Package: xcms
Authors: Johannes Rainer
Modified: 2024-10-23 19:24:55.946541
Compiled: Tue Oct 29 20:06:44 2024

1 Introduction

In a typical LC-MS-based metabolomics experiment compounds eluting from the chromatography are first ionized before being measured by mass spectrometry (MS). During the ionization different (multiple) ions can be generated from the same compound which all will be measured by MS. In general, the resulting data is then pre-processed to identify chromatographic peaks in the data and to group these across samples in the correspondence analysis. The result are distinct LC-MS features, characterized by their specific m/z and retention time range. Different ions generated during ionization will be detected as different features. Compounding aims now at grouping such features presumably representing signal from the same originating compound to reduce data set complexity (and to aid in subsequent annotation steps). General MS feature grouping functionality if defined by the MsFeatures package with additional functionality being implemented in the xcms package to enable the compounding of LC-MS data.

This document provides a simple compounding workflow using xcms. Note that the present functionality does not (yet) aggregate or combine the actual features per values, but does only define the feature groups (one per compound).

2 Compounding of LC-MS data

We demonstrate the compounding (feature grouping) functionality on the simple toy data set used also in the xcms package and provided through the faahKO package. This data set consists of samples from 4 mice with knock-out of the fatty acid amide hydrolase (FAAH) and 4 wild type mice. Pre-processing of this data set is described in detail in the main vignette of the xcms package. Below we load all required packages and the result from this pre-processing updating also the location of the respective raw data files on the current machine.

library(MSnbase)
library(xcms)
library(faahKO)
library(MsFeatures)

xmse <- loadXcmsData("xmse")

Before performing the feature grouping we inspect the result object. With featureDefinitions we can extract the results from the correspondence analysis.

featureDefinitions(xmse) |> head()
##       mzmed mzmin mzmax    rtmed    rtmin    rtmax npeaks KO WT      peakidx
## FT001 200.1 200.1 200.1 2902.634 2882.603 2922.664      2  2  0 458, 116....
## FT002 205.0 205.0 205.0 2789.901 2782.955 2796.531      8  4  4 44, 443,....
## FT003 206.0 206.0 206.0 2789.405 2781.389 2794.219      7  3  4 29, 430,....
## FT004 207.1 207.1 207.1 2718.560 2714.047 2727.347      7  4  3 16, 420,....
## FT005 233.0 233.0 233.1 3023.579 3015.145 3043.959      7  3  4 69, 959,....
## FT006 241.1 241.1 241.2 3683.299 3661.586 3695.886      8  3  4 276, 284....
##       ms_level
## FT001        1
## FT002        1
## FT003        1
## FT004        1
## FT005        1
## FT006        1

Each row in this data frame represents the definition of one feature, with its average and range of m/z and retention time. Column "peakidx" provides the index of each chromatographic peak which is assigned to the feature in the chromPeaks matrix of the result object. The featureValues function allows to extract feature values, i.e. a matrix with feature abundances, one row per feature and columns representing the samples of the present data set.

Below we extract the feature values with and without filled-in peak data. Without the gap-filled data only abundances from detected chromatographic peaks are reported. In the gap-filled data, for samples in which no chromatographic peak for a feature was detected, all signal from the m/z - retention time range defined based on the detected chromatographic peaks was integrated.

head(featureValues(xmse, filled = FALSE))
##        ko15.CDF  ko16.CDF  ko21.CDF  ko22.CDF  wt15.CDF  wt16.CDF  wt21.CDF
## FT001        NA  506848.9        NA  169955.6        NA        NA        NA
## FT002 1924712.0 1757151.0 1383416.7 1180288.2 2129885.1 1634342.0 1623589.2
## FT003  213659.3  289500.7        NA  178285.7  253825.6  241844.4  240606.0
## FT004  349011.5  451863.7  343897.8  208002.8  364609.8  360908.9        NA
## FT005  286221.4        NA  164009.0  149097.6  255697.7  311296.8  366441.5
## FT006 1160580.5        NA  380970.3  588986.4 1286883.0 1739516.6  639755.3
##        wt22.CDF
## FT001        NA
## FT002 1354004.9
## FT003  185399.5
## FT004  221937.5
## FT005  271128.0
## FT006  508546.4
head(featureValues(xmse, filled = TRUE))
##        ko15.CDF  ko16.CDF  ko21.CDF  ko22.CDF  wt15.CDF  wt16.CDF  wt21.CDF
## FT001  135162.4  506848.9  111657.3  169955.6  209929.4  141607.9  226853.7
## FT002 1924712.0 1757151.0 1383416.7 1180288.2 2129885.1 1634342.0 1623589.2
## FT003  213659.3  289500.7  164380.7  178285.7  253825.6  241844.4  240606.0
## FT004  349011.5  451863.7  343897.8  208002.8  364609.8  360908.9  226234.4
## FT005  286221.4  285857.6  164009.0  149097.6  255697.7  311296.8  366441.5
## FT006 1160580.5 1102832.6  380970.3  588986.4 1286883.0 1739516.6  639755.3
##        wt22.CDF
## FT001  138341.2
## FT002 1354004.9
## FT003  185399.5
## FT004  221937.5
## FT005  271128.0
## FT006  508546.4

In total 351 features have been defined in the present data set, many of which most likely represent signal from different ions (adducts or isotopes) of the same compound. The aim of the grouping functions of are now to define which features most likely come from the same original compound. The feature grouping functions base on the following assumptions/properties of LC-MS data:

  • Features (ions) of the same compound should have similar retention time.
  • The abundance of features (ions) of the same compound should have a similar pattern across samples, i.e. if a compound is highly concentrated in one sample and low in another, all ions from it should follow the same pattern.
  • The peak shape of extracted ion chromatograms (EIC) of features of the same compound should be similar as it should follow the elution pattern of the original compound from the LC.

The main method to perform the feature grouping is called groupFeatures which takes an xcms result object (i.e., an XcmsExperiment or XCMSnExp) as input as well as a parameter object to chose the grouping algorithm and specify its settings. xcms provides and supports the following grouping approaches:

  • SimilarRtimeParam: perform an initial grouping based on similar retention time.
  • AbundanceSimilarityParam: perform a feature grouping based on correlation of feature abundances (values) across samples.
  • EicSimilarityParam: perform a feature grouping based on correlation of EICs.

Calling groupFeatures on an xcms result object will perform a feature grouping assigning each feature in the data set to a feature group. These feature groups are stored as an additional column called "feature_group" in the featureDefinition data frame of the result object and can be accessed with the featureGroups function. Any subsequent groupFeature call will sub-group (refine) the identified feature groups further. It is thus possible to use a single grouping approach, or to combine multiple of them to generate the desired feature grouping in an incremental fashion. While the individual feature grouping algorithms can be called in any order, it is advisable to use the EicSimilarityParam as last refinement step, because it is computationally very expensive, especially if applied to a result object without pre-defined feature groups or if the feature groups are very large.

2.1 Grouping of features by similar retention time

The most intuitive and simple way to group features is based on their retention time. Before we perform this initial grouping we evaluate retention times and m/z of all features in the present data set.

plot(featureDefinitions(xmse)$rtmed, featureDefinitions(xmse)$mzmed,
     xlab = "retention time", ylab = "m/z", main = "features",
     col = "#00000080", pch = 21, bg = "#00000040")
grid()