Correct saturation by only quantifying on unsaturated isotopes in all samples under comparison #39
Comments
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Laurent asked me to create a comment here: I have had a look at the data and noticed that the saturation does not affect fragment quantities and lower abundance isotope. I would suggest the following easy to implement soultion:
Once you have applied a function of combining individual synapter analysis into MSnsets, you can apply a command of correcting saturation either on fragments or isotopes, by saying quantify on all fragments or isotopes which intensity is below certain threshold. Note that this is done AFTER several synapter analysis are combined into an MSnSet. |
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As we discussed on skype we will follow the first strategy (quantify on peptides). Now the isotopic distribution is stored in the Synapter object and the resulting MSnSet: ## isotopicDistr is formated: ion_z,ion_iso,ion_counts
tail(cbind(exprs(m), fData(m)["isotopicDistr"]))
# Synapter1 isotopicDistr
# IVDTWR 1198 2,0,838;2,1,360;1,0,30
# ESASHETPTDLRPVIPR 555 3,0,275;3,1,280;1,0,56;1,1,43
# GYDLPDEVFDENEK 775 2,0,331;2,1,293;2,2,151;1,0,37
# IFGLEK 64706 1,0,42448;1,1,17358;1,2,2956;1,3,417;2,0,1037;2,1,490;1,0,118;1,1,83
# LATMNIDIDLTSGGK 414 3,0,218;3,1,196;1,0,122
# QNLFNNTVK 1266 2,0,703;2,1,399;2,2,164;1,0,33How we realize the requantification? Do we simple sum up all counts below a threshold, e.g. for the last peptide: |
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To do requantification synapter needs to first select all ions that are present in all the runs under saturation threshold. It then sums the intensity of these ions for every run, which is the new quantitation value. For example: run1: 2,0,838;2,1,360;1,0,30 saturation threshold: 5000 there is 1 isotope present in all runs below saturation threshold: 2.1. so the new quantitation value is now 360 + 30: 4000 + 30 |
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So we just sum up common isotopes. For your example run 1 will be (360+30)/(4000+30) = 0.097 and run 2 will be 1.0? (these values are no counts anymore) What do we do if there are
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z2i0 - 10.000 z2i1 - 8000 z2i3 - 5000 z2i4 - 2000 z2i5 - 1000 for the second run the peptide intensity is low and it has the following distribution z2i0 - 5000 z2i1 - 2000 saturation threshold is 6000. Now this becomes a problem since there are no peptide ions that are common that are below saturation threshold. The only option here is to predict the correct intensity of ions above saturation threshold, by using ions below saturation threshold in run 1. There is a simple function that can generate isotopic distribution and then we say given that z2i3 - 5000 z2i4 - 2000 z2i5 - 1000, we should actually see z2i0 at 15000 and z2i1 at 10000. It should not be much work since the function is very simple (it is based on sample). See |
That's a pain! I will do it later if the whole method works as expected. Regarding 3.: In theory it sounds simple (like always) but here I used the last three peptides of my example dataset and try to calculate the original counts using theoretical proportions of the isotopes (ignoring any saturation). As you can see the calculated values are sometimes very good and sometimes (especially for the less abundant isotopes) very bad. # I changed the isotopic distribution format for easier parsing, order is equal
GYDLPDEVFDENEK 775 2_0:331;2_1:293;2_2:151;1_0:37
IFGLEK 64706 1_0:42448;1_1:17358;1_2:2956;1_3:417;2_0:1037;2_1:490;1_0:118;1_1:83
QNLFNNTVK 1266 2_0:703;2_1:399;2_2:164;1_0:33Using the library("BRAIN")
(props <- calculateIsotopicProbabilities(getAtomsFromSeq("GYDLPDEVFDENEK"), nrPeaks=3))
# [1] 0.3958128 0.3450117 0.1722195
c("2_0"=331, "2_1"=293, "2_2"=151)/props
# 2_0 2_1 2_2
# 836.2540 849.2465 876.7883
# original counts are 775
(props <- calculateIsotopicProbabilities(getAtomsFromSeq("IFGLEK"), nrPeaks=4))
# [1] 0.65718493 0.26507158 0.06427314 0.01155455
c("1_0"=42448, "1_1"=17358, "1_2"=2956, "1_3"=417)/props
# 1_0 1_1 1_2 1_3
# 64590.65 65484.20 45991.22 36089.69
# original counts are 64706
(props <- calculateIsotopicProbabilities(getAtomsFromSeq("QNLFNNTVK"), nrPeaks=3))
# [1] 0.5474634 0.3145201 0.1056640
c("2_0"=703, "2_1"=399, "2_2"=164)/props
# 2_0 2_1 2_2
# 1284.104 1268.599 1552.090
# original counts are 1266If I don't know the true counts (because I assume they are affected by saturation) I would tent to use the less abundant isotopes (because it is less likely that they are saturated) to calculate the "original" counts. But as shown above these less abundant isotopes results in very wired counts. Slowly I doubt that it would be possible to estimate the true counts in a reasonable way. BTW: Would it be save to sum up the counts of equal isotopes of different charge states? (e.g. 1_1 + 2_1 + 3_1) If that would be possible (as I assume) the results will differ even more. |
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I understand the difficulty and it is expected. Perhaps it is indeed going to be complicated to calculate the intensity of saturated ions based on non-saturated ions, due to the fact that there is a more significant noise contribution to signal for less intense isotopes. Also perhaps the situation outlined in my previous post is extremely rare and we dont even need this calculation. So I would prefer to have 2 separate functions: 1st - requantify with lower intensity isotopes, 2nd - predict intensity of higher abundant isotopes and requantify then. The bottom line is I can only test whether this functionality is useful once it is available to me. Put it in synapter and I will test it using the saturation dataset we have, |
