\HeaderA{decideTests}{Multiple Testing Across Genes and Contrasts}{decideTests}
\keyword{htest}{decideTests}
\begin{Description}\relax
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.
\end{Description}
\begin{Usage}
\begin{verbatim}
decideTests(object,method="separate",adjust.method="BH",p.value=0.05,lfc=0)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{object}] \code{MArrayLM} object output from \code{eBayes} from which the t-statistics may be extracted.
\item[\code{method}] character string specify how probes and contrasts are to be combined in the multiple testing strategy.  Choices are \code{"separate"}, \code{"global"}, \code{"hierarchical"}, \code{"nestedF"} or any partial string.
\item[\code{adjust.method}] character string specifying p-value adjustment method.  Possible values are \code{"none"}, \code{"BH"}, \code{"fdr"} (equivalent to \code{"BH"}), \code{"BY"} and \code{"holm"}. See \code{\LinkA{p.adjust}{p.adjust}} for details.
\item[\code{p.value}] numeric value between 0 and 1 giving the desired size of the test
\item[\code{lfc}] minimum log2-fold-change required
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
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 \code{tstat} correspond to genes and columns to coefficients or contrasts.

The setting \code{method="separate"} is equivalent to using \code{topTable} separately for each coefficient in the linear model fit, and will give the same lists of probes if \code{adjust.method} is the same.
\code{method="global"} will treat the entire matrix of t-statistics as a single vector of unrelated tests.
\code{method="hierarchical"} adjusts down genes and then across contrasts.
\code{method="nestedF"} adjusts down genes and then uses \code{classifyTestsF} to classify contrasts as significant or not for the selected genes.
\end{Details}
\begin{Value}
An object of class \code{\LinkA{TestResults}{TestResults}}.
This is essentially a numeric matrix with elements \code{-1}, \code{0} or \code{1} depending on whether each t-statistic is classified as significantly negative, not significant or significantly positive respectively.

If \code{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 \code{lfc=log2(1.5)} to restrict to 50\% changes or \code{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.
\end{Value}
\begin{Author}\relax
Gordon Smyth
\end{Author}
\begin{SeeAlso}\relax
An overview of multiple testing functions is given in \LinkA{08.Tests}{08.Tests}.
\end{SeeAlso}

