MetaboCoreUtils 1.4.0
Package: MetaboCoreUtils
Authors: Johannes Rainer [aut, cre] (https://orcid.org/0000-0002-6977-7147),
Michael Witting [aut] (https://orcid.org/0000-0002-1462-4426),
Andrea Vicini [aut],
Liesa Salzer [ctb] (https://orcid.org/0000-0003-0761-0656),
Sebastian Gibb [ctb] (https://orcid.org/0000-0001-7406-4443),
Michael Stravs [ctb] (https://orcid.org/0000-0002-1426-8572)
Last modified: 2022-04-26 14:32:40
Compiled: Tue Apr 26 17:04:06 2022
The MetaboCoreUtils defines metabolomics-related core functionality provided
as low-level functions to allow a data structure-independent usage across
various R packages (Rainer et al. 2022). This includes functions to calculate between ion (adduct)
and compound mass-to-charge ratios and masses or functions to work with chemical
formulas. The package provides also a set of adduct definitions and information
on some commercially available internal standard mixes commonly used in MS
experiments.
For a full list of function, see
library("MetaboCoreUtils")
ls(pos = "package:MetaboCoreUtils")## [1] "addElements" "adductNames"
## [3] "adducts" "calculateKm"
## [5] "calculateKmd" "calculateMass"
## [7] "calculateRkmd" "containsElements"
## [9] "convertMtime" "correctRindex"
## [11] "countElements" "indexRtime"
## [13] "internalStandardMixNames" "internalStandards"
## [15] "isRkmd" "isotopicSubstitutionMatrix"
## [17] "isotopologues" "mass2mz"
## [19] "mz2mass" "pasteElements"
## [21] "standardizeFormula" "subtractElements"or the reference page on the package webpage.
The package can be installed with the BiocManager package. To
install BiocManager use install.packages("BiocManager") and, after that,
BiocManager::install("MetaboCoreUtils") to install this package.
The functions defined in this package utilise basic classes with the aim of being reused in packages that provide a more formal, high-level interface.
The examples below demonstrate the basic usage of the functions from the package.
library(MetaboCoreUtils)The mass2mz and mz2mass functions allow to convert between compound masses
and ion (adduct) mass-to-charge ratios (m/z). The MetaboCoreUtils package
provides definitions of common ion adducts generated by electrospray ionization
(ESI). These can be listed with the adductNames function.
adductNames()## [1] "[M+3H]3+" "[M+2H+Na]3+" "[M+H+Na2]3+"
## [4] "[M+Na3]3+" "[M+2H]2+" "[M+H+NH4]2+"
## [7] "[M+H+K]2+" "[M+H+Na]2+" "[M+C2H3N+2H]2+"
## [10] "[M+2Na]2+" "[M+C4H6N2+2H]2+" "[M+C6H9N3+2H]2+"
## [13] "[M+H]+" "[M+Li]+" "[M+2Li-H]+"
## [16] "[M+NH4]+" "[M+H2O+H]+" "[M+Na]+"
## [19] "[M+CH4O+H]+" "[M+K]+" "[M+C2H3N+H]+"
## [22] "[M+2Na-H]+" "[M+C3H8O+H]+" "[M+C2H3N+Na]+"
## [25] "[M+2K-H]+" "[M+C2H6OS+H]+" "[M+C4H6N2+H]+"
## [28] "[2M+H]+" "[2M+NH4]+" "[2M+Na]+"
## [31] "[2M+K]+" "[2M+C2H3N+H]+" "[2M+C2H3N+Na]+"
## [34] "[3M+H]+" "[M+H-NH3]+" "[M+H-H2O]+"
## [37] "[M+H-Hexose-H2O]+" "[M+H-H4O2]+" "[M+H-CH2O2]+"
## [40] "[M]+"With that we can use the mass2mz function to calculate the m/z for a set of
compounds assuming the generation of certain ions. In the example below we
define masses for some theoretical compounds and calculate their expected m/z
assuming that ions "[M+H]+" and "[M+Na]+" are generated.
masses <- c(123, 842, 324)
mass2mz(masses, adduct = c("[M+H]+", "[M+Na]+"))## [M+H]+ [M+Na]+
## [1,] 124.0073 145.9892
## [2,] 843.0073 864.9892
## [3,] 325.0073 346.9892As a result we get a matrix with each row representing one compound and each
column the m/z for one of the defined adducts. With the mz2mass we could
perform the reverse calculation, i.e. from m/z to compound masses.
