Grouping Mass Spectrometry Features
26 April 2022
Package
MsFeatures 1.4.0
Package: MsFeatures
Authors: Johannes Rainer [aut, cre] (https://orcid.org/0000-0002-6977-7147)
Last modified: 2022-04-26 14:32:39
Compiled: Tue Apr 26 17:14:22 2022
1 Introduction
Electrospray ionization (ESI) is commonly used in mass spectrometry (MS)-based metabolomics to generate ions from the compounds to enable their detection by the MS instrument. Ionization can generate different ions (adducts) of the same original compound which are then reported as separate MS features with different mass-to-charge ratios (m/z). To reduce data set complexity (and to aid subsequent annotation steps) it is advisable to group features which supposedly represent signal from the same original compound into a single entity.
The MsFeatures package provides key concepts and functions for this feature
grouping. Methods are implemented for base R objects as well as for
Bioconductor’s SummarizedExperiment class. See also the description of the
general grouping
concept
on the package webpage for more information. Additional grouping methodology is
expected to be implemented in other R packages for data objects with additional
LC-MS related information, such as the XCMSnExp object in the xcms
package. The implementation for the SummarizedExperiment provided in this
package can be used as a reference for these additional methodology.
After definition of the feature groups, the QFeatures package could be used to aggregate their abundances into a single signal.
2 Installation
The package can be installed with the BiocManager package. To
install BiocManager use install.packages("BiocManager") and, after that,
BiocManager::install("MsFeatures") to install this package.
3 Mass Spectrometry Feature Grouping
Features from the same originating compound inherit its characteristics including its retention time (for LC or GC-MS experiments) and abundance/intensity. For the latter it is expected that features from the same compound have the same pattern of feature values/abundances across samples.
The MsFeatures package defines the groupFeatures method to perform MS
feature grouping based on the provided input data and a parameter object which
selects and defines the feature grouping algorithm. This algorithm is supposed
to assign individual features to a (single) feature group. Currently two feature
grouping approaches are implemented:
SimilarRtimeParam: group features based on similar retention times.AbundanceSimilarityParam: group features based on similar feature values/abundances across samples.
Additional algorithms, e.g. by considering also differences in features’ m/z values matching expected ions/adducts or isotopes, may be implemented in future in this or other packages.
In this document we demonstrate the feature grouping functionality on a simple
toy data set used also in the xcms package with the
raw data being provided in the faahKO data package. This data set consists of
samples from 4 mice with knock-out of the fatty acid amide hydrolase (FAAH) and
4 wild type mice. Pre-processing of this data set is described in detail in the
xcms vignette of the xcms package. Below we load all required packages and
the result from this pre-processing which is provided as a
SummarizedExperiment within this package and can be loaded with data(se).
library(MsFeatures)
library(SummarizedExperiment)
data("se")Before performing the feature grouping we inspect the result object. Feature
properties and definitions can be accessed with rowData, the feature
abundances with assay.
rowData(se)## DataFrame with 225 rows and 11 columns
## mzmed mzmin mzmax rtmed rtmin rtmax npeaks
## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## FT001 200.1 200.1 200.1 2901.63 2880.73 2922.53 2
