1 Introduction

This documents describes data import, exploration, preprocessing and analysis of LCMS experiments with xcms version >= 3. The examples and basic workflow was adapted from the original LC/MS Preprocessing and Analysis with xcms vignette from Colin A. Smith.

2 Data import

xcms supports analysis of LC/MS data from files in (AIA/ANDI) NetCDF, mzML/mzXML and mzData format. For the actual data import Bioconductor’s SRC_R[:exports both]{Biocpkg(“mzR”)} is used. For demonstration purpose we will analyze a subset of the data from [1] in which the metabolic consequences of knocking out the fatty acid amide hydrolase (FAAH) gene in mice was investigated. The raw data files (in NetCDF format) are provided with 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 mode form 200-600 m/z and 2500-4500 seconds.

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.

library(xcms)
library(faahKO)
library(RColorBrewer)
library(pander)

## Get the full path to the CDF files
cdfs <- dir(system.file("cdf", package = "faahKO"), full.names = TRUE,
        recursive = TRUE)
## Create a phenodata data.frame
pd <- data.frame(sample_name = sub(basename(cdfs), pattern = ".CDF",
                   replacement = "", fixed = TRUE),
         sample_group = c(rep("KO", 6), rep("WT", 6)),
         stringsAsFactors = FALSE) 

Subsequently we load the raw data as an OnDiskMSnExp object using the readMSData method from the MSnbase package. While the MSnbase package was originally developed for proteomics data processing, many of its functionality, including raw data import and data representation, can be shared and reused in metabolomics data analysis.

raw_data <- readMSData(files = cdfs, pdata = new("NAnnotatedDataFrame", pd),
               mode = "onDisk") 

The OnDiskMSnExp object contains general information about the number of spectra, retention times, the measured total ion current etc, but does not contain the full raw data (i.e. the m/z and intensity values from each measured spectrum). Its memory footprint is thus rather small making it an ideal object to represent large metabolomics experiments while still allowing to perform simple quality controls, data inspection and exploration as well as data sub-setting operations. The m/z and intensity values are imported from the raw data files on demand, hence the location of the raw data files should not be changed after initial data import.

3 Initial data inspection

The OnDiskMSnExp organizes the MS data by spectrum and provides the methods intensity, mz and rtime to access the raw data from the files (the measured intensity values, the corresponding m/z and retention time values). In addition, the spectra method could be used to return all data encapsulated in Spectrum classes. Below we extract the retention time values from the object.

head(rtime(raw_data)) 

## F01.S0001 F01.S0002 F01.S0003 F01.S0004 F01.S0005 F01.S0006 
##  2501.378  2502.943  2504.508  2506.073  2507.638  2509.203

All data is returned as one-dimensional vectors (a numeric vector for rtime and a list of numeric vectors for mz and intensity, each containing the values from one spectrum), even if the experiment consists of multiple files/samples. The fromFile function returns a numeric vector that provides the mapping of the values to the originating file. Below we use the fromFile indices to organize the mz values by file.

mzs <- mz(raw_data)

## Split the list by file
mzs_by_file <- split(mzs, f = fromFile(raw_data))

length(mzs_by_file) 

## [1] 12

As a first evaluation of the data we plot below 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(raw_data, aggregationFun = "max")
## Define colors for the two groups
group_colors <- brewer.pal(3, "Set1")[1:2]
names(group_colors) <- c("KO", "WT")

## Plot all chromatograms.
plot(bpis, col = group_colors[raw_data$sample_group])

The chromatogram method returned a Chromatograms 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]
head(rtime(bpi_1))

## F01.S0001 F01.S0002 F01.S0003 F01.S0004 F01.S0005 F01.S0006 
##  2501.378  2502.943  2504.508  2506.073  2507.638  2509.203

head(intensity(bpi_1)) 

## F01.S0001 F01.S0002 F01.S0003 F01.S0004 F01.S0005 F01.S0006 
##     43888     43960     43392     42632     42200     42288

The chromatogram method supports also extraction of chromatographic data from a m/z-rt slice of the MS data. In the next section we will use this method to create an extracted ion chromatogram (EIC) for a selected peak.

