1 Motivation

The SingleCellExperiment class is a lightweight Bioconductor container for storing and manipulating single-cell genomics data. It extends the RangedSummarizedExperiment class and follows similar conventions, i.e., rows should represent features (genes, transcripts, genomic regions) and columns should represent cells. It provides methods for storing dimensionality reduction results and data for alternative feature sets (e.g., synthetic spike-in transcripts, antibody-derived tags). It is the central data structure for Bioconductor single-cell packages like scater and scran.

2 Creating SingleCellExperiment instances

SingleCellExperiment objects can be created via the constructor of the same name. For example, if we have a count matrix in counts, we can simply call:

library(SingleCellExperiment)
counts <- matrix(rpois(100, lambda = 10), ncol=10, nrow=10)
sce <- SingleCellExperiment(counts)
sce
## class: SingleCellExperiment 
## dim: 10 10 
## metadata(0):
## assays(1): ''
## rownames: NULL
## rowData names(0):
## colnames: NULL
## colData names(0):
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):

In practice, it is often more useful to name the assay by passing in a named list:

sce <- SingleCellExperiment(list(counts=counts))
sce
## class: SingleCellExperiment 
## dim: 10 10 
## metadata(0):
## assays(1): counts
## rownames: NULL
## rowData names(0):
## colnames: NULL
## colData names(0):
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):

It is similarly easy to set the column and row metadata by passing values to the appropriate arguments. We will not go into much detail here as most of this is covered by the SummarizedExperiment documentation, but to give an example:

pretend.cell.labels <- sample(letters, ncol(counts), replace=TRUE)
pretend.gene.lengths <- sample(10000, nrow(counts))

sce <- SingleCellExperiment(list(counts=counts),
    colData=DataFrame(label=pretend.cell.labels),
    rowData=DataFrame(length=pretend.gene.lengths),
    metadata=list(study="GSE111111")
)
sce
## class: SingleCellExperiment 
## dim: 10 10 
## metadata(1): study
## assays(1): counts
## rownames: NULL
## rowData names(1): length
## colnames: NULL
## colData names(1): label
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):

Alternatively, we can construct a SingleCellExperiment by coercing an existing (Ranged)SummarizedExperiment object:

se <- SummarizedExperiment(list(counts=counts))
as(se, "SingleCellExperiment")
## class: SingleCellExperiment 
## dim: 10 10 
## metadata(0):
## assays(1): counts
## rownames: NULL
## rowData names(0):
## colnames: NULL
## colData names(0):
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):

Any operation that can be applied to a RangedSummarizedExperiment is also applicable to any instance of a SingleCellExperiment. This includes access to assay data via assay(), column metadata with colData(), and so on. Again, without going into too much detail:

dim(assay(sce))
## [1] 10 10
colnames(colData(sce))
## [1] "label"
colnames(rowData(sce))
## [1] "length"

To demonstrate the use of the class in the rest of the vignette, we will use the Allen data set from the scRNAseq package.

library(scRNAseq)
sce <- ReprocessedAllenData("tophat_counts")
sce
## class: SingleCellExperiment 
## dim: 20816 379 
## metadata(2): SuppInfo which_qc
## assays(1): tophat_counts
## rownames(20816): 0610007P14Rik 0610009B22Rik ... Zzef1 Zzz3
## rowData names(0):
## colnames(379): SRR2140028 SRR2140022 ... SRR2139341 SRR2139336
## colData names(22): NREADS NALIGNED ... Animal.ID passes_qc_checks_s
## reducedDimNames(0):
## mainExpName: endogenous
## altExpNames(1): ERCC

3 Adding low-dimensional representations

We compute log-transformed normalized expression values from the count matrix. (We note that many of these steps can be performed as one-liners from the scater package, but we will show them here in full to demonstrate the capabilities of the SingleCellExperiment class.)

counts <- assay(sce, "tophat_counts")
libsizes <- colSums(counts)
size.factors <- libsizes/mean(libsizes)
logcounts(sce) <- log2(t(t(counts)/size.factors) + 1)
assayNames(sce)
## [1] "tophat_counts" "logcounts"

