lcms {ptw} | R Documentation |
The lcms
data consists of a 100 x 2000 x 3 array lcms
, a
vector time
of length 2000 and a vector mz
of length 100. The
LC-MS data in the array are a subset of a larger set measured on a
tryptic digest of E. coli proteins. Peak picking leads to the object
ldms.pks (see example section).
data(lcms)
Nijmegen Proteomics Facility, Department of Laboratory Medicine, Radboud University Nijmegen Medical Centre
Bloemberg, T.G., et al. (2010) "Improved parametric time warping for Proteomics", Chemometrics and Intelligent Laboratory Systems, 104 (1), 65 – 74.
## the lcms.pks object is generated in the following way: ## Not run: data(lcms) pick.peaks <- function(x, span) { span.width <- span * 2 + 1 loc.max <- span.width + 1 - apply(embed(x, span.width), 1, which.max) loc.max[loc.max == 1 | loc.max == span.width] <- NA pks <- loc.max + 0:(length(loc.max)-1) pks <- pks[!is.na(pks)] pks.tab <- table(pks) pks.id <- as.numeric(names(pks.tab)[pks.tab > span]) cbind(rt = pks.id, I = x[pks.id]) } ## bring all samples to the same scale, copied from ptw man page lcms.scaled <- aperm(apply(lcms, c(1,3), function(x) x/mean(x) ), c(2,1,3)) lcms.s.z <- aperm(apply(lcms.scaled, c(1,3), function(x) padzeros(x, 250) ), c(2,1,3)) lcms.pks <- lapply(1:3, function(ii) { lapply(1:nrow(lcms.s.z[,,ii]), function(jj) cbind("mz" = jj, pick.peaks(lcms.s.z[jj,,ii], 5))) }) ## End(Not run)