plotQC {scater} | R Documentation |
Produce QC diagnostic plots
plotQC(object, type = "highest-expression", ...)
object |
an SingleCellExperiment object containing expression values and experimental information. Must have been appropriately prepared. |
type |
character scalar providing type of QC plot to compute: "highest-expression" (showing features with highest expression), "find-pcs" (showing the most important principal components for a given variable), "explanatory-variables" (showing a set of explanatory variables plotted against each other, ordered by marginal variance explained), or "exprs-mean-vs-freq" (plotting the mean expression levels against the frequency of expression for a set of features). |
... |
arguments passed to |
Display useful quality control plots to help with pre-processing of data and identification of potentially problematic features and cells.
a ggplot plot object
data("sc_example_counts") data("sc_example_cell_info") example_sce <- SingleCellExperiment( assays = list(counts = sc_example_counts), colData = sc_example_cell_info) example_sce <- normalize(example_sce) drop_genes <- apply(exprs(example_sce), 1, function(x) {var(x) == 0}) example_sce <- example_sce[!drop_genes, ] example_sce <- calculateQCMetrics(example_sce) plotQC(example_sce, type="high", col_by_variable="Mutation_Status") plotQC(example_sce, type="find", variable="total_features") vars <- names(colData(example_sce))[c(2:3, 5:14)] plotQC(example_sce, type="expl", variables=vars)