## FT002 205.0 205.0 205.0 2789.39 2782.30 2795.36 8
## FT003 206.0 206.0 206.0 2788.73 2780.73 2792.86 7
## FT004 207.1 207.1 207.1 2718.12 2713.21 2726.70 7
## FT005 219.1 219.1 219.1 2518.82 2517.40 2520.81 3
## ... ... ... ... ... ... ... ...
## FT221 591.30 591.3 591.3 3005.03 2992.87 3006.05 5
## FT222 592.15 592.1 592.3 3022.11 2981.91 3107.59 6
## FT223 594.20 594.2 594.2 3418.16 3359.10 3427.90 3
## FT224 595.25 595.2 595.3 3010.15 2992.87 3013.77 6
## FT225 596.20 596.2 596.2 2997.91 2992.87 3002.95 2
## KO WT peakidx ms_level
## <numeric> <numeric> <list> <integer>
## FT001 2 0 287, 679,1577,... 1
## FT002 4 4 47,272,542,... 1
## FT003 3 4 32,259,663,... 1
## FT004 4 3 19,249,525,... 1
## FT005 1 2 639, 788,1376,... 1
## ... ... ... ... ...
## FT221 2 3 349,684,880,... 1
## FT222 1 3 86,861,862,... 1
## FT223 1 2 604, 985,1543,... 1
## FT224 2 3 67,353,876,... 1
## FT225 0 2 866,1447,1643,... 1head(assay(se))## ko15.CDF ko16.CDF ko21.CDF ko22.CDF wt15.CDF wt16.CDF wt21.CDF
## FT001 159738.1 506848.88 113441.08 169955.6 216096.6 145509.7 230477.9
## FT002 1924712.0 1757150.96 1383416.72 1180288.2 2129885.1 1634342.0 1623589.2
## FT003 213659.3 289500.67 162897.19 178285.7 253825.6 241844.4 240606.0
## FT004 349011.5 451863.66 343897.76 208002.8 364609.8 360908.9 223322.5
## FT005 135978.5 25524.79 71530.84 107348.5 223951.8 134398.9 190203.8
## FT006 286221.4 289908.23 164008.97 149097.6 255697.7 311296.8 366441.5
## wt22.CDF
## FT001 140551.30
## FT002 1354004.93
## FT003 185399.47
## FT004 221937.53
## FT005 84772.92
## FT006 271128.02Columns "mzmed" and "rtmed" in the object’s rowData provide the m/z and
retention time which characterizes each feature. In total 225
features are available in the present data set, with many of them most likely
representing signal from different ions of the same compound. We aim to identify
these based on the following assumptions of the LC-MS data:
- Features (ions) of the same compound should have similar retention time.
- The abundance of features (ions) of the same compound should have a similar pattern across samples, i.e. if a compound is highly concentrated in one sample and low in another, all ions from it also should follow the same pattern.
As detailed in the general grouping
concept,
the feature grouping implemented in MsFeatures is by default intended to be
used as a stepwise approach in which each groupFeatures call further
sub-groups (and thus refines) previously defined feature groups. This enables to
either use a single algorithm for the feature grouping or to build a feature
grouping pipeline by combining different algorithms. In our example we perform
first a initial grouping of features based on similar retention time and
subsequently further refine these feature groups by requiring also similarity of
feature values across samples.
Note that it would also be possible to perform the grouping only on a subset of features instead of the full data set. An example is provided in the last section of this vignette.
3.1 Grouping of features by similar retention time
The most intuitive and simple way to group LC-MS features is based on their retention times: ionization of the compounds happens after the LC and thus all ions from the same compound should have the same retention time. The plot below shows the retention times (and m/z) of all features from the present data set.
plot(rowData(se)$rtmed, rowData(se)$mzmed,
xlab = "retention time", ylab = "m/z", main = "features",
col = "#00000060")
grid()
Figure 1: Plot of retention times and m/z for all features in the data set
As we can see there are several features with a similar retention time,
especially for lower retention times. By using groupFeatures with the
SimilarRtimeParam we can next group features if their difference in retention
time is below a certain threshold. This approach will however not only group
features representing ions from the same compound together, but also features
from different, but co-eluting compounds (i.e. different compounds with the same
retention time). Thus feature groups defined by this algorithm should be further
refined based on another feature property to reduce false positives.
For the present example, we group features with a maximal difference in
retention time of 10 seconds into a feature group. We also have to specify the
column in the object’s rowData which contains the retention times for the
features.
se <- groupFeatures(se, param = SimilarRtimeParam(10), rtime = "rtmed")The groupFeatures call on the SummarizedExperiment added the results of the
grouping into a new column called "feature_group" in the object’s
rowData. This column can also be directly accessed with the featureGroups
function. Below we print the number of features for each feature grouped defined
by the SimilarRtimeParam approach.