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Ok, I just finished the first attempt to realize this feature. It is still clumsy but I hope it is enough to start experimenting with it. default workflow## start a new, clean R session
## install devtools if you haven't done it before
install.packages("devtools")
library("devtools")
## get the latest synapter with requantification support
install_github("lgatto/synapter@saturation")
library("synapter")
## load synapterdata example files
path <- system.file("extdata", package="synapterdata")
identfiles <- list.files(path, pattern="^HDMSe", full.names=TRUE)
quantfiles <- list.files(path, pattern="^MSe.*IA", full.names=TRUE)
pep3dfiles <- list.files(path, pattern="Pep3DAMRT", full.names=TRUE)
fasta <- unzip(file.path(path, "EcoliK12_enolase_UPSsimga_NB.fasta.zip"))
set.seed(1)
s <- vector(mode="list", length=length(identfiles))
## run standard synapter workflow
system.time(
for (i in seq(along=s)) {
s[[i]] <- Synapter(list(identpeptide=identfiles[i],
quantpeptide=quantfiles[i],
quantpep3d=pep3dfiles[i],
fasta=fasta))
filterUniqueDbPeptides(s[[i]])
filterIdentPepScore(s[[i]], method="BH", fdr=0.05)
filterQuantPepScore(s[[i]], method="BH", fdr=0.05)
filterIdentProtFpr(s[[i]], fpr=0.05)
filterQuantProtFpr(s[[i]], fpr=0.05)
filterIdentPpmError(s[[i]], ppm=20)
filterQuantPpmError(s[[i]], ppm=20)
mergePeptides(s[[i]])
modelRt(s[[i]], span=0.05)
searchGrid(s[[i]])
setBestGridParams(s[[i]])
findEMRTs(s[[i]])
})
## create sample names
sets <- paste0("ups", sub("^.*([0-9]{2,})fmol_.*([0-9]{2,})_IA.*",
"\\1.\\2", basename(identfiles)))
## create msnsets
ms <- lapply(s, as, Class="MSnSet")
## update sampleNames/fvarLables of the msnsets
ms <- mapply(function(m, s) {
sampleNames(m) <- s;
m <- updateFvarLabels(m, s)
m
}, m=ms, s=sets)
## combine msnsets
system.time(
msc <- Reduce(combine, ms)
)
# ignore peptides with no isotopicDistr data for now (rescued)
#i <- which(rowSums(!is.na(fData(msc)[, grep("isotopicDistr", fvarLabels(msc))])) == ncol(msc))
#
#msc <- msc[i]Nothing new yet. requantificationThe next step would be the requantification. There is a new function called summed <- requantify(msc, saturationThreshold=Inf, method="sum")
thmean <- requantify(msc, saturationThreshold=Inf, method="th.mean")
thmedian <- requantify(msc, saturationThreshold=Inf, method="th.median")
thwmean <- requantify(msc, saturationThreshold=Inf, method="th.weighted.mean")synapter-plgs agreementAnother new function is the msc <- synapterPlgsAgreement(msc)
cols <- grepl("synapterPlgsAgreement|nIdentified|nAgree|nDisagree", fvarLabels(msc))
head(fData(msc)[, cols])What is missing/what to do next:
@pavel-shliaha I am looking forward to your feedback. |
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A little technical comment/question regarding
Do you mean |
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@lgatto: Yes, I have seen that you change some methods to functions in some of your packages. What is the reason for that? Is there any drawback of S4 methods (except that dispatching needs some time and debugging is sometimes harder)? |
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cannot find requantify function. Where does it live? |
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requantify(msc, saturationThreshold=Inf, method="sum") function does not work very well.
e.g. peptide in run 1 isotopes: 100, 10, 1 if saturation threshold is 1e3, do you jut remove the ions above that and get peptide in run 1: 100 + 10 + 1 if so than this should not be the case. You have to remove the isotopes that are above saturation threshold in one of the runs from all of the runs. So the result should be: peptide in run : 1 |
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1.) Is that really expected? I wouldn't expect that there are any original values in the r <- requantify(msc, saturationThreshold=1e3, method="sum")
notNA <- !is.na(exprs(r))
exprs(msc)[notNA] <- exprs(r)[notNA]2.) This is not true. The current behaviour is already the way you want it to be. library("devtools")
load_all("synapter")
path <- system.file("extdata", package="synapterdata")
identfiles <- list.files(path, pattern="^HDMSe", full.names=TRUE)
quantfiles <- list.files(path, pattern="^MSe.*IA", full.names=TRUE)
pep3dfiles <- list.files(path, pattern="Pep3DAMRT", full.names=TRUE)
fasta <- unzip(file.path(path, "EcoliK12_enolase_UPSsimga_NB.fasta.zip"))
set.seed(1)
s <- vector(mode="list", length=length(identfiles))
## run standard synapter workflow
system.time(
for (i in seq(along=s)) {
s[[i]] <- Synapter(list(identpeptide=identfiles[i],
quantpeptide=quantfiles[i],
quantpep3d=pep3dfiles[i],
fasta=fasta))
filterUniqueDbPeptides(s[[i]])
filterIdentPepScore(s[[i]], method="BH", fdr=0.05)
filterQuantPepScore(s[[i]], method="BH", fdr=0.05)
filterIdentProtFpr(s[[i]], fpr=0.05)
filterQuantProtFpr(s[[i]], fpr=0.05)
filterIdentPpmError(s[[i]], ppm=20)
filterQuantPpmError(s[[i]], ppm=20)
mergePeptides(s[[i]])
modelRt(s[[i]], span=0.05)
searchGrid(s[[i]])
setBestGridParams(s[[i]])
findEMRTs(s[[i]])
})
