Preprocessing of LC-MS data
Data import
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.
Initial data inspection
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 files
All 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 1278
Information 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.CDF
sampleData(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)$sample_group])
The chromatogram()
method returned a MChromatograms
object that organizes
individual Chromatogram
objects (which in fact contain the chromatographic
data) in a two-dimensional array: columns represent samples and rows
(optionally) m/z and/or retention time ranges. Below we extract the chromatogram
of the first sample and access its retention time and intensity values.
bpi_1 <- bpis[1, 1]
rtime(bpi_1) |> head()
## [1] 2501.378 2502.943 2504.508 2506.073 2507.638 2509.203
intensity(bpi_1) |> head()
## [1] 43888 43960 43392 42632 42200 42288
From the BPC above it seems that after around 4200 seconds no signal is measured
anymore. Thus, we filter below the full data set to a retention time range from
2550 to 4250 seconds using the filterRt()
function. Note that at present this
will only subset the spectra within the MsExperiment
. Subsequently we
re-create also the BPC.
faahko <- filterRt(faahko, rt = c(2550, 4250))
## Filter spectra
## creating the BPC on the subsetted data
bpis <- chromatogram(faahko, aggregationFun = "max")
We next create boxplots representing the distribution of the total ion currents
per data file. Such plots can be very useful to spot potentially problematic MS
runs. To extract this information, we use the tic()
function on the Spectra
object within faahko
and split the values by file using fromFile()
.
## Get the total ion current by file
tc <- spectra(faahko) |>
tic() |>
split(f = fromFile(faahko))
boxplot(tc, col = group_colors[sampleData(faahko)$sample_group],
ylab = "intensity", main = "Total ion current")
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$sample_name
## Perform the cluster analysis
pheatmap(cormat, annotation = ann,
annotation_color = list(group = group_colors))
The samples cluster in a pairwise manner, with the KO and WT samples for the
same sample index having the most similar BPC.
Chromatographic peak detection
Chromatographic peak detection aims at identifying all signal in each sample
created from ions of the same originating compound species. Chromatographic peak
detection can be performed in xcms with the findChromPeaks()
function and a
parameter object which defines and configures the algorithm that should be
used (see ?findChromPeaks
for a list of supported algorithms). Before running
any peak detection it is however strongly suggested to first visually inspect
the extracted ion chromatogram of e.g. internal standards or compounds known to
be present in the samples in order to evaluate and adapt the settings of the
peak detection algorithm since the default settings will not be appropriate for
most LC-MS setups.
Below we extract the EIC for one compound using the chromatogram()
function by
specifying in addition the m/z and retention time range where we would expect
the signal for that compound.
## Define the rt and m/z range of the peak area
rtr <- c(2700, 2900)
mzr <- c(334.9, 335.1)
## extract the chromatogram
chr_raw <- chromatogram(faahko, mz = mzr, rt = rtr)
plot(chr_raw, col = group_colors[chr_raw$sample_group])
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")
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)
