7.Diagnostics {limma} | R Documentation

## Diagnostics and Quality Assessment

### Description

This page gives an overview of the LIMMA functions available for microarray quality assessment and diagnostic plots.

This package provides an `anova` method which is designed for assessing the quality of an array series or of a normalization method. It is not designed to assess differential expression of individual genes. `anova` uses utility functions `bwss` and `bwss.matrix`.

The function `arrayWeights` estimates the empirical reliability of each array following a linear model fit.

Diagnostic plots can be produced by

`imageplot`
  Produces a spatial picture of any spot-specific measure from an array image. If the log-ratios are plotted, then this produces an in-silico representation of the well known false-color TIFF image of an array. `imageplot3by2` will write imageplots to files, six plots to a page.
`plotMA`
  MA-plots. One of the most useful plots of a two-color array. `plotMA3by2` will write MA-plots to files, six plots to a page. `mdplot` can also be useful for comparing two one-channel microarrays.
`plotPrintTipLoess`
  Produces a grid of MA-plots, one for each print-tip group on an array, together with the corresponding lowess curve. Intended to help visualize print-tip loess normalization.
`plotPrintorder`
  For an array, produces a scatter plot of log-ratios or log-intensities by print order.
`plotDensities`
  Individual channel densities for one or more arrays. An essential plot to accompany between array normalization, especially quantile normalization.

`plotPrintTipLoess` uses utility functions `gridr` and `gridc`. `plotDensities` uses utility function `RG.MA`.

### Author(s)

Gordon Smyth

[Package limma version 1.9.6 Index]