Everest Manual
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An introduction to Everest package Version 0.3.0 Xavier Domingo-Almenara (Maintainer) xdomingo@scripps.edu May 23, 2018 This vignette presents Everest, an R package for annotation of metabolites in untargeted LC–MS-based metabolomics. Everest allows annotation of a list of features from XCMS or other computational tools. Everest provides a full characterization of features, detecting adducts, common losses and dimmers, to computationally determine monoisotopic masses from underlying metabolites in the samples. Everest provides a list of putative identification based on accurate mass search of computed monoisotopic masses. If you use the package Everest in your analysis and publications please cite: Domingo-Almenara X. Everest, an R package for metabolite annotation in untargeted LC-MSbased metabolomics. CRAN. URL https://CRAN.R-project.org/package=everest Installation: Everest can be installed from any CRAN repository, by: # Install install.packages("everest") # Load library(everest) Support: Any enquiries, bug reports or suggestions are welcome and they should be addressed to xdomingo@scripps.edu. 1 Contents 1 Introduction 3 2 Importing feature tables 3 3 Annotation of features in untargeted LC/MS-based data 3.1 Metabolite annotation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Ohter functionalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 4 5 2 1 Introduction Everest is an R package that allows the annotation of LC–MS-based untargeted data. Everest will first group those features that stem from the same metabolite, assigning to each one an ’AlignID’ number. All these features sharing the same ’AlignID’ number are considered a single pseudo-spectra resulting from the same molecule. Next, Everest will annotate these features within each pseudo-spectra, using known monoisotopic masses for common adducts, losses and dimmers. Features that show a m/z relation will be annotated with the same ’AnnID’ number. 2 Importing feature tables Everest can work with xcmsSet object directly from XCMS, or import tables. If you have processed your data with XCMS (Offline version), and you have the xcmsSet (e.g., xset3) loaded into your R enviroment, then proceed to the next section. Otherwise, if you have processed your data and you have a CSV or Excel file (e.g., diffreport from XCMS Online) then read the following steps. First, edit the spreadsheet (Excel or CSV) so that the first column contains the features names, the second the m/z values and the third the Retention Times, and name them as: name, mz and RT, respectively. The rest of the columns should contain only the intensity or area (recommended) values of each feature in each sample. Remove any other columns or rows. Here is an example of how the table should look like: name M50T350 M51T316 M51T349 M51T3162 M51T3163 M53T350 + mz 50.22695 51.28015 51.28734 51.30070 51.31288 52.94600 RT Sample1 Sample2 349.5595 375013.1 1997693.4 316.2429 2278437.1 213624.8 349.3183 23420.0 3988480.2 316.3434 1800327.7 2046424.4 316.4545 1208414.6 1546696.9 349.5218 318827.2 1227462.8 ... RT and column names: The first column and the sample column can have any name. However, columns 2 and 3 have to follow this exact spelling: mz and RT. Also, the RT must be in seconds, so multiply the RT (rtmed) column per 60 if the data is in seconds (specially when using XCMS Online, as it is the default option) Next, the file can be loaded into R using the native read.csv2() or the read.xlsx() function from openxlsx package1 . # Load table (CSV) feature.table <- read.csv2("featureTable.csv") # Load table (tab delim TSV file) feature.table <- read.delim("featureTable.tsv") # Or an XLSX file: feature.table <- read.xlsx("featureTable.xlsx") # Make sure that it looks OK by taking a quick look: head(feature.table) If the output of head(feature.table) looks good, then we can proceed to the following section. Be careful, sometimes when loading the table, (e.g., if using TSV files) an additional column is introduced. We can remove it easily with R (see following section) 1 You can install openxslx (https://CRAN.R-project.org/package=openxlsx ) by typing the following into the R console: install.packages(”openxlsx”) 3 3 Annotation of features in untargeted LC/MS-based data In this section we show the annotation of a feature list as a result of the analysis of blood samples in positive mode with processing via XCMS. The feature table used to reproduce this example can be download from the Github repository 2 . However, the following code includes a script to automatically download the file. All the listed commands (script) to reproduce the following demo can by found by executing: help(package="everest") and then click on User guides, package vignettes and other documentation and on source from the ’Everest Manual’. 