If the gene expression measurements are already in a rectangular form, then this function allows an easy way to construct a SingleCellAssay object while still doing some sanity checking of inputs.

FromMatrix(exprsArray, cData, fData, class = "SingleCellAssay")

Arguments

exprsArray
matrix or array, columns are cells, rows are genes
cData
cellData an object that can be coerced to a DataFrame, ie, data.frame, AnnotatedDataFrame. Must have as many rows as ncol(exprsArray)
fData
featureData an object that can be coerced to a DataFrame, ie, data.frame, AnnotatedDataFrame. Must have as many rows as nrow(exprsArray).
class
desired subclass of object. Default SingleCellAssay.

Value

an object of class class

Examples

ncells <- 10 ngenes <- 5 fData <- data.frame(primerid=LETTERS[1:ngenes]) cData <- data.frame(wellKey=seq_len(ncells)) mat <- matrix(rnorm(ncells*ngenes), nrow=ngenes) sca <- FromMatrix(mat, cData, fData)
#> No dimnames in `exprsArray`, assuming `fData` and `cData` are sorted according to `exprsArray`
stopifnot(inherits(sca, 'SingleCellAssay')) stopifnot(inherits(sca, 'SummarizedExperiment0')) ##If there are mandatory keywords expected by a class, you'll have to manually set them yourself cData$ncells <- 1 fd <- FromMatrix(mat, cData, fData)
#> No dimnames in `exprsArray`, assuming `fData` and `cData` are sorted according to `exprsArray`
stopifnot(inherits(fd, 'SingleCellAssay'))