An introduction to the SingleCellExperiment class
30 April 2018
Package
SingleCellExperiment 1.2.0
Contents
1 Package Status
| Project Status |  |
| Travis CI |  |
| Test coverage |  |
2 The SingleCellExperiment class
2.1 Definition
The SingleCellExperiment class is a light-weight container for single-cell genomics data.
It extends the RangedSummarizedExperiment class with the following additional slots:
int_elementMetadataint_colDataint_metadatareducedDims
The int_ prefix describes internal slots that are not meant for direct manipulation by the user or other package developers.
Instead, they are set by other user-visible methods, which will be discussed in more detail below.
2.2 Create instances of SingleCellExperiment
There are two main ways to create instances of SingleCellExperiment.
The first is via the constructor:
library(SingleCellExperiment)
counts <- matrix(rpois(100, lambda = 10), ncol=10, nrow=10)
sce <- SingleCellExperiment(assays = 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):
## spikeNames(0):The second is via coercion from SummarizedExperiment objects.
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):
## spikeNames(0):2.3 Setting up a simple example
Here we use a subset of the allen data set from the scRNAseq package to demonstrate the use of the class.
library(scRNAseq)
data(allen)
allen## class: SummarizedExperiment
## dim: 20908 379
## metadata(2): SuppInfo which_qc
## assays(4): tophat_counts cufflinks_fpkm rsem_counts rsem_tpm
## rownames(20908): 0610007P14Rik 0610009B22Rik ... Zzef1 Zzz3
## rowData names(0):
## colnames(379): SRR2140028 SRR2140022 ... SRR2139341 SRR2139336
## colData names(22): NREADS NALIGNED ... Animal.ID passes_qc_checks_sThese data are stored as a SummarizedExperiment, so we will first coerce it into a SingleCellExperiment.
sce <- as(allen, "SingleCellExperiment")
sce## class: SingleCellExperiment
## dim: 20908 379
## metadata(2): SuppInfo which_qc
## assays(4): tophat_counts cufflinks_fpkm rsem_counts rsem_tpm
## rownames(20908): 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):
## spikeNames(0):3 Available methods for the SingleCellExperiment
3.1 Adding spike-in information
One of the main additions to SummarizedExperiment is the ability for the user to specify the rows corresponding to spike-in transcripts.
This is done with the method isSpike, using an appropriate name for the spike-in set.
isSpike(sce, "ERCC") <- grepl("^ERCC-", rownames(sce))
sce## class: SingleCellExperiment
## dim: 20908 379
## metadata(2): SuppInfo which_qc
## assays(4): tophat_counts cufflinks_fpkm rsem_counts rsem_tpm
## rownames(20908): 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):
## spikeNames(1): ERCCThe identities of the spike-in rows can be easily retrieved using the name of the spike-in set, as shown below.
The names of currently available spike-in sets can also be returned with the spikeNames method.
table(isSpike(sce, "ERCC"))##
## FALSE TRUE
## 20816 92spikeNames(sce)## [1] "ERCC"While most experimental designs use a single set of spike-ins, the class has the flexibility of including more than one set of spikes. Let us pretend that the members of the Adam gene family have been spiked-in as external genes in these data.
isSpike(sce, "Adam") <- grepl("^Adam[0-9]", rownames(sce))
sce## class: SingleCellExperiment
## dim: 20908 379
## metadata(2): SuppInfo which_qc
## assays(4): tophat_counts cufflinks_fpkm rsem_counts rsem_tpm
## rownames(20908): 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):
## spikeNames(2): ERCC Adamtable(isSpike(sce, "Adam"))##
## FALSE TRUE
## 20875 33spikeNames(sce)## [1] "ERCC" "Adam"If isSpike is used without specifying any name, it will return the union of all spike-in sets.
table(isSpike(sce))##
## FALSE TRUE
## 20783 125Similarly, running isSpike<- without specifying any name will delete all existing spike-in sets from the SingleCellExperiment, if the assigned value is NULL.