We obtain the PCA and t-SNE representations of the data and add them to the object with the reducedDims()<- method. Alternatively, we can representations one at a time with the reducedDim()<- method (note the missing s).

pca_data <- prcomp(t(logcounts(sce)), rank=50)

library(Rtsne)
set.seed(5252)
tsne_data <- Rtsne(pca_data$x[,1:50], pca = FALSE)

reducedDims(sce) <- list(PCA=pca_data$x, TSNE=tsne_data$Y)
sce
## class: SingleCellExperiment 
## dim: 20816 379 
## metadata(2): SuppInfo which_qc
## assays(2): tophat_counts logcounts
## rownames(20816): 0610007P14Rik 0610009B22Rik ... Zzef1 Zzz3
## rowData names(0):
## colnames(379): SRR2140028 SRR2140022 ... SRR2139341 SRR2139336
## colData names(22): NREADS NALIGNED ... Animal.ID passes_qc_checks_s
## reducedDimNames(2): PCA TSNE
## mainExpName: endogenous
## altExpNames(1): ERCC

The coordinates for all representations can be retrieved from a SingleCellExperiment en masse with reducedDims() or one at a time by name/index with reducedDim(). Each row of the coordinate matrix is assumed to correspond to a cell while each column represents a dimension.

reducedDims(sce)
## List of length 2
## names(2): PCA TSNE
reducedDimNames(sce)
## [1] "PCA"  "TSNE"
head(reducedDim(sce, "PCA")[,1:2])
##                   PC1       PC2
## SRR2140028  -70.23670  33.78880
## SRR2140022  -41.00366  49.76633
## SRR2140055 -133.70763   7.68870
## SRR2140083  -36.26099 113.18806
## SRR2139991 -100.86439  15.94740
## SRR2140067  -97.71210  35.77881
head(reducedDim(sce, "TSNE")[,1:2])
##                 [,1]      [,2]
## SRR2140028 10.291952 -0.573862
## SRR2140022  6.158490 -1.280543
## SRR2140055 -4.611946  5.735881
## SRR2140083  6.502360  6.623059
## SRR2139991  8.488418 -1.501760
## SRR2140067  1.832036 -3.197005

Any subsetting by column of sce_sub will also lead to subsetting of the dimensionality reduction results by cell. This is convenient as it ensures our low-dimensional results are always synchronized with the gene expression data.

dim(reducedDim(sce, "PCA"))
## [1] 379  50
dim(reducedDim(sce[,1:10], "PCA"))
## [1] 10 50

4 Convenient access to named assays

In the SingleCellExperiment, users can assign arbitrary names to entries of assays. To assist interoperability between packages, we provide some suggestions for what the names should be for particular types of data:

  • counts: Raw count data, e.g., number of reads or transcripts for a particular gene.
  • normcounts: Normalized values on the same scale as the original counts. For example, counts divided by cell-specific size factors that are centred at unity.
  • logcounts: Log-transformed counts or count-like values. In most cases, this will be defined as log-transformed normcounts, e.g., using log base 2 and a pseudo-count of 1.
  • cpm: Counts-per-million. This is the read count for each gene in each cell, divided by the library size of each cell in millions.
  • tpm: Transcripts-per-million. This is the number of transcripts for each gene in each cell, divided by the total number of transcripts in that cell (in millions).

Each of these suggested names has an appropriate getter/setter method for convenient manipulation of the SingleCellExperiment. For example, we can take the (very specifically named) tophat_counts name and assign it to counts instead:

counts(sce) <- assay(sce, "tophat_counts")
sce
## class: SingleCellExperiment 
## dim: 20816 379 
## metadata(2): SuppInfo which_qc
## assays(3): tophat_counts logcounts counts
## rownames(20816): 0610007P14Rik 0610009B22Rik ... Zzef1 Zzz3
## rowData names(0):
## colnames(379): SRR2140028 SRR2140022 ... SRR2139341 SRR2139336
## colData names(22): NREADS NALIGNED ... Animal.ID passes_qc_checks_s
## reducedDimNames(2): PCA TSNE
## mainExpName: endogenous
## altExpNames(1): ERCC
dim(counts(sce))
## [1] 20816   379

This means that functions expecting count data can simply call counts() without worrying about package-specific naming conventions.

