We compute log-transformed normalized expression values from the count matrix. Sce <- ReprocessedAllenData("tophat_counts") To demonstrate the use of the class in the rest of the vignette, we will use the Allen data set from the scRNAseq package. This includes access to assay data via assay(), column metadata with colData(), and so on.Īgain, without going into too much detail: dim(assay(sce)) # 10 10 colnames(colData(sce)) # "label" colnames(rowData(sce)) # "length" RowData=DataFrame(length=),Īlternatively, we can construct a SingleCellExperiment by coercing an existing (Ranged)SummarizedExperiment object: se <- SummarizedExperiment(list(counts=counts))Īs(se, "SingleCellExperiment") # class: SingleCellExperimentĪny operation that can be applied to a RangedSummarizedExperiment is also applicable to any instance of a SingleCellExperiment. Sce <- SingleCellExperiment(list(counts=counts),ĬolData=DataFrame(label=), We will not go into much detail here as most of this is covered by the SummarizedExperiment documentation,īut to give an example: <- sample(letters, ncol(counts), replace=TRUE)
It is similarly easy to set the column and row metadata by passing values to the appropriate arguments. In practice, it is often more useful to name the assay by passing in a named list: sce <- SingleCellExperiment(list(counts=counts))
SingleCellExperiment objects can be created via the constructor of the same name.įor example, if we have a count matrix in counts, we can simply call: library(SingleCellExperiment)Ĭounts <- matrix(rpois(100, lambda = 10), ncol=10, nrow=10) 2 Creating SingleCellExperiment instances
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