scater provides tools for visualization of single-cell transcriptomic data.
It is based on the
SingleCellExperiment class (from the SingleCellExperiment package),
and thus is interoperable with many other Bioconductor packages such as scran,
scuttle and iSEE.
To demonstrate the use of the various scater functions,
we will load in the classic Zeisel dataset:
library(scRNAseq) example_sce <- ZeiselBrainData() example_sce
## class: SingleCellExperiment ## dim: 20006 3005 ## metadata(0): ## assays(1): counts ## rownames(20006): Tspan12 Tshz1 ... mt-Rnr1 mt-Nd4l ## rowData names(1): featureType ## colnames(3005): 1772071015_C02 1772071017_G12 ... 1772066098_A12 ## 1772058148_F03 ## colData names(10): tissue group # ... level1class level2class ## reducedDimNames(0): ## altExpNames(2): ERCC repeat
Note: A more comprehensive description of the use of scater (along with other packages) in a scRNA-seq analysis workflow is available at https://osca.bioconductor.org.
Quality control to remove damaged cells and poorly sequenced libraries is a common step in single-cell analysis pipelines. We will use some utilities from the scuttle package (conveniently loaded for us when we load scater) to compute the usual quality control metrics for this dataset.
library(scater) example_sce <- addPerCellQC(example_sce, subsets=list(Mito=grep("mt-", rownames(example_sce))))
Metadata variables can be plotted against each other using the
plotColData() function, as shown below.
We expect to see an increasing number of detected genes with increasing total count.
Each point represents a cell that is coloured according to its tissue of origin.
plotColData(example_sce, x = "sum", y="detected", colour_by="tissue")