## create msnsets
ms <- lapply(s, as, Class="MSnSet")
## combine msnsets
system.time(
msc <- Reduce(combine, ms)
)
To show the selected isotopes I use some internal functions here: f <- fData(msc)[, grep("isotopicDistr", fvarLabels(msc))]
x <- .splitIsotopicDistr(unlist(f[22, ]))
# $isotopicDistr.MSe_101111_25fmol_UPS1_in_Ecoli_01
# 1_0 1_1 1_2 1_3 2_0 2_1 2_2 2_3 2_4
# 16684 6747 1885 430 137906 55855 15305 3352 549
#
# $isotopicDistr.MSe_101111_25fmol_UPS1_in_Ecoli_03
# 1_0 1_1 1_2 1_3 2_0 2_1 2_2 2_3 2_4
# 10610 4355 1409 190 136850 57326 16114 1718 325
#
# $isotopicDistr.MSe_101111_25fmol_UPS1_in_Ecoli_05
# 1_0 1_1 1_2 1_3 2_0 2_1 2_2 2_3 2_4
# 9983 4544 746 148 136386 56244 15337 3329 645
#
# $isotopicDistr.MSe_111111_50fmol_UPS1_in_Ecoli_01
# 1_0 1_1 1_2 1_3 2_0 2_1 2_2 2_3 2_4
# 12127 5041 990 177 148808 60998 16693 3656 577
#
# $isotopicDistr.MSe_111111_50fmol_UPS1_in_Ecoli_03
# 1_0 1_1 1_2 1_3 2_0 2_1 2_2 2_3 2_4 2_5
# 8180 3325 927 205 133856 54500 15275 3102 498 81
#
# $isotopicDistr.MSe_111111_50fmol_UPS1_in_Ecoli_05
# 1_0 1_1 1_2 1_3 2_0 2_1 2_2 2_3 2_4
# 10893 4425 1365 276 141491 58884 16458 3489 345
#
.commonIsotopes(x, saturationThreshold=1e2)
# character(0)
.commonIsotopes(x, saturationThreshold=1e3)
# [1] "1_3" "2_4"
# 1_2 in run 1 is the only run where 1_2 > 1800 and all 1_2 isotopes are filtered
.commonIsotopes(x, saturationThreshold=1.8e3)
# [1] "1_3" "2_4"
.commonIsotopes(x, saturationThreshold=1.9e3)
# [1] "1_2" "1_3" "2_4"Question: The current behaviour has some disadvantages. E.g. the third EMRT was seen in 5 of 6 runs with a good isotopic distribution. Because we require that the isotopes must be present in all runs (even when the EMRT was not observed at all), we could not find any common isotopes (because the first run has not any isotope) and could not requantify this EMRT: x <- .splitIsotopicDistr(unlist(f[3, ]))
# $isotopicDistr.MSe_101111_25fmol_UPS1_in_Ecoli_01
# <NA>
# NA
#
# $isotopicDistr.MSe_101111_25fmol_UPS1_in_Ecoli_03
# 1_0 1_1 1_2 1_3 2_0 2_1 2_2 2_3 2_4
# 10256 6073 1915 450 80564 48499 16732 4034 852
#
# $isotopicDistr.MSe_101111_25fmol_UPS1_in_Ecoli_05
# 1_0 1_1 1_2 1_3 2_0 2_1 2_2 2_3 2_4 2_5
# 9710 5588 1699 426 86293 46817 14378 3842 847 264
#
# $isotopicDistr.MSe_111111_50fmol_UPS1_in_Ecoli_01
# 1_0 1_1 1_2 1_3 2_0 2_1 2_2 2_3 2_4 2_5
# 9443 5068 1716 423 87367 47123 16806 4317 992 130
#
# $isotopicDistr.MSe_111111_50fmol_UPS1_in_Ecoli_03
# 1_0 1_1 1_2 1_3 2_0 2_1 2_2 2_3 2_4 2_5
# 10260 5859 1842 428 79717 46234 15919 3935 1026 138
#
# $isotopicDistr.MSe_111111_50fmol_UPS1_in_Ecoli_05
# 1_0 1_1 1_2 1_3 2_0 2_1 2_2 2_3 2_4 2_5
# 8439 5036 1629 347 68177 41550 14343 3415 751 122
#
.commonIsotopes(x, saturationThreshold=Inf)
# character(0)I would suggest to ignore NA runs (where the EMRT was not observed) and requantify all the other runs (so in this example 2 runs out of 6 would be enough for requantification). |
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I would actually like to suggest the following method to deal with the problem: imagine we have a peptide at 10, 20, 50, 100 and 500 fmol (this is actually what we see in the organelle proteomics datasets a lot), the problem is that 10 and 500 fmol do not have any isotopes in common that are not over sat threshold in 500, but are still present at 10, so correction is not possible. 50 fmol however has common isotopes with both 10 and 500fmol, so we could quantify 10 vs 50 and 50 vs 500 to solve this problem. Thus my suggestion is:
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at 1) I would suggest to let the user choose a reference run and don't try to find a run automatically. (If we try to find a run automatically it could happen that the reference run differs across peptides. Or we have to analyse all runs at once.) |
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As discussed in our last skype call I add a new requantify method: It looks for an unsaturated run (none of the isotopes is higher than the saturation threshold) with the highest summed intensity value and use this as reference. A run with intensity values above the saturation threshold would be corrected as follows:
Example: reference= "1_0:2000;1_1:200;1_2:20;1_3:2;"
satrun = "1_0:6000;1_1:1000;1_2:100;1_3:10;1_4:1"
satThreshold = 5000
# 1. step
satrun[2:4]/reference[2:4] # 1000/200, 100/20, 10/2
# 5, 5, 5
# 2. step
scaleFactor = mean(c(5, 5, 5)) # 5
# 3. step
unsatInt = reference[1]*scaleFactor # 10000
correctedInt = sum(10000, 1000, 100, 10, 1) # 11111A more complete example (ref is run 3 and sat is run 4) f <- data.frame(isotopicDistr.run1="1_0:1000;1_1:100;1_2:10;1_3:1;",
isotopicDistr.run2="1_0:800;1_1:100;1_2:10;1_3:1;",
isotopicDistr.run3="1_0:2000;1_1:200;1_2:20;1_3:2;",
isotopicDistr.run4="1_0:6000;1_1:1000;1_2:100;1_3:10;1_4:1",
stringsAsFactors=FALSE)
synapter:::.requantifyReferenceRun(f, saturationThreshold=Inf)
# isotopicDistr.run1 isotopicDistr.run2 isotopicDistr.run3 isotopicDistr.run4
# 1111 911 2222 7111
synapter:::.requantifyReferenceRun(f, saturationThreshold=5000)
# isotopicDistr.run1 isotopicDistr.run2 isotopicDistr.run3 isotopicDistr.run4
# 1111 911 2222 11111The user visible function ( refcorrected <- requantify(msc, saturationThreshold=5000, method="reference") |
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We need to review all saturation correction algorithms. Even "sum" has its bugs. "Sum" (maybe others too) should get an option to decide whether we want to remove unsaturated isotopes that are below the threshold but not present in all runs (current behaviour) or if we want to keep them: |
