xcms 4.7.0
Package: xcms
Authors: Johannes Rainer
Modified: 2025-04-04 14:16:40.870258
Compiled: Tue Apr 15 21:19:51 2025
The xcms package provides the functionality to perform the preprocessing of LC-MS, GC-MS or LC-MS/MS data in which raw signals from mzML, mzXML or CDF files are processed into feature abundances. This preprocessing includes chromatographic peak detection, sample alignment and correspondence analysis.
The first version of the package was already published in 2006 [1] and has since been updated and modernized in several rounds to better integrate it with other R-based packages for the analysis of untargeted metabolomics data. This includes version 3 of xcms that used the MSnbase package for MS data representation [2]. The most recent update (xcms version 4) enables in addition preprocessing of MS data represented by the modern MsExperiment and Spectra packages which provides an even better integration with the RforMassSpectrometry R package ecosystem simplifying e.g. also compound annotation [3].
This document describes data import, exploration and preprocessing of a simple test LC-MS data set with the xcms package version >= 4. The same functions can be applied to the older MSnbase-based workflows (xcms version 3). Additional documents and tutorials covering also other topics of untargeted metabolomics analysis are listed at the end of this document. There is also a xcms tutorial available with more examples and details. To get a complete overview of LCMS-MS analysis, an end-to-end workflow Metabonaut website, which integrate the xcms preprocessing steps with the downstream analysis, is available.
xcms supports analysis of any LC-MS(/MS) data that can be imported with the Spectra package. Such data will typically be provided in (AIA/ANDI) NetCDF, mzXML and mzML format but can, through dedicated extensions to the Spectra package, also be imported from other sources, e.g. also directly from raw data files in manufacturer’s formats.
For demonstration purpose we will analyze in this document a small subset of the data from [4] in which the metabolic consequences of the knock-out of the fatty acid amide hydrolase (FAAH) gene in mice was investigated. The raw data files (in NetCDF format) are provided through the faahKO data package. The data set consists of samples from the spinal cords of 6 knock-out and 6 wild-type mice. Each file contains data in centroid mode acquired in positive ion polarity from 200-600 m/z and 2500-4500 seconds. To speed-up processing of this vignette we will restrict the analysis to only 8 files.
Below we load all required packages, locate the raw CDF files within the
faahKO package and build a phenodata data.frame describing the
experimental setup. Generally, such data frames should contain all relevant
experimental variables and sample descriptions (including also the names of the
raw data files) and will be imported into R using either the read.table()
function (if the file is in csv or tabulator delimited text file format) or
also using functions from the readxl R package if it is in Excel file format.
library(xcms)
library(faahKO)
library(RColorBrewer)
library(pander)
library(pheatmap)
library(MsExperiment)
## Get the full path to the CDF files
cdfs <- dir(system.file("cdf", package = "faahKO"), full.names = TRUE,
recursive = TRUE)[c(1, 2, 5, 6, 7, 8, 11, 12)]
## Create a phenodata data.frame
pd <- data.frame(sample_name = sub(basename(cdfs), pattern = ".CDF",
replacement = "", fixed = TRUE),
sample_group = c(rep("KO", 4), rep("WT", 4)),
stringsAsFactors = FALSE)We next load our data using the readMsExperiment function from the
MsExperiment package.
faahko <- readMsExperiment(spectraFiles = cdfs, sampleData = pd)
faahko## Object of class MsExperiment
## Spectra: MS1 (10224)
## Experiment data: 8 sample(s)
## Sample data links:
## - spectra: 8 sample(s) to 10224 element(s).The MS spectra data from our experiment is now available as a Spectra object
within faahko. Note that this MsExperiment container could in addition to
spectra data also contain other types of data or also references to other
files. See the vignette from the MsExperiment for more
details. Also, when loading data from mzML, mzXML or CDF files, by default only
general spectra data is loaded into memory while the actual peaks data,
i.e. the m/z and intensity values are only retrieved on-the-fly from the raw
files when needed (this is similar to the MSnbase on-disk mode described in
[2]). This guarantees a low memory footprint
hence allowing to analyze also large experiments without the need of high
performance computing environments. Note that also different alternative
backends (and hence data representations) could be used for the Spectra
object within faahko with eventually even lower memory footprint, or higher
performance. See the package vignette from the Spectra package or
the SpectraTutorials tutorial for
more details on Spectra backends and how to change between them.
The MsExperiment object is a simple and flexible container for MS
experiments. The raw MS data is stored as a Spectra object that can be
accessed through the spectra() function.
spectra(faahko)## MSn data (Spectra) with 10224 spectra in a MsBackendMzR backend:
## msLevel rtime scanIndex
## <integer> <numeric> <integer>
## 1 1 2501.38 1
## 2 1 2502.94 2
## 3 1 2504.51 3
## 4 1 2506.07 4
## 5 1 2507.64 5
## ... ... ... ...
## 10220 1 4493.56 1274
## 10221 1 4495.13 1275
## 10222 1 4496.69 1276
## 10223 1 4498.26 1277
## 10224 1 4499.82 1278
## ... 33 more variables/columns.
##
## file(s):
## ko15.CDF
## ko16.CDF
## ko21.CDF
## ... 5 more filesAll spectra are organized sequentially (i.e., not by file) but the
fromFile() function can be used to get for each spectrum the information to
which of the data files it belongs. Below we simply count the number of spectra
per file.
table(fromFile(faahko))##
## 1 2 3 4 5 6 7 8
## 1278 1278 1278 1278 1278 1278 1278 1278Information on samples can be retrieved through the sampleData() function.
sampleData(faahko)## DataFrame with 8 rows and 3 columns
## sample_name sample_group spectraOrigin
## <character> <character> <character>
## 1 ko15 KO /home/bioc...
## 2 ko16 KO /home/bioc...
## 3 ko21 KO /home/bioc...
## 4 ko22 KO /home/bioc...
## 5 wt15 WT /home/bioc...
## 6 wt16 WT /home/bioc...
## 7 wt21 WT /home/bioc...
## 8 wt22 WT /home/bioc...Each row in this DataFrame represents one sample (input file). Using [ it is
possible to subset a MsExperiment object by sample. Below we subset the
faahko to the 3rd sample (file) and access its spectra and sample data.
faahko_3 <- faahko[3]
spectra(faahko_3)## MSn data (Spectra) with 1278 spectra in a MsBackendMzR backend:
## msLevel rtime scanIndex
## <integer> <numeric> <integer>
## 1 1 2501.38 1
## 2 1 2502.94 2
## 3 1 2504.51 3
## 4 1 2506.07 4
## 5 1 2507.64 5
## ... ... ... ...
## 1274 1 4493.56 1274
## 1275 1 4495.13 1275
## 1276 1 4496.69 1276
## 1277 1 4498.26 1277
## 1278 1 4499.82 1278
## ... 33 more variables/columns.
##
## file(s):
## ko21.CDFsampleData(faahko_3)## DataFrame with 1 row and 3 columns
## sample_name sample_group spectraOrigin
## <character> <character> <character>
## 1 ko21 KO /home/bioc...As a first evaluation of the data we below plot the base peak chromatogram (BPC)
for each file in our experiment. We use the chromatogram() method and set the
aggregationFun to "max" to return for each spectrum the maximal intensity
and hence create the BPC from the raw data. To create a total ion chromatogram
we could set aggregationFun to "sum".
## Get the base peak chromatograms. This reads data from the files.
bpis <- chromatogram(faahko, aggregationFun = "max")
## Define colors for the two groups
group_colors <- paste0(brewer.pal(3, "Set1")[1:2], "60")
names(group_colors) <- c("KO", "WT")
## Plot all chromatograms.
plot(bpis, col = group_colors[sampleData(faahko)$sample_group])
Figure 1: Base peak chromatogram
The chromatogram() method returned a MChromatograms object that organizes
individual Chromatogram objects (which in fact contain the chromatographic
data) in a two-dimensional array: columns represent samples and rows
(optionally) m/z and/or retention time ranges. Below we extract the chromatogram
of the first sample and access its retention time and intensity values.
bpi_1 <- bpis[1, 1]
rtime(bpi_1) |> head()## [1] 2501.378 2502.943 2504.508 2506.073 2507.638 2509.203intensity(bpi_1) |> head()## [1] 43888 43960 43392 42632 42200 42288From the BPC above it seems that after around 4200 seconds no signal is measured
anymore. Thus, we filter below the full data set to a retention time range from
2550 to 4250 seconds using the filterRt() function. Note that at present this
will only subset the spectra within the MsExperiment. Subsequently we
re-create also the BPC.
faahko <- filterRt(faahko, rt = c(2550, 4250))## Filter spectra## creating the BPC on the subsetted data
bpis <- chromatogram(faahko, aggregationFun = "max")We next create boxplots representing the distribution of the total ion currents
per data file. Such plots can be very useful to spot potentially problematic MS
runs. To extract this information, we use the tic() function on the Spectra
object within faahko and split the values by file using fromFile().
## Get the total ion current by file
tc <- spectra(faahko) |>
tic() |>
split(f = fromFile(faahko))
boxplot(tc, col = group_colors[sampleData(faahko)$sample_group],
ylab = "intensity", main = "Total ion current")
Figure 2: Distribution of total ion currents per file
In addition, we can also cluster the samples based on similarity of their base
peak chromatograms. Samples would thus be grouped based on similarity of their
LC runs. For that we need however to bin the data along the retention time
axis, since retention times will generally differ between samples. Below we use
the bin() function on the BPC to bin intensities into 2 second wide retention
time bins. The clustering is then performed using complete linkage hierarchical
clustering on the pairwise correlations of the binned base peak chromatograms.
## Bin the BPC
bpis_bin <- bin(bpis, binSize = 2)
## Calculate correlation on the log2 transformed base peak intensities
cormat <- cor(log2(do.call(cbind, lapply(bpis_bin, intensity))))
colnames(cormat) <- rownames(cormat) <- bpis_bin$sample_name
## Define which phenodata columns should be highlighted in the plot
ann <- data.frame(group = bpis_bin$sample_group)
rownames(ann) <- bpis_bin$sample_name
## Perform the cluster analysis
pheatmap(cormat, annotation = ann,
annotation_color = list(group = group_colors))
Figure 3: Grouping of samples based on similarity of their base peak chromatogram
The samples cluster in a pairwise manner, with the KO and WT samples for the same sample index having the most similar BPC.
