Construct a SingleCellAssay from a matrix or array of expression

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",
  check_sanity = TRUE, check_logged = check_sanity)

Arguments

exprsArray	matrix, or a list of matrices, or an 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.
check_sanity	(default: TRUE) Set FALSE to override sanity checks that try to ensure that the default assay is log-transformed and has at least one exact zero. See defaultAssay for details on the "default assay" which is assumed to contain log transformed data.
check_logged	alias for check_sanity

Value

an object of class class

See also

defaultAssay

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`
#> Assuming data assay in position 1, with name et is log-transformed.
stopifnot(inherits(sca, 'SingleCellAssay'))
stopifnot(inherits(sca, 'SummarizedExperiment'))
##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`
#> Assuming data assay in position 1, with name et is log-transformed.
stopifnot(inherits(fd, 'SingleCellAssay'))