Introduction

The qmtools package provides basic tools for imputation, normalization, and dimension-reduction of metabolomics data with the standard SummarizedExperiment class. It also offers several helper functions to assist visualization of data. This vignette gives brief descriptions of these tools with toy examples.

Installation

The package can be installed using BiocManager. In R session, please type BiocManager::install("qmtools").

Data preparation

To demonstrate the use of the qmtools functions, we will use the FAAH knockout LC/MS data, containing quantified LC/MS peaks from the spinal cords of 6 wild-type and 6 FAAH (fatty acid amide hydrolase) knockout mice.

library(qmtools)
#> Warning: multiple methods tables found for 'union'
#> Warning: multiple methods tables found for 'intersect'
#> Warning: multiple methods tables found for 'setdiff'
library(SummarizedExperiment)
library(vsn)
library(pls)
library(ggplot2)
library(patchwork)
set.seed(1e8)

data(faahko_se)

## Only keep the first assay for the vignette
assays(faahko_se)[2:4] <- NULL
faahko_se
#> class: SummarizedExperiment 
#> dim: 206 12 
#> metadata(0):
#> assays(1): raw
#> rownames(206): FT001 FT002 ... FT205 FT206
#> rowData names(10): mzmed mzmin ... WT peakidx
#> colnames(12): ko15.CDF ko16.CDF ... wt21.CDF wt22.CDF
#> colData names(2): sample_name sample_group

Feature filtering

Metabolomics data often contains a large number of uninformative features that can hinder downstream analysis. The removeFeatures function attempts to identify such features and remove them from the data based on missing values, quality control (QC) replicates, and blank samples with the following methods:

• Proportions of missing values: retain features if there is at least one group with a proportion of non-missing values above a cut-off.

• Relative standard deviation: remove features if QC replicates show low reproducibility.

• Intraclass correlation coefficient (ICC): retain features if a feature has relatively high variability across biological samples compared to QC replicates.

• QC/blank ratio: remove features with low abundance that may have non-biological origin.

The FAAH knockout data does not include QC and blank samples. Here, we just illustrate missing value-based filtering.

dim(faahko_se) # 206 features
#> [1] 206  12
table(colData(faahko_se)$sample_group)
#> 
#> KO WT 
#>  6  6

## Missing value filter based on 2 groups.
dim(removeFeatures(faahko_se, i = "raw",
                   group = colData(faahko_se)$sample_group,
                   cut = 0.80)) # nothing removed
#> [1] 206  12

dim(removeFeatures(faahko_se, i = "raw",
                   group = colData(faahko_se)$sample_group,
                   cut = 0.85)) # removed 65 features
#> [1] 141  12

## based on "WT" only
dim(removeFeatures(faahko_se, i = "raw",
                   group = colData(faahko_se)$sample_group,
                   levels = "WT", cut = 0.85))
#> [1] 104  12

In this vignette, we kept all features based on the cut-off: at least one group contains >= 80% of non-missing values.

Imputation

Missing values are common in metabolomics data. For example, ions may have a low abundance that does not reach the limit of detection of the instrument. Unexpected stochastic fluctuations and technical error may also cause missing values even though ions present at detectable levels. We could use the plotMiss function to explore the mechanisms generating the missing values. The bar plot on the left panel shows the amount of missing values in each samples and the right panel helps to identify the structure of missing values with a hierarchically-clustered heatmap.

## Sample group information
g <- factor(colData(faahko_se)$sample_group, levels = c("WT", "KO"))

## Visualization of missing values
plotMiss(faahko_se, i = "raw", group = g)

Overall, the knockout mice have a higher percentage of missing values. The features on top of the heatmap in general only present at the knockout mice, suggesting that some of missing values are at least not random (perhaps due to altered metabolisms by the experimental condition). In almost all cases, visualization and inspection of missing values are a time-intensive step, but greatly improve the ability to uncover the nature of missing values in data and help to choose an appropriate imputation method.