5 Adding alternative feature sets

Many scRNA-seq experiments contain sequencing data for multiple feature types beyond the endogenous genes:

  • Externally added spike-in transcripts for plate-based experiments.
  • Antibody tags for CITE-seq experiments.
  • CRISPR tags for CRISPR-seq experiments.
  • Allele information for experiments involving multiple genotypes.

Such features can be stored inside the SingleCellExperiment via the concept of “alternative Experiments”. These are nested SummarizedExperiment instances that are guaranteed to have the same number and ordering of columns as the main SingleCellExperiment itself. Data for endogenous genes and other features can thus be kept separate - which is often desirable as they need to be processed differently - while still retaining the synchronization of operations on a single object.

To illustrate, consider the case of the spike-in transcripts in the Allen data. The altExp() method returns a self-contained SingleCellExperiment instance containing only the spike-in transcripts.

altExp(sce)
## class: SingleCellExperiment 
## dim: 92 379 
## metadata(2): SuppInfo which_qc
## assays(1): tophat_counts
## rownames(92): ERCC-00002 ERCC-00003 ... ERCC-00170 ERCC-00171
## rowData names(0):
## colnames(379): SRR2140028 SRR2140022 ... SRR2139341 SRR2139336
## colData names(0):
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):

Each alternative Experiment can have a different set of assays from the main SingleCellExperiment. This is useful in cases where the other feature types must be normalized or transformed differently. Similarly, the alternative Experiments can have different rowData() from the main object.

rowData(altExp(sce))$concentration <- runif(nrow(altExp(sce)))
rowData(altExp(sce))
## DataFrame with 92 rows and 1 column
##            concentration
##                <numeric>
## ERCC-00002      0.034970
## ERCC-00003      0.979286
## ERCC-00004      0.171510
## ERCC-00009      0.577606
## ERCC-00012      0.965111
## ...                  ...
## ERCC-00164     0.2945227
## ERCC-00165     0.4449455
## ERCC-00168     0.2821027
## ERCC-00170     0.0817114
## ERCC-00171     0.4178510
rowData(sce)
## DataFrame with 20816 rows and 0 columns

We provide the splitAltExps() utility to easily split a SingleCellExperiment into new alternative Experiments. For example, if we wanted to split the RIKEN transcripts into a separate Experiment - say, to ensure that they are not used in downstream analyses without explicitly throwing out the data - we would do:

is.riken <- grepl("^[0-9]", rownames(sce))
sce <- splitAltExps(sce, ifelse(is.riken, "RIKEN", "gene"))
altExpNames(sce)
## [1] "ERCC"  "RIKEN"

Alternatively, if we want to swap the main and alternative Experiments - perhaps because the RIKEN transcripts were more interesting than expected, and we want to perform our analyses on them - we can simply use swapAltExp() to switch the RIKEN alternative Experiment with the gene expression data:

swapAltExp(sce, "RIKEN", saved="original")
## class: SingleCellExperiment 
## dim: 611 379 
## metadata(2): SuppInfo which_qc
## assays(3): tophat_counts logcounts counts
## rownames(611): 0610007P14Rik 0610009B22Rik ... 9930111J21Rik1
##   9930111J21Rik2
## rowData names(0):
## colnames(379): SRR2140028 SRR2140022 ... SRR2139341 SRR2139336
## colData names(22): NREADS NALIGNED ... Animal.ID passes_qc_checks_s
## reducedDimNames(2): PCA TSNE
## mainExpName: RIKEN
## altExpNames(2): ERCC original

6 Storing row or column pairings

A common procedure in single-cell analyses is to identify relationships between pairs of cells, e.g., to construct a nearest-neighbor graph or to mark putative physical interactions between cells. We can capture this information in the SingleCellExperiment class with the colPairs() functionality. To demonstrate, say we have 100 relationships between the cells in sce, characterized by some distance measure:

cell1 <- sample(ncol(sce), 100, replace=TRUE)
cell2 <- sample(ncol(sce), 100, replace=TRUE)
distance <- runif(100)

We store this in the SingleCellExperiment as a SelfHits object using the value metadata field to hold our data. This is easily extracted as a SelfHits or, for logical or numeric data, as a sparse matrix from Matrix.

