MTBLS2 Processing and Analysis with xcms and CAMERA and export to MetaboLights
Steffen Neumann, Andrea Thum and Christoph Boettcher
2018-05-01
- Introduction
- A few global settings
- Raw data conversion
- R and ISAtab
- ISAtab, Risa and xcms
- Grouping and Retention time correction
- QC on peaks picked
- Data imputation
- QC with some diagnostic plots
- QC with PCA
- Annotated diffreport
- Diffreport
- Combine diffreport and CAMERA spectra
- R, ISAtab, xcms and CAMERA revisited
library(Risa)
library(xcms)
library(CAMERA)
library(pcaMethods)Introduction
Indole-3-acetaldoxime (IAOx) represents an early intermediate of the biosynthesis of a variety of indolic secondary metabolites including the phytoanticipin indol-3-ylmethyl glucosinolate and the phytoalexin camalexin (3-thiazol-2’-yl-indole). Arabidopsis thaliana cyp79B2 cyp79B3 double knockout plants are completely impaired in the conversion of tryptophan to indole-3-acetaldoxime and do not accumulate IAOx-derived metabolites any longer. Consequently, comparative analysis of wild-type and cyp79B2 cyp79B3 plant lines has the potential to explore the complete range of IAOx-derived indolic secondary metabolites.
Since 2006, the Bioconductor package xcms (Smith et al, 2006) provides a rich set of algorithms for mass spectrometry data processing. Typically, xcms will create an xcmsSet object from several raw data files in an assay, which are obtained from the samples in the study.
Allowed raw data formats are netCDF, mzData, mzXML and mzML.
In this vignette, we demonstrate the processing of the MTBLS2 dataset, which was described in Neumann 2012.
A few global settings
A few things might be worth to define at the beginning of an analysis
## How many CPU cores has your machine (or cluster) ?
nSlaves=1
# prefilter <- c(3,200) ## standard
prefilter=c(6,750) ## quick-run for debuggingRaw data conversion
This can be done with the vendor tools, or the open source proteowizard converter. The preferred format should be mzML or mzData/mzXML. An overview of formats (and problems) is available at the xcms online help pages.
R and ISAtab
An ISAtab archive will contain the metadata description in several tab-separated files. (One of) the assay files contains the column Raw Spectral Data File with the paths to the mass spectral raw data files in one of the above formats.
ISAmtbls2 <- readISAtab(find.package("mtbls2"))
a.filename <- ISAmtbls2["assay.filenames"][[1]]ISAtab, Risa and xcms
With the combination of Risa and xcms, we can convert the MS raw data in an ISAtab archive into an xcmsSet:
mtbls2Set <- processAssayXcmsSet(ISAmtbls2, a.filename,
method="centWave", prefilter=prefilter,
snthr=25, ppm=25,
peakwidth=c(5,12),
nSlaves=nSlaves)## Use of argument 'nSlaves' is deprecated, please use 'BPPARAM' instead.The result is the same type of xcmsSet object:
show(mtbls2Set)## An "xcmsSet" object with 16 samples
##
## Time range: 18.4-1147.6 seconds (0.3-19.1 minutes)
## Mass range: 99.5288-1003.5005 m/z
## Peaks: 64938 (about 4059 per sample)
## Peak Groups: 0
## Sample classes: Col-0.Exp1, cyp79.Exp1, Col-0.Exp2, cyp79.Exp2
##
## Feature detection:
## o Peak picking performed on MS1.
## Profile settings: method = bin
## step = 0.1
##
## Memory usage: 6.38 MBSeveral options exist to quantify the individual intensities. For each feature, additional attributes are available, such as the minimum/maximum and average retention time and m/z values.
Grouping and Retention time correction
In the following steps, we perform a grouping: because the UPLC system used here has very stable retention times, we just use the retention time correction step as quality control of the raw data. After that, ‘fillPeaks()’ will integrate the raw data for those features, which were not detected in some of the samples.
mtbls2Set <- group(mtbls2Set, minfrac=1, bw=3)## Processing 7233 mz slices ... OKretcor(mtbls2Set, plottype="mdevden")## Performing retention time correction using 1293 peak groups.