(If non-NULL, the vector will instead be stored as a spike-in set with an empty name, in addition to removing all existing sets.)
temp <- sce
isSpike(temp) <- NULL
spikeNames(temp)## character(0)Note that the isSpike and isSpike<- methods get and set columns in int_elementMetadata and int_metadata.
This information is only relevant to package developers and not necessary for routine use of this class.
3.2 Adding size factors
One can also store size factors in the SingleCellExperiment object.
For illustration, we simply compute the total number of reads as size factors here.
Note that more sophisticated methods for computing size factors are available (see, e.g., scran).
sizeFactors(sce) <- colSums(assay(sce))
head(sizeFactors(sce))## SRR2140028 SRR2140022 SRR2140055 SRR2140083 SRR2139991 SRR2140067
## 5173863 6445002 2343379 5438526 4757468 2364851We can compute multiple size factors and store them in the object, by providing a name to sizeFactors.
This does not affect the values of the unnamed size factors.
sizeFactors(sce, "ERCC") <- colSums(assay(sce)[isSpike(sce, "ERCC"),])
head(sizeFactors(sce, "ERCC"))## SRR2140028 SRR2140022 SRR2140055 SRR2140083 SRR2139991 SRR2140067
## 224648 186208 162370 512991 278034 64975head(sizeFactors(sce)) # same as before## SRR2140028 SRR2140022 SRR2140055 SRR2140083 SRR2139991 SRR2140067
## 5173863 6445002 2343379 5438526 4757468 23648513.3 Retrieve colData and rowData information
The colData and rowData methods can be used to retrieve the stored sample- and gene-level metadata.
By default, this will only return the user-visible metadata fields, i.e., not including the fields stored in the int_* slots.