The imputation of missing values can be done with the imputeIntensity function. Several imputation methods are available such as k-Nearest Neighbor (kNN), Random Forest (RF), Bayesian PCA, and other methods available in MsCoreUtils. By default, the kNN is used to impute missing values using the Gower distance. The kNN is a distance-based algorithm that typically requires to scale the data to avoid variance-based weighing. Since the Gower distance used, the imputation can be performed with the original scales, which may be helpful to non-technical users.

se <- imputeIntensity(faahko_se, i = "raw", name = "knn", method = "knn")
se # The result was stored in assays slot: "knn"
#> class: SummarizedExperiment 
#> dim: 206 12 
#> metadata(0):
#> assays(2): raw knn
#> rownames(206): FT001 FT002 ... FT205 FT206
#> rowData names(10): mzmed mzmin ... WT peakidx
#> colnames(12): ko15.CDF ko16.CDF ... wt21.CDF wt22.CDF
#> colData names(2): sample_name sample_group

## Standardization of input does not influence the result
m <- assay(faahko_se, "raw")
knn_scaled <- as.data.frame(
    imputeIntensity(scale(m), method = "knn") # Can accept matrix as an input
)

knn_unscaled <- as.data.frame(assay(se, "knn"))

idx <- which(is.na(m[, 1]) | is.na(m[, 2])) # indices for missing values
p1 <- ggplot(knn_unscaled[idx, ], aes(x = ko15.CDF, y = ko16.CDF)) +
    geom_point() + theme_bw()
p2 <- ggplot(knn_scaled[idx, ], aes(x = ko15.CDF, y = ko16.CDF)) +
    geom_point() + theme_bw()
p1 + p2 + plot_annotation(title = "Imputed values: unscaled vs scaled")

Normalization

In metabolomics, normalization is an important part of data processing to reduce unwanted non-biological variations (e.g., variation due to sample preparation and handling). The normalizeIntensity function provides several data-driven normalization methods such as Probabilistic Quotient Normalization (PQN), Variance-Stabilizing Normalization (VSN), Cyclic LOESS normalization, and other methods available in MsCoreUtils. Here, we will apply the VSN to the imputed intensities. Note that the VSN produces glog-transformed (generalized log transform) feature intensities. The consequence of normalization can be visualized with the plotBox function.

se <- normalizeIntensity(se, i = "knn", name = "knn_vsn", method = "vsn")
se # The result was stored in assays slot: "knn_vsn"
#> class: SummarizedExperiment 
#> dim: 206 12 
#> metadata(0):
#> assays(3): raw knn knn_vsn
#> rownames(206): FT001 FT002 ... FT205 FT206
#> rowData names(10): mzmed mzmin ... WT peakidx
#> colnames(12): ko15.CDF ko16.CDF ... wt21.CDF wt22.CDF
#> colData names(2): sample_name sample_group

p1 <- plotBox(se, i = "knn", group = g, log2 = TRUE) # before normalization
p2 <- plotBox(se, i = "knn_vsn", group = g) # after normalization
p1 + p2 + plot_annotation(title = "Before vs After normalization")

Dimension-reduction

The metabolomics data generally consist of a large number of features, and dimension-reduction techniques are often used for modeling and visualization to uncover latent structure underlying many features. The reduceFeatures can be used to perform dimension-reduction of the data. Currently, Principal Component Analysis (PCA), Partial Least Square-Discriminant Analysis (PLS-DA) and t-distributed stochastic neighbor (t-SNE) are supported. The function returns a matrix containing dimension-reduced data with several attributes that can be summarized with the summary function.

## PCA
m_pca <- reduceFeatures(se, i = "knn_vsn", method = "pca", ncomp = 2)
summary(m_pca)
#> Reduction method: PCA (SVD) 
#> Input centered before PCA: TRUE 
#> Input scaled before PCA: FALSE 
#> Number of PCs calculated: 2 
#> Importance of PC(s):
#>                           PC1    PC2
#> Proportion of Variance 0.2265 0.1599
#> Cumulative Proportion  0.2265 0.3865

## PLS-DA (requires information about each sample's group)
m_plsda <- reduceFeatures(se, i = "knn_vsn", method = "plsda", ncomp = 2, y = g)
summary(m_plsda)
#> Reduction method: PLS-DA (kernelpls) 
#> Y responses: WT KO 
#> Input centered before PLS-DA: TRUE 
#> Input scaled before PLS-DA: FALSE 
#> Number of components considered: 2 
#> Amount of X variance explained by each component: 
#>              Comp 1 Comp 2
#> Explained %   21.64  14.02
#> Cumulative %  21.64  35.65

The dimension-reduction results can be plotted with the plotReduced function. Each point (label) represents a sample. Data ellipses can be visualized.

p_pca <- plotReduced(m_pca, group = g)
p_plsda <- plotReduced(m_plsda, label = TRUE, ellipse = TRUE)
p_pca + p_plsda + plot_annotation(title = "PCA and PLS-DA")

Feature clustering

For soft ionization methods such as LC/ESI-MS, a bulk of ions can be generated from an individual compound upon ionization. Because we typically interested in compounds rather than different ion species, identifying features from the same compound is necessary. The clusterFeatures function attempts to cluster metabolic features with the following steps:

1. Clusters features according to their retention times

2. Based on the initial grouping, clusters features according to the intensity correlations

After the clustering procedures, the function adds the rtime_group and feature_group columns to the rowData of SummarizedExperiment input.

se <- clusterFeatures(se, i = "knn_vsn", rtime_var = "rtmed",
                      rt_cut = 10, cor_cut = 0.7)
rowData(se)[, c("rtmed", "rtime_group", "feature_group")]
#> DataFrame with 206 rows and 3 columns
#>           rtmed rtime_group feature_group
#>       <numeric>    <factor>   <character>
#> FT001   2789.04       FG.01      FG.01.01
#> FT002   2788.31       FG.01      FG.01.02
#> FT003   2718.79       FG.02      FG.02.01
#> FT004   3024.33       FG.03      FG.03.01
#> FT005   3684.80       FG.04      FG.04.01
#> ...         ...         ...           ...
#> FT202   3001.79       FG.14      FG.14.03
#> FT203   3714.93       FG.24      FG.24.07
#> FT204   3403.03       FG.50      FG.50.04
#> FT205   3614.09       FG.49      FG.49.05
#> FT206   3817.51       FG.41      FG.41.02

By default, the retention time-based grouping is performed with a hierarchical clustering based on the Manhattan distance (i.e., differences in retention times). The equivalent steps are

rts <- rowData(se)$rtmed
rt_cut <- 10
fit <- hclust(dist(rts, "manhattan"))
plot(as.dendrogram(fit), leaflab = "none")
rect.hclust(fit, h = rt_cut)

The retention-time based grouping can also be conducted with the algorithms (groupClosest and groupConsecutive) available in the MsFeatures package.

Upon the initial grouping, each retention-based time group is further clustered according to the intensity correlations since features may be originated from different co-eluting compounds, not from a single entity. By default, the function creates a graph where correlations serve as edge weights while low correlations defined by a user-specified cut-off ignored. cor_grouping = "connected" simply assigns connected features into the same feature group whereas cor_grouping = louvain further applies the Louvain algorithm to the graph to identify densely connected features. The groupSimiarityMatrix approach from the MsFeatures package is also supported.

The feature clustering results can be visualized with the plotRTgroup function. A group of features in the same feature group will be displayed with the same color. Each vertex represents a feature and each weight represent a correlation between features.

se_connected <- clusterFeatures(se, i = "knn_vsn", rtime_var = "rtmed",
                                rt_cut = 10, cor_cut = 0.7,
                                cor_grouping = "connected")
plotRTgroup(se_connected, i = "knn_vsn", group = "FG.22")

se_louvain <- clusterFeatures(se, i = "knn_vsn", rtime_var = "rtmed",
                              rt_cut = 10, cor_cut = 0.7,
                              cor_grouping = "louvain")
plotRTgroup(se_louvain, i = "knn_vsn", group = "FG.22")

More details could be plotted by specifying type = "pairs".

plotRTgroup(se_louvain, i = "knn_vsn", group = "FG.22", type = "pairs")

The clustering results can be used to deal with the redundancy of the data with other packages such as QFeatures (aggregation of intensities) and InterpretMSSpectrum (adduct annotation).

Sample comparison

To test which metabolic features are different between two sets of samples, the compareSamples function provides a convenient way to compute empirical Bayes statistics using the limma package interface. Note that this function expects log-transformed feature intensities.

## Compute statisticis for the contrast: KO - WT
fit <- compareSamples(se, i = "knn_vsn", group = "sample_group",
                      class1 = "WT", class2 = "KO")

## List top 5 features
head(fit, 5)
#>          logFC     CI.L     CI.R  AveExpr         t      P.Value    adj.P.Val
#> FT042 3.153118 2.770529 3.535707 20.43317 17.814931 1.771892e-10 3.650097e-08
#> FT039 3.980802 3.403927 4.557677 19.87720 14.916453 1.592198e-09 1.639964e-07
#> FT063 2.217181 1.736336 2.698026 19.20748  9.967198 1.962654e-07 1.347689e-05
#> FT044 1.945845 1.269865 2.621825 18.89815  6.222304 3.185040e-05 1.640296e-03
#> FT098 2.575125 1.559026 3.591224 18.83681  5.478216 1.081528e-04 4.455894e-03
#>               B
#> FT042 14.468908
#> FT039 12.375890
#> FT063  7.579018
#> FT044  2.355507
#> FT098  1.096718

Multiple covariates can be included to incorporate important sample and experiment information.

## Include covariates
colData(se)$covar <- c(rep(c("A", "B"), 6))
compareSamples(se, i = "knn_vsn", group = "sample_group",
               covariates = "covar", class1 = "WT", class2 = "KO",
               number = 5)
#>          logFC     CI.L     CI.R  AveExpr         t      P.Value    adj.P.Val
#> FT042 3.153118 2.757256 3.548980 20.43317 17.376617 8.402759e-10 1.730968e-07
#> FT039 3.980802 3.396157 4.565447 19.87720 14.854108 5.006787e-09 5.156991e-07
#> FT063 2.217181 1.749823 2.684540 19.20748 10.349506 2.725672e-07 1.871628e-05
#> FT044 1.945845 1.236408 2.655281 18.89815  5.983604 6.682044e-05 3.441253e-03
#> FT098 2.575125 1.576222 3.574028 18.83681  5.623974 1.166301e-04 4.805159e-03
#>               B
#> FT042 13.015891
#> FT039 11.311369
#> FT063  7.328577
#> FT044  1.659229
#> FT098  1.082477

For more flexible model specifications (e.g., interaction model, multi-level model), please use a standard workflow outlined in the limma package user’s guide.

References

Colin A. Smith (2021). faahKO: Saghatelian et al. (2004) FAAH knockout LC/MS data. http://dx.doi.org/10.1021/bi0480335

Laurent Gatto, Johannes Rainer and Sebastian Gibb (2021). MsCoreUtils: Core Utils for Mass Spectrometry Data. https://github.com/RforMassSpectrometry/MsCoreUtils

Johannes Rainer (2022). MsFeatures: Functionality for Mass Spectrometry Features. https://github.com/RforMassSpectrometry/MsFeatures

Laurent Gatto and Christophe Vanderaa (2021). QFeatures: Quantitative features for mass spectrometry data. https://github.com/RforMassSpectrometry/QFeatures

Jan Lisec (2018). InterpretMSSpectrum: Interpreting High Resolution Mass Spectra. https://CRAN.R-project.org/package=InterpretMSSpectrum

Ritchie, M.E., Phipson, B., Wu, D., Hu, Y., Law, C.W., Shi, W., and Smyth, G.K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research 43(7), e47. https://bioconductor.org/packages/limma