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A question regarding reference run requantification: We have the following situation: If the saturation threshold is 400, the reference run and run500mol have not any unsaturated isotope in common. How we handle this? Sounds strange? But happens at least 2 times in your example data set, e.g. for
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"If the saturation threshold is 400, the reference run and run500mol have not any unsaturated isotope in common. How we handle this?" you cant hadle this by reference approach. First some definitions so we are on the same page. Assuming we are talking about a particular peptide: SATURATED runs are those runs that have isotopes over saturation threshold. UNSATURATED runs are those runs that have isotopes below certain threshold. REFERENCE run is unsaturated run with the highest intensity. The idea of SUM method is to use common isotopes below saturation threshold for requantification. However you will run into the problem there are no common isotopes between certain runs. This can be solved partly by reference method that does not require common isotopes between all runs, but requires common isotopes only between reference run and saturated runs. However there still can be situations in which there are no isotopes in common beow saturation theshold between reference and quantitation run, in which case requantification is impossible. |
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@pavel-shliaha asked me to look at some wired results of the detector saturation correction using the DDPHACYSTVFDK
99.92 % of the signal after reference requantification is from the 500 fmol samples. Why this happen? If you look at the As load_all("../../synapter/")
combMSnSet <- readRDS(file.path("result", "combMSnSet.RDS"))
combScheme <- list("25" = 6:7, "50" = 11:13, "75" = 14:16,
"100" = 1:3, "250" = 4:5, "500" = 8:10)
j <- unlist(combScheme)
i <- which(featureNames(combMSnSet) == "DDPHACYSTVFDK")
dd <- combMSnSet[i]
fvarLabels(dd) <- gsub("29022012_Ecoli_spikes_", "", fvarLabels(dd))
iso <- fData(dd)[grepl("isotopicDistr", fvarLabels(dd))][j]
agree <- fData(dd)[grepl("synapterPlgsAgreement", fvarLabels(dd))][j]
rownames(iso) <- rownames(agree) <- NULL
e <- exprs(dd)[, j]
r <- exprs(requantify(dd, method="reference", sat=1e5))[, j]
rownames(e) <- rownames(r) <- NULL
e
# 29022012_Ecoli_spikes_25fmol_04 29022012_Ecoli_spikes_25fmol_06
# 14090 13879
# 29022012_Ecoli_spikes_50fmol_02 29022012_Ecoli_spikes_50fmol_04
# 41539 28718
# 29022012_Ecoli_spikes_50fmol_07 29022012_Ecoli_spikes_75fmol_02
# 47001 47370
# 29022012_Ecoli_spikes_75fmol_04 29022012_Ecoli_spikes_75fmol_06
# 53378 41788
# 29022012_Ecoli_spikes_100fmol_02 29022012_Ecoli_spikes_100fmol_04
# 44478 77878
# 29022012_Ecoli_spikes_100fmol_06 29022012_Ecoli_spikes_200fmol_02
# 70682 159945
# 29022012_Ecoli_spikes_200fmol_04 29022012_Ecoli_spikes_500fmol_02
# 124565 185455
# 29022012_Ecoli_spikes_500fmol_04 29022012_Ecoli_spikes_500fmol_06
# 235531 239742
r
# 29022012_Ecoli_spikes_25fmol_04 29022012_Ecoli_spikes_25fmol_06
# 0 0
# 29022012_Ecoli_spikes_50fmol_02 29022012_Ecoli_spikes_50fmol_04
# 0 0
# 29022012_Ecoli_spikes_50fmol_07 29022012_Ecoli_spikes_75fmol_02
# 0 0
# 29022012_Ecoli_spikes_75fmol_04 29022012_Ecoli_spikes_75fmol_06
# 0 518
# 29022012_Ecoli_spikes_100fmol_02 29022012_Ecoli_spikes_100fmol_04
# 0 0
# 29022012_Ecoli_spikes_100fmol_06 29022012_Ecoli_spikes_200fmol_02
# 0 0
# 29022012_Ecoli_spikes_200fmol_04 29022012_Ecoli_spikes_500fmol_02
# 0 185455
# 29022012_Ecoli_spikes_500fmol_04 29022012_Ecoli_spikes_500fmol_06
# 235531 239742
iso
# isotopicDistr.25fmol_04 isotopicDistr.25fmol_06 isotopicDistr.50fmol_02
# 1 <NA> <NA> <NA>
# isotopicDistr.50fmol_04 isotopicDistr.50fmol_07 isotopicDistr.75fmol_02
# 1 <NA> <NA> <NA>
# isotopicDistr.75fmol_04 isotopicDistr.75fmol_06 isotopicDistr.100fmol_02
# 1 <NA> 2_0:321;2_1:197 <NA>
# isotopicDistr.100fmol_04 isotopicDistr.100fmol_06 isotopicDistr.200fmol_02
# 1 <NA> <NA> <NA>
# isotopicDistr.200fmol_04
# 1 <NA>
# isotopicDistr.500fmol_02
# 1 2_0:40003;2_1:32600;2_2:18058;2_3:6621;2_4:1735;2_5:421;2_6:127;3_0:32190;3_1:25740;3_2:18706;3_3:6798;3_4:1939;3_5:435;3_6:82
# isotopicDistr.500fmol_04
# 1 2_0:53185;2_1:45888;2_2:26425;2_3:9358;2_4:2456;2_5:531;2_6:119;3_0:36456;3_1:29678;3_2:20958;3_3:7640;3_4:2300;3_5:469;3_6:68
# isotopicDistr.500fmol_06
# 1 2_0:53234;2_1:46184;2_2:24468;2_3:8270;2_4:2146;2_5:463;3_0:38756;3_1:34138;3_2:21667;3_3:7529;3_4:2267;3_5:493;3_6:127
agree
# synapterPlgsAgreement.25fmol_04 synapterPlgsAgreement.25fmol_06
# 1 no_synapter_transfer no_synapter_transfer
# synapterPlgsAgreement.50fmol_02 synapterPlgsAgreement.50fmol_04
# 1 no_synapter_transfer no_synapter_transfer
# synapterPlgsAgreement.50fmol_07 synapterPlgsAgreement.75fmol_02
# 1 no_synapter_transfer no_synapter_transfer
# synapterPlgsAgreement.75fmol_04 synapterPlgsAgreement.75fmol_06
# 1 no_synapter_transfer no_synapter_transfer
# synapterPlgsAgreement.100fmol_02 synapterPlgsAgreement.100fmol_04
# 1 no_synapter_transfer no_synapter_transfer
# synapterPlgsAgreement.100fmol_06 synapterPlgsAgreement.200fmol_02
# 1 no_synapter_transfer no_synapter_transfer
# synapterPlgsAgreement.200fmol_04 synapterPlgsAgreement.500fmol_02
# 1 no_synapter_transfer agree
# synapterPlgsAgreement.500fmol_04 synapterPlgsAgreement.500fmol_06
# 1 agree agreeIGEEYISDLDQLR
Same problem as above but just for one 500 fmol sample. e
