R: Sigma vs A plot for microarray linear model

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. 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