plotQC: Produce QC diagnostic plots

In dynverse/scaterlegacy: Single-cell analysis toolkit for gene expression data in R

Description

Produce QC diagnostic plots

Usage

plotQC(object, type = "highest-expression", ...)

Arguments

object	an SCESet 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 plotHighestExprs, findImportantPCs, plotExplanatoryVariables and {plotExprsMeanVsFreq} as appropriate.

Details

Display useful quality control plots to help with pre-processing of data and identification of potentially problematic features and cells.

Value

a ggplot plot object

Examples

data("sc_example_counts")
data("sc_example_cell_info")
pd <- new("AnnotatedDataFrame", data=sc_example_cell_info)
rownames(pd) <- pd$Cell
example_sceset <- newSCESet(countData=sc_example_counts, phenoData=pd)
drop_genes <- apply(exprs(example_sceset), 1, function(x) {var(x) == 0})
example_sceset <- example_sceset[!drop_genes, ]
example_sceset <- calculateQCMetrics(example_sceset)
plotQC(example_sceset, type="high", col_by_variable="Mutation_Status")
plotQC(example_sceset, type="find", variable="total_features")
vars <- names(pData(example_sceset))[c(2:3, 5:14)]
plotQC(example_sceset, type="expl", variables=vars)

dynverse/scaterlegacy documentation built on Feb. 17, 2020, 5:07 a.m.