Description Multiple testing across genes and contrasts Gene Set Tests Global Tests Author(s) See Also
LIMMA provides a number of functions for multiple testing across both contrasts and genes.
The starting point is an MArrayLM
object, called fit
say, resulting from fitting a linear model and running eBayes
and, optionally, contrasts.fit
.
See 06.LinearModels or 07.SingleChannel for details.
The key function is decideTests
.
This function writes an object of class TestResults
, which is basically a matrix of -1
, 0
or 1
elements, of the same dimension as fit$coefficients
, indicating whether each coefficient is significantly different from zero.
A number of different multiple testing strategies are provided.
decideTests
calls classifyTestsF
to implement the nested F-test strategt.
selectModel
chooses between linear models for each probe using AIC or BIC criteria.
This is an alternative to hypothesis testing and can choose between non-nested models.
A number of other functions are provided to display the results of decideTests
.
The functions heatDiagram
(or the older version heatdiagram
displays the results in a heat-map style display.
This allows visual comparison of the results across many different conditions in the linear model.
The functions vennCounts
and vennDiagram
provide Venn diagram style summaries of the results.
Summary and show
method exists for objects of class TestResults
.
The results from decideTests
can also be included when the results of a linear model fit are written to a file using write.fit
.
Competitive gene set testing for an individual gene set is provided by wilcoxGST
or geneSetTest
, which permute genes.
The gene set can be displayed using barcodeplot
.
Self-contained gene set testing for an individual set is provided by roast
, which uses rotation technology, analogous to permuting arrays.
Gene set enrichment analysis for a large database of gene sets is provided by romer
.
topRomer
is used to rank results from romer
.
The functions alias2Symbol
, alias2SymbolTable
and alias2SymbolUsingNCBI
are provided to help match gene sets with microarray probes by way of official gene symbols.
The function genas
can test for associations between two contrasts in a linear model.
Given a set of p-values, the function propTrueNull
can be used to estimate the proportion of true null hypotheses.
When evaluating test procedures with simulated or known results, the utility function auROC
can be used to compute the area under the Receiver Operating Curve for the test results for a given probe.
Gordon Smyth
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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