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,
See 06.LinearModels or 07.SingleChannel for details.
The key function is
This function writes an object of class
TestResults, which is basically a matrix of
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.
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
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.
vennDiagram provide Venn diagram style summaries of the results.
show method exists for objects of class
The results from
decideTests can also be included when the results of a linear model fit are written to a file using
Competitive gene set testing for an individual gene set is provided by
geneSetTest, which permute genes.
The gene set can be displayed using
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
topRomer is used to rank results from
alias2SymbolUsingNCBI are provided to help match gene sets with microarray probes by way of official gene symbols.
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.
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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