# 29022012_Ecoli_spikes_25fmol_04 29022012_Ecoli_spikes_25fmol_06
# 17933 24056
# 29022012_Ecoli_spikes_50fmol_02 29022012_Ecoli_spikes_50fmol_04
# 61520 55124
# 29022012_Ecoli_spikes_50fmol_07 29022012_Ecoli_spikes_75fmol_02
# 55565 74681
# 29022012_Ecoli_spikes_75fmol_04 29022012_Ecoli_spikes_75fmol_06
# 77651 74222
# 29022012_Ecoli_spikes_100fmol_02 29022012_Ecoli_spikes_100fmol_04
# 89107 113455
# 29022012_Ecoli_spikes_100fmol_06 29022012_Ecoli_spikes_200fmol_02
# 105244 225785
# 29022012_Ecoli_spikes_200fmol_04 29022012_Ecoli_spikes_500fmol_02
# 215329 214903
# 29022012_Ecoli_spikes_500fmol_04 29022012_Ecoli_spikes_500fmol_06
# 252642 280309
r
# 29022012_Ecoli_spikes_25fmol_04 29022012_Ecoli_spikes_25fmol_06
# 17933.0 24056.0
# 29022012_Ecoli_spikes_50fmol_02 29022012_Ecoli_spikes_50fmol_04
# 61520.0 55124.0
# 29022012_Ecoli_spikes_50fmol_07 29022012_Ecoli_spikes_75fmol_02
# 55565.0 74681.0
# 29022012_Ecoli_spikes_75fmol_04 29022012_Ecoli_spikes_75fmol_06
# 77651.0 74222.0
# 29022012_Ecoli_spikes_100fmol_02 29022012_Ecoli_spikes_100fmol_04
# 89107.0 113455.0
# 29022012_Ecoli_spikes_100fmol_06 29022012_Ecoli_spikes_200fmol_02
# 105244.0 225785.0
# 29022012_Ecoli_spikes_200fmol_04 29022012_Ecoli_spikes_500fmol_02
# 215329.0 0.0
# 29022012_Ecoli_spikes_500fmol_04 29022012_Ecoli_spikes_500fmol_06
# 252642.0 318415.1
iso
# isotopicDistr.25fmol_04
# 1 2_0:8013;2_1:6519;2_2:2411;2_3:749;2_4:241
# isotopicDistr.25fmol_06
# 1 2_0:10824;2_1:8347;2_2:3532;2_3:861;3_0:197;3_1:175;3_2:120
# isotopicDistr.50fmol_02
# 1 2_0:27650;2_1:21582;2_2:9207;2_3:2400;2_4:502;2_5:179
# isotopicDistr.50fmol_04
# 1 2_0:23662;2_1:19309;2_2:8549;2_3:2520;2_4:674;2_5:244;3_0:108;3_1:58
# isotopicDistr.50fmol_07
# 1 2_0:24159;2_1:19193;2_2:8596;2_3:2628;2_4:802;2_5:187
# isotopicDistr.75fmol_02
# 1 2_0:32999;2_1:26262;2_2:11456;2_3:3084;2_4:654;2_5:226
# isotopicDistr.75fmol_04
# 1 2_0:34799;2_1:27407;2_2:11170;2_3:3322;2_4:727;3_0:144;3_1:82
# isotopicDistr.75fmol_06
# 1 2_0:32917;2_1:25482;2_2:11430;2_3:3056;2_4:830;2_5:227;3_0:170;3_1:110
# isotopicDistr.100fmol_02
# 1 2_0:39872;2_1:30827;2_2:13170;2_3:3771;2_4:972;2_5:236;3_0:188;3_1:71
# isotopicDistr.100fmol_04
# 1 2_0:50963;2_1:39987;2_2:16566;2_3:4480;2_4:1097;3_0:228;3_1:134
# isotopicDistr.100fmol_06
# 1 2_0:46435;2_1:38035;2_2:15497;2_3:4051;2_4:1019;2_5:207
# isotopicDistr.200fmol_02
# 1 2_0:97225;2_1:80863;2_2:35200;2_3:9747;2_4:2216;2_5:455;2_6:79
# isotopicDistr.200fmol_04
# 1 2_0:91501;2_1:76143;2_2:32803;2_3:10120;2_4:2229;2_5:504;2_6:143;3_0:653;3_1:560;3_2:430;3_3:160;3_4:83
# isotopicDistr.500fmol_02
# 1 <NA>
# isotopicDistr.500fmol_04
# 1 1_0:401;1_1:372;2_0:94256;2_1:83636;2_2:51768;2_3:17209;2_4:3779;2_5:807;2_6:104;3_0:128;3_1:100;3_2:82
# isotopicDistr.500fmol_06
# 1 1_0:413;1_1:379;2_0:103359;2_1:95753;2_2:56364;2_3:18257;2_4:4074;2_5:751;2_6:150;3_0:361;3_1:259;3_2:189
agree
# synapterPlgsAgreement.25fmol_04 synapterPlgsAgreement.25fmol_06
# 1 agree agree
# synapterPlgsAgreement.50fmol_02 synapterPlgsAgreement.50fmol_04
# 1 agree agree
# synapterPlgsAgreement.50fmol_07 synapterPlgsAgreement.75fmol_02
# 1 agree agree
# synapterPlgsAgreement.75fmol_04 synapterPlgsAgreement.75fmol_06
# 1 agree agree
# synapterPlgsAgreement.100fmol_02 synapterPlgsAgreement.100fmol_04
# 1 agree agree
# synapterPlgsAgreement.100fmol_06 synapterPlgsAgreement.200fmol_02
# 1 agree agree
# synapterPlgsAgreement.200fmol_04 synapterPlgsAgreement.500fmol_02
# 1 agree no_synapter_transfer
# synapterPlgsAgreement.500fmol_04 synapterPlgsAgreement.500fmol_06
# 1 agree agreeBTW: the "sum" requantification isn't working for these both examples (resulting in zero intensity for all samples). |
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Thanks for having a look into it. In both cases it is the artifact of plotting requantified results, not of requantification itself. What I mean is that the code to plot requantification is just for the paper it is not a part of basic synapter functionality (unless we want to give visualisaion tool to the user?) About the peptides:
|
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@sgibb still too peptides are requantified by sum but not by reference: "FFQELR" "ESNEITIIINPYRETVCFSVEPVK". Can u have a look? |
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It is the other way around: both are requantified by reference but not by sum (what makes absolutely sense to me): ## fetch peptide of interest
poi <- combMSNset[grep("^FFQELR$", featureNames(combMSNset)),]
## grep isotopic distribution
iso <- fData(poi)[, grep("isotopicDistr", fvarLabels(combMSNset))]
m <- synapter:::.isotopicDistr2matrix(iso)
m
# 1_0 1_1 1_2 1_3 1_4 2_0 2_1 2_2 2_3
# isotopicDistr.S130423_05 632 307 NA NA NA 6099 2445 696 NA
# isotopicDistr.S130423_07 NA NA NA NA NA NA NA NA NA
# isotopicDistr.S130423_09 37909 16706 2711 501 174 8502 3463 677 125
# isotopicDistr.S130423_11 NA NA NA NA NA NA NA NA NA
# isotopicDistr.S130423_13 NA NA NA NA NA NA NA NA NA
# isotopicDistr.S130423_06 NA NA NA NA NA NA NA NA NA
# isotopicDistr.S130423_08 NA NA NA NA NA 5538 1675 NA NA
# isotopicDistr.S130423_10 NA NA NA NA NA 4842 1439 484 NA
# isotopicDistr.S130423_12 NA NA NA NA NA 4128 1625 371 NA
# isotopicDistr.S130423_14 NA NA NA NA NA 3386 1290 349 NA
exprs(requantify(poi, method="sum", saturationThreshold=3e4))
