glimmaMA.MArrayLM: Glimma MA Plot
In Glimma: Interactive HTML graphics

Description Usage Arguments Details Value See Also Examples

Draws a two-panel interactive MA plot from an MArrayLM object. This is a special case of the glimmaXY plot.

## S3 method for class 'MArrayLM'
glimmaMA(
  x,
  dge = NULL,
  counts = dge$counts,
  groups = dge$samples$group,
  coef = ncol(x$coefficients),
  status = limma::decideTests(x),
  anno = x$genes,
  display.columns = NULL,
  status.cols = c("dodgerblue", "silver", "firebrick"),
  sample.cols = NULL,
  p.adj.method = "BH",
  transform.counts = c("logcpm", "cpm", "rpkm", "none"),
  main = colnames(x)[coef],
  xlab = "logCPM",
  ylab = "logFC",
  html = NULL,
  width = 920,
  height = 920,
  ...
)

`x`	`MArrayLM` object from which summary statistics are extracted from to create summary (left) plot.
`dge`	`DGEList` object with `nrow(x)` rows from which expression values are extracted from to create expression (right) plot. Gene counts are taken from `dge$counts` and sample groups from `dge$samples$group`.
`counts`	numeric matrix with `nrow(x)` rows containing gene expression values. This can be used to replace raw gene counts from `dge$counts` with transformed counts e.g. logCPM or logRPKM values.
`groups`	vector of length `ncol(dge)` representing categorisation of samples in expression plot.
`coef`	integer indicating the column in `x` from the summary plot is created.
`status`	vector of length `nrow(x)` indicating the status of each gene. By default genes in the summary plot are coloured based on its differential expression status using an adjusted p-value cutoff of 5% by calling the `limma::decideTests` function, where the value of -1 marks down-regulated genes, 0 marks genes with no expression difference, and 1 marks up-regulated genes.
`anno`	dataframe with `nrow(x)` rows containing gene annotations.
`display.columns`	character vector containing names of columns from `anno` from which to display in mouseover tooltips and table.
`status.cols`	vector of length 3 containing valid CSS strings for colours associated with `status` in the order of -1, 0 and 1.
`sample.cols`	character vector of length `ncol(counts)` containing valid CSS strings for colours associated with each sample to be displayed on the expression plot. If left unspecified, samples will be coloured according to `groups`.
`p.adj.method`	character string specifying p-value adjustment method.
`transform.counts`	the type of transform used on the counts log-cpm by default. `edgeR::cpm(counts, log=TRUE)`; defaults to FALSE.
`main`	character string for the main title of summary plot.
`xlab`	character string for the x-axis label of summary plot.
`ylab`	character string for the y-axis label of summary plot.
`html`	character string for naming HTML file for exportation of widget. The extension should be included in the file name e.g. "file.html".
`width`	numeric value indicating width of widget in pixels.
`height`	numeric value indicating width of height in pixels.
`...`	addition unused arguments.

The summary plot on the left represents gene-wise log-fold-change (logFC) on the y-axis versus average gene expression calculated as log-counts-per-million (logCPM) values. We call our summary plot an MA plot because this type of plot was originally referred to as an MA plot in the limma package, with the M-value representing logFC and A-value representing average expression - it has since been renamed to MD plot in the limma package. The expression plot on the right displays sample expression values for a single gene. Interactions with the htmlwidget include clicking on genes (points) in the summary plot to bring up associated sample expression values in the expression plot, as well as the summary statistics in the table below. Alternatively, users can interact with the table by clicking on genes (rows) to highlight genes in the summary plot, as well as bring up associated sample expression values in the expression plot. Briefly, other interactive features include a search box for the table, buttons to save plots and data (summary statistics and expression values), additional pop-up information when hovering on points in plots, and rescaling of the y-axis in the expression plot.

htmlwidget object or NULL if html argument is specified.

glimmaMA, glimmaMA.DGEExact, glimmaMA.DGELRT, glimmaMA.DESeqDataSet

dge <- readRDS(
  system.file("RNAseq123/dge.rds", package = "Glimma"))
design <- readRDS(
  system.file("RNAseq123/design.rds", package = "Glimma"))
contr.matrix <- readRDS(
  system.file("RNAseq123/contr.matrix.rds", package = "Glimma"))

v <- limma::voom(dge, design)
vfit <- limma::lmFit(v, design)
vfit <- limma::contrasts.fit(vfit, contrasts = contr.matrix)
efit <- limma::eBayes(vfit)

glimmaMA(efit, dge = dge)