresponds to (counter-)clockwise rotation.
• mirrorImg(x, axis) expects as axis a character string specifying
  whether to mirror horizontally ("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))