table(featureGroups(se))##
## FG.001 FG.002 FG.003 FG.004 FG.005 FG.006 FG.007 FG.008 FG.009 FG.010 FG.011
## 3 3 3 3 2 4 5 6 4 2 5
## FG.012 FG.013 FG.014 FG.015 FG.016 FG.017 FG.018 FG.019 FG.020 FG.021 FG.022
## 3 4 3 5 3 3 5 3 3 3 3
## FG.023 FG.024 FG.025 FG.026 FG.027 FG.028 FG.029 FG.030 FG.031 FG.032 FG.033
## 3 3 6 3 3 3 3 2 3 3 4
## FG.034 FG.035 FG.036 FG.037 FG.038 FG.039 FG.040 FG.041 FG.042 FG.043 FG.044
## 3 2 2 3 2 2 4 2 2 2 3
## FG.045 FG.046 FG.047 FG.048 FG.049 FG.050 FG.051 FG.052 FG.053 FG.054 FG.055
## 4 2 3 3 3 2 2 3 4 2 3
## FG.056 FG.057 FG.058 FG.059 FG.060 FG.061 FG.062 FG.063 FG.064 FG.065 FG.066
## 2 2 2 3 2 3 2 2 2 3 2
## FG.067 FG.068 FG.069 FG.070 FG.071 FG.072 FG.073 FG.074 FG.075 FG.076 FG.077
## 2 3 2 2 2 3 2 2 1 1 1
## FG.078 FG.079 FG.080 FG.081 FG.082 FG.083 FG.084
## 1 1 1 1 1 1 1We also calculate the mean retention time for all the feature groups and order them increasingly.
split(rowData(se)$rtmed, featureGroups(se)) |>
vapply(FUN = mean, numeric(1)) |>
sort()## FG.005 FG.012 FG.062 FG.076 FG.069 FG.007 FG.018 FG.027
## 2506.301 2511.821 2519.745 2595.503 2623.786 2684.671 2686.835 2688.495
## FG.064 FG.057 FG.066 FG.074 FG.003 FG.023 FG.004 FG.001
## 2694.089 2718.799 2731.648 2751.041 2787.291 2788.022 2788.625 2788.949
## FG.011 FG.048 FG.014 FG.075 FG.084 FG.080 FG.060 FG.021
## 2790.232 2793.065 2799.149 2832.560 2860.496 2871.735 2881.899 2899.457
## FG.063 FG.033 FG.071 FG.079 FG.050 FG.051 FG.039 FG.010
## 2915.107 2924.922 2936.602 2953.627 2998.303 3005.543 3009.864 3011.159
## FG.040 FG.019 FG.006 FG.067 FG.036 FG.078 FG.037 FG.013
## 3015.635 3022.568 3027.254 3038.254 3044.682 3057.242 3075.361 3079.912
## FG.017 FG.083 FG.044 FG.073 FG.070 FG.008 FG.068 FG.082
## 3088.353 3114.721 3128.047 3142.710 3161.133 3170.219 3184.355 3203.298
## FG.029 FG.009 FG.055 FG.002 FG.042 FG.016 FG.035 FG.054
## 3217.009 3226.662 3242.698 3258.465 3261.208 3263.141 3265.660 3284.004
## FG.043 FG.038 FG.052 FG.077 FG.056 FG.028 FG.061 FG.022
## 3289.081 3295.654 3300.775 3311.799 3321.354 3324.717 3335.200 3353.939
## FG.031 FG.081 FG.041 FG.026 FG.015 FG.030 FG.047 FG.034
## 3358.597 3372.908 3383.014 3395.552 3405.585 3407.192 3410.212 3417.383
## FG.024 FG.049 FG.059 FG.058 FG.045 FG.046 FG.065 FG.053
## 3422.675 3428.323 3435.389 3442.423 3447.411 3457.582 3465.580 3472.813
## FG.032 FG.020 FG.025 FG.072
## 3478.609 3483.910 3497.730 3510.664Note that the differences in retention times between the feature groups can be
smaller than the used cut-off (10 seconds in our case). If we were not happy
with this feature grouping and would like to repeat it we would need to drop the
"feature_group" column in the object’s rowData with
rowData(se)$feature_group <- NULL and repeat the feature grouping with
different settings. This is required, because by default groupFeatures will
refine previous feature grouping results but not overwrite them.
