vignettes/globalstd.Rmd
globalstd.Rmd
pmd
package use Paired Mass Distance (PMD) relationship to analysis the GC/LC-MS based non-targeted data. PMD means the distance between two masses or mass to charge ratios. In mass spectrometry, PMD would keep the same value between two masses and two mass to charge ratios(m/z). There are two kinds of PMD involved in this package: PMD from the same compound and PMD from different compounds. In GC/LC-MS or XCMS based non-targeted data analysis, peaks could be separated by chronograph and same compound means ions from similar retention times or ions co-eluted by certain column.
For MS1 full scan data, we could build retention time(RT) bins to assign peaks into different RT groups by retention time hierarchical clustering analysis. For each RT group, the peaks should come from same compounds or co-elutes. If certain PMD appeared in multiple RT groups, it would be related to the relationship about adducts, neutral loss, isotopologues or common fragments ions.
The peaks from different retention time groups would like to be different compounds separated by chronograph. The PMD would reflect the relationship about homologous series or chemical reactions.
GlobalStd algorithm use the PMD within same RT group to find independent peaks among certain data set. Then, structure/reaction directed analysis use PMD from different RT groups to screen important compounds or reactions.
The input data should be a list
object with at least two elements from a peaks list:
mz
, high resolution mass spectrometry is requiredrt
However, I suggested to add intensity and group information to the list for validation of PMD analysis.
In this package, a data set from in vivo solid phase micro-extraction(SPME) was attached. This data set contain 9 samples from 3 fish with triplicates samples for each fish. Here is the data structure:
library(pmd)
data("spmeinvivo")
str(spmeinvivo)
#> List of 4
#> $ data : num [1:1459, 1:9] 1095 10439 10154 2797 90211 ...
#> ..- attr(*, "dimnames")=List of 2
#> .. ..$ : chr [1:1459] "100.1/170" "100.5/86" "101/85" "103.1/348" ...
#> .. ..$ : chr [1:9] "1405_Fish1_F1" "1405_Fish1_F2" "1405_Fish1_F3" "1405_Fish2_F1" ...
#> $ group:'data.frame': 9 obs. of 2 variables:
#> ..$ sample_name : chr [1:9] "1405_Fish1_F1" "1405_Fish1_F2" "1405_Fish1_F3" "1405_Fish2_F1" ...
#> ..$ sample_group: chr [1:9] "fish1" "fish1" "fish1" "fish2" ...
#> $ mz : num [1:1459] 100 101 101 103 104 ...
#> $ rt : num [1:1459] 170.2 86.3 84.9 348.1 48.8 ...
You could build this list
or mzrt
object from the xcms
objects via enviGCMS
package. When you have a xcmsSet
object or XCMSnExp
object named xset
, you could use enviGCMS::getmzrt(xset)
to get such list. Of course you could build such list by yourself.
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 common fragments ions. The third step is to screen the independent peaks.
Here is a workflow for this algorithm:
knitr::include_graphics('https://yufree.github.io/presentation/figure/GlobalStd.png')
pmd <- getpaired(spmeinvivo)
#> 75 retention time cluster found.
#> 369 paired masses found
#> 5 unique within RT clusters high frequency PMD(s) used for further investigation.
#> The unique within RT clusters high frequency PMD(s) is(are) 28.03 21.98 44.03 17.03 18.01.
#> 719 isotopologue(s) related paired mass found.
#> 492 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. Retention time cluster cutoff should fit the peak picking algorithm. For HPLC, 10 is suggested and 5 could be used for UPLC.
Global PMD’s retention time group numbers should be around 20 percent of the retention time cluster numbers. For example, if you find 100 retention time clusters, I suggested you use 20 as the cutoff of empirical global PMD’s retention time group numbers. If you don’t specifically assign a value to ng
, the algorithm will select such recommendation by default setting.