Note that chromatogram reads the raw data from each file to calculate the chromatogram. The bpi and tic methods on the other hand do not read any data from the raw files but use the respective information that was provided in the header definition of the input files.

Below we create boxplots representing the distribution of total ion currents per file. Such plots can be very useful to spot problematic or failing MS runs.

## Get the total ion current by file
tc <- split(tic(raw_data), f = fromFile(raw_data))
boxplot(tc, col = group_colors[raw_data$sample_group],
    ylab = "intensity", main = "Total ion current") 

Figure 1: Distribution of total ion currents per file

4 Chromatographic peak detection

Next we perform the chromatographic peak detection using the centWave algorithm [2]. Before running the peak detection it is however strongly suggested to visually inspect e.g. the extracted ion chromatogram of internal standards or known compounds to evaluate and adapt the peak detection settings since the default settings will not be appropriate for most LCMS experiments. The two most critical parameters for centWave are the peakwidth (expected range of chromatographic peak widths) and ppm (maximum expected deviation of m/z values of centroids corresponding to one chromatographic peak; this is usually much larger than the ppm specified by the manufacturer) parameters. To evaluate the typical chromatographic peak width we plot the EIC for one peak.

## 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(raw_data, mz = mzr, rt = rtr)
plot(chr_raw, col = group_colors[chr_raw$sample_group]) 

Figure 2: 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. for a specific retention time) no signal was measured in the respective mz range. This is reflected by the lines not being drawn as continuous lines in the plot above.

The peak above has a width of about 50 seconds. The peakwidth parameter should be set to accommodate the expected widths of peak in the data set. We set it to 20,80 for the present example data set.

For the ppm parameter we extract the full MS data (intensity, retention time and m/z values) corresponding to the above peak.

## Extract the MS data for the region.
msd_raw <- extractMsData(raw_data, mz = mzr, rt = rtr)
plotMsData(msd_raw[[1]]) 

Figure 3: Visualization of the raw MS data for one peak
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 one would see the m/z values (lower panel) scatter around the real m/z value of the compound. It is suggested to inspect the ranges of m/z values for many compounds (either internal standards or compounds known to be present in the sample) and define the ppm parameter for centWave according to these.

Below we perform the chromatographic peak detection using the findChromPeaks method. The submitted parameter object defines which algorithm will be used and allows to define the settings for this algorithm. Note that we set the argument noise to 1000 to slightly speed up the analysis by considering only signals with a value larger than 1000 in the peak detection step.

cwp <- CentWaveParam(peakwidth = c(30, 80), noise = 1000)
xdata <- findChromPeaks(raw_data, param = cwp) 

The results are returned as an XCMSnExp object which extends the OnDiskMSnExp object by storing also LC/GC-MS preprocessing results. This means also that all methods to sub-set and filter the data or to access the (raw) data are inherited from the OnDiskMSnExp object. The results from the chromatographic peak detection can be accessed with the chromPeaks method.

head(chromPeaks(xdata)) 

##         mz mzmin mzmax       rt    rtmin    rtmax       into       intb
## [1,] 236.1 236.1 236.1 2520.158 2501.378 2553.022  301289.53  268211.58
## [2,] 362.9 362.9 362.9 2596.840 2587.451 2604.665   19916.60   19674.79
## [3,] 302.0 302.0 302.0 2617.185 2587.451 2648.484  699458.52  687162.27
## [4,] 316.2 316.2 316.2 2668.828 2661.003 2676.653   51310.09   50854.60
## [5,] 370.1 370.1 370.1 2673.523 2643.789 2706.387  458247.09  423667.40
## [6,] 360.0 360.0 360.0 2682.913 2635.964 2718.907 9034381.61 8518838.59
##        maxo sn sample is_filled
## [1,]  12957 13      1         0
## [2,]   1453 12      1         0
## [3,]  30552 73      1         0
## [4,]   4267 17      1         0
## [5,]  25672 16      1         0
## [6,] 317568 20      1         0

The returned matrix provides the m/z and retention time range for each identified chromatographic peak as well as the integrated signal intensity (“into”) and the maximal peak intensitity (“maxo”). Columns “sample” contains the index of the sample in the object/experiment in which the peak was identified.

