glMDPlot {Glimma} | R Documentation

## Glimma MD Plot

### Description

Draw an interactive MD plot

### Usage

    glMDPlot(x, ...)

### Arguments

`x` | the DE object to plot.
`...` | additional arguments affecting the plots produced. See specific methods for detailed arguments.

### Value

Draws a two-panel interactive MD plot in an html page. The left plot shows the log-fold-change vs average expression. The right plot shows the expression levels of a particular gene of each sample. Hovering over points on left plot will plot expression level for corresponding gene, clicking on points will fix the expression plot to gene. Clicking on rows on the table has the same effect as clicking on the corresponding gene in the plot.

### Author(s)

Shian Su

### See Also

`glMDPlot.default`, `glMDPlot.DGELRT`, `glMDPlot.DGEExact`, `glMDPlot.MArrayLM`, `glMDPlot.DESeqDataSet`

### Examples

    library(limma)
    library(edgeR)

    data(lymphomaRNAseq)
    x <- lymphomaRNAseq

    sel <- rowSums(cpm(x$counts)>0.5)>=3
    x <- x[sel,]

    genotype <- relevel(x$samples$group, "Smchd1-null")
    x <- calcNormFactors(x, method="TMM")
    des <- model.matrix(~genotype)

    ## Apply voom with sample quality weights and fit linear model
    v <- voomWithQualityWeights(x, design=des, normalize.method="none", plot=FALSE)
    vfit <- lmFit(v,des)

    ## Apply treat relative to a fold-change of 1.5
    vtfit <- treat(vfit,lfc=log2(1.5))
    vfit <- eBayes(vfit)
    results <- decideTests(vfit,p.value=0.01)

    glMDPlot(vfit, counts=x$counts, anno=x$genes, groups=genotype, samples=1:7,
             status=results[,2], main="MD plot: Wild-type vs Smchd1",
             display.columns=c("Symbols", "GeneID", "GeneName"),
             folder="Smchd1-Lymphoma")

[Package Glimma version 1.14.0 Index]