Chromatographic peak detection aims at identifying all signal in each sample
created from ions of the same originating compound species. Chromatographic peak
detection can be performed in xcms with the findChromPeaks() function and a
parameter object which defines and configures the algorithm that should be
used (see ?findChromPeaks for a list of supported algorithms). Before running
any peak detection it is however strongly suggested to first visually inspect
the extracted ion chromatogram of e.g. internal standards or compounds known to
be present in the samples in order to evaluate and adapt the settings of the
peak detection algorithm since the default settings will not be appropriate for
most LC-MS setups.
Below we extract the EIC for one compound using the chromatogram() function by
specifying in addition the m/z and retention time range where we would expect
the signal for that compound.
## Define the rt and m/z range of the peak area
rtr <- c(2700, 2900)
mzr <- c(334.9, 335.1)
## extract the chromatogram
chr_raw <- chromatogram(faahko, mz = mzr, rt = rtr)
plot(chr_raw, col = group_colors[chr_raw$sample_group])
Figure 4: Extracted ion chromatogram for one peak
Note that Chromatogram objects extracted by the chromatogram() method
contain an NA value if in a certain scan (i.e. in a spectrum for a specific
retention time) no signal was measured in the respective m/z range. This is
reflected by the lines not being drawn as continuous lines in the plot above.
The peak above has thus a width of about 50 seconds. We can use this information
to define the peakwidth parameter of the centWave peak detection method
[5] that we will use for chromatographic peak detection on our
data. The peakwidth parameter allows to define the expected lower and upper
width (in retention time dimension) of chromatographic peaks. For our data we
set it thus to peakwidth = c(20, 80). The second important parameter for
centWave is ppm which defines the expected maximum deviation of m/z values
of the centroids that should be included into one chromatographic peak (note
that this is not the precision of m/z values provided by the MS instrument
manufacturer).
For the ppm parameter we extract the full MS data (intensity, retention time
and m/z values) corresponding to the above peak. To this end we first filter the
raw object by retention time, then by m/z and finally plot the object with type = "XIC" to produce the plot below. We use the pipe (|>) operator to better
illustrate the corresponding workflow.
faahko |>
filterRt(rt = rtr) |>
filterMz(mz = mzr) |>
plot(type = "XIC")
Figure 5: Visualization of the raw MS data for one peak
For each plot: upper panel: chromatogram plotting the intensity values against the retention time, lower panel m/z against retention time plot. The individual data points are colored according to the intensity.
In the present data there is actually no variation in the m/z values. Usually the m/z values of the individual centroids (lower panel) in the plots above would scatter around the real m/z value of the compound (in an intensity dependent manner).
The first step of the centWave algorithm defines regions of interest (ROI)
that are subsequently screened for the presence of chromatographic peaks. These
ROIs are defined based on the difference of m/z values of centroids from
consecutive scans (spectra). In detail, centroids from consecutive scans are
included into a ROI if the difference between their m/z and the mean m/z of the
ROI is smaller than the user defined ppm parameter. A reasonable choice for
the ppm could thus be the maximal m/z difference of data points from
neighboring scans/spectra that are part of a chromatographic peak for an
internal standard of known compound. It is suggested to inspect the ranges of
m/z values for several compounds (either internal standards or compounds known
to be present in the sample) and define the ppm parameter for centWave
according to these. See also this
tutorial for additional
information and examples on choosing and testing peak detection settings.
Chromatographic peak detection can also be performed on extracted ion
chromatograms, which helps testing and tuning peak detection settings. Note
however that peak detection on EICs does not involve the first step of
centWave described above and will thus not consider the ppm
parameter. Also, since less data is available to the algorithms, background
signal estimation is performed differently and different settings for snthresh
will need to be used (generally a lower snthresh will be used for EICs since
the estimated background signal tends to be higher for data subsets than for the
full data). Below we perform the peak detection with the findChromPeaks()
function on the EIC generated above. The submitted parameter object defines
which algorithm will be used and allows to define the settings for this
algorithm. We use a CentWaveParam parameter object to use and configure the
centWave algorithm with default settings, except for snthresh.
xchr <- findChromPeaks(chr_raw, param = CentWaveParam(snthresh = 2))We can access the identified chromatographic peaks with the chromPeaks()
function.
chromPeaks(xchr)## mz mzmin mzmax rt rtmin rtmax into intb maxo
## mzmin 335 334.9 335.1 2781.505 2761.160 2809.674 412134.255 355516.374 16856
## mzmin 335 334.9 335.1 2786.199 2764.290 2812.803 1496244.206 1391821.332 58736
## mzmin 335 334.9 335.1 2734.556 2714.211 2765.855 21579.367 18449.428 899
## mzmin 335 334.9 335.1 2797.154 2775.245 2815.933 159058.782 150289.314 6295
## mzmin 335 334.9 335.1 2784.635 2761.160 2808.109 54947.545 37923.532 2715
## mzmin 335 334.9 335.1 2859.752 2845.668 2878.532 13895.212 13874.868 905
## mzmin 335 334.9 335.1 2897.311 2891.051 2898.876 5457.155 5450.895 995
## mzmin 335 334.9 335.1 2819.064 2808.109 2834.713 19456.914 19438.134 1347
## mzmin 335 334.9 335.1 2789.329 2762.725 2808.109 174473.311 91114.975 8325
## mzmin 335 334.9 335.1 2786.199 2764.290 2812.803 932645.211 569236.246 35856
## mzmin 335 334.9 335.1 2792.461 2768.987 2823.760 876585.527 511324.134 27200
## mzmin 335 334.9 335.1 2789.329 2773.680 2806.544 89582.569 73871.386 5427
## sn row column
## mzmin 13 1 1
## mzmin 20 1 2
## mzmin 4 1 3
## mzmin 12 1 3
## mzmin 2 1 4
## mzmin 904 1 4
## mzmin 994 1 4
## mzmin 1576 1 4
## mzmin 3 1 5
## mzmin 2 1 6
## mzmin 2 1 7
## mzmin 6 1 8Parallel to the chromPeaks() matrix there is also a chromPeakData() data
frame that allows to add arbitrary annotations to each chromatographic peak,
such as e.g. the MS level in which the peak was detected:
chromPeakData(xchr)## DataFrame with 12 rows and 4 columns
## ms_level is_filled row column
## <integer> <logical> <integer> <integer>
## mzmin 1 FALSE 1 1
## mzmin 1 FALSE 1 2
## mzmin 1 FALSE 1 3
## mzmin 1 FALSE 1 3
## mzmin 1 FALSE 1 4
## ... ... ... ... ...
## mzmin 1 FALSE 1 4
## mzmin 1 FALSE 1 5
## mzmin 1 FALSE 1 6
## mzmin 1 FALSE 1 7
## mzmin 1 FALSE 1 8Below we plot the EIC along with all identified chromatographic peaks using the
plot() function on the result object from above. Additional parameters
peakCol and peakBg allow to define a foreground and background (fill) color
for each identified chromatographic peak in the provided result object (i.e., we
need to define one color for each row of chromPeaks(xchr) - column "column"
(or "sample" if present) in that peak matrix specifies the sample in which the
peak was identified).
## Define a color for each sample
sample_colors <- group_colors[xchr$sample_group]
## Define the background color for each chromatographic peak
bg <- sample_colors[chromPeaks(xchr)[, "column"]]
## Parameter `col` defines the color of each sample/line, `peakBg` of each
## chromatographic peak.
plot(xchr, col = sample_colors, peakBg = bg)
Figure 6: Signal for an example peak
Red and blue colors represent KO and wild type samples, respectively. Peak area of identified chromatographic peaks are highlighted in the sample group color.
If we are happy with the settings we can use them for the peak detection on the
full data set (i.e. on the MsExperiment object with the data for the whole
experiment). Note that below we set the argument prefilter to c(6, 5000) and
noise to 5000 to reduce the run time of this vignette. With this setting we
consider only ROIs with at least 6 centroids with an intensity larger than 5000
for the centWave chromatographic peak detection.
cwp <- CentWaveParam(peakwidth = c(20, 80), noise = 5000,
prefilter = c(6, 5000))
faahko <- findChromPeaks(faahko, param = cwp)The results of findChromPeaks() on a MsExperiment object are returned as an
XcmsExperiment object. This object extends MsExperiment directly (hence
providing the same access to all raw data) and contains all xcms
preprocessing results. Note also that additional rounds of chromatographic peak
detections could be performed and their results being added to existing peak
detection results by additional calls to findChromPeaks() on the result object
and using parameter add = TRUE.
The chromPeaks function can also here be used to access the results from the
chromatographic peak detection. Below we show the first 6 identified
chromatographic peaks.
chromPeaks(faahko) |>
head()## mz mzmin mzmax rt rtmin rtmax into intb maxo sn
## CP0001 594.0 594.0 594.0 2601.535 2581.191 2637.529 161042.2 146073.3 7850 11
## CP0002 577.0 577.0 577.0 2604.665 2581.191 2626.574 136105.2 128067.9 6215 11
## CP0003 307.0 307.0 307.0 2618.750 2592.145 2645.354 284782.4 264907.0 16872 20
## CP0004 302.0 302.0 302.0 2617.185 2595.275 2640.659 687146.6 669778.1 30552 43
## CP0005 370.1 370.1 370.1 2673.523 2643.789 2700.127 449284.6 417225.3 25672 17
## CP0006 427.0 427.0 427.0 2675.088 2643.789 2684.478 283334.7 263943.2 11025 13
## sample
## CP0001 1
## CP0002 1
## CP0003 1
## CP0004 1
## CP0005 1
## CP0006 1Columns of this chromPeaks() matrix might differ depending on the used peak
detection algorithm. Columns that all algorithms have to provide are: "mz",
"mzmin", "mzmax", "rt", "rtmin" and "rtmax" that define the m/z and
retention time range of the chromatographic peak (i.e. all mass peaks within
that area are used to integrate the peak signal) as well as the m/z and
retention time of the peak apex. Other mandatory columns are "into" and
"maxo" with the absolute integrated peak signal and the maximum peak
intensity. Finally, "sample" provides the index of the sample in which the
peak was identified.