# S130423_05 S130423_07 S130423_09 S130423_11 S130423_13 S130423_06
# FFQELR NA NA NA NA NA NA
# S130423_08 S130423_10 S130423_12 S130423_14
# FFQELR NA NA NA NA
exprs(requantify(poi, method="reference", saturationThreshold=3e4))
# S130423_05 S130423_07 S130423_09 S130423_11 S130423_13 S130423_06
# FFQELR 10179 NA 70768 NA NA NA
# S130423_08 S130423_10 S130423_12 S130423_14
# FFQELR NA 6765 6124 NA |
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Having problem applying the rescaleForTop3() function. Below is a figure of three datasets:
I had a look at a peptide I thin is requantified incorrectly:
as you can see the sum functionality has decreased the ratio from 0.2883538 to 0.207168 as expected but top3 requantitation has put it back up to 0.3473387. This probably should not happen. The code is in: |
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It seems that it is not a bug but a feature. You could find the modified code in the same directory. |
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Another figure for the same fact (please note that the colours have different meanings now: blue: no saturation; red: saturation in one sample (A or B; that is the important fraction that cause the "strange" behaviour in the bottom left panel); green: saturation in both samples: |
basic informationlibrary (synapter)
library (MSnbase)
combMSNset <- readRDS ("refCombMSNSet.RDS")
satThreshold <- 3e4
## samples
a <- 1:5
b <- 6:10
## fetch peptide of interest
poi <- combMSNset[grep("^AGMVAGVIVNR$", featureNames(combMSNset)),]
## grep isotopic distribution
iso <- fData(poi)[, grep("isotopicDistr", fvarLabels(combMSNset))]
isom <- synapter:::.isotopicDistr2matrix(iso)
isom## all samples "a" are not saturated but all of "b"
synapter:::.runsUnsaturated(t(isom), saturationThreshold=satThreshold)## run requantification and rescaling
req <- requantify(poi, method="sum", saturationThreshold=satThreshold,
onlyCommonIsotopes=FALSE)
top3 <- rescaleForTop3(poi, req, satThreshold, onlyForSaturatedRuns=TRUE)
top3all <- rescaleForTop3(poi, req, satThreshold, onlyForSaturatedRuns=FALSE)intensity tabletab <- rbind(exprs(poi), exprs(req), exprs(top3), exprs(top3all))
rownames(tab) <- c("initial", "sum", "top3", "top3all")
knitr::kable(tab)
plot intensities plot(NA, xlim=c(14, 18), ylim=c(-3, 3),
xlab="intensity sample B", ylab="A/B")
abline(h=c(1, 0, -2), col="#808080")
col <- c("#e41a1c", "#377eb8", "#4daf4a", "#984ea3")
points(log2(tab[, b]), log2(tab[, a]/tab[, b]), col=col, pch=20)
legend("topright", legend=rownames(tab), col=col, pch=20)
intensity ratiosratios <- rbind(exprs(poi)[, a]/exprs(poi)[, b],
exprs(req)[, a]/exprs(req)[, b],
exprs(top3)[, a]/exprs(top3)[, b],
exprs(top3all)[, a]/exprs(top3all)[, b])
rownames(ratios) <- c("initial", "sum", "top3", "top3all")
knitr::kable(ratios)
detailed walkthrough for onlyForSaturatedRuns=TRUE## exprs before requantification
eBefore <- exprs(poi)
eBefore## exprs after requantification
eAfter <- exprs(req)
eAfter## grep isotopic information
isotop <- as.matrix(iso)
isotop## if we want to handle only saturated runs we have to know which ones are
## unsaturated (this code block is skipped for onlyForSaturatedRuns=FALSE
unsat <- t(apply(isotop, 1, function(x)synapter:::.runsUnsaturated(t(synapter:::.isotopicDistr2matrix(x)), saturationThreshold=satThreshold)))
unsat## replace requantified values of unsaturated runs with their original
## intensities
eAfter[unsat] <- eBefore[unsat]
eAfter## calculation proportions
prop <- eAfter/rowSums(eAfter, na.rm=TRUE)
prop## calculate correction factor
cf <- eBefore/prop
cfcfm <- rowMeans(cf, na.rm=TRUE)
cfm## calculate new intensities
eNew <- cfm * prop
eNew## replace unsaturated runs with original values
## (this code block is skipped for onlyForSaturatedRuns=FALSE
eNew[unsat] <- eBefore[unsat]
eNewdetailed walkthrough for onlyForSaturatedRuns=FALSE## exprs before requantification
eBefore <- exprs(poi)
eBefore## exprs after requantification
eAfter <- exprs(req)
eAfter## calculation proportions
prop <- eAfter/rowSums(eAfter, na.rm=TRUE)
prop## calculate correction factor
cf <- eBefore/prop
cfcfm <- rowMeans(cf, na.rm=TRUE)
cfm## calculate new intensities
eNew <- cfm * prop
eNew |
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Hey Sebastian I think u pinpointed the problem yourself here. It is indeed incorrect to include saturated runs into calculation of correction factor. I believe you got confused: onlyForSaturTedRuns refers to whether u have to requantify all peptides or only those above saturation threshold, the correction factor should always be computed on unsaturated peptides only. This is analogous to isotopic correction we perform in theoretical methods. |
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Hey Sebastian, please execute this code and tell me what you see library(synapter)
combMSNSet <- readRDS ("Y://RAW/pvs22//_QTOF_DATA_data3//synapter2paper//kuharev2015//synapter2//output//UDMSE//refcombMSNSet.RDS")
satThresholdIon <- 3e4
satCorrected <- sapply (c ("sat", "sum", "reference"),
function (x) NULL)
satCorrected[[1]] <- combMSNSet
satCorrected[[2]] <- requantify (combMSNSet, method = "sum",
saturationThreshold= satThresholdIon,
onlyCommonIsotopes=FALSE)
satCorrected[[3]] <- requantify (combMSNSet, method = "reference",
saturationThreshold= satThresholdIon)
# function to find requantified row
findDifferentquant <- function (MSnSet1, MSnSet2){
selA <- c ()