Below we use the data from this table to calculate some per-file summaries.

summary_fun <- function(z) {
    c(peak_count = nrow(z), rt = quantile(z[, "rtmax"] - z[, "rtmin"]))
}
T <- lapply(split.data.frame(chromPeaks(xdata),
                 f = chromPeaks(xdata)[, "sample"]),
        FUN = summary_fun)
T <- do.call(rbind, T)
rownames(T) <- basename(fileNames(xdata))
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.")) 

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 the chromatographic peaks for the 3rd sample.

plotChromPeaks(xdata, file = 3) 

Figure 4: Identified chromatographic peaks in the m/z by retention time space for one sample

To get a global overview of the peak detection we can plot the frequency of identified peaks per file along the retention time axis. This allows to identify time periods along the MS run in which a higher number of peaks was identified and evaluate whether this is consistent across files.

plotChromPeakImage(xdata) 

Figure 5: 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.

Next we highlight the identified chromatographic peaks for the example peak from before. Evaluating such plots on a list of peaks corresponding to known peaks or internal standards helps to ensure that peak detection settings were appropriate and correctly identified the expected peaks.

plot(chr_raw, col = group_colors[chr_raw$sample_group], lwd = 2)
highlightChromPeaks(xdata, border = group_colors[chr_raw$sample_group],
            lty = 3, rt = rtr, mz = mzr) 

Figure 6: 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.

Note that we can also specifically extract identified chromatographic peaks for a selected region by providing the respective m/z and retention time ranges with the mz and rt arguments in the chromPeaks method.

pander(chromPeaks(xdata, mz = mzr, rt = rtr),
       caption = paste("Identified chromatographic peaks in a selected ",
               "m/z and retention time range.")) 

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(xdata)[, "into"]),
          f = chromPeaks(xdata)[, "sample"])
boxplot(ints, varwidth = TRUE, col = group_colors[xdata$sample_group],
    ylab = expression(log[2]~intensity), main = "Peak intensities")
grid(nx = NA, ny = NULL) 

Figure 7: Peak intensity distribution per sample

5 Alignment

The time at which analytes elute in the chromatography can vary between samples (and even compounds). Such a difference was already observable in the extracted ion chromatogram plot shown as an example in the previous section. The alignment step, also referred to as retention time correction, aims at adjusting this by shifting signals along the retention time axis to align the signals between different samples within an experiment.

A plethora of alignment algorithms exist (see [3]), with some of them being implemented also in xcms. The method to perform the alignment/retention time correction in xcms is adjustRtime which uses different alignment algorithms depending on the provided parameter class. In the example below we use the obiwarp method [4] to align the samples. We use a binSize = 0.6 which creates warping functions in mz bins of 0.6. Also here it is advisable to modify the settings for each experiment and evaluate if retention time correction did align internal controls or known compounds properly.

xdata <- adjustRtime(xdata, param = ObiwarpParam(binSize = 0.6)) 

adjustRtime, besides calculating adjusted retention times for each spectrum, does also adjust the reported retention times of the identified chromatographic peaks.

To extract the adjusted retention times we can use the adjustedRtime method, or simply the rtime method that, if present, returns by default adjusted retention times from an XCMSnExp object.

## Extract adjusted retention times
head(adjustedRtime(xdata))

## F01.S0001 F01.S0002 F01.S0003 F01.S0004 F01.S0005 F01.S0006 
##  2501.378  2502.958  2504.538  2506.118  2507.699  2509.280

## Or simply use the rtime method
head(rtime(xdata)) 

## F01.S0001 F01.S0002 F01.S0003 F01.S0004 F01.S0005 F01.S0006 
##  2501.378  2502.958  2504.538  2506.118  2507.699  2509.280

Raw retention times can be extracted from an XCMSnExp containing aligned data with rtime(xdata, adjusted = FALSE).

To evaluate the impact of the alignment we plot the BPC on the adjusted data. In addition we plot the differences of the adjusted- to the raw retention times per sample using the plotAdjustedRtime function.

