\HeaderA{twilight.teststat}{Interface to the test statistics provided within 'twilight'.}{twilight.teststat}
\keyword{nonparametric}{twilight.teststat}
\begin{Description}\relax
A function to compute two-sample t, Z and fold change equivalent test statistics (paired or unpaired) and correlation coefficients.
\end{Description}
\begin{Usage}
\begin{verbatim}
twilight.teststat(xin, yin, method = "fc", paired = FALSE, s0 = NULL)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{xin}] Either an expression set (\code{ExpressionSet}) or a data matrix with rows corresponding to features and columns corresponding to samples. 
\item[\code{yin}] A numerical vector containing class labels. The higher label denotes the case, the lower label the control samples to test case vs. control. For correlation scores, \code{yin} can be any numerical vector of length equal to the number of samples. 
\item[\code{method}] Character string: \code{"fc"} for fold change equivalent test (that is log ratio test), \code{"t"} for t-test, and \code{"z"} for Z-test of Tusher et al. (2001). With \code{"pearson"} or \code{"spearman"}, the test statistic is either Pearson's correlation coefficient or Spearman's rank correlation coefficient.
\item[\code{paired}] Logical value. Depends on whether the samples are paired. Ignored if \code{method="pearson"} or \code{method="spearman"}.
\item[\code{s0}] Fudge factor for variance correction in the Z-test. Takes effect only if \code{method="z"}. If \code{s0=NULL}: The fudge factor is set to the median of the pooled standard deviations.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
Please see vignette for detailed information.
\end{Details}
\begin{Value}
Returns a list with two components: a numerical vector of observed test statistics \code{observed}. Each entry corresponds to one row of the input data matrix. Also, the estimated fudge factor \code{s0} is returned. In any other case except \code{method="z"}, \code{s0} is zero.
\end{Value}
\begin{Author}\relax
Stefanie Scheid \url{http://www.molgen.mpg.de/~scheid}
\end{Author}
\begin{References}\relax
Scheid S and Spang R (2004): A stochastic downhill search algorithm for estimating the local false discovery rate, \emph{IEEE TCBB} \bold{1(3)}, 98--108.

Scheid S and Spang R (2005): twilight; a Bioconductor package for estimating the local false discovery rate, \emph{Bioinformatics} \bold{21(12)}, 2921--2922. 

Scheid S and Spang R (2006): Permutation filtering: A novel concept for significance analysis of large-scale genomic data, in: Apostolico A, Guerra C, Istrail S, Pevzner P, and Waterman M (Eds.): \emph{Research in Computational Molecular Biology: 10th Annual International Conference, Proceedings of RECOMB 2006, Venice, Italy, April 2-5, 2006}. Lecture Notes in Computer Science vol. 3909, Springer, Heidelberg, pp. 338-347.

Tusher VG, Tibshirani R and Chu G (2001): Significance analysis of mircroarrays applied to the ionizing response, \emph{PNAS} \bold{98(9)}, 5116--5121.
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{twilight.pval}{twilight.pval}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
### Z-test on random values
M <- matrix(rnorm(20000),nrow=1000)
id <- c(rep(1,10),rep(0,10))
stat <- twilight.teststat(M,id,method="z")

### Pearson correlation
id <- 1:20
stat <- twilight.teststat(M,id,method="pearson")
\end{ExampleCode}
\end{Examples}

