This is the constructor for the class. This class intends to ease the analysis of single cell assays, in which multiple, exchangeable, cells from an experimental unit (patient, or organism) are assayed along several (or many) dimensions, such as genes. A few examples of this might be Fluidigm gene expression chips, or single cell sequencing experiments. The chief functionality is to make it easy to keep cellular-level metadata linked to the measurements through cellData
and phenoData
. There are also subsetting and splitting measures to coerce between a SingleCellAssay, and a SCASet.
SingleCellAssay(dataframe = NULL, idvars = NULL, primerid = NULL,
measurement = NULL, id = numeric(0), cellvars = NULL,
featurevars = NULL, phenovars = NULL, ...)
A 'flattened' data.frame
or data.table
containing columns giving cell and feature identifiers and a measurement column
character vector naming columns that uniquely identify a cell
character vector of length 1 that names the column that identifies what feature (i.e. gene) was measured
character vector of length 1 that names the column containing the measurement
An identifier (eg, experiment name) for the resulting object
Character vector naming columns containing additional cellular metadata
Character vector naming columns containing additional feature metadata
Character vector naming columns containing additional phenotype metadata
additional arguments are ignored
SingleCellAssay object
# NOT RUN {
## See FluidigmAssay for examples
# }
# NOT RUN {
example(FluidigmAssay)
# }
Run the code above in your browser using DataLab