Additional annotations for each individual peak can be extracted with the
chromPeakData() function. This data frame could also be used to add/store
arbitrary annotations for each detected peak (that don’t necessarily need to be
numeric).
chromPeakData(faahko)## DataFrame with 2908 rows and 2 columns
## ms_level is_filled
## <integer> <logical>
## CP0001 1 FALSE
## CP0002 1 FALSE
## CP0003 1 FALSE
## CP0004 1 FALSE
## CP0005 1 FALSE
## ... ... ...
## CP2904 1 FALSE
## CP2905 1 FALSE
## CP2906 1 FALSE
## CP2907 1 FALSE
## CP2908 1 FALSEBased on the publication by Kumler et al. published in 2023 [6], new
quality metrics (beta_cor and beta_snr) were added to xcms. beta_cor
indicates how bell-shaped the chromatographic peak is and beta_snr is
estimating the signal-to-noise ratio using the residuals from this fit. These
metrics can be calculated during peak picking by setting verboseBetaColumns = TRUE in the CentWaveParam object, or they can be calculated afterwards by
using the chromPeakSummary() function with the XcmsExperiment object and
the BetaDistributionParam parameter object as input:
beta_metrics <- chromPeakSummary(faahko, BetaDistributionParam())
head(beta_metrics)## beta_cor beta_snr
## CP0001 0.9865868 5.349210
## CP0002 0.9401467 4.928835
## CP0003 0.9846982 5.783004
## CP0004 0.9883246 6.044377
## CP0005 0.6972246 5.293175
## CP0006 0.1358883 4.995241The result returned by chromPeakSummary() is thus a numeric matrix with the
values for these quality estimates, one row for each chromatographic peak.
Using summary statistics, one can explore the distribution of these metrics in
the data.
summary(beta_metrics)## beta_cor beta_snr
## Min. :-0.8541 Min. :3.921
## 1st Qu.: 0.7390 1st Qu.:5.157
## Median : 0.9442 Median :5.594
## Mean : 0.7904 Mean :5.662
## 3rd Qu.: 0.9771 3rd Qu.:6.097
## Max. : 0.9990 Max. :7.983
## NA's :6 NA's :6Visual inspection gives a better idea of what these metrics represent in terms
of peak quality in the data at hand. This information can be used to e.g.
filter out peaks that don’t meet a chosen quality metric threshold. In order to
plot the detected peaks, their EIC can be extracted with the function
chromPeakChromatograms. An example of a peak with a high beta_cor and for
a peak with a low beta_cor score is given below.
beta_metrics[c(4, 6), ]## beta_cor beta_snr
## CP0004 0.9883246 6.044377
## CP0006 0.1358883 4.995241eics <- chromPeakChromatograms(
faahko, peaks = rownames(chromPeaks(faahko))[c(4, 6)])peak_1 <- eics[1]
peak_2 <- eics[2]
par(mfrow = c(1, 2))
plot(peak_1)
plot(peak_2)
Figure 7: Plots of high and low quality peaks
Left: peak CP0004 with a beta_cor = 0.98, right: peak CP0006 with a beta_cor = 0.13.
Peak detection will not always work perfectly for all types of peak shapes
present in the data set leading to peak detection artifacts, such as (partially
or completely) overlapping peaks or artificially split peaks (common issues
especially for centWave). xcms provides the refineChromPeaks() function
that can be called on peak detection results in order to refine (or clean)
peak detection results by either removing identified peaks not passing a certain
criteria or by merging artificially split or partially or completely overlapping
chromatographic peaks. Different algorithms are available that can again be
configured with their respective parameter objects: CleanPeaksParam and
FilterIntensityParam allow to remove peaks with their retention time range or
intensity being below a specified threshold, respectively. With
MergeNeighboringPeaksParam it is possible to merge chromatographic peaks and
hence remove many of the above mentioned (centWave) peak detection
artifacts. See also ?refineChromPeaks for more information and help on the
different methods.
Below we post-process the peak detection results merging peaks that overlap in a
4 second window per file and for which the signal between them is lower than 75%
of the smaller peak’s maximal intensity. See the ?MergeNeighboringPeaksParam
help page for a detailed description of the settings and the approach.
mpp <- MergeNeighboringPeaksParam(expandRt = 4)
faahko_pp <- refineChromPeaks(faahko, mpp)An example for a merged peak is given below.
mzr_1 <- 305.1 + c(-0.01, 0.01)
chr_1 <- chromatogram(faahko[1], mz = mzr_1)## Processing chromatographic peakschr_2 <- chromatogram(faahko_pp[1], mz = mzr_1)## Processing chromatographic peakspar(mfrow = c(1, 2))
plot(chr_1)
plot(chr_2)
Figure 8: Result from the peak refinement step
Left: data before processing, right: after refinement. The splitted peak was merged into one.
centWave thus detected originally 3 chromatographic peaks in the m/z slice
(left panel in the plot above) and peak refinement with
MergeNeighboringPeaksParam and the specified settings merged the first two
peaks into a single one (right panel in the figure above). Other close peaks,
with a lower intensity between them, were however not merged (see below).
mzr_1 <- 496.2 + c(-0.01, 0.01)
chr_1 <- chromatogram(faahko[1], mz = mzr_1)## Processing chromatographic peakschr_2 <- chromatogram(faahko_pp[1], mz = mzr_1)## Processing chromatographic peakspar(mfrow = c(1, 2))
plot(chr_1)
plot(chr_2)
Figure 9: Result from the peak refinement step
Left: data before processing, right: after refinement. The peaks were not merged.
It is also possible to perform the peak refinement on extracted ion
chromatograms. This could again be used to test and fine-tune the settings for
the parameter and to avoid potential problematic behavior. The minProp
parameter for example has to be carefully chosen to avoid merging of isomer
peaks (like in the example above). With the default minProp = 0.75 only peaks
are merged if the signal between the two peaks is higher than 75% of the
smaller peak’s maximal intensity. Setting this value too low could eventually
result in merging of isomers as shown below.
#' Too low minProp could cause merging of isomers!
res <- refineChromPeaks(chr_1, MergeNeighboringPeaksParam(minProp = 0.05))
chromPeaks(res)## mz mzmin mzmax rt rtmin rtmax into intb maxo sn
## CPM1 496.2 496.19 496.21 3384.012 3294.809 3412.181 45940118 NA 1128960 177
## sample row column
## CPM1 1 1 1plot(res)
Thus, before running such a peak refinement evaluate that isomers present in the data set were not wrongly merged based on the chosen settings.
Before proceeding we next replace the faahko object with the results from the
peak refinement step.
faahko <- faahko_ppBelow we use the data from the chromPeaks() matrix to calculate per-file
summaries of the peak detection results, such as the number of peaks per file as
well as the distribution of the retention time widths.
summary_fun <- function(z)
c(peak_count = nrow(z), rt = quantile(z[, "rtmax"] - z[, "rtmin"]))
T <- chromPeaks(faahko) |>
split.data.frame(f = chromPeaks(faahko)[, "sample"]) |>
lapply(FUN = summary_fun) |>
do.call(what = rbind)
rownames(T) <- basename(fileNames(faahko))
pandoc.table(
T,
caption = paste0("Summary statistics on identified chromatographic",
" peaks. Shown are number of identified peaks per",
" sample and widths/duration of chromatographic ",
"peaks."))| peak_count | rt.0% | rt.25% | rt.50% | rt.75% | rt.100% | |
|---|---|---|---|---|---|---|
| ko15.CDF | 397 | 10.95 | 34.43 | 42.25 | 53.21 | 319.2 |
| ko16.CDF | 520 | 10.95 | 32.86 | 42.25 | 53.21 | 151.8 |
| ko21.CDF | 207 | 10.95 | 42.25 | 50.08 | 65.73 | 164.3 |
| ko22.CDF | 233 | 10.95 | 37.56 | 46.95 | 59.47 | 147.1 |
| wt15.CDF | 403 | 10.95 | 32.86 | 42.25 | 53.21 | 161.2 |
| wt16.CDF | 361 | 10.95 | 35.99 | 45.38 | 57.9 | 162.8 |
| wt21.CDF | 227 | 10.95 | 35.21 | 48.51 | 64.16 | 172.1 |
| wt22.CDF | 328 | 10.95 | 35.99 | 45.38 | 57.9 | 228.5 |
While by default chromPeaks() will return all identified chromatographic peaks
in a result object it is also possible to extract only chromatographic peaks for
a specified m/z and/or rt range:
chromPeaks(faahko, mz = c(334.9, 335.1), rt = c(2700, 2900))## mz mzmin mzmax rt rtmin rtmax into intb maxo sn
## CP0038 335 335 335 2781.505 2761.160 2809.674 412134.3 383167.4 16856 23
## CP0494 335 335 335 2786.199 2764.290 2812.803 1496244.2 1461187.3 58736 72
## CP1025 335 335 335 2797.154 2775.245 2815.933 159058.8 149229.6 6295 13
## CP1964 335 335 335 2786.199 2764.290 2812.803 932645.2 915333.8 35856 66
## CP2349 335 335 335 2792.461 2768.987 2823.760 876585.5 848569.1 27200 36
## sample
## CP0038 1
## CP0494 2
## CP1025 3
## CP1964 6
## CP2349 7We can also plot the location of the identified chromatographic peaks in the
m/z - retention time space for one file using the plotChromPeaks()
function. Below we plot this information for the third sample.
plotChromPeaks(faahko, file = 3)
Figure 10: Identified chromatographic peaks in the m/z by retention time space for one sample
As a general overview of the peak detection results we can in addition visualize
the number of identified chromatographic peaks per file along the retention time
axis. Parameter binSize allows to define the width of the bins in rt dimension
in which peaks should be counted. This number of chromatographic peaks within
each bin is then shown color-coded in the resulting plot.
plotChromPeakImage(faahko, binSize = 10)
Figure 11: Frequency of identified chromatographic peaks along the retention time axis
The frequency is color coded with higher frequency being represented by yellow-white. Each line shows the peak frequency for one file.
