An apply-like function for ExpressionSet and related structures.
esApply is a wrapper to apply for use with ExpressionSets.
The application of a function to rows of an expression array usually involves
variables in pData. esApply uses a special evaluation paradigm
to make this easy. The function FUN may reference any data in
pData by name.
esApply(X, MARGIN, FUN, ...)
X |
An instance of class |
MARGIN |
The margin to apply to, either 1 for rows (samples) or 2 for columns (features). |
FUN |
Any function |
... |
Additional parameters for |
The pData from X is installed in an
environment. This environment is installed as the environment of
FUN. This will then provide bindings for any symbols in
FUN that are the same as the names of the pData of
X. If FUN has an environment already it is retained but
placed after the newly created environment. Some variable shadowing
could occur under these circumstances.
The result of with(pData(x), apply(exprs(X), MARGIN, FUN, ...)).
V.J. Carey <stvjc@channing.harvard.edu>, R. Gentleman
data(sample.ExpressionSet)
## sum columns of exprs
res <- esApply(sample.ExpressionSet, 1, sum)
## t-test, spliting samples by 'sex'
f <- function(x) {
xx <- split(x, sex)
t.test(xx[[1]], xx[[2]])$p.value
}
res <- esApply(sample.ExpressionSet, 1, f)
## same, but using a variable passed in the function call
f <- function(x, s) {
xx <- split(x, s)
mean(xx[[1]]) - mean(xx[[2]])
}
sex <- sample.ExpressionSet[["sex"]]
res <- esApply(sample.ExpressionSet, 1, f, s = sex)
# obtain the p-value of the t-test for sex difference
mytt.demo <- function(y) {
ys <- split(y, sex)
t.test(ys[[1]], ys[[2]])$p.value
}
sexPValue <- esApply(sample.ExpressionSet, 1, mytt.demo)
# obtain the p-value of the slope associated with score, adjusting for sex
# (if we were concerned with sign we could save the z statistic instead at coef[3,3]
myreg.demo <- function(y) {
summary(lm(y ~ sex + score))$coef[3,4]
}
scorePValue <- esApply(sample.ExpressionSet, 1, myreg.demo)
# a resampling method
resamp <- function(ESET) {
ntiss <- ncol(exprs(ESET))
newind <- sample(1:ntiss, size = ntiss, replace = TRUE)
ESET[newind,]
}
# a filter
q3g100filt <- function(eset) {
apply(exprs(eset), 1, function(x) quantile(x,.75) > 100)
}
# filter after resampling and then apply
set.seed(123)
rest <- esApply({bool <- q3g100filt(resamp(sample.ExpressionSet)); sample.ExpressionSet[bool,]},
1, mytt.demo)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.