colData(sce)## DataFrame with 379 rows and 22 columns
## NREADS NALIGNED RALIGN TOTAL_DUP PRIMER
## <numeric> <numeric> <numeric> <numeric> <numeric>
## SRR2140028 13743900 13011100 94.6681 51.11 0.0148148
## SRR2140022 14078700 13521900 96.0454 55.9157 0.00853083
## SRR2140055 5842930 5135980 87.9008 59.1126 0.0356112
## SRR2140083 16784400 15585800 92.8587 55.3076 0.0209695
## SRR2139991 11558600 10864300 93.9929 50.2258 0.016408
## ... ... ... ... ... ...
## SRR2139325 12875700 11307000 87.8172 70.3564 0.0453119
## SRR2139373 9699400 8964140 92.4196 45.5249 0.0216694
## SRR2139379 6175660 5728080 92.7526 45.2652 0.0217132
## SRR2139341 28038500 26320000 93.8711 65.1959 0.0270482
## SRR2139336 7878700 7467200 94.7772 56.9675 0.0190784
## PCT_RIBOSOMAL_BASES PCT_CODING_BASES PCT_UTR_BASES
## <numeric> <numeric> <numeric>
## SRR2140028 1e-06 0.216848 0.265609
## SRR2140022 0 0.263052 0.310332
## SRR2140055 3e-06 0.207086 0.327241
## SRR2140083 1e-06 0.129243 0.253681
## SRR2139991 0 0.257729 0.276831
## ... ... ... ...
## SRR2139325 1.2e-05 0.211253 0.269041
## SRR2139373 1e-06 0.220541 0.254625
## SRR2139379 0 0.253996 0.289924
## SRR2139341 0 0.297193 0.367713
## SRR2139336 1e-06 0.369537 0.407216
## PCT_INTRONIC_BASES PCT_INTERGENIC_BASES PCT_MRNA_BASES
## <numeric> <numeric> <numeric>
## SRR2140028 0.369509 0.148033 0.482457
## SRR2140022 0.290329 0.136288 0.573384
## SRR2140055 0.291128 0.174542 0.534327
## SRR2140083 0.444594 0.172481 0.382924
## SRR2139991 0.323493 0.141946 0.53456
## ... ... ... ...
## SRR2139325 0.34636 0.173333 0.480295
## SRR2139373 0.385409 0.139423 0.475167
## SRR2139379 0.3254 0.13068 0.54392
## SRR2139341 0.190673 0.144421 0.664906
## SRR2139336 0.077377 0.14587 0.776752
## MEDIAN_CV_COVERAGE MEDIAN_5PRIME_BIAS MEDIAN_3PRIME_BIAS
## <numeric> <numeric> <numeric>
## SRR2140028 0.507749 0.14181 0.409045
## SRR2140022 0.488182 0.145024 0.41916
## SRR2140055 0.729874 0.069846 0.54856
## SRR2140083 0.781878 0 0.697916
## SRR2139991 0.48292 0.160644 0.413018
## ... ... ... ...
## SRR2139325 0.754565 0.049809 0.45433
## SRR2139373 0.504545 0.162197 0.518013
## SRR2139379 0.521732 0.136669 0.458149
## SRR2139341 0.491965 0.155719 0.551382
## SRR2139336 0.517592 0.148329 0.445874
## MEDIAN_5PRIME_TO_3PRIME_BIAS driver_1_s dissection_s
## <numeric> <character> <character>
## SRR2140028 0.425234 Scnn1a-Tg3-Cre L4
## SRR2140022 0.41926 Scnn1a-Tg3-Cre L4
## SRR2140055 0.257657 Scnn1a-Tg3-Cre All
## SRR2140083 0.01825 Scnn1a-Tg3-Cre L4
## SRR2139991 0.462171 Scnn1a-Tg3-Cre L4
## ... ... ... ...
## SRR2139325 0.413165 Ntsr1-Cre_GN220 L6a
## SRR2139373 0.356451 Ntsr1-Cre_GN220 L6a
## SRR2139379 0.367889 Ntsr1-Cre_GN220 L6a
## SRR2139341 0.330835 Ntsr1-Cre_GN220 L6a
## SRR2139336 0.390592 Ntsr1-Cre_GN220 L6a
## Core.Type Primary.Type Secondary.Type Animal.ID
## <character> <character> <character> <integer>
## SRR2140028 Intermediate L4 Scnn1a L4 Ctxn3 133632
## SRR2140022 Core L4 Scnn1a 133632
## SRR2140055 Intermediate L5a Tcerg1l L5a Batf3 151560
## SRR2140083 NA NA NA NA
## SRR2139991 Intermediate L4 Scnn1a L4 Ctxn3 126846
## ... ... ... ... ...
## SRR2139325 Intermediate L6a Sla L6a Mgp 175613
## SRR2139373 Core L6a Sla 132856
## SRR2139379 Core L6a Sla 132856
## SRR2139341 Core L6a Sla 132856
## SRR2139336 Core L6a Sla 132856
## passes_qc_checks_s
## <character>
## SRR2140028 Y
## SRR2140022 Y
## SRR2140055 Y
## SRR2140083 N
## SRR2139991 Y
## ... ...
## SRR2139325 Y
## SRR2139373 Y
## SRR2139379 Y
## SRR2139341 Y
## SRR2139336 YrowData(sce)## DataFrame with 20908 rows and 0 columnsHowever, it is sometimes useful to retrieve a DataFrame with the internal fields, i.e., spike-in and size factor information.
This can be achieved by specifying internal=TRUE.