## Get the base peak chromatograms.
bpis_adj <- chromatogram(xdata, aggregationFun = "max")
par(mfrow = c(2, 1), mar = c(4.5, 4.2, 1, 0.5))
plot(bpis_adj, col = group_colors[bpis_adj$sample_group])
## Plot also the difference of adjusted to raw retention time.
plotAdjustedRtime(xdata, col = group_colors[xdata$sample_group]) 

Figure 8: Obiwarp aligned data
Base peak chromatogram after alignment (top) 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.

Alternatively we could use the peak groups alignment method that adjusts the retention time by aligning previously identified hook peaks (chromatographic peaks present in most/all samples). Ideally, these hook peaks should span most part of the retention time range. Below we first restore the raw retention times (also of the identified peaks) using the dropAdjustedRtime methods. Note that a drop* method is available for each preprocessing step allowing to remove the respective results from the XCMSnExp object.b

## Does the object have adjusted retention times?
hasAdjustedRtime(xdata)

## [1] TRUE

## Drop the alignment results.
xdata <- dropAdjustedRtime(xdata)

## Does the object have adjusted retention times?
hasAdjustedRtime(xdata) 

## [1] FALSE

As noted above the peak groups method requires peak groups (features) present in most samples to perform the alignment. We thus have to perform a first correspondence run to identify such peaks (details about the algorithm used are presented in the next section). We use here again default settings, but it is strongly advised to adapt the parameters for each data set. 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(xdata))).

## Correspondence: group peaks across samples.
pdp <- PeakDensityParam(sampleGroups = xdata$sample_group,
            minFraction = 0.8)
xdata <- groupChromPeaks(xdata, param = pdp)

## Now the retention time correction.
pgp <- PeakGroupsParam(minFraction = 0.85)

## Get the peak groups that would be used for alignment.
xdata <- adjustRtime(xdata, param = pgp)

Note also that we could use the adjustedRtimePeakGroups method on the object before alignment to evaluate on which features (peak groups) the alignment would be performed. This can be useful to test different settings for the peak groups algorithm. Also, it is possible to manually select or define certain peak groups (i.e. their retention times per sample) and provide this matrix to the PeakGroupsParam class with the peakGroupsMatrix argument.

Below plot the difference between raw and adjusted retention times using the plotAdjustedRtime function, which, if the peak groups method is used for alignment, also highlights the peak groups used in the adjustment.

## Plot the difference of adjusted to raw retention time.
plotAdjustedRtime(xdata, col = group_colors[xdata$sample_group],
          peakGroupsCol = "grey", peakGroupsPch = 1) 

Figure 9: Peak groups aligned data

At last we evaluate the impact of the alignment on the test peak.

par(mfrow = c(2, 1))
## Plot the raw data
plot(chr_raw, col = group_colors[chr_raw$sample_group])

## Extract the chromatogram from the adjusted object
chr_adj <- chromatogram(xdata, rt = rtr, mz = mzr)
plot(chr_adj, col = group_colors[chr_raw$sample_group]) 

Figure 10: Example extracted ion chromatogram before (top) and after alignment (bottom)

6 Correspondence

The final step in the metabolomics preprocessing is the correspondence that matches detected chromatographic peaks between samples (and depending on the settings, also within samples if they are adjacent). The method to perform the correspondence in xcms is groupChromPeaks. We will use the peak density method [5] to group chromatographic peaks. The algorithm combines chromatographic peaks depending on the density of peaks along the retention time axis within small slices along the mz dimension. To illustrate this we plot below the chromatogram for an mz slice with multiple chromatographic peaks within each sample. We use below a value of 0.4 for the minFraction parameter hence only chromatographic peaks present in at least 40% of the samples per sample group are grouped into a feature. The sample group assignment is specified with the sampleGroups argument.

