08.Tests {limma} | R Documentation

## Topic: Hypothesis Testing for Linear Models

### Description

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.

### Multiple testing across genes and contrasts

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. The function calls other functions `classifyTestsF`, `classifyTestsP` and `classifyTestsT` which implement particular strategies. The function `FStat` provides an alternative interface to `classifyTestsF` to extract only the overall moderated F-statistic.

`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`.

### Gene Set Tests

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.

### Global Tests

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.

### Author(s)

Gordon Smyth

### See Also

01.Introduction, 02.Classes, 03.ReadingData, 04.Background, 05.Normalization, 06.LinearModels, 07.SingleChannel, 08.Tests, 09.Diagnostics, 10.GeneSetTests, 11.RNAseq

[Package limma version 3.36.5 Index]