As stated above, this initial grouping on retention times put features from the same, but also from different co-eluting compounds into the same feature group. We thus next refine the feature groups requiring also feature abundances across samples to be correlated.
3.2 Grouping of features by abundance correlation across samples
Features representing ions of the same compound are expected to have correlated
feature values (intensities, abundances) across samples. groupFeatures with
AbundanceSimilarityParam allows to group features with similar abundance
patterns. This approach performs a pairwise similarity calculation and puts
features with a similarity >= threshold into the same feature group. By
calling this function on the previous result object the initial feature groups
will be refined, by eventually splitting them based on the (missing)
correlation of feature abundances.
We below evaluate the correlation between individual features indicating also the previously defined feature groups.
library(pheatmap)
fvals <- log2(assay(se))
cormat <- cor(t(fvals), use = "pairwise.complete.obs")
ann <- data.frame(fgroup = featureGroups(se))
rownames(ann) <- rownames(cormat)
res <- pheatmap(cormat, annotation_row = ann, cluster_rows = TRUE,
cluster_cols = TRUE)
Figure 2: Correlation of features based on their abundances
As expected, the clustering based on the feature abundances does not perfectly match the retention time-based feature grouping. Many features grouped based on retention time have a low, or even negative correlation of feature abundances across samples hence most likely representing features from different, but co-eluting compounds. On the other hand, many features are highly correlated, but have a different retention time and can thus also not represent signal from ions of the same compound. Thus, each single approach has its drawbacks, but combination them can reduce the number of wrongly grouped features.
We thus next perform the feature grouping with AbundanceSimilarityParam on the
result object to refine the retention time-based feature groups. The approach
can be further customized by providing a function to calculate feature
similarities with parameter simFun (by default cor will be used to calculate
similarities using Pearson’s correlation). Parameter transform allows to
specify a function to transform feature abundances prior similarity
calculation. By default the feature values are taken as-is, but below we use
transform = log2 to perform the calculations in log2 scale. With threshold = 0.7 we ensure that only features with a correlation coefficient >= 0.7 are
assigned to the same feature group. Finally, parameter i would allow to
specify the assay in the SummarizedExperiment that contains the feature
abundances on which similarities should be calculated. See the
AbundanceSimilarityParam help page for a full listing of the parameters and
more details.
se <- groupFeatures(se, AbundanceSimilarityParam(threshold = 0.7,
transform = log2), i = 1)
table(featureGroups(se))##