## Define the mz slice.
mzr <- c(305.05, 305.15)

## Extract and plot the chromatograms
chr_mzr <- chromatogram(xdata, mz = mzr, rt = c(2500, 4000))
par(mfrow = c(3, 1), mar = c(1, 4, 1, 0.5))
cols <- group_colors[chr_mzr$sample_group]
plot(chr_mzr, col = cols, xaxt = "n", xlab = "")
## Highlight the detected peaks in that region.
highlightChromPeaks(xdata, mz = mzr, col = cols, type = "point", pch = 16)
## Define the parameters for the peak density method
pdp <- PeakDensityParam(sampleGroups = xdata$sample_group,
            minFraction = 0.4, bw = 30)
par(mar = c(4, 4, 1, 0.5))
plotChromPeakDensity(xdata, mz = mzr, col = cols, param = pdp,
             pch = 16, xlim = c(2500, 4000))
## Use a different bw
pdp <- PeakDensityParam(sampleGroups = xdata$sample_group,
            minFraction = 0.4, bw = 20)
plotChromPeakDensity(xdata, mz = mzr, col = cols, param = pdp,
             pch = 16, xlim = c(2500, 4000)) 

Figure 11: Example for peak density correspondence
Upper panel: chromatogram for an mz slice with multiple chromatographic peaks. Middle and 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 above shows the extracted ion chromatogram for each sample with the detected peaks highlighted. The middle and lower plot shows the retention time for each detected peak within the different samples. The black solid line represents the density distribution of detected peaks along the retention times. Peaks combined into features (peak groups) are indicated with grey rectangles. Different values for the bw parameter of the PeakDensityParam were used: bw = 30 in the middle and bw = 20 in the lower panel. With the default value for the parameter bw the two neighboring chromatographic peaks would be grouped into the same feature, while with a bw of 20 they would be grouped into separate features. This grouping depends on the parameters for the density function and other parameters passed to the algorithm with the PeakDensityParam.

## Perform the correspondence
pdp <- PeakDensityParam(sampleGroups = xdata$sample_group,
            minFraction = 0.4, bw = 20)
xdata <- groupChromPeaks(xdata, param = pdp)

The results from the correspondence can be extracted using the featureDefinitions method, that returns a DataFrame with the definition of the features (i.e. the mz and rt ranges and, in column peakidx, the index of the chromatographic peaks in the chromPeaks matrix for each feature).

## Extract the feature definitions
featureDefinitions(xdata)

## DataFrame with 312 rows and 10 columns
##           mzmed     mzmin     mzmax     rtmed     rtmin     rtmax    npeaks
##       <numeric> <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## FT001     205.0     205.0     205.0  2790.917  2790.259  2791.337        12
## FT002     206.0     206.0     206.0  2790.765  2787.954  2794.075        11
## FT003     207.1     207.1     207.1  2720.761  2718.267  2723.954         8
## FT004     236.1     236.1     236.1  2526.732  2523.803  2527.559         5
## FT005     241.1     241.1     241.2  3678.876  3664.149  3683.063         9
## ...         ...       ...       ...       ...       ...       ...       ...
## FT308     594.2     594.2     594.2  3405.347  3395.291  3410.289         3
## FT309     594.3     594.3     594.3  3613.401  3608.979  3619.980         6
## FT310     595.2     595.2     595.3  3004.515  3000.080  3019.169         7
## FT311     596.3     596.3     596.4  3818.529  3813.580  3826.519         7
## FT312     597.4     597.3     597.4  3814.927  3809.072  3818.574         5
##              KO        WT            peakidx
##       <numeric> <numeric>             <list>
## FT001         6         6     43,416,912,...
## FT002         4         6   25,1232,1244,...
## FT003         4         4   399,881,1482,...
## FT004         2         3     1,376,1943,...
## FT005         4         2  253,1122,1397,...
## ...         ...       ...                ...
## FT308         0         3     2797,3095,3638
## FT309         3         2  217,1605,1865,...
## FT310         2         4    63,495,2026,...
## FT311         4         2  306,1145,1147,...
## FT312         1         3 1150,2917,2919,...

The featureValues method returns a matrix with rows being features and columns samples. The content of this matrix can be defined using the value argument. Setting value = "into" returns a matrix with the integrated signal of the peaks corresponding to a feature in a sample. Any column name of the chromPeaks matrix can be passed to the argument value. Below we extract the integrated peak intensity per feature/sample.