Take care of the retention time cluster with lots of peaks. In this case, such cluster could be co-eluted compounds on certain column. It would be wise to trim the retention time window for high quality peaks. Another important hint is that pre-filter your peak list by black samples or other quality control samples. Otherwise the running time would be long and lots of pmd relationship would be just from noise.
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.
plotpaired(pmd)
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)
}
This is an easy way to find potential adducts of the data by high frequency PMD from the same compound. For example, 21.98 Da could be the mass distances between \([M+H]^+\) and \([M+Na]^+\). In this case, user could find the potential adducts or neutral loss even when they have no preferred adducts list. If one adduct exist in certain analytical system, the high frequency PMD will reveal such relationship. The high frequency PMD list could also be used to check the fragmental pattern of in-source reactions as long as such patterns are popular among all collected ions.
You could use getstd
function to get the independent peaks. Independent peaks mean the peaks list removing the redundant peaks such as adducts, neutral loss, isotopologues and comment fragments ions found by PMD analysis in STEP2. Ideally, those peaks could be molecular ions while they might still contain redundant peaks.
std <- getstd(pmd)
#> 8 retention group(s) have single peaks. 14 23 32 33 54 55 56 75
#> 11 group(s) with multiple peaks while no isotope/paired relationship 4 5 7 8 11 41 42 49 68 72 73
#> 9 group(s) with multiple peaks with isotope without paired relationship 2 9 22 26 52 62 64 66 70
#> 4 group(s) with paired relationship without isotope 1 10 15 18
#> 43 group(s) with paired relationship and isotope 3 6 12 13 16 17 19 20 21 24 25 27 28 29 30 31 34 35 36 37 38 39 40 43 44 45 46 47 48 50 51 53 57 58 59 60 61 63 65 67 69 71 74
#> 291 std mass found.
Here you could plot the peaks by plotstd
function to show the distribution of independent peaks:
plotstd(std)
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')
Original GlobalStd algorithm only use mass to charge ratio and retention time of peaks to select independent peaks. However, if intensity data across samples are available, correlation coefficient of paired ions could be used to further filter the random noise in high frequency PMDs. You could set up cutoff of Pearson Correlation Coefficient between peaks to refine the peaks selected by GlobalStd within same retention time groups. In this case, the numbers of selected independent peaks will be further reduced. When you use this parameter, make sure the intensity data are from real samples instead of blank samples, which will affect the calculation of correlation coefficient.
std2 <- getstd(pmd,corcutoff = 0.9)
#> 8 retention group(s) have single peaks. 14 23 32 33 54 55 56 75
#> 23 group(s) with multiple peaks while no isotope/paired relationship 2 4 5 7 8 10 11 15 18 26 35 39 41 42 49 50 59 62 68 69 70 72 73
#> 14 group(s) with multiple peaks with isotope without paired relationship 9 12 22 24 27 28 34 51 52 57 60 64 66 71
#> 3 group(s) with paired relationship without isotope 1 53 74
#> 27 group(s) with paired relationship and isotope 3 6 13 16 17 19 20 21 25 29 30 31 36 37 38 40 43 44 45 46 47 48 58 61 63 65 67
#> 120 std mass found.
You need to check the GlobalStd algorithm’s results by principal components analysis(PCA). If we removed too much peaks containing information, the score plot of reduced data set would show great changes.
library(enviGCMS)
par(mfrow = c(2,2),mar = c(4,4,2,1)+0.1)
plotpca(std$data,lv = as.numeric(as.factor(std$group$sample_group)),main = "all peaks")
plotpca(std$data[std$stdmassindex,],lv = as.numeric(as.factor(std$group$sample_group)),main = paste(sum(std$stdmassindex),"independent peaks"))
plotpca(std2$data[std2$stdmassindex,],lv = as.numeric(as.factor(std$group$sample_group)),main = paste(sum(std2$stdmassindex),"reduced independent peaks"))
You might find original GlobalStd algorithm show a similar PCA score plot with original data while GlobalStd algorithm considering intensity data seems change the profile. The major reason is that correlation coefficient option in the algorithm will remove the paired ions without strong correlation. It will be aggressive to remove low intensity peaks, which are vulnerable by baseline noise. However, such options would be helpful if you only concern high quality peaks for following analysis. Otherwise, original GlobalStd will keep the most information for explorer purpose.
