\name{ttestCtData}
\Rdversion{1.1}
\alias{ttestCtData}

\title{Differentially expressed features with qPCR: t-test}
\description{Function for calculating t-test and p-values across two groups for the features present in high-throughput qPCR data, such as from TaqMan Low Density Arrays.}

\usage{
ttestCtData(q, groups = NULL, calibrator, alternative = "two.sided", paired = FALSE, replicates = TRUE, sort = TRUE, stringent = TRUE, p.adjust = "BH", ...)
}

\arguments{
  \item{q}{qPCRset object.}
  \item{groups}{factor, assigning each sample to one of two groups.}
  \item{calibrator}{which of the two groups is to be considered as the reference and not the test? Defaults to the first group in \code{groups}.}
  \item{alternative}{character string (first letter is enough), specifying the alternative hypothesis, "two.sided" (default), "greater" or "less".}
  \item{paired}{logical, should a paired t-test be used.}
  \item{replicates}{logical, if replicated genes are present on the array, the statistics will be calculated for all the replicates combined, rather than the individual wells.}
    \item{sort}{boolean, should the output be sorted by p-values.}
  \item{stringent}{boolean, for flagging results as "Undetermined". See details.}
  \item{p.adjust}{character string, which method to use for p-value adjsutment for multiple testing. See details.}
  \item{\dots}{any other arguments will be passed to the \code{\link{t.test}} function.}
}

\details{Once the Ct values have been normalised, differential expression  can be calculated. This function deals with just the simple case, where there are two types of samples to compare. For more complex studies, see \code{limmaCtData}.

All results are assigned to a category, either "OK" or "Undetermined" depending on the input Ct values. If \code{stringent=TRUE} any unreliable or undetermined measurements among technical and biological replicates will result in the final result being "Undetermined". For \code{stringent=FALSE} the result will be "OK" unless at least half of the Ct values for a given gene are unreliable/undetermined.

The argument \code{p.adjust} is passed on to the \code{\link{p.adjust}} function. Options include e.g. "BH" (Benjamini & Hochberg, the default), "fdr" and "bonferroni". See \code{\link{p.adjust}} for more information on the individual methods.
}

\value{A data.frame containing the following information:
	\item{genes}{The names of the features on the card.}
	\item{feature.pos}{The \code{featurePos} of the genes. If replicated genes are used, the feature positions will be concatenated together.}
	\item{t.test}{The value of the t-test.}
	\item{p.value}{The corresponding p-value.}
	\item{ddCt}{The delta delta Ct values.}
	\item{FC}{The fold change; 2^(-ddCt).}
	\item{meanCalibrator}{The average expression level of each gene in the calibrator sample(s).}
	\item{meanTarget)}{The average expression level of each gene in the target sample(s).}
	\item{categoryCalibrator}{The category of the Ct values ("OK", "Undetermine") across the calibrator.}
	\item{categoryTarget}{Ditto for the target.}
}

\author{Heidi Dvinge}

\seealso{\code{\link{t.test}}, \code{\link{limmaCtData}}, \code{\link{mannwhitneyCtData}}. \code{\link{plotCtRQ}} and \code{\link{plotCtSignificance}} can be used for visualising the results.}

\examples{
# Load example preprocessed data
data(qPCRpros)
# Test between two groups, collapsing replicated features
diff.exp <- ttestCtData(qPCRpros[,1:4], groups=factor(c("A", "B", "B", "A")), calibrator="B")
diff.exp[1:10,]
# The same test, taking replicated features individually
diff.exp <- ttestCtData(qPCRpros[,1:4], groups=factor(c("A", "B", "B", "A")), calibrator="B", replicates=FALSE)
# Using another method for p-value ajustment
diff.exp <- ttestCtData(qPCRpros[,1:4], groups=factor(c("A", "B", "B", "A")), calibrator="B", p.adjust="holm")
}

\keyword{htest  }