QC on peaks picked
A first QC step is the visual inspection of intensities across the samples. Alternatively to a boxplot, one could also create histograms/density plots.
boxplot(groupval(mtbls2Set, value="into")+1,
col=as.numeric(sampclass(mtbls2Set))+1,
log="y", las=2)
Data imputation
After grouping, peaks might be missing/not found in some samples. fillPekas() will impute them, using the consensus mz and RT from the other samples.
mtbls2Set <- fillPeaks(mtbls2Set, nSlaves=nSlaves)## Use of argument 'nSlaves' is deprecated, please use 'BPPARAM' instead.## /tmp/RtmpjmdujM/Rinst35d231374c3d/mtbls2/mzData/MSpos-Ex2-Col0-48h-Ag-1_1-A,2_01_9827.mzData
## method: bin
## step: 0.1
## /tmp/RtmpjmdujM/Rinst35d231374c3d/mtbls2/mzData/MSpos-Ex2-Col0-48h-Ag-2_1-A,3_01_9829.mzData
## method: bin
## step: 0.1
## /tmp/RtmpjmdujM/Rinst35d231374c3d/mtbls2/mzData/MSpos-Ex2-Col0-48h-Ag-3_1-A,4_01_9831.mzData
## method: bin
## step: 0.1
## /tmp/RtmpjmdujM/Rinst35d231374c3d/mtbls2/mzData/MSpos-Ex2-Col0-48h-Ag-4_1-A,2_01_9833.mzData
## method: bin
## step: 0.1## /tmp/RtmpjmdujM/Rinst35d231374c3d/mtbls2/mzData/MSpos-Ex2-cyp79-48h-Ag-1_1-B,3_01_9828.mzData
## method: bin
## step: 0.1
## /tmp/RtmpjmdujM/Rinst35d231374c3d/mtbls2/mzData/MSpos-Ex2-cyp79-48h-Ag-2_1-B,4_01_9830.mzData
## method: bin
## step: 0.1
## /tmp/RtmpjmdujM/Rinst35d231374c3d/mtbls2/mzData/MSpos-Ex2-cyp79-48h-Ag-3_1-B,3_01_9832.mzData
## method: bin
## step: 0.1
## /tmp/RtmpjmdujM/Rinst35d231374c3d/mtbls2/mzData/MSpos-Ex2-cyp79-48h-Ag-4_1-B,4_01_9834.mzData
## method: bin
## step: 0.1## /tmp/RtmpjmdujM/Rinst35d231374c3d/mtbls2/mzData/MSpos-Ex1-cyp79-48h-Ag-1_1-B,1_01_9819.mzData
## method: bin
## step: 0.1
## /tmp/RtmpjmdujM/Rinst35d231374c3d/mtbls2/mzData/MSpos-Ex1-cyp79-48h-Ag-2_1-B,2_01_9821.mzData
## method: bin
## step: 0.1
## /tmp/RtmpjmdujM/Rinst35d231374c3d/mtbls2/mzData/MSpos-Ex1-cyp79-48h-Ag-3_1-B,1_01_9823.mzData
## method: bin
## step: 0.1
## /tmp/RtmpjmdujM/Rinst35d231374c3d/mtbls2/mzData/MSpos-Ex1-cyp79-48h-Ag-4_1-B,2_01_9825.mzData
## method: bin
## step: 0.1## /tmp/RtmpjmdujM/Rinst35d231374c3d/mtbls2/mzData/MSpos-Ex1-Col0-48h-Ag-1_1-A,1_01_9818.mzData
## method: bin
## step: 0.1
## /tmp/RtmpjmdujM/Rinst35d231374c3d/mtbls2/mzData/MSpos-Ex1-Col0-48h-Ag-2_1-A,1_01_9820.mzData
## method: bin
## step: 0.1
## /tmp/RtmpjmdujM/Rinst35d231374c3d/mtbls2/mzData/MSpos-Ex1-Col0-48h-Ag-3_1-A,1_01_9822.mzData
## method: bin
## step: 0.1
## /tmp/RtmpjmdujM/Rinst35d231374c3d/mtbls2/mzData/MSpos-Ex1-Col0-48h-Ag-4_1-A,1_01_9824.mzData
## method: bin
## step: 0.1The final xcmsSet represents a rectangular matrix of mass spectral features, which were detected (or imputed) across the samples. The dimensionality is M * N, where M denotes the number of samples in the assay, and N the number of features grouped across the samples.