GlobalStd algorithm in pmd
package could be treated as a method to extract pseudo spectra. You could use getcluster
to get peaks groups information for all GlobalStd peaks. This function would consider the merge of GlobalStd peaks when certain peak is involved in multiple clusters. Then you could choose export peaks with the highest intensities or base peaks in each GlobalStd merged peaks groups. Meanwhile, you could also include the correlation coefficient cutoff to further improve the data quality.
stdcluster <- getcluster(std)
# extract pseudospectra for std peak 71
idx <- unique(stdcluster$cluster$largei[stdcluster$cluster$i==71])
plot(stdcluster$cluster$mz[stdcluster$cluster$largei==idx],stdcluster$cluster$ins[stdcluster$cluster$largei==idx],type = 'h',xlab = 'm/z',ylab = 'intensity',main = 'pseudo spectra for GlobalStd peak 71')
# export peaks with the highest intensities in each GlobalStd peaks groups.
data <- stdcluster$data[stdcluster$stdmassindex2,]
# considering the correlation coefficient cutoff
stdcluster2 <- getcluster(std, corcutoff = 0.9)
# considering the correlation coefficient cutoff for both psedospectra extraction and GlobalStd algorithm
stdcluster3 <- getcluster(std2, corcutoff = 0.9)
We supplied getcorcluster
to find peaks groups by correlation analysis only. The base peaks of correlation cluster were selected to stand for the compounds.
corcluster <- getcorcluster(spmeinvivo)
#> 75 retention time cluster found.
# extract pseudospectra 1@46
peak <- corcluster$cluster[corcluster$cluster$largei == '1@46',]
plot(peak$ins~peak$mz,type = 'h',xlab = 'm/z',ylab = 'intensity',main = 'pseudo spectra for correlation cluster')
Then we could compare the compare reduced result using PCA similarity factor. A good peak selection algorithm could show a high PCA similarity factor compared with original data set while retain the minimized number of peaks.
par(mfrow = c(3,3),mar = c(4,4,2,1)+0.1)
plotpca(std$data[std$stdmassindex,],lv = as.numeric(as.factor(std$group$sample_group)),main = paste(sum(std$stdmassindex),"independent peaks"))
plotpca(std$data[stdcluster$stdmassindex2,],lv = as.numeric(as.factor(std$group$sample_group)),main = paste(sum(stdcluster$stdmassindex2),"independent base peaks"))
plotpca(std$data[stdcluster2$stdmassindex2,],lv = as.numeric(as.factor(std$group$sample_group)),main = paste(sum(stdcluster2$stdmassindex2),"independent reduced base peaks"))
plotpca(std$data[corcluster$stdmassindex,],lv = as.numeric(as.factor(std$group$sample_group)),main = paste(sum(corcluster$stdmassindex),"peaks without correlationship"))
plotpca(std$data[corcluster$stdmassindex2,],lv = as.numeric(as.factor(std$group$sample_group)),main = paste(sum(corcluster$stdmassindex2),"base peaks without correlationship"))
plotpca(std$data,lv = as.numeric(as.factor(std$group$sample_group)),main = paste(nrow(std$data),"all peaks"))
plotpca(std$data[stdcluster3$stdmassindex2,],lv = as.numeric(as.factor(std$group$sample_group)),main = paste(sum(stdcluster3$stdmassindex2),"reduced independent base peaks"))
pcasf(std$data, std$data[std$stdmassindex,])
#> pcasf
#> 0.9993497
pcasf(std$data, std$data[stdcluster$stdmassindex2,])
#> pcasf
#> 0.9993578
pcasf(std$data, std$data[stdcluster2$stdmassindex2,])
#> pcasf
#> 0.999346
pcasf(std$data, std$data[corcluster$stdmassindex,])
#> pcasf
#> 0.9471586
pcasf(std$data, std$data[corcluster$stdmassindex2,])
#> pcasf
#> 0.9497193
pcasf(std$data, std$data[stdcluster3$stdmassindex2,])
#> pcasf
#> 0.713527
In this case, five peaks selection algorithms are fine to stand for the original peaks with PCA similarity score larger than 0.9. However, the independent base peaks retain the most information with relative low numbers of peaks.