colData(sce, internal=TRUE)## DataFrame with 379 rows and 24 columns
## NREADS NALIGNED RALIGN TOTAL_DUP PRIMER
## <numeric> <numeric> <numeric> <numeric> <numeric>
## SRR2140028 13743900 13011100 94.6681 51.11 0.0148148
## SRR2140022 14078700 13521900 96.0454 55.9157 0.00853083
## SRR2140055 5842930 5135980 87.9008 59.1126 0.0356112
## SRR2140083 16784400 15585800 92.8587 55.3076 0.0209695
## SRR2139991 11558600 10864300 93.9929 50.2258 0.016408
## ... ... ... ... ... ...
## SRR2139325 12875700 11307000 87.8172 70.3564 0.0453119
## SRR2139373 9699400 8964140 92.4196 45.5249 0.0216694
## SRR2139379 6175660 5728080 92.7526 45.2652 0.0217132
## SRR2139341 28038500 26320000 93.8711 65.1959 0.0270482
## SRR2139336 7878700 7467200 94.7772 56.9675 0.0190784
## PCT_RIBOSOMAL_BASES PCT_CODING_BASES PCT_UTR_BASES
## <numeric> <numeric> <numeric>
## SRR2140028 1e-06 0.216848 0.265609
## SRR2140022 0 0.263052 0.310332
## SRR2140055 3e-06 0.207086 0.327241
## SRR2140083 1e-06 0.129243 0.253681
## SRR2139991 0 0.257729 0.276831
## ... ... ... ...
## SRR2139325 1.2e-05 0.211253 0.269041
## SRR2139373 1e-06 0.220541 0.254625
## SRR2139379 0 0.253996 0.289924
## SRR2139341 0 0.297193 0.367713
## SRR2139336 1e-06 0.369537 0.407216
## PCT_INTRONIC_BASES PCT_INTERGENIC_BASES PCT_MRNA_BASES
## <numeric> <numeric> <numeric>
## SRR2140028 0.369509 0.148033 0.482457
## SRR2140022 0.290329 0.136288 0.573384
## SRR2140055 0.291128 0.174542 0.534327
## SRR2140083 0.444594 0.172481 0.382924
## SRR2139991 0.323493 0.141946 0.53456
## ... ... ... ...
## SRR2139325 0.34636 0.173333 0.480295
## SRR2139373 0.385409 0.139423 0.475167
## SRR2139379 0.3254 0.13068 0.54392
## SRR2139341 0.190673 0.144421 0.664906
## SRR2139336 0.077377 0.14587 0.776752
## MEDIAN_CV_COVERAGE MEDIAN_5PRIME_BIAS MEDIAN_3PRIME_BIAS
## <numeric> <numeric> <numeric>
## SRR2140028 0.507749 0.14181 0.409045
## SRR2140022 0.488182 0.145024 0.41916
## SRR2140055 0.729874 0.069846 0.54856
## SRR2140083 0.781878 0 0.697916
## SRR2139991 0.48292 0.160644 0.413018
## ... ... ... ...
## SRR2139325 0.754565 0.049809 0.45433
## SRR2139373 0.504545 0.162197 0.518013
## SRR2139379 0.521732 0.136669 0.458149
## SRR2139341 0.491965 0.155719 0.551382
## SRR2139336 0.517592 0.148329 0.445874
## MEDIAN_5PRIME_TO_3PRIME_BIAS driver_1_s dissection_s
## <numeric> <character> <character>
## SRR2140028 0.425234 Scnn1a-Tg3-Cre L4
## SRR2140022 0.41926 Scnn1a-Tg3-Cre L4
## SRR2140055 0.257657 Scnn1a-Tg3-Cre All
## SRR2140083 0.01825 Scnn1a-Tg3-Cre L4
## SRR2139991 0.462171 Scnn1a-Tg3-Cre L4
## ... ... ... ...
## SRR2139325 0.413165 Ntsr1-Cre_GN220 L6a
## SRR2139373 0.356451 Ntsr1-Cre_GN220 L6a
## SRR2139379 0.367889 Ntsr1-Cre_GN220 L6a
## SRR2139341 0.330835 Ntsr1-Cre_GN220 L6a
## SRR2139336 0.390592 Ntsr1-Cre_GN220 L6a
## Core.Type Primary.Type Secondary.Type Animal.ID
## <character> <character> <character> <integer>
## SRR2140028 Intermediate L4 Scnn1a L4 Ctxn3 133632
## SRR2140022 Core L4 Scnn1a 133632
## SRR2140055 Intermediate L5a Tcerg1l L5a Batf3 151560
## SRR2140083 NA NA NA NA
## SRR2139991 Intermediate L4 Scnn1a L4 Ctxn3 126846
## ... ... ... ... ...
