2.1 Rationale

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.

Methods to get or set the internal fields are exported for use by developers of packages that depend on SingleCellExperiment. This allows dependent packages to store their own custom fields that are not meant to be directly accessible by the user. However, this requires some care to avoid conflicts between packages.

2.2 Conflicts between packages

The concern is that package A and B both define methods that get/set an internal field X in a SingleCellExperiment instance. Consider the following example object:

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):

Assume that we have functions that set an internal field X in packages A and B.

# Function in package A:
AsetX <- function(sce) {
    int_colData(sce)$X <- runif(ncol(sce))
    sce
}

# Function in package B:
BsetX <- function(sce) {
    int_colData(sce)$X <- sample(LETTERS, ncol(sce), replace=TRUE)
    sce
}

If both of these functions are called, one will clobber the output of the other. This may lead to nonsensical results in downstream procedures.

sce2 <- AsetX(sce)
int_colData(sce2)$X

##  [1] 0.2099273 0.3622541 0.4772182 0.3795951 0.4070312 0.4911640 0.3034656
##  [8] 0.9995554 0.3009144 0.2914477

sce2 <- BsetX(sce2)
int_colData(sce2)$X

##  [1] "B" "L" "E" "X" "Z" "K" "Q" "K" "T" "Z"

2.3 Using “Inception-style” nesting

We recommend using nested DataFrames to store internal fields in the column-level metadata. The name of the nested element should be set to the package name, thus avoiding clashes between fields with the same name from different packages.

AsetX_better <- function(sce) {
    int_colData(sce)$A <- DataFrame(X=runif(ncol(sce)))
    sce
}

BsetX_better <- function(sce) {
    choice <- sample(LETTERS, ncol(sce), replace=TRUE)
    int_colData(sce)$B <- DataFrame(X=choice)
    sce
}

sce2 <- AsetX_better(sce)
sce2 <- BsetX_better(sce2)
int_colData(sce2)$A$X 

##  [1] 0.45433559 0.52673957 0.94013308 0.49054995 0.24740638 0.67396501
##  [7] 0.61584069 0.02948517 0.38389955 0.04578058

int_colData(sce2)$B$X 

##  [1] "B" "L" "M" "A" "J" "L" "B" "I" "G" "X"

The same approach can be applied to the row-level metadata, e.g., for some per-row field Y.

AsetY_better <- function(sce) {
    int_elementMetadata(sce)$A <- DataFrame(Y=runif(nrow(sce)))
    sce
}

BsetY_better <- function(sce) {
    choice <- sample(LETTERS, nrow(sce), replace=TRUE)
    int_elementMetadata(sce)$B <- DataFrame(Y=choice)
    sce
}

sce2 <- AsetY_better(sce)
sce2 <- BsetY_better(sce2)
int_elementMetadata(sce2)$A$Y 

##  [1] 0.3205707 0.9655682 0.2536934 0.7950062 0.4590836 0.8831779 0.9069343
##  [8] 0.6680549 0.4033529 0.2023641

int_elementMetadata(sce2)$B$Y

##  [1] "R" "A" "B" "M" "F" "Q" "S" "B" "N" "F"

For the object-wide metadata, a nested list is usually sufficient.

AsetZ_better <- function(sce) {
    int_metadata(sce)$A <- list(Z = "Aaron")
    sce
}

BsetZ_better <- function(sce) {
    int_metadata(sce)$B <- list(Z = "Davide")
    sce
}

sce2 <- AsetZ_better(sce)
sce2 <- BsetZ_better(sce2)
int_metadata(sce2)$A$Z

## [1] "Aaron"

int_metadata(sce2)$B$Z

## [1] "Davide"

In this manner, both A and B can set their internal X, Y and Z without interfering with each other. Of course, this strategy assumes that packages do not have the same names as some of the in-built internal fields (which would be very unfortunate).

4.1 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.

4.2 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.