getsda
function could be used to perform Structure/reaction directed analysis. The cutoff of frequency is automate found by PMD network analysis with the largest mean distance of all nodes.
sda <- getsda(std)
#> PMD frequency cutoff is 6 by PMD network analysis with largest network average distance 6.67 .
#> 53 groups were found as high frequency PMD group.
#> 0 was found as high frequency PMD.
#> 1.98 was found as high frequency PMD.
#> 2.01 was found as high frequency PMD.
#> 2.02 was found as high frequency PMD.
#> 6.97 was found as high frequency PMD.
#> 11.96 was found as high frequency PMD.
#> 12 was found as high frequency PMD.
#> 13.98 was found as high frequency PMD.
#> 14.02 was found as high frequency PMD.
#> 14.05 was found as high frequency PMD.
#> 15.99 was found as high frequency PMD.
#> 16.03 was found as high frequency PMD.
#> 19.04 was found as high frequency PMD.
#> 28.03 was found as high frequency PMD.
#> 30.05 was found as high frequency PMD.
#> 31.99 was found as high frequency PMD.
#> 33.02 was found as high frequency PMD.
#> 37.02 was found as high frequency PMD.
#> 42.05 was found as high frequency PMD.
#> 48.04 was found as high frequency PMD.
#> 48.98 was found as high frequency PMD.
#> 49.02 was found as high frequency PMD.
#> 54.05 was found as high frequency PMD.
#> 56.06 was found as high frequency PMD.
#> 56.1 was found as high frequency PMD.
#> 58.04 was found as high frequency PMD.
#> 58.08 was found as high frequency PMD.
#> 58.11 was found as high frequency PMD.
#> 63.96 was found as high frequency PMD.
#> 66.05 was found as high frequency PMD.
#> 68.06 was found as high frequency PMD.
#> 70.04 was found as high frequency PMD.
#> 70.08 was found as high frequency PMD.
#> 74.02 was found as high frequency PMD.
#> 80.03 was found as high frequency PMD.
#> 82.08 was found as high frequency PMD.
#> 88.05 was found as high frequency PMD.
#> 91.1 was found as high frequency PMD.
#> 93.12 was found as high frequency PMD.
#> 94.1 was found as high frequency PMD.
#> 96.09 was found as high frequency PMD.
#> 101.05 was found as high frequency PMD.
#> 108.13 was found as high frequency PMD.
#> 110.11 was found as high frequency PMD.
#> 112.16 was found as high frequency PMD.
#> 116.08 was found as high frequency PMD.
#> 122.15 was found as high frequency PMD.
#> 124.16 was found as high frequency PMD.
#> 126.14 was found as high frequency PMD.
#> 144.18 was found as high frequency PMD.
#> 148.04 was found as high frequency PMD.
#> 150.2 was found as high frequency PMD.
#> 173.18 was found as high frequency PMD.
