08.Tests {limma} | R Documentation

## 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.

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 is provided by `geneSetTest`, which permutes genes, while self-contained gene set testing is provided by `roast`, which randomly rotates arrays.

The function `alias2Symbol` is provided to help match gene sets with microarray probes by way of official gene symbols.

### Other Functions

Given a set of p-values, the function `convest` 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

[Package limma version 2.18.3 Index]