Hey Isam,
Thanks for the advice.
It really looks like if we are collecting too much noise at low abundance.
We decided to use the density algorithm for our further approach and tried to optimize some parameters like bw and mzwid.
xsneg <- xcmsSet("C:/LCMS_Processed_Data/data_processing/20130327_Test/netCDF_data/negative", method="centWave", ppm=4, peakwidth=c(3,8), snthr=3, nSlaves=8)
xspos <- xcmsSet("C:/LCMS_Processed_Data/data_processing/20130327_Test/netCDF_data/positive", method="centWave", ppm=4, peakwidth=c(3,8), snthr=3, nSlaves=8)
gnego <- group(xsneg, method="density", bw=0.01, mzwid=0.003, max=100, minsamp = 4)
gposo <- group(xspos, method="density", bw=0.01, mzwid=0.003, max=100, minsamp = 4)
neg <- xsAnnotate(gnego, nSlaves=8, polarity="negative") #, calcCiS=TRUE, calcIso=FALSE, calcCaS=FALSE)
neg <- groupFWHM(neg)
neg <- groupCorr(neg)
neg <- findAdducts(neg, polarity="negative", mzabs=0.002)
neg <- findIsotopes(neg, mzabs = 0.002)
pos <- xsAnnotate(gposo, nSlaves=8, polarity="positive")#, calcCiS=TRUE, calcIso=FALSE, calcCaS=FALSE)
pos <- groupFWHM(pos)
pos <- groupCorr(pos)
pos <- findAdducts(pos, polarity="positive", mzabs=0.002)
pos <- findIsotopes(pos, mzabs = 0.002)
combipos <- combinexsAnnos(pos, neg, pos=TRUE, tol=2, ruleset=NULL)
combineg <- combinexsAnnos(pos, neg, pos=FALSE, tol=2, ruleset=NULL)
It resulted in around 30 thousand features in positive and 10 thousand features in negative mode being present in all four samples (around one third are isotope peaks).
Cheers,
Dominic