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Last data update: 2014.03.03

R: Produce QC diagnostic plots

plotQC

R Documentation

Produce QC diagnostic plots

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`, `plotImportantPCs`, `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)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(scater)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: ggplot2

Attaching package: 'scater'

The following object is masked from 'package:stats':

    filter

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/scater/plotQC.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plotQC
> ### Title: Produce QC diagnostic plots
> ### Aliases: plotQC
> 
> ### ** 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)
The variable filter_on_total_counts only has one unique value, so R^2 is not meaningful.
This variable will not be plotted.
The variable filter_on_total_features only has one unique value, so R^2 is not meaningful.
This variable will not be plotted.
The variable exprs_feature_controls only has one unique value, so R^2 is not meaningful.
This variable will not be plotted.
The variable pct_exprs_feature_controls only has one unique value, so R^2 is not meaningful.
This variable will not be plotted.
The variable filter_on_pct_exprs_feature_controls only has one unique value, so R^2 is not meaningful.
This variable will not be plotted.
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>