\HeaderA{backgroundCorrect}{Correct Intensities for Background}{backgroundCorrect}
\keyword{models}{backgroundCorrect}
\begin{Description}\relax
Background correct microarray expression intensities.
\end{Description}
\begin{Usage}
\begin{verbatim}
backgroundCorrect(RG, method="subtract", offset=0, printer=RG$printer, verbose=TRUE)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{RG}] an \code{\LinkA{RGList}{RGList}} object or a unclassed list containing the same components as an \code{RGList}
\item[\code{method}] character string specifying correction method.  Possible values are \code{"none"}, \code{"subtract"}, \code{"half"}, \code{"minimum"}, \code{"movingmin"}, \code{"edwards"}, \code{"normexp"} or \code{"rma"}.
\item[\code{offset}] numeric value to add to intensities
\item[\code{printer}] a list containing printer layout information, see \code{\LinkA{PrintLayout-class}{PrintLayout.Rdash.class}}
\item[\code{verbose}] logical, should progress be reported to standard output
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
If \code{method="none"} then the corrected intensities are equal to the foreground intensities, i.e., the background intensities are treated as zero.
If \code{method="subtract"} then this function simply subtracts the background intensities from the foreground intensities which is the usual background correction method.
If \code{method="movingmin"} then the background estimates are replaced with the minimums of the backgrounds of the spot and its eight neighbors, i.e., the background is replaced by a moving minimum of 3x3 grids of spots.

The remaining methods are all designed to produce positive corrected intensities.
If \code{method="half"} then any intensity which is less than 0.5 after background subtraction is reset to be equal to 0.5.
If \code{method="minimum"} then any intensity which is zero or negative after background subtraction is set equal to half the minimum of the positive corrected intensities for that array.
If \code{method="edwards"} a log-linear interpolation method is used to adjust lower intensities as in Edwards (2003).
If \code{method="normexp"} a convolution of normal and exponential distributions is fitted to the foreground intensities using the background intensities as a covariate, and the expected signal given the observed foreground becomes the corrected intensity.
This results in a smooth monotonic transformation of the background subtracted intensities such that all the corrected intensities are positive.
See Smyth (2005) and \code{\LinkA{normexp.fit}{normexp.fit}} for more details.

The \code{offset} can be used to add a constant to the intensities before log-transforming, so that the log-ratios are shrunk towards zero at the lower intensities.
This may eliminate or reverse the usual 'fanning' of log-ratios at low intensities associated with local background subtraction.

Background correction (background subtraction) is also performed by the \code{\LinkA{normalizeWithinArrays}{normalizeWithinArrays}} method for \code{RGList} objects, so it is not necessary to call \code{backgroundCorrect} directly unless one wants to use a method other than simple subtraction.
Calling \code{backgroundCorrect} before \code{normalizeWithinArrays} will over-ride the default background correction.
\end{Details}
\begin{Value}
An \code{RGList} object in which components \code{R} and \code{G} are background corrected
and components \code{Rb} and \code{Gb} are removed.
\end{Value}
\begin{Author}\relax
Gordon Smyth
\end{Author}
\begin{References}\relax
Edwards, D. E. (2003). Non-linear normalization and background correction in one-channel cDNA microarray studies
\emph{Bioinformatics} 19, 825-833. 

Smyth, G. K. (2005). Limma: linear models for microarray data. In:  \emph{Bioinformatics and Computational Biology Solutions using R and Bioconductor}, R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, W. Huber (eds.), Springer, New York, pages 397-420. 

Yang, Y. H., Buckley, M. J., Dudoit, S., and Speed, T. P. (2002). Comparison of methods for image analysis on cDNA microarray data. \emph{Journal of Computational and Graphical Statistics} 11, 108-136.

Yang, Y. H., Buckley, M. J., and Speed, T. P. (2001). Analysis of microarray images. \emph{Briefings in Bioinformatics} 2, 341-349.
\end{References}
\begin{SeeAlso}\relax
An overview of background correction functions is given in \code{\LinkA{04.Background}{04.Background}}.
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
RG <- new("RGList", list(R=c(1,2,3,4),G=c(1,2,3,4),Rb=c(2,2,2,2),Gb=c(2,2,2,2)))
backgroundCorrect(RG)
backgroundCorrect(RG, method="half")
backgroundCorrect(RG, method="minimum")
backgroundCorrect(RG, offset=5)
\end{ExampleCode}
\end{Examples}