## Extract the into column for each feature.
head(featureValues(xdata, value = "into"))

##        ko15.CDF  ko16.CDF  ko18.CDF  ko19.CDF  ko21.CDF  ko22.CDF  wt15.CDF
## FT001 2024095.6 1832137.0 1756373.2 1275015.4 1558397.4 1274955.4 2220917.7
## FT002  229446.1        NA        NA  148895.1  169782.6  181300.6  271403.0
## FT003        NA  470042.7  349545.6        NA  356775.9  240606.3  370510.3
## FT004  301289.5  250053.3        NA        NA        NA        NA  293360.4
## FT005  784970.1        NA 1377891.5  659602.9        NA  528679.9        NA
## FT006  681973.4  266391.1  103640.3        NA        NA  132666.7  682289.2
##        wt16.CDF  wt18.CDF   wt19.CDF  wt21.CDF  wt22.CDF
## FT001 1717218.1 1871280.3 1551566.88 1713396.7 1438088.9
## FT002  261254.3  237429.1  225250.02  244356.5  188864.7
## FT003  388938.3  312991.1         NA        NA  251502.7
## FT004  190338.9        NA         NA  305942.5        NA
## FT005 1698777.3        NA  704491.40        NA        NA
## FT006        NA  186512.1   25193.37        NA 1462255.2

This feature matrix contains NA for samples in which no chromatographic peak was detected in the feature’s m/z-rt region. While in many cases there might indeed be no peak signal in the respective region, it might also be that there is signal, but the peak detection algorithm failed to detect a chromatographic peak. xcms provides the fillChromPeaks method to fill in intensity data for such missing values from the original files. The filled in peaks are added to the chromPeaks matrix and are flagged with an 1 in the "is_filled" column. Below we perform such a filling-in of missing peaks.

## Filling missing peaks using default settings. Alternatively we could
## pass a FillChromPeaksParam object to the method.
xdata <- fillChromPeaks(xdata)

head(featureValues(xdata))

##       ko15.CDF ko16.CDF ko18.CDF ko19.CDF ko21.CDF ko22.CDF wt15.CDF
## FT001       43      416      912     1242     1500     1727     1978
## FT002       25     3892     3986     1232     1495     1724     1968
## FT003     3798      399      881     4069     1482     1707     1962
## FT004        1      376     3987     4070     4185     4310     1943
## FT005      253     3893     1122     1397     4186     1880     4415
## FT006       46      424      916     4071     4187     1728     1984
##       wt16.CDF wt18.CDF wt19.CDF wt21.CDF wt22.CDF
## FT001     2350     2705     3000     3270     3508
## FT002     2341     2693     2996     3267     3501
## FT003     2327     2673     4742     4858     3482
## FT004     2304     4627     4743     3235     4990
## FT005     2540     4628     3184     4859     4991
## FT006     4527     2706     3004     4860     3519

For features without detected peaks in a sample, the method extracts all intensities in the mz-rt region of the feature, integrates the signal and adds a filled-in peak to the chromPeaks matrix. No peak is added if no signal is measured/available for the mz-rt region of the feature. For these, even after filling in missing peak data, a NA is reported in the featureValues matrix.

Below we compare the number of missing values before and after filling in missing values. We can use the parameter filled of the featureValues method to define whether or not filled-in peak values should be returned too.

## Missing values before filling in peaks
apply(featureValues(xdata, filled = FALSE), MARGIN = 2,
      FUN = function(z) sum(is.na(z)))

## ko15.CDF ko16.CDF ko18.CDF ko19.CDF ko21.CDF ko22.CDF wt15.CDF wt16.CDF 
##       98       97       84      118      133      111      117      105 
## wt18.CDF wt19.CDF wt21.CDF wt22.CDF 
##      121      120      139      113

## Missing values after filling in peaks
apply(featureValues(xdata), MARGIN = 2,
      FUN = function(z) sum(is.na(z)))

## ko15.CDF ko16.CDF ko18.CDF ko19.CDF ko21.CDF ko22.CDF wt15.CDF wt16.CDF 
##        4        3        1        2        8        6        5        5 
## wt18.CDF wt19.CDF wt21.CDF wt22.CDF 
##        6        4        7        8

At last we perform 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(featureValues(xdata, value = "into"))

## 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
cols <- group_colors[xdata$sample_group]
pcSummary <- summary(pc)
plot(pc$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$importance[2, 2] * 100,
                   digits = 3), " % variance"),
     col = "darkgrey", bg = cols, cex = 2)
grid()
text(pc$x[, 1], pc$x[,2], labels = xdata$sample_name, col = "darkgrey",
     pos = 3, cex = 2)

Figure 12: 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).