Note that extracting ion chromatorams from an xcms result object will in
addition to the chromatographic data also extract any identified chromatographic
peaks within that region. This can thus also be used to validate and verify that
the used peak detection settings identified e.g. peaks for known compounds or
internal standards properly. Below we extract the ion chromatogram for the m/z -
rt region above and access the detected peaks in that region using the
chromPeaks() function.
chr_ex <- chromatogram(faahko, mz = mzr, rt = rtr)
chromPeaks(chr_ex)## mz mzmin mzmax rt rtmin rtmax into intb maxo sn
## CP0038 335 335 335 2781.505 2761.160 2809.674 412134.3 383167.4 16856 23
## CP0494 335 335 335 2786.199 2764.290 2812.803 1496244.2 1461187.3 58736 72
## CP1025 335 335 335 2797.154 2775.245 2815.933 159058.8 149229.6 6295 13
## CP1964 335 335 335 2786.199 2764.290 2812.803 932645.2 915333.8 35856 66
## CP2349 335 335 335 2792.461 2768.987 2823.760 876585.5 848569.1 27200 36
## sample row column
## CP0038 1 1 1
## CP0494 2 1 2
## CP1025 3 1 3
## CP1964 6 1 6
## CP2349 7 1 7We can also plot this extracted ion chromatogram which will also visualize all identified chromatographic peaks in that region.
sample_colors <- group_colors[chr_ex$sample_group]
plot(chr_ex, col = group_colors[chr_raw$sample_group], lwd = 2,
peakBg = sample_colors[chromPeaks(chr_ex)[, "sample"]])
Figure 12: Signal for an example peak
Red and blue colors represent KO and wild type samples, respectively. The signal area of identified chromatographic peaks are filled with a color.
Chromatographic peaks can be visualized also in other ways: by setting peakType = "rectangle" the they are indicated with a rectangle instead of the default
highlighting option (peakType = "polygon") used above. As a third alternative
it would also possible to just indicate the apex position for each identified
chromatographic peak with a single point (peakType = "point"). Below we plot
the data again using peakType = "rectangle".
plot(chr_ex, col = sample_colors, peakType = "rectangle",
peakCol = sample_colors[chromPeaks(chr_ex)[, "sample"]],
peakBg = NA)
Figure 13: Signal for an example peak
Red and blue colors represent KO and wild type samples, respectively. The rectangles indicate the identified chromatographic peaks per sample.
Finally we plot also the distribution of peak intensity per sample. This allows to investigate whether systematic differences in peak signals between samples are present.
## Extract a list of per-sample peak intensities (in log2 scale)
ints <- split(log2(chromPeaks(faahko)[, "into"]),
f = chromPeaks(faahko)[, "sample"])
boxplot(ints, varwidth = TRUE, col = sample_colors,
ylab = expression(log[2]~intensity), main = "Peak intensities")
grid(nx = NA, ny = NULL)
Figure 14: Peak intensity distribution per sample
Over and above the signal of the identified chromatographic peaks is comparable across samples, with the exception of samples 3, 4 and, to some degree, also 7 that show slightly higher average intensities, but also a lower number of detected peaks (indicated by the smaller width of the boxes).
Note that in addition to the above described identification of chromatographic
peaks, it is also possible to manually define and add chromatographic peaks
with the manualChromPeaks() function (see ?manualChromPeaks help page for
more information).
The time at which analytes elute in the chromatography can vary between samples (and even compounds). Such differences were already visible in the BPC and even the extracted ion chromatogram plot in the previous section. The alignment step, also referred to as retention time correction, aims to adjust these differences by shifting signals along the retention time axis and aligning them between different samples within an experiment.
A plethora of alignment algorithms exist (see [7]), with some of
them being also implemented in xcms. Alignment of LC-MS data can be performed
in xcms using the adjustRtime() method and an algorithm-specific parameter
class (see ?adjustRtime for an overview of available methods in xcms).
In the example below we use the obiwarp method [8] to align the
samples. We use a binSize = 0.6 which creates warping functions in m/z bins of
0.6. Also here it is advisable to modify and adapt the settings for each
experiment.
faahko <- adjustRtime(faahko, param = ObiwarpParam(binSize = 0.6))Note that adjustRtime(), besides calculating adjusted retention times for each
spectrum, adjusts also the retention times of the identified chromatographic
peaks in the xcms result object. Adjusted retention times of individual
spectra can be extracted from the result object using either the
adjustedRtime() function or using rtime() with parameter adjusted = TRUE
(the default):
## Extract adjusted retention times
adjustedRtime(faahko) |> head()## [1] 2551.457 2553.089 2554.720 2556.352 2557.983 2559.615## Or simply use the rtime method
rtime(faahko) |> head()## [1] 2551.457 2553.089 2554.720 2556.352 2557.983 2559.615## Get raw (unadjusted) retention times
rtime(faahko, adjusted = FALSE) |> head()## [1] 2551.457 2553.022 2554.586 2556.151 2557.716 2559.281To evaluate the impact of the alignment we plot the BPC on the adjusted data. In
addition we plot also the differences between the adjusted and the raw retention
times per sample using the plotAdjustedRtime() function. To disable the
automatic extraction of all identified chromatographic peaks by the
chromatogram() function (which would not make much sense for a BPC) we use
chromPeaks = "none" below.
## Get the base peak chromatograms.
bpis_adj <- chromatogram(faahko, aggregationFun = "max", chromPeaks = "none")
par(mfrow = c(3, 1), mar = c(4.5, 4.2, 1, 0.5))
plot(bpis, col = sample_colors)
grid()
plot(bpis_adj, col = sample_colors)
grid()
## Plot also the difference of adjusted to raw retention time.
plotAdjustedRtime(faahko, col = sample_colors)
grid()
Figure 15: Obiwarp aligned data
Base peak chromatogram before (top) and after alignment (middle) and difference between adjusted and raw retention times along the retention time axis (bottom).
Too large differences between adjusted and raw retention times could indicate poorly performing samples or alignment.
At last we evaluate also the impact of the alignment on the test peak.
par(mfrow = c(2, 1))
## Plot the raw data
plot(chr_raw, col = sample_colors)
grid()
## Extract the chromatogram from the adjusted object
chr_adj <- chromatogram(faahko, rt = rtr, mz = mzr)
plot(chr_adj, col = sample_colors, peakType = "none")
grid()
Figure 16: Example extracted ion chromatogram before (top) and after alignment (bottom)
Note: xcms result objects (XcmsExperiment as well as XCMSnExp) contain
both the raw and the adjusted retention times for all spectra and subset
operation will in many cases drop adjusted retention times. Thus it might
sometimes be useful to immediately replace the raw retention times in the
data using the applyAdjustedRtime() function.
For some experiments it might be better to perform the alignment based on only a subset of the available samples, e.g. if pooled QC samples were injected at regular intervals or if the experiment contains blanks. All alignment methods in xcms support such a subset-based alignment in which retention time shifts are estimated on only a specified subset of samples followed by an alignment of the whole data set based on the aligned subset.
The subset of samples for such an alignment can be specified with the parameter
subset of the PeakGroupsParam or ObiwarpParam object. Parameter
subsetAdjust allows to specify the method by which the left-out samples
will be adjusted. There are currently two options available:
subsetAdjust = "previous": adjust the retention times of a non-subset
sample based on the alignment results of the previous subset sample (e.g. a
QC sample). If samples are e.g. in the order A1, B1, B2, A2, B3,
B4 with A representing QC samples and B study samples, using
subset = c(1, 4) and subsetAdjust = "previous" would result in all A
samples to be aligned with each other and non-subset samples B1 and B2
being adjusted based on the alignment result of subset samples A1 and B3
and B4 on those of A2.
subsetAdjust = "average": adjust retention times of non-subset samples based
on an interpolation of the alignment results of the previous and subsequent
subset sample. In the example above, B1 would be adjusted based on the
average of adjusted retention times between subset (QC) samples A1 and
A2. Since there is no subset sample after non-subset samples B3 and B4
these will be adjusted based on the alignment results of A2 alone. Note
that a weighted average is used to calculate the adjusted retention time
averages, which uses the inverse of the difference of the index of the
non-subset sample to the subset samples as weights. Thus, if we have a
setup like A1, B1, B2, A2 the adjusted retention times of A1
would get a larger weight than those of A2 in the adjustment of
non-subset sample B1 causing it’s adjusted retention times to be closer
to those of A1 than to A2. See below for examples.
Important: both cases require a meaningful/correct ordering of the samples within the object (i.e., samples should be ordered by injection index).
The examples below aim to illustrate the effect of these alignment options. We
assume samples 1, 4 and 7 in the faahKO data set to be QC pool samples. We
thus want to perform the alignment based on these samples and subsequently
adjust the retention times of the left-out samples (2, 3, 5, 6 and 8) based on
interpolation of the results from the neighboring subset (QC) samples. After
initial peak grouping we perform the subset-based alignment with the peak
groups method by passing the indices of the QC samples with the subset
parameter to the PeakGroupsParam function and set subsetAdjust = "average"
to adjust the study samples based on interpolation of the alignment results from
neighboring subset/QC samples.
Note that for any subset-alignment all parameters such as minFraction are
relative to the subset, not the full experiment!
Below we first remove any previous alignment results with the
dropAdjustedRtime() function to allow a fresh alignment using the subset-based
option outlined above. In addition to removing adjusted retention times for all
spectra, this function will also restore the original retention times for
identified chromatographic peaks.
faahko <- dropAdjustedRtime(faahko)
## Define the experimental layout
sampleData(faahko)$sample_type <- "study"
sampleData(faahko)$sample_type[c(1, 4, 7)] <- "QC"For an alignment with the peak groups method an initial peak grouping
(correspondence) analysis is required, because the algorithm estimates retention
times shifts between samples using the retention times of hook peaks
(i.e. chromatographic peaks present in most/all samples). Here we use the
default settings for an peak density method-based correspondence, but it is
strongly advised to adapt the parameters for each data set (details in the next
section). The definition of the sample groups (i.e. assignment of individual
samples to the sample groups in the experiment) is mandatory for the
PeakDensityParam. If there are no sample groups in the experiment,
sampleGroups should be set to a single value for each file (e.g. rep(1, length(fileNames(faahko))).
