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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