plotSA {limma} | R Documentation

## Sigma vs A plot for microarray linear model

### Description

Plot residual standard deviation versus average log expression for a fitted microarray linear model.

### Usage

    plotSA(fit, xlab = "Average log-expression", ylab = "sqrt(sigma)", zero.weights = FALSE,
           pch = 16, cex = 0.3, col = c("black","red"), ...)

### Arguments

`fit` | an `MArrayLM` object.
`xlab` | label for x-axis
`ylab` | label for y-axis
`zero.weights` | logical, should genes with all zero weights be plotted?
`pch` | vector of codes for plotting characters.
`cex` | numeric, vector of expansion factors for plotting characters.
`col` | plotting colors for regular and outlier variances respectively.
`...` | any other arguments are passed to `plot`

### Details

This plot is used to check the mean-variance relationship of the expression data, after fitting a linear model. A scatterplot of residual-variances vs average log-expression is created. The plot is especially useful for examining the mean-variance trend estimated by `eBayes` or `treat` with `trend=TRUE`. It can be considered as a routine diagnostic plot in the limma-trend pipeline.

If robust empirical Bayes was used to create `fit`, then outlier variances are highlighted in the color given by `col[2]`.

The y-axis is square-root `fit$sigma`, where `sigma` is the estimated residual standard deviation. The y-axis therefore corresponds to quarter-root variances. The y-axis was changed from log2-variance to quarter-root variance in limma version 3.31.21. The quarter-root scale matches the similar plot produced by the `voom` function and gives a better plot when some of the variances are close to zero.

See `points` for possible values for `pch` and `cex`.

### Value

A plot is created on the current graphics device.

### Author(s)

Gordon Smyth

### See Also

`eBayes`

An overview of diagnostic functions available in LIMMA is given in 09.Diagnostics.

[Package limma version 3.43.8 Index]