## Initial peak grouping. Use sample_type as grouping variable
pdp_subs <- PeakDensityParam(sampleGroups = sampleData(faahko)$sample_type,
minFraction = 0.9)
faahko <- groupChromPeaks(faahko, param = pdp_subs)
## Define subset-alignment options and perform the alignment
pgp_subs <- PeakGroupsParam(
minFraction = 0.85,
subset = which(sampleData(faahko)$sample_type == "QC"),
subsetAdjust = "average", span = 0.4)
faahko <- adjustRtime(faahko, param = pgp_subs)Below we plot the results of the alignment highlighting the subset samples in
green. This nicely shows how the interpolation of the subsetAdjust = "average"
works: retention times of sample 2 are adjusted based on those from subset
sample 1 and 4, giving however more weight to the closer subset sample 1 which
results in the adjusted retention times of 2 being more similar to those of
sample 1. Sample 3 on the other hand gets adjusted giving more weight to the
second subset sample (4).
clrs <- rep("#00000040", 8)
clrs[sampleData(faahko)$sample_type == "QC"] <- c("#00ce0080")
par(mfrow = c(2, 1), mar = c(4, 4.5, 1, 0.5))
plot(chromatogram(faahko, aggregationFun = "max", chromPeaks = "none"),
col = clrs)
grid()
plotAdjustedRtime(faahko, col = clrs, peakGroupsPch = 1,
peakGroupsCol = "#00ce0040")
grid()
Figure 17: Subset-alignment results with option average
Difference between adjusted and raw retention times along the retention time axis. Samples on which the alignment models were estimated are shown in green, study samples in grey.
Option subsetAdjust = "previous" would adjust the retention times of a
non-subset sample based on a single subset sample (the previous), which results
in most cases in the adjusted retention times of the non-subset sample being
highly similar to those of the subset sample which was used for adjustment.
Correspondence is usually the final step in LC-MS data preprocessing in which
data, presumably representing signal from the same originating ions, is matched
across samples. As a result, chromatographic peaks from different samples with
similar m/z and retention times get grouped into LC-MS features. The function
to perform the correspondence in xcms is called groupChromPeaks() that again
supports different algorithms which can be selected and configured with a
specific parameter object (see ?groupChromPeaks for an overview). For our
example we will use the peak density method [1] that, within small
slices along the m/z dimension, combines chromatographic peaks depending on the
density of these peaks along the retention time axis. To illustrate this, we
simulate below the peak grouping for an m/z slice containing multiple
chromatoghaphic peaks within each sample using the plotChromPeakDensity()
function and a PeakDensityParam object with parameter minFraction = 0.4
(features are only defined if in at least 40% of samples a chromatographic peak
was present) - parameter sampleGroups is used to define to which sample group
each sample belongs.
## Define the mz slice.
mzr <- c(305.05, 305.15)
## Extract and plot the chromatograms
chr_mzr <- chromatogram(faahko, mz = mzr)
## Define the parameters for the peak density method
pdp <- PeakDensityParam(sampleGroups = sampleData(faahko)$sample_group,
minFraction = 0.4, bw = 30)
plotChromPeakDensity(chr_mzr, col = sample_colors, param = pdp,
peakBg = sample_colors[chromPeaks(chr_mzr)[, "sample"]],
peakCol = sample_colors[chromPeaks(chr_mzr)[, "sample"]],
peakPch = 16)
Figure 18: Example for peak density correspondence
Upper panel: chromatogram for an mz slice with multiple chromatographic peaks. lower panel: identified chromatographic peaks at their retention time (x-axis) and index within samples of the experiments (y-axis) for different values of the bw parameter. The black line represents the peak density estimate. Grouping of peaks (based on the provided settings) is indicated by grey rectangles.
The upper panel in the plot shows the extracted ion chromatogram for each sample
with the detected peaks highlighted. The retention times for the individual
chromatographic peaks in each sample (y-axis being the index of the sample in
the data set) are shown in the lower panel with the solid black line
representing the density estimate for the distribution of detected peaks along
the retention time. Parameter bw defines the smoothness of this
estimation. The grey rectangles indicate which chromatographic peaks would be
grouped into a feature (each grey rectangle thus representing one feature). This
type of visualization is thus ideal to test, validate and optimize
correspondence settings on manually defined m/z slices before applying them to
the full data set. For the tested m/z slice the settings seemed to be OK and we
are thus applying them to the full data set below. Especially the parameter bw
will be very data set dependent (or more specifically LC-dependent) and should
be adapted to each data set.
Another important parameter is binSize that defines the size of the m/z slices
(bins) within which peaks are being grouped. This parameter thus defines the
required similarity in m/z values for the chromatographic peaks that are then
assumed to represent signal from the same (type of ion of a) compound and hence
evaluated for grouping. By default, a constant m/z bin size is used, but by
changing parameter ppm to a value larger than 0, m/z-relative bin sizes would
be used instead (i.e., the bin size will increase with the m/z value hence
better representing the measurement error/precision of some MS instruments). The
bin sizes (and subsequently the m/z width of the defined features) would then
reach a maximal value of binSize plus ppm parts-per-million of the largest
m/z value of any chromatographic peak in the data set.
See also the xcms tutorial for more examples and details.
## Perform the correspondence using fixed m/z bin sizes.
pdp <- PeakDensityParam(sampleGroups = sampleData(faahko)$sample_group,
minFraction = 0.4, bw = 30)
faahko <- groupChromPeaks(faahko, param = pdp)As an alternative we perform the correspondence using m/z relative bin sizes.
## Drop feature definitions and re-perform the correspondence
## using m/z-relative bin sizes.
faahko_ppm <- groupChromPeaks(
dropFeatureDefinitions(faahko),
PeakDensityParam(sampleGroups = sampleData(faahko)$sample_group,
minFraction = 0.4, bw = 30, ppm = 10))The results will be mostly similar, except for the higher m/z range (in which larger m/z bins will be used). Below we plot the m/z range for features against their median m/z. For the present data set (acquired with a triple quad instrument) no clear difference can be seen for the two approaches hence we proceed the analysis with the fixed bin size setting. A stronger relationship would be expected for example for data measured on TOF instruments.
## Calculate m/z width of features
mzw <- featureDefinitions(faahko)$mzmax - featureDefinitions(faahko)$mzmin
mzw_ppm <- featureDefinitions(faahko_ppm)$mzmax -
featureDefinitions(faahko_ppm)$mzmin
plot(featureDefinitions(faahko_ppm)$mzmed, mzw_ppm,
xlab = "m/z", ylab = "m/z width", pch = 21,
col = "#0000ff20", bg = "#0000ff10")
points(featureDefinitions(faahko)$mzmed, mzw, pch = 21,
col = "#ff000020", bg = "#ff000010")
Figure 19: Relationship between a feature’s m/z and the m/z width (max - min m/z) of the feature
Red points represent the results with the fixed m/z bin size, blue with the m/z-relative bin size.
Results from the correspondence analysis can be accessed with the
featureDefinitions() and featureValues() function. The former returns a data
frame with general information on each of the defined features, with each row
being one feature and columns providing information on the median m/z and
retention time as well as the indices of the chromatographic peaks assigned to
the feature in column "peakidx". Below we show the information on the first 6
features.
featureDefinitions(faahko) |> 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, 1161
## 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 1The featureValues() function returns a matrix with rows being features and
columns samples. The content of this matrix can be defined using the value
argument which can be any column name in the chromPeaks() matrix. With the
default value = "into" a matrix with the integrated signal of the peaks
corresponding to a feature in a sample are returned. This is then generally used
as the intensity matrix for downstream analysis. Below we extract the
intensities for the first 6 features.
featureValues(faahko, value = "into") |> head()## 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 508546.4As we can see we have several missing values in this feature matrix. Missing values are reported if in one sample no chromatographic peak was detected in the m/z - rt region of the feature. This does however not necessarily mean that there is no signal for that specific ion in that sample. The chromatographic peak detection algorithm could also just have failed to identify any peak in that region, e.g. because the signal was too noisy or too low. Thus it is advisable to perform, after correspondence, also a gap-filling (see next section).
The performance of peak detection, alignment and correspondence should always be
evaluated by inspecting extracted ion chromatograms e.g. of known compounds,
internal standards or identified features in general. The
featureChromatograms() function allows to extract chromatograms for each
feature present in featureDefinitions(). The returned MChromatograms object
contains an ion chromatogram for each feature (each row containing the data for
one feature) and sample (each column representing containing data for one
sample). Parameter features allows to define specific features for which the
EIC should be returned. These can be specified with their index or their ID
(i.e. their row name in the featureDefinitions() data frame. If features is
not defined, EICs are returned for all features in a data set, which can
take also a considerable amount of time. Below we extract the chromatograms for
the first 4 features.
feature_chroms <- featureChromatograms(faahko, features = 1:4)
feature_chroms## XChromatograms with 4 rows and 8 columns
## ko15.CDF ko16.CDF ko21.CDF ko22.CDF
## <XChromatogram> <XChromatogram> <XChromatogram> <XChromatogram>
## [1,] peaks: 0 peaks: 1 peaks: 0 peaks: 1
## [2,] peaks: 1 peaks: 1 peaks: 1 peaks: 1
## [3,] peaks: 1 peaks: 1 peaks: 0 peaks: 1
## [4,] peaks: 1 peaks: 1 peaks: 1 peaks: 1
## wt15.CDF wt16.CDF wt21.CDF wt22.CDF
## <XChromatogram> <XChromatogram> <XChromatogram> <XChromatogram>
## [1,] peaks: 0 peaks: 0 peaks: 0 peaks: 0
## [2,] peaks: 1 peaks: 1 peaks: 1 peaks: 1
## [3,] peaks: 1 peaks: 1 peaks: 1 peaks: 1
## [4,] peaks: 1 peaks: 1 peaks: 0 peaks: 1
## phenoData with 4 variables
## featureData with 4 variables
## - - - xcms preprocessing - - -
## Chromatographic peak detection:
## method: centWave
## Correspondence:
## method: chromatographic peak density
## 4 feature(s) identified.And plot the extracted ion chromatograms. We again use the group color for each identified peak to fill the area.