3.1 Metabolite annotation The following code will download the DEMO feature table and proceed with the metabolite annotation. library(everest) # Download the feature table (if not downloaded manually before): download.file("https://github.com/xdomingoal/everest-data/raw/master/MTBLS20.tsv" , "MTBLS20.tsv") # Load the table (we have to remove the first column of the table, as explained before): feature.table <- read.delim("featureTable.tsv")[,-1] # Annotate ex <- evAnnotate(data.table = feature.table, ion.mode = "pos", min.correlation = 0.7, max.time.dist = 2, ppm.error = 20) The results can be accessed throught the function annoTable(), and we can save the resulting file as an Excel or CSV file: # Access the annotation results: anTab <- annoTable(ex) # Write the table as an excel file: require(openxlsx) write.xlsx(anTab, file = "results.xlsx") The resulting table will contain four additional columns: AlignID: All the features sharing the same AlignID are considered to stem from the same metabolite. AnnID: Within the same AlignID, these features sharing the same AnnID are considered to stem from a unique monoisotopic mass. A unique AlignID may have more than one AnnID, and each AnnID number leads to a different monoisotopic m/z (see example below). Isotope: Indicates whether or not this feature is an isotopic peak. toMSMS: Indicates whether or not Everest recommends these feature for subsequent tandem MS fragmentation. 2 URL to download the file. https://github.com/xdomingoal/everest-data/raw/master/MTBLS20.tsv (click on File/Save File As on your web explorer if the file does not automatically downloads.) 4 To annotate a xset3 object from XCMS (offline version), use the following code: # Annotate an xset3 object from XCMS ex <- evAnnotate(xcmsSet = xset3, ion.mode = "pos", min.correlation = 0.7, max.time.dist = 2, ppm.error = 20) 3.2 Ohter functionalities Two other functions are included in , showAn and showGroup. The first displays access to a summary of the annotation results for a specific feature. Let us consider that we want to know the annotation of the feature ’M170T276 8’, in that case: showAn(ex, "M170T276_8") AlignID AnnID Feature 525 1 M152T276 525 1 M170T276_8 525 1 M192T276 525 1 M339T276 525 1 M153T276 525 2 M152T276 525 2 M170T276_8 525 2 M192T276 525 2 M153T276 Mass 152.0344 170.0448 192.0268 339.0852 153.0376 152.0344 170.0448 192.0268 153.0376 Rt 276.4 276.4 276.1 276.1 276.3 276.4 276.4 276.1 276.3 Adduct IsoCount (M+H)+[-H2O] 1 (M+H)+ 0 (M+Na)+ 0 (2M+H)+ 0 M+1 0 (M+Na)+[-H2O] 1 (M+Na)+ 0 [M-H+2Na]+ 0 M+1 0 Score 99 99 99 99 99 99 99 99 99 mIsoMass Isotope toMSMS 169.0382 169.0382 yes 169.0382 169.0382 169.0382 yes 147.0562 147.0562 147.0562 147.0562 yes In this example, the queried feature is shown together with the other features sharing the same AlignID number. We observe that the queried feature is a protonated (M+H) specie, and that shares its AnnID number with other four features, which correspond to adducts, losses, dimmers and isotopes. Additionally, other four features within the same AlignID group share a different AnnID number. This indicates that these AlignID group could actually be another metabolite (protonated specie). Everest ranks the different annotation hypothesis, and in this case, proposes the queried feature for subsquent tandem MS fragmentation (toMSMS column). The Isocount column indicates how many isotopic peaks have been found for that feature, whereas the mIsoMass indicates the monoisotopic mass to which all the features whithin a unique AnnID point to. Of note, decimals in this example are truncated for illustration purposes. The second function, showGroup retrieves a summary of the group (AlignID) of that feature, and it contains specific information such as the pseudo-spectra or the list of features that compose that group. 5
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