7 Further data processing and analysis

Normalizing features’ signal intensities is required, but at present not (yet) supported in xcms (some methods might be added in near future). Also, 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.

8 Additional details and notes

For a detailed description of the new data objects and changes/improvements compared to the original user interface see the new_functionality vignette.

processHistory(xdata) 

## [[1]]
## Object of class "XProcessHistory"
##  type: Peak detection 
##  date: Sat Mar  3 18:19:52 2018 
##  info:  
##  fileIndex: 1,2,3,4,5,6,7,8,9,10,11,12 
##  Parameter class: CentWaveParam 
##  MS level(s) 1 
## 
## [[2]]
## Object of class "XProcessHistory"
##  type: Peak grouping 
##  date: Sat Mar  3 18:20:51 2018 
##  info:  
##  fileIndex: 1,2,3,4,5,6,7,8,9,10,11,12 
##  Parameter class: PeakDensityParam 
## 
## [[3]]
## Object of class "XProcessHistory"
##  type: Retention time correction 
##  date: Sat Mar  3 18:20:53 2018 
##  info:  
##  fileIndex: 1,2,3,4,5,6,7,8,9,10,11,12 
##  Parameter class: PeakGroupsParam 
##  MS level(s) 1 
## 
## [[4]]
## Object of class "XProcessHistory"
##  type: Peak grouping 
##  date: Sat Mar  3 18:21:05 2018 
##  info:  
##  fileIndex: 1,2,3,4,5,6,7,8,9,10,11,12 
##  Parameter class: PeakDensityParam 
## 
## [[5]]
## Object of class "XProcessHistory"
##  type: Missing peak filling 
##  date: Sat Mar  3 18:21:07 2018 
##  info:  
##  fileIndex: 1,2,3,4,5,6,7,8,9,10,11,12 
##  Parameter class: FillChromPeaksParam

ph <- processHistory(xdata, type = "Retention time correction")

ph 

## [[1]]
## Object of class "XProcessHistory"
##  type: Retention time correction 
##  date: Sat Mar  3 18:20:53 2018 
##  info:  
##  fileIndex: 1,2,3,4,5,6,7,8,9,10,11,12 
##  Parameter class: PeakGroupsParam 
##  MS level(s) 1

## Access the parameter
processParam(ph[[1]])

## Object of class:  PeakGroupsParam 
## Parameters:
##  minFraction: 0.85 
##  extraPeaks: 1 
##  smooth: loess 
##  span: 0.2 
##  family: gaussian 
##  number of peak groups: 50

subs <- filterFile(xdata, file = c(2, 4))

## Do we have identified chromatographic peaks?
hasChromPeaks(subs) 

## [1] TRUE

## Do we still have features?
hasFeatures(subs)

## [1] FALSE

## Do we still have adjusted retention times?
hasAdjustedRtime(subs) 

## [1] FALSE

subs <- filterFile(xdata, keepAdjustedRtime = TRUE)

hasAdjustedRtime(subs) 

## [1] TRUE

subs <- filterRt(xdata, rt = c(3000, 3500))

range(rtime(subs)) 

## [1] 3000.011 3499.909

hasAdjustedRtime(subs) 

## [1] TRUE

hasChromPeaks(subs)

## [1] TRUE

range(chromPeaks(subs)[, "rt"]) 

## [1] 3000.011 3499.909

## Extract all data from the 3rd file.
one_file <- filterFile(xdata, file = 3)

one_file_2 <- xdata[fromFile(xdata) == 3]

