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
arrayWeights estimates the empirical reliability of each array following a linear model fit.
Diagnostic plots can be produced by
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.
Plots foreground versus background log-intensies.
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.
MA-plots, essentially the same as mean-difference plots.
plotMA3by2 will write MA-plots to files, six plots to a page.
Scatterplots with highlights.
This is the underlying engine for
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.
For an array, produces a scatter plot of log-ratios or log-intensities by print order.
Individual channel densities for one or more arrays. An essential plot to accompany between array normalization, especially quantile normalization.
Multidimensional scaling plot for a set of arrays. Useful for visualizing the relationship between the set of samples.
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
plotDensities uses utility function
01.Introduction, 02.Classes, 03.ReadingData, 04.Background, 05.Normalization, 06.LinearModels, 07.SingleChannel, 08.Tests, 09.Diagnostics, 10.GeneSetTests, 11.RNAseq
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