## SRR2139325 Intermediate L6a Sla L6a Mgp 175613
## SRR2139373 Core L6a Sla 132856
## SRR2139379 Core L6a Sla 132856
## SRR2139341 Core L6a Sla 132856
## SRR2139336 Core L6a Sla 132856
## passes_qc_checks_s size_factor size_factor_ERCC
## <character> <numeric> <numeric>
## SRR2140028 Y 5173863 224648
## SRR2140022 Y 6445002 186208
## SRR2140055 Y 2343379 162370
## SRR2140083 N 5438526 512991
## SRR2139991 Y 4757468 278034
## ... ... ... ...
## SRR2139325 Y 4377966 331955
## SRR2139373 Y 3393227 79524
## SRR2139379 Y 2529501 37021
## SRR2139341 Y 13972642 439580
## SRR2139336 Y 4838243 163414rowData(sce, internal=TRUE)## DataFrame with 20908 rows and 3 columns
## is_spike_ERCC is_spike is_spike_Adam
## <logical> <logical> <logical>
## 1 FALSE FALSE FALSE
## 2 FALSE FALSE FALSE
## 3 FALSE FALSE FALSE
## 4 FALSE FALSE FALSE
## 5 FALSE FALSE FALSE
## ... ... ... ...
## 20904 FALSE FALSE FALSE
## 20905 FALSE FALSE FALSE
## 20906 FALSE FALSE FALSE
## 20907 FALSE FALSE FALSE
## 20908 FALSE FALSE FALSESee below for some discussion of why an internal storage mechanism is used here.
3.4 Adding low-dimensional representations
For simplicity and speed, we work on a subset of 100 genes. To avoid ending up with only uninteresting genes, we extract the 100 genes with maximal variance in the log-transformed counts.
library(magrittr)
assay(sce) %>% log1p %>% rowVars -> vars
names(vars) <- rownames(sce)
vars <- sort(vars, decreasing = TRUE)
sce_sub <- sce[names(vars[1:100]),]
sce_sub## class: SingleCellExperiment
## dim: 100 379
## metadata(2): SuppInfo which_qc
## assays(4): tophat_counts cufflinks_fpkm rsem_counts rsem_tpm
## rownames(100): Lamp5 Fam19a1 ... Rnf2 Zfp35
## rowData names(0):
## colnames(379): SRR2140028 SRR2140022 ... SRR2139341 SRR2139336
## colData names(22): NREADS NALIGNED ... Animal.ID passes_qc_checks_s
## reducedDimNames(0):
## spikeNames(2): ERCC AdamWe obtain the PCA and t-SNE representations of the data and add them to the object with the reducedDims method.
library(Rtsne)
set.seed(5252)
pca_data <- prcomp(t(log1p(assay(sce_sub))))
tsne_data <- Rtsne(pca_data$x[,1:50], pca = FALSE)
reducedDims(sce_sub) <- SimpleList(PCA=pca_data$x, TSNE=tsne_data$Y)
sce_sub## class: SingleCellExperiment
## dim: 100 379
## metadata(2): SuppInfo which_qc
## assays(4): tophat_counts cufflinks_fpkm rsem_counts rsem_tpm
## rownames(100): Lamp5 Fam19a1 ... Rnf2 Zfp35
## rowData names(0):
## colnames(379): SRR2140028 SRR2140022 ... SRR2139341 SRR2139336
## colData names(22): NREADS NALIGNED ... Animal.ID passes_qc_checks_s
## reducedDimNames(2): PCA TSNE
## spikeNames(2): ERCC AdamThe stored coordinates can be retrieved by name or by numerical index. Each row of the coordinate matrix is assumed to correspond to a cell, while each column represents a dimension.
reducedDims(sce_sub)## List of length 2
## names(2): PCA TSNEreducedDimNames(sce_sub)## [1] "PCA" "TSNE"head(reducedDim(sce_sub, "PCA")[,1:2])## PC1 PC2
## SRR2140028 17.557295 -7.717162
## SRR2140022 21.468975 -1.198212
## SRR2140055 4.303756 -11.360330
## SRR2140083 21.440479 -9.435868
## SRR2139991 15.592089 -11.043989
## SRR2140067 16.539336 -9.831779head(reducedDim(sce_sub, "TSNE")[,1:2])## [,1] [,2]
## SRR2140028 16.47207 -3.0408912
## SRR2140022 13.31402 -1.3113644
## SRR2140055 17.82551 4.5114364
## SRR2140083 14.88531 -0.2405083
## SRR2139991 14.32553 1.3716557
## SRR2140067 16.29199 2.6580009Any subsetting by column of sce_sub will also lead to subsetting of the dimensionality reduction results by cell.
