decideTests: Multiple Testing Across Genes and Contrasts

In limma: Linear Models for Microarray Data

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.

method="separate" will apply multiple testing adjustments to each column of p-values separately. 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 according to overall F-tests and then uses classifyTestsF to classify contrasts as significant or not for the selected genes.

The default method="separate" and adjust.method="BH" settings are appropriate for most analyses. method="global" is useful when it is important that the same t-statistic cutoff should correspond to statistical significance for all the contrasts. The "nestedF" method was proposed by Michaud et al (2008) and achieves better consistency between contrasts than the other methods. It provides formal error rate control at the gene level but not for individual contrasts. See the classifyTestsF help page for more detail about the "nestedF" method.

If object is a MArrayLM linear model fit, then the "hierarchical" method conducts row-wise F-tests and then proceeds to t-tests for those rows with significant F-tests. The multiple testing adjustment is applied initially to the F-tests and then, with an adjusted level, to the t-tests for each significant row.

Also see the limma User's Guide for a discussion of the statistical properties of the various adjustment 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 is not generally 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

References

Michaud, J, Simpson, KM, Escher, R, Buchet-Poyau, K, Beissbarth, T, Carmichael, C, Ritchie, ME, Schutz, F, Cannon, P, Liu, M, Shen, X, Ito, Y, Raskind, WH, Horwitz, MS, Osato, M, Turner, DR, Speed, TP, Kavallaris, M, Smyth, GK, and Scott, HS (2008). Integrative analysis of RUNX1 downstream pathways and target genes. BMC Genomics 9, 363.

See Also

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

limma documentation built on Nov. 8, 2020, 8:28 p.m.