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## msiSlices
## create intensity matrix for each slice from a list of MassSpectrum/MassPeaks
## objects
##
## params:
## x: list of MassSpectrum/MassPeaks objects
## center: position(s) of interest (in mass values)
## tolerance: aggregate data around center +/- tolerance
## method: aggregate method
## adjust: set coordinates to 1,1 (if there are empty/missing spectra before)
##
## returns:
## an array (dim: x, y, z=slice nr)
##
msiSlices <- function(x, center, tolerance, method=c("sum", "mean", "median"),
adjust=TRUE) {
x <- suppressWarnings(trim(x, range=range(center) + c(-tolerance, tolerance)))
.msiSlices(m=.as.matrix.MassObjectList(x),
coord=coordinates(x, adjust=adjust),
center=center, tolerance=tolerance, method=method)
}
.msiSlices <- function(m, coord, center, tolerance,
method=c("sum", "mean", "median")) {
method <- match.arg(method)
fun <- switch(method,
"sum" = rowSums,
"mean" = rowMeans,
"median" = function(x, ...).colMedians(t(x), ...))
n <- unname(apply(coord, MARGIN=2L, FUN=max))
l <- pmin(findInterval(center - tolerance - .Machine$double.eps,
attr(m, "mass")) + 1L, ncol(m))
r <- findInterval(center + tolerance + .Machine$double.eps, attr(m, "mass"))
slices <- array(NA_real_, dim=c(x=n[1L], y=n[2L], z=length(center)))
for (i in seq_along(center)) {
slices[cbind(coord, i)] <- fun(m[, l[i]:r[i], drop=FALSE], na.rm=TRUE)
}
attr(slices, "center") <- center
attr(slices, "tolerance") <- tolerance
attr(slices, "method") <- method
slices
}
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