5.LinearModels {limma} | R Documentation |
This page gives an overview of the LIMMA functions available to fit linear models and to interpret the results.
The core of this package is the fitting of gene-wise linear models to microarray data. The basic idea is to estimate log-ratios between two or more target RNA samples simultaneously. See the LIMMA User's Guide for several case studies.
The function designMatrix
is provided to assist with creation of an appropriate design matrix for two-color microarray experiments using a common reference.
Design matrices for Affymetrix or single-color arrays can be easily created using the ordinary R command model.matrix
.
For the direct two-color designs the design matrix needs to be created by hand.
There are four functions in the package which fit linear models:
lmFit
lm.series
rlm.series
lm.series
using robust regression as implemented by the rlm
function in the MASS package.gls.series
duplicateCorrelation
or dupcor.series
are used to estimate the inter-duplicate correlation before using gls.series
.
Each of these functions accepts essentially the same argument list and produces a fitted model object of the same form.
The first function lmFit
formally produces an object of class MArrayLM
.
The other three functions are lower level functions which produce similar output but in unclassed lists.
The main argument is the design matrix which specifies which target RNA samples were applied to each channel on each array. There is considerable freedom to choose the design matrix - there is always more than one choice which is correct provided it is interpreted correctly. The fitted model object consists of coefficients, standard errors and residual standard errors for each gene.
All the functions which fit linear models use unwrapdups
which provides an unified method for handling duplicate spots.
Once a linear model has been fit using an appropriate design matrix, the command makeContrasts
may be used to form a contrast matrix to make comparisons of interest.
The fit and the contrast matrix are used by contrasts.fit
to compute fold changes and t-statistics for the contrasts of interest.
This is a way to compute all possible pairwise comparisons between treatments for example in an experiment which compares many treatments to a common reference.
After fitting a linear model, the standard errors are moderated using a simple empirical Bayes model using ebayes
or eBayes
.
A moderated t-statistic and a log-odds of differential expression is computed for each contrast for each gene.
ebayes
and eBayes
use internal functions fitFDist
, tmixture.matrix
and tmixture.vector
.
The function zscoreT
is sometimes used for computing z-score equivalents for t-statistics so as to place t-statistics with different degrees of freedom on the same scale.
zscoreGamma
is used the same way with standard deviations instead of t-statistics.
These functions are for research purposes rather than for routine use.
After the above steps the results may be displayed or further processed using:
toptable
classifyTests
classifyTestsT
and classifyTestsP
are simpler methods using cutoffs for the t-statistics or p-values individually.heatdiagram
classifyTests
.vennCounts
classifyTests
and counts the number of genes in each classification.vennDiagram
classifyTests
or vennCounts
and produces a Venn diagram plot.Gordon Smyth
Smyth, G. K. (2003). Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. http://www.statsci.org/smyth/pubs/ebayes.pdf
Smyth, G. K., Michaud, J., and Scott, H. (2003). The use of within-array duplicate spots for assessing differential expression in microarray experiments. http://www.statsci.org/smyth/pubs/dupcor.pdf