Function optimizes Extraction windows for DIA/SWATH so we have the same number of precursor per window. This optimization is based on spectral library data or non redundant .blib files (Bibliospec).
library(prozor)
data("masses") cdsw <- Cdsw(masses , nbins = 25, digits = 1) cdsw$plot()
knitr::kable(cdsw$asTable())
constError <- cdsw$error()
quantile
Same number of MS1 precursors in each window
cdsw$quantile_breaks()
cdsw$plot()
knitr::kable(cdsw$asTable())
quantileError <- cdsw$error()
Using this method the window start and end is shifted to a mass range with as few MS1 peaks as possible.
knitr::kable(cdsw$optimizeWindows(maxbin = 10, plot = TRUE) )
cdsw$sampling_breaks(maxwindow = 100,plot = TRUE)
cdsw$plot()
knitr::kable(cdsw$asTable())
knitr::kable(cdsw$optimizeWindows(maxbin = 10, plot = TRUE) )
mixedError <- cdsw$error()
We compare the optimal number of MS1 peaks per SWATH window (same in each window) with the numbers obtained by using all of the 3 methods implemented.
barplot(c(const = constError$score1, quantile = quantileError$score1, mixed = mixedError$score1),ylab = "Manhattan distance") barplot(c(const = constError$score2, quantile = quantileError$score2, mixed = mixedError$score2),ylab = "Euclidean distance")
We can see that Method 3 has a relatively small error although it is able to fulfill constraints such as maximum window size.
sessionInfo()
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