apply: S3 generic apply method for msSet Class

In zeehio/msProcess: Protein Mass Spectra Processing

Description

The apply function in S-PLUS is S3 generic, but it is not so in R. For the msProcess package, the apply function is overloaded to be an S3 generic function, relying on UseMethod("apply") to distribute the call. The apply.default function is defined to be base::apply, so if the class of the X input is not "msSet" then the standard R definition will be used.

Usage

apply(X, MARGIN, FUN, ..., type="intensity", pre=NULL, covar=NULL)

Arguments

X	an msSet object.
MARGIN	an integer denoting the dimension over which the given function is applied. Use MARGIN=1 for rows and MARGIN=2 for columns.
FUN	a function to be applied to the specified array sections, or a character string giving the name of the function.
...	any arguments to FUN. They are passed unchanged to each call of FUN and include their names.
type	a character string specifying the name of the array in the msSet object list to operate over. A typical value is "intensity" or "noise", but the name of any legitimate matrix attached to the primary msSet object list can be used for type. Default: "intensity" (the intensity matrix).
pre	a function that is applied to the matrix prior to processing the data. Typical examples would be pre=t (matrix transpose), pre=log (log of matrix), etc. Default: NULL (no function is applied a priori).
covar	a named list of additional matrices to be parsed in the same manner as the primary matrix (specified by type). The contents of the covar matrices are also sent to the FUN function as an input argument with the same name as that supplied in the covar list. As an example, assuming x is an object of class msSet that contains the matrices x$intensity and x$z, then the call: apply(x, MARGIN=1, FUN="foo", type="intensity", covar=list(z=z)), will ultimately result in calls foo(x$intensity[i,], z=x$z[i,]), where i=1:numRows(x$intensity). Note that the matrix x$z need only have the same number of rows in this case since MARGIN=1, but need not necessarily contain the same number of columns, i.e., restrictions on the dimensions of covar matrices not specfied by MARGIN are controlled by the FUN function. Default: NULL (no covariate matrices).

Value

a matrix containing the result of the FUN function applied to the matrix of type type found in the original msSet object X.

See Also

msList, msSet.

Examples

if (!exists("qcset")) data("qcset", package="msProcess")

# find the means of each spectrum
# and convert the result to a single-row matrix
means <- apply(qcset, MARGIN=2, mean)
nc <- NCOL(qcset$intensity)
means <- matrix(means, ncol=nc)
print(means)

# add the means (single-row) matrix to the original
# msSet object and verify its existence
z <- msSet(qcset, means=means)
names(z)
is.matrix(z$means)

# to illustrate the use of the 'covar' argument in apply,
# create a faux function that finds tha maximum absolute
# difference between each spectrum and its mean value
foo <- function(x,meanvals) max(abs(x-meanvals))
maxdiff <- as.vector(apply(z, MARGIN=2, FUN=foo, covar=list(meanvals=means)))
print(maxdiff)

# verify the results: should get vector of nc zeros
unlist(lapply(seq(along=maxdiff), function(i,z,maxdiff)
  vecnorm(max(abs(z$intensity[,i]-z$means[,i])) - maxdiff[i]),
  z=z, maxdiff=maxdiff))

zeehio/msProcess documentation built on May 4, 2019, 10:15 p.m.