--- title: "Processing quantitative metabolomics data with the qmtools package" author: - name: Jaehyun Joo affiliation: University of Pennsylvania email: jaehyunjoo@outlook.com output: BiocStyle::html_document: toc_float: true vignette: > %\VignetteIndexEntry{Quantitative metabolomics data processing} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>", message = FALSE, fig.height = 10, fig.width = 10 ) ``` # 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 `r CRANpkg("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](https://pubs.acs.org/doi/10.1021/bi0480335) data, containing quantified LC/MS peaks from the spinal cords of 6 wild-type and 6 FAAH (fatty acid amide hydrolase) knockout mice. ```{r setup} library(qmtools) 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 ``` # 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. ```{r filtering} dim(faahko_se) # 206 features table(colData(faahko_se)$sample_group) ## Missing value filter based on 2 groups. dim(removeFeatures(faahko_se, i = "raw", group = colData(faahko_se)$sample_group, cut = 0.80)) # nothing removed dim(removeFeatures(faahko_se, i = "raw", group = colData(faahko_se)$sample_group, cut = 0.85)) # removed 65 features ## based on "WT" only dim(removeFeatures(faahko_se, i = "raw", group = colData(faahko_se)$sample_group, levels = "WT", cut = 0.85)) ``` In this vignette, we kept all features based on the cut-off: at least one group contains >= 80% of non-missing values. # Imputation Missing values are common in metabolomics data. For example, ions may have a low abundance that does not reach the limit of detection of the instrument. Unexpected stochastic fluctuations and technical error may also cause missing values even though ions present at detectable levels. We could use the `plotMiss` function to explore the mechanisms generating the missing values. The bar plot on the left panel shows the amount of missing values in each samples and the right panel helps to identify the structure of missing values with a hierarchically-clustered heatmap. ```{r plotMiss, fig.wide = TRUE, fig.height = 5} ## Sample group information g <- factor(colData(faahko_se)$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 `r Biocpkg("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. ```{r knn, fig.wide = TRUE, fig.height = 5} se <- imputeIntensity(faahko_se, i = "raw", name = "knn", method = "knn") se # The result was stored in assays slot: "knn" ## 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 `r Biocpkg("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. ```{r vsn, fig.wide = TRUE, fig.height = 5} se <- normalizeIntensity(se, i = "knn", name = "knn_vsn", method = "vsn") se # The result was stored in assays slot: "knn_vsn" 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. ```{r PCA} ## PCA m_pca <- reduceFeatures(se, i = "knn_vsn", method = "pca", ncomp = 2) summary(m_pca) ``` ```{r PLSDA} ## 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) ``` The dimension-reduction results can be plotted with the `plotReduced` function. Each point (label) represents a sample. Data ellipses can be visualized. ```{r plotReduced, fig.wide = TRUE, fig.height = 5} 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. ```{r clusterFeatures} se <- clusterFeatures(se, i = "knn_vsn", rtime_var = "rtmed", rt_cut = 10, cor_cut = 0.7) rowData(se)[, c("rtmed", "rtime_group", "feature_group")] ``` 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 ```{r rtime hclust, fig.wide = TRUE, fig.height = 5} 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 `r Biocpkg("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 `r Biocpkg("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. ```{r connected, fig.small = TRUE} 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") ``` ```{r louvain, fig.small = TRUE} 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"`. ```{r pairs, fig.wide = FALSE} 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 `r Biocpkg("QFeatures")` (aggregation of intensities) and `r CRANpkg("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 `r Biocpkg("limma")` package interface. Note that this function expects log-transformed feature intensities. ```{r compareSamples} ## 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) ``` Multiple covariates can be included to incorporate important sample and experiment information. ```{r compareSamples covariates} ## 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) ``` For more flexible model specifications (e.g., interaction model, multi-level model), please use a standard workflow outlined in the `r Biocpkg("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 # Session info {-} ```{r session info} sessionInfo() ```