Such largest mean distance of all nodes is calculated for top 1 to 100 (if possible) high frequency PMDs. Here is a demo for the network generation process.
library(igraph)
#>
#> Attaching package: 'igraph'
#> The following objects are masked from 'package:stats':
#>
#> decompose, spectrum
#> The following object is masked from 'package:base':
#>
#> union
cdf <- sda$sda
# get the PMDs and frequency
pmds <- as.numeric(names(sort(table(cdf$diff2),decreasing = T)))
freq <- sort(table(cdf$diff2),decreasing = T)
# filter the frequency larger than 10 for demo
pmds <- pmds[freq>10]
cdf <- sda$sda[sda$sda$diff2 %in% pmds,]
g <- igraph::graph_from_data_frame(cdf,directed = F)
l <- igraph::layout_with_fr(g)
for(i in 1:length(pmds)){
g2 <- igraph::delete_edges(g,which(E(g)$diff2%in%pmds[1:i]))
plot(g2,edge.width=1,vertex.label="",vertex.size=1,layout=l,main=paste('Top',length(pmds)-i,'high frequency PMDs'))
}
Here we could find more and more compounds will be connected with more high frequency PMDs. Meanwhile, the mean distance of all network nodes will increase. However, some PMDs are generated by random combination of ions. In this case, if we included those PMDs for the network, the mean distance of all network nodes will decrease. Here, the largest mean distance means no more information will be found for certain data set and such value is used as the cutoff for high frequency PMDs selection.
You could use plotstdsda
to show the distribution of the selected paired peaks.
plotstdsda(sda)
You could also use index to show the distribution of certain PMDs.
par(mfrow = c(1,3),mar = c(4,4,2,1)+0.1)
plotstdsda(sda,sda$sda$diff2 == 2.02)
plotstdsda(sda,sda$sda$diff2 == 28.03)
plotstdsda(sda,sda$sda$diff2 == 58.04)
Structure/reaction directed analysis could be directly performed on all the peaks, which is slow to process:
sdaall <- getsda(spmeinvivo)
#> PMD frequency cutoff is 104 by PMD network analysis with largest network average distance 14.06 .
#> 6 groups were found as high frequency PMD group.
#> 0 was found as high frequency PMD.
#> 2.02 was found as high frequency PMD.
#> 28.03 was found as high frequency PMD.
#> 31.01 was found as high frequency PMD.
#> 58.04 was found as high frequency PMD.
#> 116.08 was found as high frequency PMD.
par(mfrow = c(1,3),mar = c(4,4,2,1)+0.1)
plotstdsda(sdaall,sdaall$sda$diff2 == 2.02)
plotstdsda(sdaall,sdaall$sda$diff2 == 28.03)
plotstdsda(sdaall,sdaall$sda$diff2 == 58.04)
Structure/Reaction directed analysis could also use correlation to restrict the paired ions. However, similar to GlobalStd algorithm, such cutoff will remove low intensity data. Researcher should have a clear idea to use this cutoff.
sda2 <- getsda(std, corcutoff = 0.9)
#> PMD frequency cutoff is 6 by PMD network analysis with largest network average distance 6.67 .
#> 41 groups were found as high frequency PMD group.
#> 0 was found as high frequency PMD.
#> 1.98 was found as high frequency PMD.
#> 2.01 was found as high frequency PMD.
#> 2.02 was found as high frequency PMD.
#> 11.96 was found as high frequency PMD.
#> 12 was found as high frequency PMD.
#> 13.98 was found as high frequency PMD.
#> 14.02 was found as high frequency PMD.
#> 14.05 was found as high frequency PMD.
#> 15.99 was found as high frequency PMD.
#> 16.03 was found as high frequency PMD.
#> 19.04 was found as high frequency PMD.
#> 28.03 was found as high frequency PMD.
#> 30.05 was found as high frequency PMD.
#> 31.99 was found as high frequency PMD.
#> 33.02 was found as high frequency PMD.
#> 42.05 was found as high frequency PMD.
#> 48.98 was found as high frequency PMD.
#> 49.02 was found as high frequency PMD.
#> 54.05 was found as high frequency PMD.
#> 56.06 was found as high frequency PMD.
#> 58.04 was found as high frequency PMD.
#> 58.08 was found as high frequency PMD.
#> 63.96 was found as high frequency PMD.
#> 66.05 was found as high frequency PMD.
#> 68.06 was found as high frequency PMD.
#> 70.08 was found as high frequency PMD.
#> 74.02 was found as high frequency PMD.
#> 80.03 was found as high frequency PMD.
#> 82.08 was found as high frequency PMD.
#> 88.05 was found as high frequency PMD.
#> 93.12 was found as high frequency PMD.
#> 94.1 was found as high frequency PMD.
#> 96.09 was found as high frequency PMD.
#> 108.13 was found as high frequency PMD.
#> 110.11 was found as high frequency PMD.
#> 112.16 was found as high frequency PMD.
#> 116.08 was found as high frequency PMD.
#> 122.15 was found as high frequency PMD.
#> 124.16 was found as high frequency PMD.
#> 126.14 was found as high frequency PMD.
plotstdsda(sda2)
When you only have data of peaks without retention time or compounds list, structure/reaction directed analysis could also be done by getrda
function.
sda <- getrda(spmeinvivo$mz[std$stdmassindex])
#> 15209 pmd found.
#> 3 pmd used.
globalstd
function is a wrap function to process GlobalStd algorithm and structure/reaction directed analysis in one line. All the plot function could be directly used on the list
objects from globalstd
function. If you want to perform structure/reaction directed analysis, set the sda=T
in the globalstd
function.
result <- globalstd(spmeinvivo, sda=FALSE)
#> 75 retention time cluster found.
#> 369 paired masses found
#> 5 unique within RT clusters high frequency PMD(s) used for further investigation.
#> The unique within RT clusters high frequency PMD(s) is(are) 28.03 21.98 44.03 17.03 18.01.
#> 719 isotopologue(s) related paired mass found.
#> 492 multi-charger(s) related paired mass found.
#> 8 retention group(s) have single peaks. 14 23 32 33 54 55 56 75
#> 11 group(s) with multiple peaks while no isotope/paired relationship 4 5 7 8 11 41 42 49 68 72 73
#> 9 group(s) with multiple peaks with isotope without paired relationship 2 9 22 26 52 62 64 66 70
#> 4 group(s) with paired relationship without isotope 1 10 15 18
#> 43 group(s) with paired relationship and isotope 3 6 12 13 16 17 19 20 21 24 25 27 28 29 30 31 34 35 36 37 38 39 40 43 44 45 46 47 48 50 51 53 57 58 59 60 61 63 65 67 69 71 74
#> 291 std mass found.
Independent peaks are supposing generated from different compounds. We could use those peaks for MS/MS analysis instead of DIA or DDA. Here we need multiple injections for one sample since it might be impossible to get all ions’ fragment ions in one injection with good sensitivity. You could use gettarget
to generate the index for the injections and output the peaks for each run.
# you need retention time for independent peaks
index <- gettarget(std$rt[std$stdmassindex])
#> You need 10 injections!
# output the ions for each injection
table(index)
#> index
#> 1 2 3 4 5 6 7 8 9 10
#> 28 33 31 33 35 23 15 40 30 23
# show the ions for the first injection
std$mz[index==1]
#> [1] 103.0547 125.9874 132.0050 149.9530 152.0578 156.1777 156.9622 172.1705
#> [9] 175.0873 175.1481 175.1482 177.1636 181.1597 184.9858 186.1854 191.1801
#> [17] 192.1604 196.4492 208.1693 209.9806 219.0540 226.1823 228.1973 236.1626
#> [25] 242.2863 242.2863 252.1237 254.2122 262.1453 270.3185 270.3185 273.8902
#> [33] 274.8744 280.2641 281.0520 294.2054 299.1113 300.1148 300.2046 303.2325
#> [41] 304.9038 307.1107 307.9421 309.0913 309.3159 312.3261 320.3322 323.3221
#> [49] 329.0928 334.3101 337.3298 340.3593 349.3476 350.3426 359.2410 361.2353
#> [57] 368.3395 381.1324 383.1414 394.8754 395.2208 401.3420 401.3421 417.2462
#> [65] 421.2521 422.2952 424.0815 426.3146 429.3192 430.8888 431.0687 432.3878
#> [73] 435.3471 442.3376 447.9910 449.1148 463.3975 471.3317 485.2901 494.8112
#> [81] 505.1055 505.3342 522.3557 525.9831 538.3435 549.3617 555.2922 567.1783
#> [89] 567.3904 576.8454 577.1267 600.4401 607.4028 608.4054 608.4285 613.1827
#> [97] 616.4645 621.4195 622.4229 628.8597 634.8774 639.8539 652.8473 655.8704
#> [105] 668.8690 675.5084 680.4633 691.4631 703.3651 703.6382 713.4467 716.5241
#> [113] 737.3591 760.2210 762.3925 779.5153 780.8078 790.5883 791.1193 816.5102
#> [121] 831.6037 832.3212 832.8212 836.6816 839.3409 841.8196 845.5232 853.7083
#> [129] 858.6636 867.4427 874.3049 878.3781 889.4890 900.3092 911.7489 924.7338
#> [137] 942.7638 943.7976 974.8148 975.8147 998.7737
std$rt[index==1]
#> [1] 348.1340 76.4910 49.4910 1079.6400 219.5120 405.3890 145.5380
#> [8] 478.9360 511.2940 614.4130 453.1780 594.2680 615.0530 85.4930
#> [15] 501.3300 638.8870 337.3920 145.1850 611.4110 147.8780 169.5380
#> [22] 611.4120 453.1570 430.6770 780.5760 573.8035 591.4830 451.6785
#> [29] 491.1510 501.6500 447.3925 145.9680 218.5540 576.6950 617.4140
#> [36] 452.0005 447.6060 447.6060 172.2230 586.1240 145.4960 212.7520
#> [43] 146.3215 568.7680 594.0550 636.9570 639.0990 639.3130 212.3870
#> [50] 608.1990 595.1260 658.6000 659.2440 625.9840 213.7505 550.5530
#> [57] 644.2430 447.8210 605.5200 217.1550 613.7680 672.1020 632.8410
#> [64] 444.6075 634.3425 639.5290 583.9830 404.5330 557.1970 549.6980
#> [71] 762.3630 612.4845 581.9475 656.2430 212.6570 717.1020 582.3755
#> [78] 540.8660 582.6970 890.7680 762.4675 422.9630 546.4830 639.0980
#> [85] 537.2240 418.7850 512.7070 762.3630 213.3840 215.1975 819.6200
#> [92] 455.1500 434.9630 434.7490 613.3395 819.1920 454.9350 454.9350
#> [99] 455.1500 213.5990 213.7270 214.1780 215.5645 213.3340 213.5090
#> [106] 468.2215 468.4360 525.0090 213.5480 638.8855 525.0090 481.0790
#> [113] 213.5480 700.3870 485.8675 519.6690 214.7290 492.8650 639.9560
#> [120] 213.3340 503.6870 213.5140 213.7130 646.6000 213.6370 213.8945
#> [127] 517.5090 646.6610 646.7630 494.3650 638.7790 381.9255 213.6405
#> [134] 213.5480 650.4570 636.9560 636.9060 214.0705 213.7130 213.7270
#> [141] 215.6570
An interactive document has been included in this package to perform PMD analysis. You need to prepare a csv file with m/z and retention time of peaks. Such csv file could be generated by run enviGCMS::getcsv()
on the list
object from enviGCMS::getmzrt(xset)
function. The xset
should be XCMSnExp
object or xcmsSet
object. You could also generate the csv file by enviGCMS::getmzrt(xset,name = 'test')
. You will find the csv file in the working dictionary named test.csv
.
Then you could run runPMD()
to start the Graphical user interface(GUI) for GlobalStd algorithm and structure/reaction directed analysis.