dim(reducedDim(sce_sub, "PCA"))## [1] 379 100dim(reducedDim(sce_sub[,1:10], "PCA"))## [1] 10 1003.5 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-transformednormcounts, 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: 20908 379
## metadata(2): SuppInfo which_qc
## assays(5): tophat_counts cufflinks_fpkm rsem_counts rsem_tpm counts
## rownames(20908): 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):
## spikeNames(2): ERCC Adamdim(counts(sce))## [1] 20908 379This means that functions expecting count data can simply call counts() without worrying about package-specific naming conventions.
4 Design decisions
4.1 Package scope
By design, the scope of this package is limited to defining the SingleCellExperiment class and some minimal getter and setter methods.
For this reason, we leave it to developers of specialized packages to provide more advanced methods for the SingleCellExperiment class.
For example, scater defines a number of specific methods such as normalize and dplyr-like verbs.
We also leave it to the developers of existing packages to provide coercion methods from their classes to SingleCellExperiment.
4.2 Why internal storage?
We use an internal storage mechanism to protect the spike-in and size factor fields from direct manipulation by the user.
This ensures that, e.g., only a call to sizeFactors<- can change the size factors.
The same effect could be achieved by reserving a subset of columns (or column names) as “private” in colData() and rowData(), though this is not easily implemented.
The internal storage avoids situations where users or functions can silently overwrite these important metadata fields during manipulations of rowData or colData.
This can result in bugs that are difficult to track down, particularly in long workflows involving many functions.
It also allows us to add new methods and metadata types to SingleCellExperiment without worrying about overwriting user-supplied metadata in existing objects.
4.3 What’s up with reducedDims?
We use a SimpleList as the reducedDims slot to allow for multiple dimensionality reduction results.
One can imagine that different dimensionality reduction techniques will be useful for different aspects of the analysis, e.g., t-SNE for visualization, PCA for pseudo-time inference.
We see reducedDims as a similar slot to assays() in that multiple matrices can be stored, though the dimensionality reduction results need not have the same number of dimensions.
4.4 Why derive from a RangedSummarizedExperiment?
We decided to extend RangedSummarizedExperiment rather than SummarizedExperiment because for certain assays it will be essential to have rowRanges().
Even for RNA-seq, it is sometimes useful to have rowRanges() and other classes to define the genomic coordinates, e.g., DESeqDataSet in the DESeq2 package.
An alternative would have been to have two classes, SingleCellExperiment and RangedSingleCellExperiment.
However, this seems like an unnecessary duplication as having a class with default empty rowRanges seems good enough when one does not need rowRanges.
5 Session Info
sessionInfo()## R version 3.5.0 (2018-04-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.7-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.7-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 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] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] Rtsne_0.13 magrittr_1.5
## [3] scRNAseq_1.5.0 SingleCellExperiment_1.2.0
## [5] SummarizedExperiment_1.10.0 DelayedArray_0.6.0
## [7] BiocParallel_1.14.0 matrixStats_0.53.1
## [9] Biobase_2.40.0 GenomicRanges_1.32.0
## [11] GenomeInfoDb_1.16.0 IRanges_2.14.0
## [13] S4Vectors_0.18.0 BiocGenerics_0.26.0
## [15] BiocStyle_2.8.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.16 knitr_1.20 XVector_0.20.0
## [4] zlibbioc_1.26.0 lattice_0.20-35 stringr_1.3.0
## [7] tools_3.5.0 grid_3.5.0 xfun_0.1
## [10] htmltools_0.3.6 yaml_2.1.18 rprojroot_1.3-2
## [13] digest_0.6.15 bookdown_0.7 Matrix_1.2-14
## [16] GenomeInfoDbData_1.1.0 bitops_1.0-6 RCurl_1.95-4.10
## [19] evaluate_0.10.1 rmarkdown_1.9 stringi_1.1.7
## [22] compiler_3.5.0 backports_1.1.2