Cdsw-class {prozor} | R Documentation |
Compute dynamic swath windows
Description
initialize
create equidistant breaks
quantile breaks
sampling breaks
barplot showing the number of precursors per window
Table with window boundaries and statistics
summary of the binning process (see objectiveMS1Function for more details)
moves window start and end to region with as few as possible precursor masses
shows the generated DIA cycle
Arguments
list |
of masses |
nbins |
number of bins default 25 |
maxwindow |
largest window size |
minwindow |
smallest window size |
digits |
mass precision default 2 |
digigits |
mass precision |
max |
number of bins |
plot |
default TRUE |
overlap |
size of window overlap default 1 m/z |
Value
array of masses
array with masses
array with masses
data.frame with columns: - from (window start) - to (window end) - mid (window centre), width (window width) - counts expected number of precursors
list with optimization scores
data.frame with optimized windows
Fields
masses
MS1 masses
breaks
the breaks
nbins
number of bins
digits
mass accuracy in result
Methods
asTable(overlap = 1)
make windows
error()
show error
optimizeWindows(digits = 1, maxbin = 15, plot = FALSE, overlap = 0)
optimizes the windows
quantile_breaks(digits = 2)
same number of MS1 in each window but might violate hard constraints
sampling_breaks(maxwindow = 150, minwindow = 5, digits = 2, plot = FALSE)
starts with quantile breaks but mixes with uniform data to satisfy had constraints
Examples
data(masses)
cdsw <- Cdsw(masses)
tmp <- cdsw$sampling_breaks(maxwindow=100,plot=TRUE)
cdsw$plot()
cdsw$asTable()
cdsw$breaks
cdsw$optimizeWindows()
cdsw$showCycle()