decideTests: Multiple Testing Across Genes and Contrasts

In richierocks/limma2: Linear Models for Microarray Data

Description

Classify a series of related t-statistics as up, down or not significant. A number of different multiple testing schemes are offered which adjust for multiple testing down the genes as well as across contrasts for each gene.

Usage

decideTests(object,method="separate",adjust.method="BH",p.value=0.05,lfc=0)

Arguments

object	MArrayLM object output from eBayes from which the t-statistics may be extracted.
method	character string specify how probes and contrasts are to be combined in the multiple testing strategy. Choices are "separate", "global", "hierarchical", "nestedF" or any partial string.
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 desired size of the test
lfc	minimum log2-fold-change required

Details

These functions implement multiple testing procedures for determining whether each statistic in a matrix of t-statistics should be considered significantly different from zero. Rows of tstat correspond to genes and columns to coefficients or contrasts.

The setting method="separate" is equivalent to using topTable separately for each coefficient in the linear model fit, and will give the same lists of probes 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 respectively.

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.

Author(s)

Gordon Smyth

See Also

An overview of multiple testing functions is given in 08.Tests.

richierocks/limma2 documentation built on May 27, 2019, 8:47 a.m.