The lack of consistency in the format in which chemical formulas are written
poses a big problem comparing formulas coming from different resources. The
MetaboCoreUtils package provides functions to standardize formulas as well
as combine formulas or substract elements from formulas. Below we use an
artificial example to show this functionality. First we standardize a chemical
formula with the standardizeFormula function.
frml <- "Na3C4"
frml <- standardizeFormula(frml)
frml## Na3C4
## "C4Na3"Next we add "H2O" to the formula using the addElements function.
frml <- addElements(frml, "H2O")
frml## [1] "C4H2ONa3"We can also substract elements with the subtractElements function:
frml <- subtractElements(frml, "H")
frml## [1] "C4HONa3"The counts for individual elements in a chemical formula can be calculated with
the countElements function.
countElements(frml)## $C4HONa3
## C H O Na
## 4 1 1 3Lipids and other homologous series based on fatty acyls can be found in data by
using Kendrick mass defects (KMD) or referenced kendrick mass defects
(RKMD). The MetaboCoreUtils package provides functions to calculate everything
around Kendrick mass defects. The following example calculates the KMD and RKMD
for three lipids (PC(16:0/18:1(9Z)), PC(16:0/18:0), PS(16:0/18:1(9Z))) and
checks, if they fit the RKMD of PCs detected as [M+H]+ adducts.
lipid_masses <- c(760.5851, 762.6007, 762.5280)
calculateKmd(lipid_masses)## [1] 0.7358239 0.7491732 0.6765544Next the RKMD is calculated and checked if it fits to a specific range. RKMDs are either 0 or negative integers according to the number of double bonds in the lipids, e.g. -2 if two double bonds are present in the lipids.
lipid_rkmd <- calculateRkmd(lipid_masses)
isRkmd(lipid_rkmd)## [1] TRUE TRUE FALSERetention times are often not directly comparable between two LC-MS systems,
even if nominally the same separation method is used. Conversion of retention
times to retetion indices can overcome this issue. The MetaboCoreUtils package
provides a function to perform this conversion. Below we use an example based on
indexing with a homologoues series af N-Alkyl-pyridinium sulfonates (NAPS).
rti <- read.table(system.file("retentionIndex",
"rti.txt",
package = "MetaboCoreUtils"),
header = TRUE,
sep = "\t")
rtime <- read.table(system.file("retentionIndex",
"metabolites.txt",
package = "MetaboCoreUtils"),
header = TRUE,
sep = "\t")A data.frame with the retetion times of the NAPS and their respective index
value is required.
head(rti)## rtime rindex
## 1 1.14 100
## 2 1.18 200
## 3 1.38 300
## 4 2.11 400
## 5 4.34 500
## 6 5.92 600The indexing is peformed using the function indexRtime.
rtime$rindex_r <- indexRtime(rtime$rtime, rti)For comparison the manual calculated retention indices are included.
head(rtime)## name rtime rindex_manual rindex_r
## 1 VITAMIN D2 NA NA NA
## 2 SQUALENE 15.66 1709.8765 1709.8765
## 3 4-COUMARATE 6.26 629.3103 629.3103
## 4 NONANOATE 11.73 1244.5783 1244.5783
## 5 ESTRADIOL-17ALPHA 10.27 1065.4321 1065.4321
## 6 CAPRYLATE 10.67 1114.8148 1114.8148Conditions that shall be compared by the retention index might not perfectly
match. In case the deviation is linear a simple two-point correction can be
applied to the data. This is performed by the function correctRindex. The
correction requires two reference standards and their measured RIs and reference
RIs.
ref <- data.frame(rindex = c(1709.8765, 553.7975),
refindex = c(1700, 550))
rtime$rindex_cor <- correctRindex(rtime$rindex_r, ref)If you would like to contribute any low-level functionality, please open a GitHub issue to discuss it. Please note that any contributions should follow the style guide and will require an appropriate unit test.
If you wish to reuse any functions in this package, please just go ahead. If you would like any advice or seek help, please either open a GitHub issue.
## R version 4.2.0 RC (2022-04-19 r82224)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
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## BLAS: /home/biocbuild/bbs-3.15-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.15-bioc/R/lib/libRlapack.so
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##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] MetaboCoreUtils_1.4.0 BiocStyle_2.24.0
##
## loaded via a namespace (and not attached):
## [1] knitr_1.38 cluster_2.1.3 magrittr_2.0.3
## [4] BiocGenerics_0.42.0 MsCoreUtils_1.8.0 MASS_7.3-57
## [7] clue_0.3-60 R6_2.5.1 rlang_1.0.2
## [10] fastmap_1.1.0 stringr_1.4.0 tools_4.2.0
## [13] xfun_0.30 cli_3.3.0 jquerylib_0.1.4
## [16] htmltools_0.5.2 yaml_2.3.5 digest_0.6.29
## [19] bookdown_0.26 BiocManager_1.30.17 sass_0.4.1
## [22] S4Vectors_0.34.0 evaluate_0.15 rmarkdown_2.14
## [25] stringi_1.7.6 compiler_4.2.0 bslib_0.3.1
## [28] stats4_4.2.0 jsonlite_1.8.0Rainer, Johannes, Andrea Vicini, Liesa Salzer, Jan Stanstrup, Josep M. Badia, Steffen Neumann, Michael A. Stravs, et al. 2022. “A Modular and Expandable Ecosystem for Metabolomics Data Annotation in R.” Metabolites 12 (2): 173. https://doi.org/10.3390/metabo12020173.