QC with some diagnostic plots
QC of mass accuracy and retention time consistency
plotQC(mtbls2Set)
QC with PCA
In addition to the boxplot for QC, we can also check a hierarchical clustering and the PCA of the samples.
sdThresh <- 4.0 ## Filter low-standard deviation rows for plot
data <- log(groupval(mtbls2Set, value="into")+1)
pca.result <- pca(data, nPcs=3)
plotPcs(pca.result, type="loadings",
col=as.numeric(sampclass(mtbls2Set))+1)
Annotated diffreport
an <- xsAnnotate(mtbls2Set,
sample=seq(1,length(sampnames(mtbls2Set))),
nSlaves=nSlaves)
an <- groupFWHM(an)
an <- findIsotopes(an) # optional but recommended.
an <- groupCorr(an,
graphMethod="lpc",
calcIso = TRUE,
calcCiS = TRUE,
calcCaS = TRUE,
cor_eic_th=0.5)
## Setup ruleSet
rs <- new("ruleSet")
rs@ionlistfile <- file.path(find.package("mtbls2"), "lists","ions.csv")
rs@neutraladditionfile <- file.path(find.package("mtbls2"), "lists","neutraladdition.csv")
rs@neutrallossfile <- file.path(find.package("mtbls2"), "lists","neutralloss.csv")
rs <- readLists(rs)
rs <- setDefaultParams(rs)
rs <- generateRules(rs)
an <- findAdducts(an,
rules=rs@rules,
polarity="positive")Diffreport
dr <- diffreport(mtbls2Set, sortpval=FALSE, filebase="mtbls2diffreport", eicmax=20 )## Create profile matrix with method 'bin' and step 0.1 ... OK
## No need to perform retention time correction, raw and corrected rt are identical for MSpos-Ex1-Col0-48h-Ag-1_1-A,1_01_9818.mzData.
## Create profile matrix with method 'bin' and step 0.1 ... OK
## No need to perform retention time correction, raw and corrected rt are identical for MSpos-Ex1-Col0-48h-Ag-2_1-A,1_01_9820.mzData.
## Create profile matrix with method 'bin' and step 0.1 ... OK
## No need to perform retention time correction, raw and corrected rt are identical for MSpos-Ex1-Col0-48h-Ag-3_1-A,1_01_9822.mzData.
## Create profile matrix with method 'bin' and step 0.1 ... OK
## No need to perform retention time correction, raw and corrected rt are identical for MSpos-Ex1-Col0-48h-Ag-4_1-A,1_01_9824.mzData.
## Create profile matrix with method 'bin' and step 0.1 ... OK
## No need to perform retention time correction, raw and corrected rt are identical for MSpos-Ex1-cyp79-48h-Ag-1_1-B,1_01_9819.mzData.
## Create profile matrix with method 'bin' and step 0.1 ... OK
## No need to perform retention time correction, raw and corrected rt are identical for MSpos-Ex1-cyp79-48h-Ag-2_1-B,2_01_9821.mzData.
## Create profile matrix with method 'bin' and step 0.1 ... OK
## No need to perform retention time correction, raw and corrected rt are identical for MSpos-Ex1-cyp79-48h-Ag-3_1-B,1_01_9823.mzData.
## Create profile matrix with method 'bin' and step 0.1 ... OK
## No need to perform retention time correction, raw and corrected rt are identical for MSpos-Ex1-cyp79-48h-Ag-4_1-B,2_01_9825.mzData.## Warning in dir.create(eicdir): 'mtbls2diffreport_eic' already exists## Warning in dir.create(boxdir): 'mtbls2diffreport_box' already existscspl <- getPeaklist(an)
annotatedDiffreport <- cbind(dr, cspl)Combine diffreport and CAMERA spectra
interestingPspec <- tapply(seq(1, nrow(annotatedDiffreport)),
INDEX=annotatedDiffreport[,"pcgroup"],
FUN=function(x, a) {m <- median(annotatedDiffreport[x, "pvalue"]);
p <- max(annotatedDiffreport[x, "pcgroup"]);
as.numeric(c(pvalue=m,pcgroup=p))},
annotatedDiffreport)
interestingPspec <- do.call(rbind, interestingPspec)
colnames(interestingPspec) <- c("pvalue", "pcgroup")
o <- order(interestingPspec[,"pvalue"])
pdf("interestingPspec.pdf")
dummy <- lapply(interestingPspec[o[1:40], "pcgroup"],
function(x) {suppressWarnings(plotPsSpectrum(an, pspec=x, maxlabel=5))})
dev.off()## png
## 2R, ISAtab, xcms and CAMERA revisited
These attributes and the intensity matrix could already be exported to conform to the specification for the ``metabolite assignment file’’ in the mzTab format used in MetaboLights. Currently, this functionality is not included in xcms. A prototype snippet is the following:
pl <- annotatedDiffreport
charge <- sapply(an@isotopes, function(x) {
ifelse( length(x) > 0, x$charge, NA)
})
abundance <- groupval(an@xcmsSet, value="into")
##
## load ISA assay files
##
a.samples <- ISAmtbls2["samples.per.assay.filename"][[ a.filename ]]
##
## These columns are defined by mzTab
##
maf.std.colnames <- c("identifier", "chemical_formula", "description",
"mass_to_charge", "fragmentation", "charge", "retention_time",
"taxid", "species", "database", "database_version", "reliability",
"uri", "search_engine", "search_engine_score", "modifications",
"smallmolecule_abundance_sub", "smallmolecule_abundance_stdev_sub",
"smallmolecule_abundance_std_error_sub")
##
## Plus the columns for the sample intensities
##
all.colnames <- c(maf.std.colnames, a.samples)
##
## Now assemble new maf
##
l <- nrow(pl)