for (i in 1:nrow (MSnSet1)) if (any (exprs(MSnSet1)[i, ] != exprs(MSnSet2)[i, ], na.rm = TRUE)) selA <- c (selA, i)
return (selA)
}
diffSum <- findDifferentquant(satCorrected[[1]], satCorrected[[2]])
diffRef <- findDifferentquant(satCorrected[[1]], satCorrected[[3]])
length (diffSum)
length (diffRef)I see
and
|
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@pavel-shliaha I got the same output. Now I finally understand what you mean with I looked into ## fetch peptide of interest
poi <- combMSNset[grep("^FFQELR$", featureNames(combMSNset)),]
## grep isotopic distribution
iso <- fData(poi)[, grep("isotopicDistr", fvarLabels(combMSNset))]
m <- synapter:::.isotopicDistr2matrix(iso)
m
# 1_0 1_1 1_2 1_3 1_4 2_0 2_1 2_2 2_3
# isotopicDistr.S130423_05 632 307 NA NA NA 6099 2445 696 NA
# isotopicDistr.S130423_07 NA NA NA NA NA NA NA NA NA
# isotopicDistr.S130423_09 37909 16706 2711 501 174 8502 3463 677 125
# isotopicDistr.S130423_11 NA NA NA NA NA NA NA NA NA
# isotopicDistr.S130423_13 NA NA NA NA NA NA NA NA NA
# isotopicDistr.S130423_06 NA NA NA NA NA NA NA NA NA
# isotopicDistr.S130423_08 NA NA NA NA NA 5538 1675 NA NA
# isotopicDistr.S130423_10 NA NA NA NA NA 4842 1439 484 NA
# isotopicDistr.S130423_12 NA NA NA NA NA 4128 1625 371 NA
# isotopicDistr.S130423_14 NA NA NA NA NA 3386 1290 349 NAIt is a special case: all but one run (S130423_09) are unsaturated. Now the algorithm looks for the best reference run (which is the one with the most unsaturated isotopes: S130423_09 (8 unsaturated and 1 saturated)). Subsequently the new intensity values are calculated for run S130423_09 (all other runs are unsaturated and not touched at all). The correction factor between the unsaturated isotopes of S130423_09 and the reference (S130423_09, too) is |
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For the I hope the onlyForSaturatedRuns problem is fixed now: |
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there is a problem with theoretical method correction for some peptides, e.g. ILFDYSK. I have run the code and the correction is in the table presented below library (synapter) combMSNSet <- readRDS ("Y://RAW/pvs22//_QTOF_DATA_data3//synapter2paper//kuharev2015//synapter2//output//UDMSE//refcombMSNSet.RDS") satThresholdIon <- 3e4 satCorrected[[1]] <- combMSNSet satCorrected[[2]] <- requantify (combMSNSet, method = "th.mean", satCorrected[[3]] <- requantify (combMSNSet, method = "th.median", satCorrected[[4]] <- requantify (combMSNSet, method = "th.weighted.mean", xx <- rbind (exprs (satCorrected[[1]])[featureNames (satCorrected[[2]]) == "ILFDYSK", ], row.names(xx) <- names (satCorrected)
could you please produce for "ILFDYSK" a detailed procedure of what is happening here, like you did for previous peptides when we had a problem? |
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sorry yet another issue sum methodlibrary (synapter) combMSNSet <- readRDS ("Y://RAW/pvs22//_QTOF_DATA_data3//synapter2paper//kuharev2015//synapter2//output//UDMSE//refcombMSNSet.RDS") satCorrected[[1]] <- combMSNSet satCorrected[[2]] <- requantify (combMSNSet, method = "sum", exprs (satCorrected[[2]])[5, ] all are NA! so requantification FAILED!poi <- combMSNSet[5,] m
but this shows requantification is possible |
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The combMSNset <- readRDS("refCombMSNSet.RDS")
## fetch peptide of interest
poi <- combMSNset[grep("^ILFDYSK$", featureNames(combMSNset)),]
## grep isotopic distribution
iso <- fData(poi)[, grep("isotopicDistr", fvarLabels(combMSNset))]
x <- synapter:::.isotopicDistr2matrix(iso)
saturationThreshold <- 3e4
unsat <- .isUnsaturatedIsotope(x, saturationThreshold=saturationThreshold)
# 1_0 1_1 1_2 2_0 2_1
#isotopicDistr.S130423_05 NA NA NA FALSE FALSE
#isotopicDistr.S130423_07 NA NA NA FALSE FALSE
#isotopicDistr.S130423_09 NA NA NA FALSE FALSE
#isotopicDistr.S130423_11 NA NA NA FALSE FALSE
#isotopicDistr.S130423_13 NA NA NA FALSE FALSE
#isotopicDistr.S130423_06 TRUE TRUE TRUE FALSE FALSE
#isotopicDistr.S130423_08 TRUE TRUE NA FALSE FALSE
#isotopicDistr.S130423_10 TRUE TRUE NA FALSE TRUE
#isotopicDistr.S130423_12 NA NA NA FALSE TRUE
#isotopicDistr.S130423_14 NA NA NA FALSE TRUEAbove we see the first problem: There are not any isotopes below the saturation threshold for the first 5 runs. So we can't predict anything here (explains the x
# 1_0 1_1 1_2 2_0 2_1
#isotopicDistr.S130423_05 NA NA NA 70214 40605
#isotopicDistr.S130423_07 NA NA NA 64751 41339
#isotopicDistr.S130423_09 NA NA NA 66143 34858
#isotopicDistr.S130423_11 NA NA NA 54542 30211
#isotopicDistr.S130423_13 NA NA NA 47453 30213
#isotopicDistr.S130423_06 12833 5303 955 63133 33950
#isotopicDistr.S130423_08 9116 4281 NA 53849 31806
#isotopicDistr.S130423_10 9077 3418 NA 50046 24914
#isotopicDistr.S130423_12 NA NA NA 40965 20147
#isotopicDistr.S130423_14 NA NA NA 31636 19936
x <- x * unsat
# 1_0 1_1 1_2 2_0 2_1
#isotopicDistr.S130423_05 NA NA NA 0 0
#isotopicDistr.S130423_07 NA NA NA 0 0
#isotopicDistr.S130423_09 NA NA NA 0 0
#isotopicDistr.S130423_11 NA NA NA 0 0
#isotopicDistr.S130423_13 NA NA NA 0 0
#isotopicDistr.S130423_06 12833 5303 955 0 0
#isotopicDistr.S130423_08 9116 4281 NA 0 0
#isotopicDistr.S130423_10 9077 3418 NA 0 24914
#isotopicDistr.S130423_12 NA NA NA 0 20147
#isotopicDistr.S130423_14 NA NA NA 0 19936And here we see the second problem. Allmost all two-charged ions are saturated (and not used for the prediction) that's why our predicted intensities for The The isotopic matrix of the fifth peptide ( 2_0 2_1 2_2 2_3 2_4 3_0 3_1 3_2 3_3 3_4