colPair(sce, "relationships") <- SelfHits(
    cell1, cell2, nnode=ncol(sce), value=distance)
colPair(sce, "relationships")
## SelfHits object with 100 hits and 1 metadata column:
##              from        to |     value
##         <integer> <integer> | <numeric>
##     [1]         2       224 | 0.3155391
##     [2]         2       311 | 0.0554668
##     [3]        12       200 | 0.6148850
##     [4]        16       304 | 0.0174597
##     [5]        21       244 | 0.9801941
##     ...       ...       ... .       ...
##    [96]       349       369 |  0.662352
##    [97]       349       372 |  0.839914
##    [98]       352        10 |  0.540362
##    [99]       360       172 |  0.392147
##   [100]       360       237 |  0.626605
##   -------
##   nnode: 379
class(colPair(sce, asSparse=TRUE))
## [1] "dgCMatrix"
## attr(,"package")
## [1] "Matrix"

A particularly useful feature is that the indices of the interacting cells are automatically remapped when sce is subsetted. This ensures that the pairings are always synchronized with the identities of the cells in sce.

sub <- sce[,50:300]
colPair(sub) # grabs the first pairing, if no 'type' is supplied.
## SelfHits object with 46 hits and 1 metadata column:
##             from        to |      value
##        <integer> <integer> |  <numeric>
##    [1]         7       157 |   0.748443
##    [2]        11         2 |   0.260945
##    [3]        18       203 |   0.857025
##    [4]        19       217 |   0.967591
##    [5]        32       249 |   0.608411
##    ...       ...       ... .        ...
##   [42]       236         3 | 0.17149734
##   [43]       240       245 | 0.98800819
##   [44]       245        52 | 0.00164622
##   [45]       248       208 | 0.53441707
##   [46]       250        65 | 0.31503674
##   -------
##   nnode: 251

Similar functionality is available for pairings between rows via the rowPairs() family of functions, which is potentially useful for representing coexpression or regulatory networks.

7 Additional metadata fields

The SingleCellExperiment class provides the sizeFactors() getter and setter methods, to set and retrieve size factors from the colData of the object. Each size factor represents the scaling factor applied to a cell to normalize expression values prior to downstream comparisons, e.g., to remove the effects of differences in library size and other cell-specific biases. These methods are primarily intended for programmatic use in functions implementing normalization methods, but users can also directly call this to inspect or define the size factors for their analysis.

# Making up some size factors and storing them:
sizeFactors(sce) <- 2^rnorm(ncol(sce))
summary(sizeFactors(sce))
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.1336  0.6761  1.0082  1.2365  1.5034  9.8244
# Deleting the size factors:
sizeFactors(sce) <- NULL
sizeFactors(sce)
## NULL

The colLabels() getter and setters methods allow applications to set and retrieve cell labels from the colData. These labels can be derived from cluster annotations, assigned by classification algorithms, etc. and are often used in downstream visualization and analyses. While labels can be stored in any colData field, the colLabels() methods aim to provide some informal standardization to a default location that downstream functions can search first when attempting to retrieve annotations.

# Making up some labels and storing them:
colLabels(sce) <- sample(letters, ncol(sce), replace=TRUE)
table(colLabels(sce))
## 
##  a  b  c  d  e  f  g  h  i  j  k  l  m  n  o  p  q  r  s  t  u  v  w  x  y  z 
## 18 14 15 13 16 12 19 14 10 12 14 13 16 10 25 22 15  8 12 19 14 19 12 17 11  9
# Deleting the labels:
colLabels(sce) <- NULL
colLabels(sce)
## NULL

In a similar vein, we provide the rowSubset() function for users to set and get row subsets from the rowData. This will store any vector of gene identities (e.g., row names, integer indices, logical vector) in the SingleCellExperiment object for retrieval and use by downstream functions. Users can then easily pack multiple feature sets into the same object for synchronized manipulation.

