GlobalStd algorithm
GlobalStd algorithm try to find independent peaks among certain peaks list. The first step is retention time hierarchical clustering analysis. The second step is to find the relationship among adducts, neutral loss, isotopologues and commen fragments ions. The third step is to screen the independent peaks.
Retention time hierarchical clustering
pmd <- getpaired(spmeinvivo, rtcutoff = 10, ng = 10)
#> 75 retention time cluster found.
#> 443 paired masses found
#> 10 unique PMD(s) used for further investigation.
#> 631 isotopologue(s) related paired mass found.
#> 685 multi-charger(s) related paired mass found.
plotrtg(pmd)
This plot would show the distribution of RT groups. The rtcutoff in function getpaired could be used to set the cutoff of the distances in retention time hierarchical clustering analysis.
Relationship among adducts, neutral loss, isotopologues and commen fragments ions
The ng in function getpaired could be used to set cutoff of global PMD’s retention time group numbers. If ng is 10, at least 10 of the retention time groups should contain the shown PMD relationship. You could use plotpaired to show the distribution.
You could also show the distribution of PMD relationship by index:
# show the unique PMD found by getpaired function
for(i in 1:length(unique(pmd$paired$diff2))){
diff <- unique(pmd$paired$diff2)[i]
index <- pmd$paired$diff2 == diff
plotpaired(pmd,index)
}
Screen the independent peaks
You could use getstd function to get the independent peaks.
std <- getstd(pmd)
#> 11 group(s) with multiple peaks while no isotope/paired relationship
#> 8 group(s) with multiple peaks with isotope without paired relationship
#> 4 group(s) with paired relationship without isotope
#> 44 group(s) with paired relationship and isotope
#> 277 std mass found.
#> 8 retention group(s) have single peaks.Here you could plot the peaks by plotstd function to show the distribution of independent peaks:
You could also plot the peaks distribution by assign a retention time group via plotstdrt:
par(mfrow = c(2,3))
plotstdrt(std,rtcluster = 23,main = 'Retention time group 23')
plotstdrt(std,rtcluster = 9,main = 'Retention time group 9')
plotstdrt(std,rtcluster = 18,main = 'Retention time group 18')
plotstdrt(std,rtcluster = 67,main = 'Retention time group 67')
plotstdrt(std,rtcluster = 49,main = 'Retention time group 49')
plotstdrt(std,rtcluster = 6,main = 'Retention time group 6')
Validation by principal components analysis(PCA)
You need to check the GlobalStd algorithm’s results by principal components analysis(PCA).
library(enviGCMS)
par(mfrow = c(1,2),mar = c(4,4,2,1)+0.1)
plotpca(std$data,lv = as.numeric(std$group$class),main = substitute(paste(italic('in vivo'), " SPME samples(all peaks)")))
plotpca(std$data[std$stdmassindex,],lv = as.numeric(std$group$class),main = substitute(paste(italic('in vivo'), " SPME samples(selected peaks)")))
