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\section*{A note on {\tt esApply}}
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{\tt exprSet}s are complex objects. We will think
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of them as linked arrays: the \Robject{exprs} element
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of an \Rclass{exprSet} is $G \times N$, where $G$ is
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the number of genes on a chip and $N$ is the number
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of tissues analyzed, and the \Robject{pData} element
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of the associated \Robject{phenoData} element is $N \times p$,
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where $p$ is the number of phenotypic or demographic, etc.,
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Abstractly, we are often interested in evaluating
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functions $f(y;x)$ where $y$ is an $N$-vector of
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expression results for a specific gene and $x$ is
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an $N$-dimensional structure, coordinated with $y$, that
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distinguishes elements of $y$ for processing in the function $f$.
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A basic problem is to guarantee that the $j$th element
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of $y$ is correctly associated with the $j$th component of $x$.
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<<R.hide, results=hide, echo=FALSE>>=
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As an example, let's consider \Robject{eset} which is an %'
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\Rclass{exprSet} supplied with Biobase. We will print
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the first $N$-vector of gene expressions and
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print(exprs(eset)[1,])
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print(pData(eset)[1:2,1:3])
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Now let's see how expressions %'
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and a covariate are related:
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print( rbind(exprs(eset[1,]),
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cov1=t(pData(eset))[1,]))
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A function that evaluates the difference in median expression across
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strata defined using an abstract covariate \Robject{x} is
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medContr <- function( y, x ) {
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median(ys[[1]]) - median(ys[[2]])
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We can apply this to a small
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\Rclass{exprSet} that gives back the data listed above:
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print(apply(exprs(eset[1,,drop=F]),1,
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medContr, pData(eset)[["cov1"]]))
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That's a bit clumsy. This is where %'
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\Rfunction{esApply} comes in. We pay for some
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simplicity by following a strict protocol
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for the definition of the statistical
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function to be applied.
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medContr1 <- function(y) {
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median(ys[[1]]) - median(ys[[2]])
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print(esApply( eset, 1, medContr1 )[1])
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The manual page on \Rfunction{esApply} has a number of
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additional examples that show how applicable functions
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can be constructed and used. The important thing to
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note is that the applicable functions {\em know} the names of
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the covariates in the \Robject{pData} dataframe.
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This is achieved by having an environment populated with all the
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variables in the \Robject{phenoData} component of the \Rclass{exprSet} put
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in as the environment of the function that will be applied. If that
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function already has an environment we retain that but in the second
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position. Thus, there is some potential for variable shadowing.
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