R: Multiple Testing Across Genes and Contrasts

decideTests {limma}

R Documentation

Multiple Testing Across Genes and Contrasts

Description

Identify which genes are significantly differentially expressed for each contrast from a fit object containing p-values and test statistics. A number of different multiple testing strategies are offered that adjust for multiple testing down the genes as well as across contrasts for each gene.

Usage

## S3 method for class 'MArrayLM'
decideTests(object, method = "separate", adjust.method = "BH", p.value = 0.05,
            lfc = 0, ...)
## Default S3 method:
decideTests(object, method = "separate", adjust.method = "BH", p.value = 0.05,
            lfc = 0, coefficients = NULL, cor.matrix = NULL, tstat = NULL, df = Inf,
            genewise.p.value = NULL, ...)

Arguments

`object`	a numeric matrix of p-values or an `MArrayLM` object from which p-values and t-statistics can be extracted.
`method`	character string specifying how genes and contrasts are to be combined in the multiple testing scheme. Choices are `"separate"`, `"global"`, `"hierarchical"` or `"nestedF"`.
`adjust.method`	character string specifying p-value adjustment method. Possible values are `"none"`, `"BH"`, `"fdr"` (equivalent to `"BH"`), `"BY"` and `"holm"`. See `p.adjust` for details.
`p.value`	numeric value between 0 and 1 giving the required family-wise error rate or false discovery rate.
`lfc`	numeric, minimum absolute log2-fold-change required.
`coefficients`	numeric matrix of coefficients or log2-fold-changes. Of same dimensions as `object`.
`cor.matrix`	correlation matrix of coefficients. Square matrix of dimension `ncol(object)`.
`tstat`	numeric matrix of t-statistics. Of same dimensions as `object`.
`df`	numeric vector of length `nrow(object)` giving degrees of freedom for the t-statistics.
`genewise.p.value`	numeric vector of length `nrow(object)` containing summary gene-level p-values for use with `method="hierarchical"`.
`...`	other arguments are not used.

Details

This function can be applied to a matrix of p-values but is more often applied to an MArrayLM fit object produced by eBayes or treat. In either case, rows of object correspond to genes and columns to coefficients or contrasts.

This function applies a multiple testing procedure and a significance level cutoff to the statistics contained in object. It implements a number of multiple testing procedures for determining whether each statistic should be considered significantly different from zero.

The setting method="separate" is equivalent to using topTable separately for each coefficient in the linear model fit, and will identify the same probes as significantly differentially expressed if adjust.method is the same. method="global" will treat the entire matrix of t-statistics as a single vector of unrelated tests. method="hierarchical" adjusts down genes and then across contrasts. method="nestedF" adjusts down genes and then uses classifyTestsF to classify contrasts as significant or not for the selected genes. Please see the limma User's Guide for a discussion of the statistical properties of these methods.

Value

An object of class TestResults. This is essentially a numeric matrix with elements -1, 0 or 1 depending on whether each t-statistic is classified as significantly negative, not significant or significantly positive.

If lfc>0 then contrasts are judged significant only when the log2-fold change is at least this large in absolute value. For example, one might choose lfc=log2(1.5) to restrict to 50% changes or lfc=1 for 2-fold changes. In this case, contrasts must satisfy both the p-value and the fold-change cutoff to be judged significant.

Note

Although this function enables users to set p-value and lfc cutoffs simultaneously, this combination criterion not usually recommended. Unless the fold changes and p-values are very highly correlated, the addition of a fold change cutoff can increase the family-wise error rate or false discovery rate above the nominal level. Users wanting to use fold change thresholding are recommended to use treat instead of eBayes and to leave lfc at the default value when using decideTests.

Author(s)

Gordon Smyth