09.Diagnostics {limma} | R Documentation

## Topic: 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.

`plotFB`

  Plots foreground versus background log-intensies.

`plotMD`

  Mean-difference plots. Very versatile plot. For two color arrays, this plots the M-values vs A-values. For single channel technologies, this plots one column of log-expression values vs the average of the other columns. For fitted model objects, this plots a log-fold-change versus average log-expression. `mdplot` can also be useful for comparing two one-channel microarrays.

`plotMA`

  MA-plots, essentially the same as mean-difference plots. `plotMA3by2` will write MA-plots to files, six plots to a page.

`plotWithHighlights`

  Scatterplots with highlights. This is the underlying engine for `plotMD` and `plotMA`.

`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.

`plotMDS`

  Multidimensional scaling plot for a set of arrays. Useful for visualizing the relationship between the set of samples.

`plotSA`

  Sigma vs A plot. After a linear model is fitted, this checks constancy of the variance with respect to intensity level.

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

### Author(s)

Gordon Smyth

### See Also

01.Introduction, 02.Classes, 03.ReadingData, 04.Background, 05.Normalization, 06.LinearModels, 07.SingleChannel, 08.Tests, 09.Diagnostics, 10.GeneSetTests, 11.RNAseq

[Package limma version 3.36.5 Index]