## FG.001.001 FG.001.002 FG.002.001 FG.002.002 FG.002.003 FG.003.001 FG.003.002
## 2 1 1 1 1 2 1
## FG.004.001 FG.005.001 FG.006.001 FG.006.002 FG.007.001 FG.007.002 FG.007.003
## 3 2 3 1 2 1 1
## FG.007.004 FG.008.001 FG.008.002 FG.008.003 FG.008.004 FG.009.001 FG.009.002
## 1 3 1 1 1 3 1
## FG.010.001 FG.011.001 FG.011.002 FG.011.003 FG.012.001 FG.013.001 FG.013.002
## 2 2 2 1 3 2 1
## FG.013.003 FG.014.001 FG.014.002 FG.015.001 FG.015.002 FG.015.003 FG.015.004
## 1 2 1 2 1 1 1
## FG.016.001 FG.016.002 FG.017.001 FG.017.002 FG.018.001 FG.018.002 FG.018.003
## 2 1 2 1 2 1 1
## FG.018.004 FG.019.001 FG.019.002 FG.019.003 FG.020.001 FG.020.002 FG.021.001
## 1 1 1 1 2 1 1
## FG.021.002 FG.021.003 FG.022.001 FG.022.002 FG.022.003 FG.023.001 FG.023.002
## 1 1 1 1 1 2 1
## FG.024.001 FG.024.002 FG.024.003 FG.025.001 FG.025.002 FG.025.003 FG.025.004
## 1 1 1 2 1 1 1
## FG.025.005 FG.026.001 FG.026.002 FG.026.003 FG.027.001 FG.027.002 FG.027.003
## 1 1 1 1 1 1 1
## FG.028.001 FG.028.002 FG.029.001 FG.029.002 FG.030.001 FG.030.002 FG.031.001
## 2 1 2 1 1 1 3
## FG.032.001 FG.032.002 FG.032.003 FG.033.001 FG.033.002 FG.033.003 FG.033.004
## 1 1 1 1 1 1 1
## FG.034.001 FG.034.002 FG.034.003 FG.035.001 FG.035.002 FG.036.001 FG.036.002
## 1 1 1 1 1 1 1
## FG.037.001 FG.037.002 FG.038.001 FG.039.001 FG.040.001 FG.040.002 FG.041.001
## 2 1 2 2 2 2 1
## FG.041.002 FG.042.001 FG.042.002 FG.043.001 FG.044.001 FG.044.002 FG.045.001
## 1 1 1 2 2 1 1
## FG.045.002 FG.045.003 FG.045.004 FG.046.001 FG.047.001 FG.047.002 FG.047.003
## 1 1 1 2 1 1 1
## FG.048.001 FG.048.002 FG.048.003 FG.049.001 FG.049.002 FG.050.001 FG.050.002
## 1 1 1 2 1 1 1
## FG.051.001 FG.052.001 FG.052.002 FG.053.001 FG.053.002 FG.053.003 FG.054.001
## 2 2 1 2 1 1 1
## FG.054.002 FG.055.001 FG.055.002 FG.055.003 FG.056.001 FG.056.002 FG.057.001
## 1 1 1 1 1 1 2
## FG.058.001 FG.058.002 FG.059.001 FG.059.002 FG.059.003 FG.060.001 FG.060.002
## 1 1 1 1 1 1 1
## FG.061.001 FG.061.002 FG.062.001 FG.062.002 FG.063.001 FG.064.001 FG.064.002
## 2 1 1 1 2 1 1
## FG.065.001 FG.065.002 FG.066.001 FG.066.002 FG.067.001 FG.067.002 FG.068.001
## 2 1 1 1 1 1 3
## FG.069.001 FG.070.001 FG.070.002 FG.071.001 FG.071.002 FG.072.001 FG.072.002
## 2 1 1 1 1 2 1
## FG.073.001 FG.073.002 FG.074.001 FG.075.001 FG.076.001 FG.077.001 FG.078.001
## 1 1 2 1 1 1 1
## FG.079.001 FG.080.001 FG.081.001 FG.082.001 FG.083.001 FG.084.001
## 1 1 1 1 1 1Many of the larger retention time-based feature groups have been splitted into
two or more sub-groups based on the correlation of their feature abundances. We
evaluate this for one specific feature group "FG.003" by plotting their
pairwise correlation.
fts <- grep("FG.003", featureGroups(se))
pairs(t(fvals[fts, ]), gap = 0.1, main = "FG.003")
Figure 3: Pairwise correlation plot for features initially grouped into the feature group FG.003
A high correlation can be observed between FT035 and FT051 while they are not correlated with feature FT013. We next evaluate the feature grouping for another example.
fts <- grep("FG.008", featureGroups(se))
pairs(t(fvals[fts, ]), gap = 0.1, main = "FG.008")
Figure 4: Pairwise correlation plot for features initially grouped into the feature group FG.008
The results are less clear than for the previous example, still, some features seem to be correlated with each other while others are not. Generally, the abundance correlation approach in this data set suffers from the low number of sample (8 in total). Also, the approach works better for features with a high variance (biologically or technically) across samples.