## rt rtmin rtmax into intb maxo sn row column
## [1,] 2781.505 2761.160 2809.674 412134.255 355516.374 16856 13 1 1
## [2,] 2786.199 2764.290 2812.803 1496244.206 1391821.332 58736 20 1 2
## [3,] 2734.556 2714.211 2765.855 21579.367 18449.428 899 4 1 3
## [4,] 2797.154 2775.245 2815.933 159058.782 150289.314 6295 12 1 3
## [5,] 2784.635 2761.160 2808.109 54947.545 37923.532 2715 2 1 4
## [6,] 2859.752 2845.668 2878.532 13895.212 13874.868 905 904 1 4
## [7,] 2897.311 2891.051 2898.876 5457.155 5450.895 995 994 1 4
## [8,] 2819.064 2808.109 2834.713 19456.914 19438.134 1347 1576 1 4
## [9,] 2789.329 2762.725 2808.109 174473.311 91114.975 8325 3 1 5
## [10,] 2786.199 2764.290 2812.803 932645.211 569236.246 35856 2 1 6
## [11,] 2792.461 2768.987 2823.760 876585.527 511324.134 27200 2 1 7
## [12,] 2789.329 2773.680 2806.544 89582.569 73871.386 5427 6 1 8
Parallel 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>
## 1 1 FALSE 1 1
## 2 1 FALSE 1 2
## 3 1 FALSE 1 3
## 4 1 FALSE 1 3
## 5 1 FALSE 1 4
## ... ... ... ... ...
## 8 1 FALSE 1 4
## 9 1 FALSE 1 5
## 10 1 FALSE 1 6
## 11 1 FALSE 1 7
## 12 1 FALSE 1 8
Below 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)
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 1
Columns 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 FALSE
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 peaks
chr_2 <- chromatogram(faahko_pp[1], mz = mzr_1)
## Processing chromatographic peaks
par(mfrow = c(1, 2))
plot(chr_1)
plot(chr_2)
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 peaks
chr_2 <- chromatogram(faahko_pp[1], mz = mzr_1)
## Processing chromatographic peaks
par(mfrow = c(1, 2))
plot(chr_1)
plot(chr_2)
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 1
plot(res)
![](data:image/png;base64,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)
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_pp
Below 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."))
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 7
We 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)
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)
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 7
We 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"]])
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)
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)
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).
Alignment
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 [6]), 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 [7] 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 2559.281
To 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()
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()
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.
Subset-based alignment
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()
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
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)
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")
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 1
The 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 508546.4
As 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"]])
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 8
Gap filling
Missing 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 508546.4
Final result
While 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: GenomeInfoDb
res <- 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_type
The 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 1
Annotations 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... study
Finally, 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 508546.4
Since 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 508546.4
Finally, 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: Wed Jun 5 20:24:14 2024
## 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: Wed Jun 5 20:24:17 2024
## 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: Wed Jun 5 20:24:34 2024
## 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: Wed Jun 5 20:24:34 2024
## 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: Wed Jun 5 20:24:41 2024
## 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: Wed Jun 5 20:24:48 2024
## info:
## fileIndex: 1,2,3,4,5,6,7,8
## Parameter class: ChromPeakAreaParam
## MS level(s) 1
This 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: Wed Jun 5 20:24:14 2024
## info:
## fileIndex: 1,2,3,4,5,6,7,8
## Parameter class: CentWaveParam
## MS level(s) 1
These 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] FALSE
At 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$sample_name, col = "darkgrey",
pos = 3, cex = 2)
We can see the expected separation between the KO and WT samples on PC2. On PC1
samples separate based on their ID, samples with an ID <= 18 from samples with
an ID > 18. This separation might be caused by a technical bias
(e.g. measurements performed on different days/weeks) or due to biological
properties of the mice analyzed (sex, age, litter mates etc).
Further data processing and analysis
Quality-based filtering of features
When dealing with metabolomics results, it is often necessary to filter features
based on certain criteria. These criteria are typically derived from statistical
formulas applied to full rows of data, where each row represents a feature. The
filterFeatures()
function provides a robust solution for filtering features
based on these conventional quality assessment criteria. It supports multiple
types of filtering, allowing users to tailor the filtering process to their
specific needs, all controlled by the filter
argument. This function and its
implementations are applicable to both XcmsExperiment
results objects and
SummarizedExperiment
objects.
We will demonstrate how to use the filterFeatures()
function to perform
quality assessment and filtering on both the faahko
and res
variables
defined above. The filter
argument can accommodate various types of input,
each determining the specific type of quality assessment and filtering to be
performed.