# Packs integer or character vectors into the rowData:
rowSubset(sce, "my gene set 1") <- 1:10
which(rowSubset(sce, "my gene set 1"))
##  [1]  1  2  3  4  5  6  7  8  9 10
# Easy to delete:
rowSubset(sce, "my gene set 1") <- NULL
rowSubset(sce, "my gene set 1")
## NULL

Session information

sessionInfo()
## R Under development (unstable) (2021-11-10 r81171)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.3 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.15-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.15-bioc/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] Rtsne_0.15                  scRNAseq_2.9.2             
##  [3] SingleCellExperiment_1.17.2 SummarizedExperiment_1.25.2
##  [5] Biobase_2.55.0              GenomicRanges_1.47.5       
##  [7] GenomeInfoDb_1.31.1         IRanges_2.29.1             
##  [9] S4Vectors_0.33.4            BiocGenerics_0.41.2        
## [11] MatrixGenerics_1.7.0        matrixStats_0.61.0         
## [13] BiocStyle_2.23.0           
## 
## loaded via a namespace (and not attached):
##  [1] ProtGenerics_1.27.0           bitops_1.0-7                 
##  [3] bit64_4.0.5                   filelock_1.0.2               
##  [5] progress_1.2.2                httr_1.4.2                   
##  [7] tools_4.2.0                   bslib_0.3.1                  
##  [9] utf8_1.2.2                    R6_2.5.1                     
## [11] lazyeval_0.2.2                DBI_1.1.1                    
## [13] withr_2.4.2                   tidyselect_1.1.1             
## [15] prettyunits_1.1.1             bit_4.0.4                    
## [17] curl_4.3.2                    compiler_4.2.0               
## [19] xml2_1.3.2                    DelayedArray_0.21.2          
## [21] rtracklayer_1.55.0            bookdown_0.24                
## [23] sass_0.4.0                    rappdirs_0.3.3               
## [25] Rsamtools_2.11.0              stringr_1.4.0                
## [27] digest_0.6.28                 rmarkdown_2.11               
## [29] XVector_0.35.0                pkgconfig_2.0.3              
## [31] htmltools_0.5.2               ensembldb_2.19.3             
## [33] dbplyr_2.1.1                  fastmap_1.1.0                
## [35] rlang_0.4.12                  RSQLite_2.2.8                
## [37] shiny_1.7.1                   jquerylib_0.1.4              
## [39] BiocIO_1.5.0                  generics_0.1.1               
## [41] jsonlite_1.7.2                BiocParallel_1.29.2          
## [43] dplyr_1.0.7                   RCurl_1.98-1.5               
## [45] magrittr_2.0.1                GenomeInfoDbData_1.2.7       
## [47] Matrix_1.3-4                  Rcpp_1.0.7                   
## [49] fansi_0.5.0                   lifecycle_1.0.1              
## [51] stringi_1.7.5                 yaml_2.2.1                   
## [53] zlibbioc_1.41.0               BiocFileCache_2.3.0          
## [55] AnnotationHub_3.3.6           grid_4.2.0                   
## [57] blob_1.2.2                    parallel_4.2.0               
## [59] promises_1.2.0.1              ExperimentHub_2.3.4          
## [61] crayon_1.4.2                  lattice_0.20-45              
## [63] Biostrings_2.63.0             GenomicFeatures_1.47.2       
## [65] hms_1.1.1                     KEGGREST_1.35.0              
## [67] knitr_1.36                    pillar_1.6.4                 
## [69] rjson_0.2.20                  biomaRt_2.51.0               
## [71] XML_3.99-0.8                  glue_1.5.0                   
## [73] BiocVersion_3.15.0            evaluate_0.14                
## [75] BiocManager_1.30.16           png_0.1-7                    
## [77] vctrs_0.3.8                   httpuv_1.6.3                 
## [79] purrr_0.3.4                   assertthat_0.2.1             
## [81] cachem_1.0.6                  xfun_0.28                    
## [83] mime_0.12                     xtable_1.8-4                 
## [85] AnnotationFilter_1.19.0       restfulr_0.0.13              
## [87] later_1.3.0                   tibble_3.1.6                 
## [89] GenomicAlignments_1.31.2      AnnotationDbi_1.57.1         
## [91] memoise_2.0.0                 ellipsis_0.3.2               
## [93] interactiveDisplayBase_1.33.0