plot(feature_chroms, col = sample_colors,
peakBg = sample_colors[chromPeaks(feature_chroms)[, "sample"]])
Figure 20: Extracted ion chromatograms for features 1 to 4
To access the EICs of the second feature we can simply subset the
feature_chroms object.
eic_2 <- feature_chroms[2, ]
chromPeaks(eic_2)## mz mzmin mzmax rt rtmin rtmax into intb maxo sn
## CP0048 205 205 205 2791.873 2771.300 2815.623 1924712 1850331 84280 64
## CP0495 205 205 205 2795.796 2773.702 2821.043 1757151 1711473 68384 69
## CP1033 205 205 205 2796.531 2774.506 2821.697 1383417 1334570 47384 54
## CP1255 205 205 205 2789.405 2769.019 2812.924 1180288 1126958 48336 32
## CP1535 205 205 205 2782.955 2762.596 2806.444 2129885 2054677 93312 44
## CP1967 205 205 205 2787.464 2767.135 2812.486 1634342 1566379 67984 53
## CP2350 205 205 205 2790.396 2763.847 2821.630 1623589 1531573 49208 28
## CP2601 205 205 205 2787.273 2766.970 2812.261 1354005 1299188 55712 35
## sample row column
## CP0048 1 1 1
## CP0495 2 1 2
## CP1033 3 1 3
## CP1255 4 1 4
## CP1535 5 1 5
## CP1967 6 1 6
## CP2350 7 1 7
## CP2601 8 1 8Missing values in LC-MS data are in many cases the result of the chromatographic
peak detection algorithm failing to identify peaks (because of noisy or low
intensity signal). The aim of the gap filling step is to reduce the number of
such missing values by integrating signals from the original data files for
samples in which no chromatographic peak was found from the m/z - rt region
where signal from the ion is expected. Gap filling can be performed in xcms
with the fillChromPeaks() function and a parameter object selecting and
configuring the gap filling algorithm. The method of choice is
ChromPeakAreaParam that integrates the signal (in samples in which no
chromatographic peak was found for a feature) in the m/z - rt region that is
defined based on the m/z and retention time ranges of all detected
chromatographic peaks of that specific feature. The lower m/z limit of the area
is defined as the lower quartile (25% quantile) of the "mzmin" values of all
peaks of the feature, the upper m/z value as the upper quartile (75% quantile)
of the "mzmax" values, the lower rt value as the lower quartile (25% quantile)
of the "rtmin" and the upper rt value as the upper quartile (75% quantile) of
the "rtmax" values. Below we perform this gap filling on our test data and
extract the feature values for the first 6 features after gap filling. An NA
is reported if no signal is measured at all for a specific sample.
faahko <- fillChromPeaks(faahko, param = ChromPeakAreaParam())
featureValues(faahko, value = "into") |> head()## 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 508546.4While we can continue using the xcms result set for further analysis
(e.g. also for feature grouping with the MsFeatures package; see
the LC-MS feature grouping vignette for details) we could also extract all
results as a SummarizedExperiment object. This is the standard data
container for Bioconductor defined in the SummarizedExperiment
package and integration with other Bioconductor packages might thus be easier
using that type of object. Below we use the quantify() function to extract the
xcms preprocessing results as such a SummarizedExperiment
object. Internally, the featureValues() function is used to generate the
feature value matrix. We can pass any parameters from that function to the
quantify() call. Below we use value = "into" and method = "sum" to report
the integrated peak signal as intensity and to sum these values in samples in
which more than one chromatographic peak was assigned to a feature (for that
option it is important to run refineChromPeaks() like described above to merge
overlapping peaks in each sample).
library(SummarizedExperiment)## Loading required package: MatrixGenerics## Loading required package: matrixStats##
## Attaching package: 'matrixStats'## The following objects are masked from 'package:Biobase':
##
## anyMissing, rowMedians##
## Attaching package: 'MatrixGenerics'## The following objects are masked from 'package:matrixStats':
##
## colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
## colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
## colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
## colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
## colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
## colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
## colWeightedMeans, colWeightedMedians, colWeightedSds,
## colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
## rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
## rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
## rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
## rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
## rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
## rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
## rowWeightedSds, rowWeightedVars## The following object is masked from 'package:Biobase':
##
## rowMedians## Loading required package: GenomicRanges## Loading required package: IRanges##
## Attaching package: 'IRanges'## The following object is masked from 'package:xcms':
##
## distance## Loading required package: GenomeInfoDbres <- quantify(faahko, value = "into", method = "sum")
res## class: SummarizedExperiment
## dim: 351 8
## metadata(6): '' '' ... '' ''
## assays(1): raw
## rownames(351): FT001 FT002 ... FT350 FT351
## rowData names(10): mzmed mzmin ... WT ms_level
## colnames(8): ko15.CDF ko16.CDF ... wt21.CDF wt22.CDF
## colData names(4): sample_name sample_group spectraOrigin sample_typeThe information from featureDefinitions() is now stored in the rowData() of
this object. The rowData() provides annotations and information for each
row in the SummarizedExperiment (which in our case are the features).
rowData(res)## DataFrame with 351 rows and 10 columns
## mzmed mzmin mzmax rtmed rtmin rtmax npeaks
## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## FT001 200.1 200.1 200.1 2902.63 2882.60 2922.66 2
## FT002 205.0 205.0 205.0 2789.90 2782.95 2796.53 8
## FT003 206.0 206.0 206.0 2789.40 2781.39 2794.22 7
## FT004 207.1 207.1 207.1 2718.56 2714.05 2727.35 7
## FT005 233.0 233.0 233.1 3023.58 3015.14 3043.96 7
## ... ... ... ... ... ... ... ...
## FT347 595.25 595.2 595.3 3010.60 2992.76 3014.37 6
## FT348 596.20 596.2 596.2 2997.78 2992.76 3002.79 2
## FT349 596.30 596.3 596.3 3819.83 3811.52 3836.39 4
## FT350 597.40 597.4 597.4 3820.88 3817.76 3826.13 3
## FT351 599.30 599.3 599.3 4071.41 4044.94 4125.32 3
## KO WT ms_level
## <numeric> <numeric> <integer>
## FT001 2 0 1
## FT002 4 4 1
## FT003 3 4 1
## FT004 4 3 1
## FT005 3 4 1
## ... ... ... ...
## FT347 2 3 1
## FT348 0 2 1
## FT349 2 2 1
## FT350 1 2 1
## FT351 1 2 1Annotations for columns (in our case samples) are stored as
colData(). In this data frame each row contains annotations for one sample
(and hence one column in the feature values matrix).
colData(res)## DataFrame with 8 rows and 4 columns
## sample_name sample_group spectraOrigin sample_type
## <character> <character> <character> <character>
## ko15.CDF ko15 KO /home/bioc... QC
## ko16.CDF ko16 KO /home/bioc... study
## ko21.CDF ko21 KO /home/bioc... study
## ko22.CDF ko22 KO /home/bioc... QC
## wt15.CDF wt15 WT /home/bioc... study
## wt16.CDF wt16 WT /home/bioc... study
## wt21.CDF wt21 WT /home/bioc... QC
## wt22.CDF wt22 WT /home/bioc... studyFinally, the feature matrix is stored as an assay within the object. Note
that a SummarizedExperiment can have multiple assays which have to be numeric
matrices with the number of rows and columns matching the number of features and
samples, respectively. Below we list the names of the available assays.
assayNames(res)## [1] "raw"And we can access the actual data using the assay() function, optionally also
providing the name of the assay we want to access. Below we show the first 6
lines of that matrix.
assay(res) |> head()## 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 1923307.8 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 508546.4Since a SummarizedExperiment supports multiple assays, we in addition add also
the feature value matrix without filled-in values (i.e. feature intensities
that were added by the gap filling step).
assays(res)$raw_nofill <- featureValues(faahko, filled = FALSE, method = "sum")With that we have now two assays in our result object.
assayNames(res)## [1] "raw" "raw_nofill"And we can extract the feature values without gap-filling:
assay(res, "raw_nofill") |> head()## 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 1923307.8 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 508546.4Finally, a history of the full processing with xcms is available as metadata
in the SummarizedExperiment.
metadata(res)## [[1]]
## Object of class "XProcessHistory"
## type: Peak detection
## date: Tue Apr 15 21:20:21 2025
## info:
## fileIndex: 1,2,3,4,5,6,7,8
## Parameter class: CentWaveParam
## MS level(s) 1
##
## [[2]]
## Object of class "XProcessHistory"
## type: Peak refinement
## date: Tue Apr 15 21:20:30 2025
## info:
## fileIndex: 1,2,3,4,5,6,7,8
## Parameter class: MergeNeighboringPeaksParam
## MS level(s) 1
##
## [[3]]
## Object of class "XProcessHistory"
## type: Peak grouping
## date: Tue Apr 15 21:20:49 2025
## info:
## fileIndex: 1,2,3,4,5,6,7,8
## Parameter class: PeakDensityParam
## MS level(s) 1
##
## [[4]]
## Object of class "XProcessHistory"
## type: Retention time correction
## date: Tue Apr 15 21:20:49 2025
## info:
## fileIndex: 1,2,3,4,5,6,7,8
## Parameter class: PeakGroupsParam
## MS level(s) 1
##
## [[5]]
## Object of class "XProcessHistory"
## type: Peak grouping
## date: Tue Apr 15 21:20:57 2025
## info:
## fileIndex: 1,2,3,4,5,6,7,8
## Parameter class: PeakDensityParam
## MS level(s) 1
##
## [[6]]
## Object of class "XProcessHistory"
## type: Missing peak filling
## date: Tue Apr 15 21:21:04 2025
## info:
## fileIndex: 1,2,3,4,5,6,7,8
## Parameter class: ChromPeakAreaParam
## MS level(s) 1This same information can also be extracted from the xcms result object using
the processHistory() function. Below we extract the information for the first
processing step.
processHistory(faahko)[[1]]## Object of class "XProcessHistory"
## type: Peak detection
## date: Tue Apr 15 21:20:21 2025
## info:
## fileIndex: 1,2,3,4,5,6,7,8
## Parameter class: CentWaveParam
## MS level(s) 1These processing steps contain also the individual parameter objects used for the analysis, hence allowing to exactly reproduce the analysis.
processHistory(faahko)[[1]] |> processParam()## Object of class: CentWaveParam
## Parameters:
## - ppm: [1] 25
## - peakwidth: [1] 20 80
## - snthresh: [1] 10
## - prefilter: [1] 6 5000
## - mzCenterFun: [1] "wMean"
## - integrate: [1] 1
## - mzdiff: [1] -0.001
## - fitgauss: [1] FALSE
## - noise: [1] 5000
## - verboseColumns: [1] FALSE
## - roiList: list()
## - firstBaselineCheck: [1] TRUE
## - roiScales: numeric(0)
## - extendLengthMSW: [1] FALSE
## - verboseBetaColumns: [1] FALSEAt last we perform also a principal component analysis to evaluate the grouping of the samples in this experiment. Note that we did not perform any data normalization hence the grouping might (and will) also be influenced by technical biases.