The RsdFilter
enable users to filter features based on their relative
standard deviation (coefficient of variation) for a specified threshold
. It
is recommended to base the computation on quality control (QC) samples,
as demonstrated below:
# Set up parameters for RsdFilter
rsd_filter <- RsdFilter(threshold = 0.3,
qcIndex = sampleData(faahko)$sample_type == "QC")
# Apply the filter to faakho object
filtered_faahko <- filterFeatures(object = faahko, filter = rsd_filter)
## 255 features were removed
# Now apply the same strategy to the res object
rsd_filter <- RsdFilter(threshold = 0.3, qcIndex = res$sample_type == "QC")
filtered_res <- filterFeatures(object = res, filter = rsd_filter, assay = "raw")
## 260 features were removed
All features with an RSD (CV) strictly larger than 0.3 in QC samples were thus
removed from the data set.
The DratioFilter
can be used to filter features based on the D-ratio or
dispersion ratio, which compares the standard deviation in QC samples to that
in study samples.
# Set up parameters for DratioFilter
dratio_filter <- DratioFilter(
threshold = 0.5,
qcIndex = sampleData(filtered_faahko)$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$sample_type == "study")
filtered_res <- filterFeatures(object = filtered_res,
filter = dratio_filter)
## 38 features were removed
All features with an D-ratio strictly larger than 0.5 were thus removed from
the data set.
The PercentMissingFilter
allows to filter features based on the percentage of
missing values for each feature. This function takes as an input the parameter
f
which is supposed to be a vector of length equal to the length of the object
(i.e. number of samples) with the sample type for each. The function then
computes the percentage of missing values per sample groups and filters
features based on this. Features with a percent of missing values larger than
the threshold in all sample groups will be removed. Another option is to base
this quality assessment and filtering only on QC samples.
Both examples are shown below:
# To set up parameter `f` to filter only based on QC samples
f <- sampleData(filtered_faakho)$sample_type
f[f != "QC"] <- NA
# To set up parameter `f` to filter per sample type excluding QC samples
f <- sampleData(filtered_faakho)$sample_type
f[f == "QC"] <- NA
missing_filter <- PercentMissingFilter(threshold = 30,
f = f)
# Apply the filter to faakho object
filtered_faakho <- filterFeatures(object = filtered_faakho,
filter = missing_filter)
## 0 features were removed
# Apply the filter to res object
missing_filter <- PercentMissingFilter(threshold = 30,
f = f)
filtered_res <- filterFeatures(object = filtered_res,
filter = missing_filter)
## 0 features were removed
Here, 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-3.20-bioc/R/site-library
## xcms /tmp/RtmpgGmtUB/Rinst30857b1411477
## QFeatures /home/biocbuild/bbs-3.20-bioc/R/site-library
##
##
## Using the first match ...
?BlankFlag
Normalization
Normalizing features’ signal intensities is required, but at present not (yet)
supported in xcms
(some methods might be added in near future). It is advised
to use the SummarizedExperiment
returned by the quantify()
method for any
further data processing, as this type of object stores feature definitions,
sample annotations as well as feature abundances in the same object. For the
identification of e.g. features with significant different
intensities/abundances it is suggested to use functionality provided in other R
packages, such as Bioconductor’s excellent limma package.
Alignment to an external reference dataset
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 removed
ref_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 3683.299
nrow(ref_mz_rt)
## [1] 347
This 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()
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)
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]]$obs)
![](data:image/png;base64,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The overlay of BPC above provides insight into the correlation between accurate
alignment and the presence of peaks matching with lamas
. For this particular
sample no chromatographic peaks were matched to the lamas
between 2500 and
3000 seconds and hence the alignment in that region was not good. For the second
file, chrom peaks could also be matched in that region resulting in a better
alignment.
par(mfrow = c(1, 1))
plot(bpc[1, 2], col = "#00000080", main = "Distribution CP matched to Lamas")
points(rtime(bpc_tst_adj[1, 2]), intensity(bpc_tst_adj[1, 2]), type = "l",
col = "#0000ff80")
grid()
abline(v = mtch[[2]]$obs)
![](data:image/png;base64,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)
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()
![](data:image/png;base64,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)