isotopicDistr.S130423_05 24557 19414 8161 3387 1424 66080 59557 33660 21962 9124
isotopicDistr.S130423_07 26498 21050 9577 3939 1922 68076 58367 42972 24453 13588
isotopicDistr.S130423_09 NA NA NA NA NA NA NA NA NA NA
isotopicDistr.S130423_11 16756 16158 5719 2899 1341 56830 53333 30881 16859 7490
isotopicDistr.S130423_13 17117 12276 5233 2072 NA 48460 41008 30369 13261 8023
isotopicDistr.S130423_06 24851 17418 9016 NA NA 65222 56007 37009 NA NA
isotopicDistr.S130423_08 19034 13436 6568 NA NA 52526 44629 32459 NA NA
isotopicDistr.S130423_10 14319 11793 5010 NA NA 55761 47528 25765 NA NA
isotopicDistr.S130423_12 15707 13055 5352 NA NA 49521 45595 26457 NA NA
isotopicDistr.S130423_14 17540 12890 6435 NA NA 44465 43195 29950 NA NAAs you see the third run |
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for sum method can you please no consider runs, for which we dont have peptide identity i.e. all the isotopes are NA. Simply ignore the line or convert to 0. This is the final fix I need to finish the paper. For the theroretical methods we need to think what to do in instances like the one above where requantification is not possible. |
Converting to 0 is not advisable. If a line is ignored, this would need to be recorded somewhere, or reported to the used at the very least. The best is to keep all features, but set those that don't return any value to |
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sorry guys did not mean to tell you how to code |
No worries - I just wanted to make sure we stay away from wild 0-imputation. |
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Ok, now missing runs (runs without any recorded intensity value) are ignored for combMSNset <- readRDS("refCombMSNSet.RDS")
## fetch peptide of interest
poi <- combMSNset[5,]
## grep isotopic distribution
iso <- fData(poi)[, grep("isotopicDistr", fvarLabels(combMSNset))]
# NONE
exprs(poi)
# FALSE
exprs(requantify(poi, method="sum", saturationThreshold=3e4, onlyCommon=FALSE))
# TRUE
exprs(requantify(poi, method="sum", saturationThreshold=3e4, onlyCommon=TRUE))
|
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Hey Sebastian. Problems again. Please have a look. satThresholdIon <- 3e4
combMSNSet2 <- readRDS ("Y://RAW/pvs22//_QTOF_DATA_data3//synapter2paper//kuharev2015//synapter2//output//UDMSE//combMSNSet.RDS")
combMSNSet2 <- requantify (combMSNSet2, method = "sum",
saturationThreshold= satThresholdIon,
onlyCommonIsotopes=FALSE)getting the following message:
already tried restarting R session and reinstalling synapter. Can you reproduce that? |
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I never see this kind of error before. It seems that the peptide Nevertheless it was a bug, that the functions could not handle entries without any non- @pavel-shliaha: Why we didn't recognize this before. Where does |
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lets keep this issue open for now |
requantify (readRDS("Y://RAW/pvs22//_QTOF_DATA_data3//synapter2paper//kuharev2015//synapter2_intensity//output//UDMSE//refcombMSNSetNS.RDS"),
method = "th.mean",
saturationThreshold= 3e4)returns
could you please have a look |
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I have currently no access to prot-filesrv1 (networkmanager-strongswan plugin seems to not accept/send the password). Can you send me the |
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shared the file with you via google drive |
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Sorry, but I can't reproduce the error. Just works for me. Could you try again and directly call |
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@sgibb started having trouble with the theoretical method as soon as I updated synapter. I have shared the file with you through google drive. testMSNSet <- readRDS ("MSnbaseProblemRequant.RDS") satThresholdIon <- 3e4 requantify (testMSNSet, method = "th.mean", error: Error in Mod(z$values) : non-numeric argument to function |
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Sorry guys now it works after restarting R session (but I also restarted before posting). I am not sure but perhaps this intermittent error has smth to do with the BRAIN package. |
The prototype of suggested function:
requantify <- function (synapterObj , satThreshold, minIsotopes)
synapterObj - list of synapter objects
satThreshold - intensity over which accurate ion recording is not possible, due to saturation
minIsotopes - minimum number of isotopes accepted for requantitation
For every peptide the function will find common unsaturated ions from supplied synapter objects, and requqntify EMRT using these ions only. satThreshold is used to determine which ions saturate. minIsotopes is the minimum number of isotopes that are sufficient to requantify (i.e. sometimes only few isotopes will be seen in all samples under saturation and over LOD)
Important: the information on peptide isotopes is not presented in current synapter objects! Pep3D file that is loaded in synapter is filtered so that for each EMRT only one isotope is preserved. This means that synapter object will have to be modified to include this information. Current consensus is that a list should be created each element of which represents a single EMRT and contains information on ion intensities from those EMRTs
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