maf <- data.frame(identifier = character(l),
chemical_formula = character(l),
description = character(l),
mass_to_charge = pl$mz,
fragmentation = character(l),
charge = charge,
retention_time = pl$rt,
taxid = character(l),
species = character(l),
database = character(l),
database_version = character(l),
reliability = character(l),
uri = character(l),
search_engine = character(l),
search_engine_score = character(l),
modifications = character(l),
smallmolecule_abundance_sub = character(l),
smallmolecule_abundance_stdev_sub = character(l),
smallmolecule_abundance_std_error_sub = character(l),
abundance, stringsAsFactors=FALSE)##
## Make sure maf table is quoted properly,
## and add to the ISAmtbls2 assay file.
##
maf_character <- apply(maf, 2, as.character)
write.table(maf_character,
file=paste(tempdir(), "/a_mtbl2_metabolite profiling_mass spectrometry_maf.csv", sep=""),
row.names=FALSE, col.names=all.colnames,
quote=TRUE, sep="\t", na="\"\"")
ISAmtbls2 <- updateAssayMetadata(ISAmtbls2, a.filename,
"Metabolite Assignment File",
paste(tempdir(), "/a_mtbl2_metabolite profiling_mass spectrometry_maf.csv", sep=""))
write.assay.file(ISAmtbls2, a.filename)sessionInfo()## R version 3.5.0 (2018-04-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.7-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.7-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] Rmpi_0.6-7 pcaMethods_1.72.0 CAMERA_1.36.0
## [4] xcms_3.2.0 MSnbase_2.6.0 ProtGenerics_1.12.0
## [7] mzR_2.14.0 BiocParallel_1.14.0 Risa_1.22.0
## [10] affy_1.58.0 biocViews_1.48.0 Rcpp_0.12.16
## [13] Biobase_2.40.0 BiocGenerics_0.26.0
##
## loaded via a namespace (and not attached):
## [1] vsn_3.48.0 splines_3.5.0 foreach_1.4.4
## [4] Formula_1.2-2 stats4_3.5.0 latticeExtra_0.6-28
## [7] RBGL_1.56.0 yaml_2.1.18 impute_1.54.0
## [10] pillar_1.2.2 backports_1.1.2 lattice_0.20-35
## [13] limma_3.36.0 RUnit_0.4.31 digest_0.6.15
## [16] RColorBrewer_1.1-2 checkmate_1.8.5 colorspace_1.3-2
## [19] htmltools_0.3.6 preprocessCore_1.42.0 Matrix_1.2-14
## [22] plyr_1.8.4 MALDIquant_1.17 XML_3.98-1.11
## [25] pkgconfig_2.0.1 zlibbioc_1.26.0 scales_0.5.0
## [28] RANN_2.5.1 affyio_1.50.0 tibble_1.4.2
## [31] htmlTable_1.11.2 IRanges_2.14.0 ggplot2_2.2.1
## [34] nnet_7.3-12 lazyeval_0.2.1 MassSpecWavelet_1.46.0
## [37] survival_2.42-3 magrittr_1.5 evaluate_0.10.1
## [40] doParallel_1.0.11 MASS_7.3-50 foreign_0.8-70
## [43] graph_1.58.0 BiocInstaller_1.30.0 tools_3.5.0
## [46] data.table_1.10.4-3 stringr_1.3.0 S4Vectors_0.18.0
## [49] munsell_0.4.3 cluster_2.0.7-1 compiler_3.5.0
## [52] mzID_1.18.0 rlang_0.2.0 grid_3.5.0
## [55] RCurl_1.95-4.10 iterators_1.0.9 rstudioapi_0.7
## [58] htmlwidgets_1.2 igraph_1.2.1 bitops_1.0-6
## [61] base64enc_0.1-3 rmarkdown_1.9 gtable_0.2.0
## [64] codetools_0.2-15 multtest_2.36.0 gridExtra_2.3
## [67] knitr_1.20 Hmisc_4.1-1 rprojroot_1.3-2
## [70] stringi_1.1.7 rpart_4.1-13 acepack_1.4.1