## Is the content the same?
all.equal(spectra(one_file), spectra(one_file_2)) 

## [1] TRUE

## Subsetting with filterFile preserves chromatographic peaks
head(chromPeaks(one_file))

##         mz mzmin mzmax       rt    rtmin    rtmax     into     intb  maxo sn
## [1,] 402.1 402.1 402.1 2515.464 2501.379 2548.328 213455.2 185881.2  8038 11
## [2,] 339.0 339.0 339.0 2515.464 2501.379 2540.503 162264.4 158324.9  5638 14
## [3,] 354.0 354.0 354.0 2515.464 2501.379 2532.678 173130.4 165213.5  6334 11
## [4,] 352.9 352.9 352.9 2517.029 2501.379 2534.243 525993.0 522760.1 20488 55
## [5,] 316.0 316.0 316.0 2517.029 2501.379 2535.808 741708.6 726925.4 27816 47
## [6,] 333.0 333.0 333.0 2515.464 2501.379 2545.198 968861.6 951705.4 33992 49
##      sample is_filled
## [1,]      1         0
## [2,]      1         0
## [3,]      1         0
## [4,]      1         0
## [5,]      1         0
## [6,]      1         0

## Subsetting with [ not
head(chromPeaks(one_file_2)) 

## Warning in .local(object, ...): No chromatographic peaks available.

## NULL

subs <- xdata[20:30, keepAdjustedRtime = TRUE]

## Warning in xdata[20:30, keepAdjustedRtime = TRUE]: Removed preprocessing
## results

hasAdjustedRtime(subs)

## [1] TRUE

## Access adjusted retention times:
rtime(subs)

## F01.S0020 F01.S0021 F01.S0022 F01.S0023 F01.S0024 F01.S0025 F01.S0026 
##  2538.347  2539.912  2541.477  2543.042  2544.607  2546.172  2547.737 
## F01.S0027 F01.S0028 F01.S0029 F01.S0030 
##  2549.302  2550.867  2552.432  2553.997

## Access raw retention times:
rtime(subs, adjusted = FALSE) 

## F01.S0020 F01.S0021 F01.S0022 F01.S0023 F01.S0024 F01.S0025 F01.S0026 
##  2531.112  2532.677  2534.242  2535.807  2537.372  2538.937  2540.502 
## F01.S0027 F01.S0028 F01.S0029 F01.S0030 
##  2542.067  2543.632  2545.197  2546.762

## Extract a single spectrum
xdata[[14]] 

## Object of class "Spectrum1"
##  Retention time: 42:9 
##  MSn level: 1 
##  Total ion count: 445 
##  Polarity: NA

x_list <- split(xdata, f = fromFile(xdata), keepAdjustedRtime = TRUE)

lengths(x_list)

##    1    2    3    4    5    6    7    8    9   10   11   12 
## 1278 1278 1278 1278 1278 1278 1278 1278 1278 1278 1278 1278

lapply(x_list, hasAdjustedRtime) 

## $`1`
## [1] TRUE
## 
## $`2`
## [1] TRUE
## 
## $`3`
## [1] TRUE
## 
## $`4`
## [1] TRUE
## 
## $`5`
## [1] TRUE
## 
## $`6`
## [1] TRUE
## 
## $`7`
## [1] TRUE
## 
## $`8`
## [1] TRUE
## 
## $`9`
## [1] TRUE
## 
## $`10`
## [1] TRUE
## 
## $`11`
## [1] TRUE
## 
## $`12`
## [1] TRUE

xdata_by_file <- splitByFile(xdata, f = factor(1:length(fileNames(xdata))))

lapply(xdata_by_file, hasChromPeaks) 

## $`1`
## [1] TRUE
## 
## $`2`
## [1] TRUE
## 
## $`3`
## [1] TRUE
## 
## $`4`
## [1] TRUE
## 
## $`5`
## [1] TRUE
## 
## $`6`
## [1] TRUE
## 
## $`7`
## [1] TRUE
## 
## $`8`
## [1] TRUE
## 
## $`9`
## [1] TRUE
## 
## $`10`
## [1] TRUE
## 
## $`11`
## [1] TRUE
## 
## $`12`
## [1] TRUE

register(bpstart(MulticoreParam(2))) 

register(bpstart(SnowParam(2)))