## Extract the features and log2 transform them
ft_ints <- log2(assay(res, "raw"))
## Perform the PCA omitting all features with an NA in any of the
## samples. Also, the intensities are mean centered.
pc <- prcomp(t(na.omit(ft_ints)), center = TRUE)
## Plot the PCA
pcSummary <- summary(pc)
plot(pc$x[, 1], pc$x[,2], pch = 21, main = "",
xlab = paste0("PC1: ", format(pcSummary$importance[2, 1] * 100,
digits = 3), " % variance"),
ylab = paste0("PC2: ", format(pcSummary$importance[2, 2] * 100,
digits = 3), " % variance"),
col = "darkgrey", bg = sample_colors, cex = 2)
grid()
text(pc$x[, 1], pc$x[,2], labels = res$sample_name, col = "darkgrey",
pos = 3, cex = 2)
Figure 21: PCA for the faahKO data set, un-normalized intensities
We can see the expected separation between the KO and WT samples on PC2. On PC1 samples separate based on their ID, samples with an ID <= 18 from samples with an ID > 18. This separation might be caused by a technical bias (e.g. measurements performed on different days/weeks) or due to biological properties of the mice analyzed (sex, age, litter mates etc).
When dealing with metabolomics results, it is often necessary to filter features
based on certain criteria. These criteria are typically derived from statistical
formulas applied to full rows of data, where each row represents a feature. The
filterFeatures() function provides a robust solution for filtering features
based on these conventional quality assessment criteria. It supports multiple
types of filtering, allowing users to tailor the filtering process to their
specific needs, all controlled by the filter argument. This function and its
implementations are applicable to both XcmsExperiment results objects and
SummarizedExperiment objects.
We will demonstrate how to use the filterFeatures() function to perform
quality assessment and filtering on both the faahko and res variables
defined above. The filter argument can accommodate various types of input,
each determining the specific type of quality assessment and filtering to be
performed.
The PercentMissingFilter allows to filter features based on the percentage of
missing values for each feature. This function takes as an input the parameter
f which is supposed to be a vector of length equal to the length of the object
(i.e. number of samples) with the sample type for each. The function then
computes the percentage of missing values per sample groups and filters
features based on this. Features with a percent of missing values larger than
the threshold in all sample groups will be removed. Another option is to base
this quality assessment and filtering only on QC samples.
Both examples are shown below:
# To set up parameter `f` to filter only based on QC samples
f <- sampleData(faahko)$sample_type
f[f != "QC"] <- NA
# To set up parameter `f` to filter per sample type excluding QC samples
f <- sampleData(faahko)$sample_type
f[f == "QC"] <- NA
missing_filter <- PercentMissingFilter(threshold = 30, f = f)
# Apply the filter to faakho object
filtered_faahko <- filterFeatures(object = faahko, filter = missing_filter)## 3 features were removed# Apply the filter to res object
missing_filter <- PercentMissingFilter(threshold = 30, f = f)
filtered_res <- filterFeatures(object = res, filter = missing_filter)## 3 features were removedHere, no feature was removed, meaning that all the features had less than 30%
of NA values in at least one of the sample type.
Although not directly relevant to this experiment, the BlankFlag filter can be
used to flag features based on the intensity relationship between blank and QC
samples. More information can be found in the documentation of the filter:
# Retrieve documentation for the main function and the specific filter.
?filterFeatures## Help on topic 'filterFeatures' was found in the following packages:
##
## Package Library
## ProtGenerics /home/biocbuild/bbs-3.22-bioc/R/site-library
## xcms /tmp/Rtmpy6AWGw/Rinst291f5b65ffef25
## QFeatures /home/biocbuild/bbs-3.22-bioc/R/site-library
##
##
## Using the first match ...?BlankFlagThe RsdFilter enable users to filter features based on their relative
standard deviation (coefficient of variation) for a specified threshold. It
is recommended to base the computation on quality control (QC) samples,
as demonstrated below:
# Set up parameters for RsdFilter
rsd_filter <- RsdFilter(threshold = 0.3,
qcIndex = sampleData(filtered_faahko)$sample_type == "QC")
# Apply the filter to faakho object
filtered_faahko <- filterFeatures(object = filtered_faahko, filter = rsd_filter)## 252 features were removed# Now apply the same strategy to the res object
rsd_filter <- RsdFilter(threshold = 0.3, qcIndex = filtered_res$sample_type == "QC")
filtered_res <- filterFeatures(object = filtered_res, filter = rsd_filter, assay = "raw")## 257 features were removedAll features with an RSD (CV) strictly larger than 0.3 in QC samples were thus removed from the data set.
The DratioFilter can be used to filter features based on the D-ratio or
dispersion ratio, which compares the standard deviation in QC samples to that
in study samples.
# Set up parameters for DratioFilter
dratio_filter <- DratioFilter(
threshold = 0.5,
qcIndex = sampleData(filtered_faahko)$sample_type == "QC",
studyIndex = sampleData(filtered_faahko)$sample_type == "study")
# Apply the filter to faahko object
filtered_faakho <- filterFeatures(object = filtered_faahko,
filter = dratio_filter)## 38 features were removed# Now same but for the res object
dratio_filter <- DratioFilter(
threshold = 0.5,
qcIndex = filtered_res$sample_type == "QC",
studyIndex = filtered_res$sample_type == "study")
filtered_res <- filterFeatures(object = filtered_res,
filter = dratio_filter)## 38 features were removedAll features with an D-ratio strictly larger than 0.5 were thus removed from the data set.
In certain experiments, aligning two different datasets is necessary. This
can involve comparing runs of the same samples conducted across different
laboratories or runs with MS2 recorded after the initial MS1 run. Across
laboratories and over time, the same samples may result in variation in
retention time, especially because the LC system can be quite unstable. In these
cases, an alignment step using the adjustRtime() function with the
LamaParam parameter can allow the user to perform this type of alignment.
We will go through this step by step below.
Let’s load an already analyzed dataset ref and our previous dataset before
alignment, which will be tst. We will first restrict their retention time
range to be the same for both dataset.
ref <- loadXcmsData("xmse")
tst <- loadXcmsData("faahko_sub2")Now, we will attempt to align these two samples with the previous dataset. The
first step is to extract landmark features (referred to as lamas). To achieve
this, we will identify the features present in every QC sample of the ref
dataset. To do so, we will categorize (using factor()) our data by
sample_type and only retain the QC samples. This variable will be utilized to
filter the features using the PercentMissingFilter parameter within the
filterFeatures() function (see section above for more information on this
method)
f <- sampleData(ref)$sample_type
f[f != "QC"] <- NA
ref <- filterFeatures(ref, PercentMissingFilter(threshold = 0, f = f))## 4 features were removedref_mz_rt <- featureDefinitions(ref)[, c("mzmed","rtmed")]
head(ref_mz_rt)## mzmed rtmed
## FT001 200.1 2902.634
## FT002 205.0 2789.901
## FT003 206.0 2789.405
## FT004 207.1 2718.560
## FT005 233.0 3023.579
## FT006 241.1 3683.299nrow(ref_mz_rt)## [1] 347This is what the lamas input should look like for alignment. In terms of
how this method works, the alignment algorithm matches chromatographic peaks
from the experimental data to the lamas, fitting a model based on this
match to adjust their retention times and minimize differences between
the two datasets.
Now we can define our param object LamaParama to prepare for the
alignment. Parameters such as tolerance, toleranceRt, and ppm relate
to the matching between chromatographic peaks and lamas. Other parameters
are related to the type of fitting generated between these data points.
