Visualizing Metabolomics Data: Graphical Insights

Metabolomics is a powerful approach for studying small molecules present in biological systems. Analyzing metabolomics data requires robust statistical methods and effective visualization techniques to extract meaningful insights. Graphical representation plays a crucial role in interpreting complex metabolomics data, allowing researchers to identify patterns, trends, and relationships within the data. In this article, we will explore commonly used graphs in metabolomics data analysis.

Metabolomics data can be generated using various analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy. Before analysis, data preprocessing steps such as normalization and scaling are often performed to remove technical variation and improve data quality.

Univariate Analysis Graphs

Univariate analysis focuses on exploring individual variables (metabolites) one at a time. This approach helps researchers understand the distribution, variability, and significance of each metabolite independently.

Histograms

Histograms are widely used to visualize the distribution of metabolite intensities within a sample or across samples. Each histogram consists of bins representing intervals of metabolite intensities, and the height of each bin represents the frequency of metabolites falling within that intensity range. Histograms provide valuable insights into the central tendency, dispersion, and skewness of metabolite data. They also help identify any outliers or unusual patterns in the data distribution.

Box Plots

Box plots, also known as box-and-whisker plots, are effective for visually summarizing the distribution of metabolite abundances. Each box plot displays the median, quartiles (25th and 75th percentiles), and potential outliers of the metabolite intensities. The central box represents the interquartile range (IQR), with the median line inside. The whiskers extend to the minimum and maximum non-outlier values, or a certain multiple of the IQR from the quartiles. Box plots are particularly useful for comparing the distribution of metabolites between different groups or experimental conditions.

Scatter Plots

Scatter plots are essential for visualizing the relationship between two metabolites or between a metabolite and a phenotypic trait. