Paired Mass Distance(PMD) analysis for GC/LC-MS based non-targeted analysis
Introduction of Paired Mass Distance analysis
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.
PMD from the same compound
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.
PMD from different compounds
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.
Data format
The input data should be a list object with at least two
elements from a peaks list:
- mass to charge ratio with name of
mz, high resolution mass spectrometry is required - retention time with name of
rt
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
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:
STEP1: Retention time hierarchical clustering
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.
STEP2: Relationship among adducts, neutral loss, isotopologues and common 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)
}
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.
STEP3: Screen the independent peaks
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:
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')
Extra filter with correlation coefficient cutoff
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.Validation by principal components analysis(PCA)
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$stdmassindex),"reduced independent peaks"))
You might find original GlobalStd algorithm show a similar PCA score plot with original data while GlobalStd algorithm considering intensity data seems change the profile. The major reason is that correlation coefficient option in the algorithm will remove the paired ions without strong correlation. It will be aggressive to remove low intensity peaks, which are vulnerable by baseline noise. However, such options would be helpful if you only concern high quality peaks for following analysis. Otherwise, original GlobalStd will keep the most information for explorer purpose.
Comparison with other pseudo spectra extraction method
GlobalStd algorithm in pmd package could be treated as a
method to extract pseudo spectra. You could use getcluster
to get peaks groups information for all GlobalStd peaks. This function
would consider the merge of GlobalStd peaks when certain peak is
involved in multiple clusters. Then you could choose export peaks with
the highest intensities or base peaks in each GlobalStd merged peaks
groups. Meanwhile, you could also include the correlation coefficient
cutoff to further improve the data quality.
stdcluster <- getcluster(std)
# extract pseudospectra for std peak 71
idx <- unique(stdcluster$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.
Structure/Reaction directed analysis
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)$diff2%in%pmds[1:i]))
plot(g2,edge.width=1,vertex.label="",vertex.size=1,layout=l,main=paste('Top',length(pmds)-i,'high frequency PMDs'))
}
Here we could find more and more compounds will be connected with more high frequency PMDs. Meanwhile, the mean distance of all network nodes will increase. However, some PMDs are generated by random combination of ions. In this case, if we included those PMDs for the network, the mean distance of all network nodes will decrease. Here, the largest mean distance means no more information will be found for certain data set and such value is used as the cutoff for high frequency PMDs selection.
You could use plotstdsda to show the distribution of the
selected paired peaks.
You could also use index to show the distribution of certain PMDs.
par(mfrow = c(1,3),mar = c(4,4,2,1)+0.1)
plotstdsda(sda,sda$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)
Extra filter with correlation coefficient cutoff
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)
Structure/reaction directed analysis for peaks/compounds only data
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)
#> 164462 pmd found.
#> 20 pmd used.
# check high frequency pmd
colnames(sda)
#> [1] "0" "1.001" "1.002" "1.003" "1.004" "2.015" "2.016"
#> [8] "14.015" "17.026" "18.011" "21.982" "28.031" "28.032" "44.026"
#> [15] "67.987" "67.988" "88.052" "116.192" "135.974" "135.975"
# get certain pmd related m/z
idx <- sda[,'2.016']
# show the m/z
spmeinvivo$mz[idx]
#> [1] 118.0651 118.0652 120.0812 159.1575 162.0552 170.0330 170.0932 170.1541
#> [9] 174.1363 174.9917 175.0873 176.0305 176.0418 181.9872 184.1695 188.6484
#> [17] 192.1487 192.1604 226.9522 226.9523 228.1969 228.1973 259.1148 261.1317
#> [25] 270.3185 271.3217 272.3230 272.3234 273.8902 274.8744 284.2955 285.3002
#> [33] 285.3002 286.3101 286.3101 291.0712 293.1755 294.9392 296.2961 304.3081
#> [41] 305.2480 305.3118 308.0889 308.2953 308.2954 309.1672 309.2046 315.1781
#> [49] 317.9344 319.3005 319.3002 319.9302 320.3041 320.3322 321.3165 322.3185
#> [57] 323.3221 324.3266 325.3294 327.2022 327.3449 329.0052 331.0031 350.3426
#> [65] 352.3214 352.3215 353.3244 354.3365 355.0696 359.2410 361.2353 372.3197
#> [73] 375.3066 383.2804 383.3723 384.3350 385.2753 385.3480 387.2851 397.1907
#> [81] 399.3274 400.9174 401.3420 403.2859 432.8860 433.2781 445.8289 447.1173
#> [89] 451.3633 462.8615 522.3557 524.1178 525.9831 526.4841 705.7223 708.8218
#> [97] 976.3139 976.8122 982.7763Wrap function for GlobalStd algorithm
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.Use independent peaks for MS/MS validation (PMDDA)
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$stdmassindex])
#> You need 10 injections!
# output the ions for each injection
table(index)
#> index
#> 1 2 3 4 5 6 7 8 9 10
#> 20 33 33 35 28 23 24 38 26 31
# show the ions for the first injection
std$mz[index==1]
#> [1] 143.9602 156.9622 158.1546 161.0967 170.0931 174.0392 175.1481 208.1696
#> [9] 211.1698 212.2025 212.2026 214.9181 229.6753 237.6615 240.2335 242.2863
#> [17] 242.2863 252.1237 252.9981 258.9254 271.3217 281.0520 282.0531 284.2955
#> [25] 288.9232 295.1910 300.1148 320.8698 326.3060 327.0091 327.0777 328.0132
#> [33] 338.3435 341.2662 342.3095 344.3500 350.3426 359.2410 359.3151 365.8035
#> [41] 392.2873 401.3421 403.2859 406.2928 416.2862 418.9952 422.2952 448.1186
#> [49] 452.2768 454.2924 454.4255 460.3112 492.8839 496.3410 500.3789 501.2808
#> [57] 505.3342 522.3557 523.1141 534.4711 564.3304 577.1267 578.1272 582.1904
#> [65] 597.8743 602.3103 608.4054 654.2092 660.3160 663.4897 664.4577 665.4664
#> [73] 684.2033 694.6505 696.8477 698.7711 703.6382 713.4467 714.4484 731.6537
#> [81] 774.5655 791.1193 792.3470 798.3265 813.8323 825.3507 832.3212 875.4962
#> [89] 881.3220 883.7184 884.1633 884.7221 915.3139 927.8191 934.3019 937.8098
#> [97] 943.7976 974.8148 975.1471 985.4324
std$rt[index==1]
#> [1] 85.4930 145.5380 470.3640 490.7225 226.0540 216.6520 614.4130 415.6780
#> [9] 452.9630 639.1000 594.4850 76.9220 639.3150 639.6355 639.1010 631.5560
#> [17] 573.8035 591.4830 509.7940 144.6690 858.8350 617.4140 617.8400 613.7700
#> [25] 213.5480 518.1520 447.6060 218.2260 570.2690 509.5330 559.9820 509.1510
#> [33] 639.1010 564.2670 612.2680 638.9930 625.9840 213.7505 612.4820 639.2065
#> [41] 665.0280 632.8410 570.0540 610.9840 169.8320 819.1920 639.5290 717.1020
#> [49] 482.3650 504.0090 628.5540 169.9670 213.7130 536.5800 422.9630 477.4360
#> [57] 422.9630 546.4830 688.6000 639.0990 531.3305 819.6200 819.4060 762.4675
#> [65] 213.2320 469.3330 434.7490 819.4060 448.2490 633.5910 800.0760 528.0080
#> [73] 883.2670 639.1000 213.7270 218.0975 638.8855 525.0090 525.0080 595.1270
#> [81] 492.5440 639.9560 213.4660 213.7260 214.5090 367.0375 213.5140 213.0800
#> [89] 214.8590 632.1990 213.5090 632.0920 214.9660 213.5105 213.6405 213.4465
#> [97] 214.0705 213.7130 213.4215 388.0330Shiny application
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.
Conclusion
pmd package could be used to reduce the redundancy peaks
for GC/LC-MS based research and perform structure/reaction directed
analysis to screen known and unknown important compounds or
reactions.
- Introduction of Paired Mass Distance analysis
- Data format
- GlobalStd algorithm
- Validation by principal components analysis(PCA)
- Comparison with other pseudo spectra extraction method
- Structure/Reaction directed analysis
- Wrap function for GlobalStd algorithm
- Use independent peaks for MS/MS validation (PMDDA)
- Shiny application
- Conclusion