More details on each parameter and the overall method can be found by
searching ?adjustRtime. Below is an example using default parameters.
param <- LamaParama(lamas = ref_mz_rt, method = "loess", span = 0.5,
outlierTolerance = 3, zeroWeight = 10, ppm = 20,
tolerance = 0, toleranceRt = 20, bs = "tp")
#' input into `adjustRtime()`
tst_adjusted <- adjustRtime(tst, param = param)
tst_adjusted <- applyAdjustedRtime(tst_adjusted)We extract the base peak chromatogram (BPC) to visualize and evaluate the alignment:
#' evaluate the results with BPC
bpc <- chromatogram(ref, chromPeaks = "none")
bpc_tst_raw <- chromatogram(tst, chromPeaks = "none")
bpc_tst_adj <- chromatogram(tst_adjusted, chromPeaks = "none")We generate plots to visually compare the alignment to the reference dataset (black) both before (red) and after (blue) adjustment:
#' BPC of a sample
par(mfrow = c(1, 2), mar = c(4, 2.5, 1, 0.5))
plot(bpc[1, 1], col = "#00000080", main = "Before Alignment")
points(rtime(bpc_tst_raw[1, 1]), intensity(bpc_tst_raw[1, 1]), type = "l",
col = "#ff000080")
grid()
plot(bpc[1, 1], col = "#00000080", main = "After Alignment")
points(rtime(bpc_tst_adj[1, 1]), intensity(bpc_tst_adj[1, 1]), type = "l",
col = "#0000ff80")
grid()
It appears that certain time intervals (2500 to 3000 and 3500 to 4500 seconds)
exhibit better alignment than others. This variance can be elucidated by
examining the distribution of matched peaks, as illustrated below. The
matchLamaChromPeaks() function facilitates the assessment of how well the
lamas correspond with the chromatographic peaks in each file. This analysis
can be conducted prior to any adjustments.
param <- matchLamasChromPeaks(tst, param = param)
mtch <- matchedRtimes(param)
#' BPC of the first sample with matches to lamas overlay
par(mfrow = c(1, 1))
plot(bpc[1, 1], col = "#00000080", main = "Distribution CP matched to Lamas")
points(rtime(bpc_tst_adj[1, 1]), intensity(bpc_tst_adj[1, 1]), type = "l",
col = "#0000ff80")
grid()
abline(v = mtch[[1]]$obs)
The overlay of BPC above provides insight into the correlation between accurate
alignment and the presence of peaks matching with lamas. For this particular
sample no chromatographic peaks were matched to the lamas between 2500 and
3000 seconds and hence the alignment in that region was not good. For the second
file, chrom peaks could also be matched in that region resulting in a better
alignment.
par(mfrow = c(1, 1))
plot(bpc[1, 2], col = "#00000080", main = "Distribution CP matched to Lamas")
points(rtime(bpc_tst_adj[1, 2]), intensity(bpc_tst_adj[1, 2]), type = "l",
col = "#0000ff80")
grid()
abline(v = mtch[[2]]$obs)
Furthermore, a more detailed examination of the matching and the model used for
fitting each file is possible. Numerical information can be obtained using the
summarizeLamaMatch() function. From this, the percentage of chromatographic
peaks utilized for alignment can be computed relative to the total number of
peaks in the file. Additionally, it is feasible to directly plot() the param
object for the file of interest, showcasing the distribution of these
chromatographic peaks along with the fitted model line.
#' access summary of matches and model information
summary <- summarizeLamaMatch(param)
summary## Total_peaks Matched_peaks Total_lamas Model_summary
## 1 87 34 347 30, c(0.....
## 2 100 51 347 48, c(0.....
## 3 61 34 347 33, c(0.....#' coverage for each file
summary$Matched_peaks / summary$Total_peaks * 100## [1] 39.08046 51.00000 55.73770#' access the information on the model of for the first file
summary$Model_summary[[1]]## Call:
## loess(formula = ref ~ obs, data = rt_map, weights = weights,
## span = span)
##
## Number of Observations: 30
## Equivalent Number of Parameters: 7.64
## Residual Standard Error: 2.235
## Trace of smoother matrix: 8.45 (exact)
##
## Control settings:
## span : 0.5
## degree : 2
## family : gaussian
## surface : interpolate cell = 0.2
## normalize: TRUE
## parametric: FALSE
## drop.square: FALSE#' Plot obs vs. ref with fitting line
plot(param, index = 1L, main = "ChromPeaks versus Lamas for the first file",
colPoint = "red")
abline(0, 1, lty = 3, col = "grey")
grid()
xcms result objects can be subset/filtered by sample using the [
method or one of the filter* functions (although the XcmsExperiment supports
at present only few selected filter functions). In some cases filtering can
remove preprocessing results, but most filter functions support parameters
keepFeatures and keepAdjustedRtime that can be set to TRUE to avoid their
removal.
Most functions in xcms support parallel processing, which is enabled by
default and is performed, for most operations, on a per-file basis. Parallel
processing is handled and configured by the BiocParallel Bioconductor package
and can be globally defined for an R session. Note that, while all data objects
are designed to have a low memory footprint by loading only peak data into
memory when needed, parallel processing will increase this demand: in general,
the full data for as many files as there are parallel processes are loaded into
memory. This needs also to be considered, when the number of parallel processes
is defined.
Unix-based systems (Linux, macOS) support multicore-based parallel
processing. To configure it globally we register() the parameter class. Note
also that bpstart() is used below to initialize the parallel processes.
register(bpstart(MulticoreParam(2)))Windows supports only socket-based parallel processing:
register(bpstart(SnowParam(2)))MSnbase-based xcms version 3Spectra is used as the main container for the (raw) MS data. Thus changing
backends is supported at any stage of the analysis (e.g. to load all data into
memory or to load data from remote databases).Some of the documentations listed here are still based on xcms version 3 but will be subsequently updated.
R packages used for this document are listed below.
sessionInfo()## R version 4.5.0 beta (2025-04-02 r88102)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.22-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB 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
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] SummarizedExperiment_1.39.0 GenomicRanges_1.61.0
## [3] GenomeInfoDb_1.45.0 IRanges_2.43.0
## [5] MatrixGenerics_1.21.0 matrixStats_1.5.0
## [7] RColorBrewer_1.1-3 MetaboCoreUtils_1.17.0
## [9] MsBackendMgf_1.17.0 MsExperiment_1.11.0
## [11] pander_0.6.6 Spectra_1.19.0
## [13] MassSpecWavelet_1.75.0 pheatmap_1.0.12
## [15] faahKO_1.47.1 MSnbase_2.35.0
## [17] ProtGenerics_1.41.0 S4Vectors_0.47.0
## [19] mzR_2.43.0 Rcpp_1.0.14
## [21] Biobase_2.69.0 BiocGenerics_0.55.0
## [23] generics_0.1.3 MsFeatures_1.17.0
## [25] xcms_4.7.0 BiocParallel_1.43.0
## [27] BiocStyle_2.37.0
##
## loaded via a namespace (and not attached):
## [1] DBI_1.2.3 rlang_1.1.6
## [3] magrittr_2.0.3 clue_0.3-66
## [5] compiler_4.5.0 vctrs_0.6.5
## [7] reshape2_1.4.4 stringr_1.5.1
## [9] pkgconfig_2.0.3 crayon_1.5.3
## [11] fastmap_1.2.0 magick_2.8.6
## [13] XVector_0.49.0 rmarkdown_2.29
## [15] UCSC.utils_1.5.0 preprocessCore_1.71.0
## [17] tinytex_0.57 purrr_1.0.4
## [19] xfun_0.52 MultiAssayExperiment_1.35.0
## [21] cachem_1.1.0 jsonlite_2.0.0
## [23] progress_1.2.3 DelayedArray_0.35.0
## [25] prettyunits_1.2.0 parallel_4.5.0
## [27] cluster_2.1.8.1 R6_2.6.1
## [29] bslib_0.9.0 stringi_1.8.7
## [31] limma_3.65.0 jquerylib_0.1.4
## [33] iterators_1.0.14 bookdown_0.43
## [35] knitr_1.50 BiocBaseUtils_1.11.0
## [37] Matrix_1.7-3 igraph_2.1.4
## [39] tidyselect_1.2.1 abind_1.4-8
## [41] yaml_2.3.10 doParallel_1.0.17
## [43] codetools_0.2-20 affy_1.87.0
## [45] lattice_0.22-7 tibble_3.2.1
## [47] plyr_1.8.9 signal_1.8-1
## [49] evaluate_1.0.3 pillar_1.10.2
## [51] affyio_1.79.0 BiocManager_1.30.25
## [53] foreach_1.5.2 MALDIquant_1.22.3
## [55] ncdf4_1.24 hms_1.1.3
## [57] ggplot2_3.5.2 munsell_0.5.1
## [59] scales_1.3.0 glue_1.8.0
## [61] lazyeval_0.2.2 tools_4.5.0
## [63] mzID_1.47.0 QFeatures_1.19.0
## [65] vsn_3.77.0 fs_1.6.6
## [67] XML_3.99-0.18 grid_4.5.0
## [69] impute_1.83.0 tidyr_1.3.1
## [71] MsCoreUtils_1.21.0 colorspace_2.1-1
## [73] GenomeInfoDbData_1.2.14 PSMatch_1.13.0
## [75] cli_3.6.4 S4Arrays_1.9.0
## [77] dplyr_1.1.4 AnnotationFilter_1.33.0
## [79] pcaMethods_2.1.0 gtable_0.3.6
## [81] sass_0.4.10 digest_0.6.37
## [83] SparseArray_1.9.0 farver_2.1.2
## [85] htmltools_0.5.8.1 lifecycle_1.0.4
## [87] httr_1.4.7 statmod_1.5.0
## [89] MASS_7.3-651. Smith CA, Want EJ, O’Maille G, Abagyan R, Siuzdak G: XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Analytical chemistry 2006, 78:779–787.
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3. Rainer J, Vicini A, Salzer L, Stanstrup J, Badia JM, Neumann S, Stravs MA, Verri Hernandes V, Gatto L, Gibb S, Witting M: A Modular and Expandable Ecosystem for Metabolomics Data Annotation in R. Metabolites 2022, 12:173.
4. Saghatelian A, Trauger SA, Want EJ, Hawkins EG, Siuzdak G, Cravatt BF: Assignment of endogenous substrates to enzymes by global metabolite profiling. Biochemistry 2004, 43:14332–9.
5. Tautenhahn R, Böttcher C, Neumann S: Highly sensitive feature detection for high resolution LC/MS. BMC Bioinformatics 2008, 9:504.
6. Kumler W, Hazelton BJ, Ingalls AE: Picky with peakpicking: Assessing chromatographic peak quality with simple metrics in metabolomics. BMC Bioinformatics 2023, 24:404.
7. Smith R, Ventura D, Prince JT: LC-MS alignment in theory and practice: a comprehensive algorithmic review. Briefings in bioinformatics 2013, 16:bbt080–117.
8. Prince JT, Marcotte EM: Chromatographic alignment of ESI-LC-MS proteomics data sets by ordered bijective interpolated warping. Analytical chemistry 2006, 78:6140–6152.