The table below lists the retention time, m/z and group assignment for these features.
tmp <- as.data.frame(rowData(se)[fts, c("rtmed", "mzmed", "feature_group")])
tmp <- tmp[order(tmp$feature_group), ]
knitr::kable(tmp)| rtmed | mzmed | feature_group | |
|---|---|---|---|
| FT097 | 3173.240 | 385.1 | FG.008.001 |
| FT163 | 3170.171 | 502.1 | FG.008.001 |
| FT165 | 3170.171 | 503.1 | FG.008.001 |
| FT074 | 3167.106 | 361.1 | FG.008.002 |
| FT077 | 3170.675 | 362.1 | FG.008.003 |
| FT100 | 3169.951 | 386.1 | FG.008.004 |
The difference in m/z between features FT163 and FT165, both being assigned to the same feature group, is ~ 1 suggesting that one of the two is in fact a (C13) isotope of the other feature.
3.3 Performing feature grouping on a subset of features
Sometimes it might not be needed or required to perform the feature grouping on the full data set but only on a subset of interesting features (i.e. those with significant differences in feature abundances between sample groups). This has also the advantage of a larger range of feature values across samples which supports the abundance similarity-based feature grouping.
Feature grouping on a subset of features can be performed by manually assigning
all features of interest to an initial feature group and setting the feature
group for all other features to NA. As an example we perform below the feature
grouping only features 30-60.
featureGroups(se) <- NA_character_
featureGroups(se)[30:60] <- "FG"
se <- groupFeatures(se, SimilarRtimeParam(10), rtime = "rtmed")This did not refine this initial, manually specified feature group by the
retention time-based grouping. Features with NA value in their feature group
column are skipped. As a result we get the following grouping:
featureGroups(se)## [1] NA NA NA NA NA NA NA NA
## [9] NA NA NA NA NA NA NA NA
## [17] NA NA NA NA NA NA NA NA
## [25] NA NA NA NA NA "FG.009" "FG.005" "FG.010"
## [33] "FG.002" "FG.011" "FG.001" "FG.007" "FG.004" "FG.004" "FG.005" "FG.002"
## [41] "FG.005" "FG.012" "FG.008" "FG.002" "FG.007" "FG.013" "FG.014" "FG.008"
## [49] "FG.015" "FG.004" "FG.001" "FG.002" "FG.003" "FG.003" "FG.016" "FG.002"
## [57] "FG.006" "FG.017" "FG.006" "FG.018" NA NA NA NA
## [65] NA NA NA NA NA NA NA NA
## [73] NA NA NA NA NA NA NA NA
## [81] NA NA NA NA NA NA NA NA
## [89] NA NA NA NA NA NA NA NA
## [97] NA NA NA NA NA NA NA NA
## [105] NA NA NA NA NA NA NA NA
## [113] NA NA NA NA NA NA NA NA
## [121] NA NA NA NA NA NA NA NA
## [129] NA NA NA NA NA NA NA NA
## [137] NA NA NA NA NA NA NA NA
## [145] NA NA NA NA NA NA NA NA
## [153] NA NA NA NA NA NA NA NA
## [161] NA NA NA NA NA NA NA NA
## [169] NA NA NA NA NA NA NA NA
## [177] NA NA NA NA NA NA NA NA
## [185] NA NA NA NA NA NA NA NA
## [193] NA NA NA NA NA NA NA NA
## [201] NA NA NA NA NA NA NA NA
## [209] NA NA NA NA NA NA NA NA
## [217] NA NA NA NA NA NA NA NA
## [225] NASession information
## R version 4.2.0 RC (2022-04-19 r82224)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
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## attached base packages:
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## other attached packages:
## [1] pheatmap_1.0.12 SummarizedExperiment_1.26.0
## [3] Biobase_2.56.0 GenomicRanges_1.48.0
## [5] GenomeInfoDb_1.32.0 IRanges_2.30.0
## [7] S4Vectors_0.34.0 BiocGenerics_0.42.0
## [9] MatrixGenerics_1.8.0 matrixStats_0.62.0
## [11] MsFeatures_1.4.0 BiocStyle_2.24.0
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## loaded via a namespace (and not attached):
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## [40] scales_1.2.0 htmltools_0.5.2 MASS_7.3-57
## [43] colorspace_2.0-3 stringi_1.7.6 munsell_0.5.0
## [46] RCurl_1.98-1.6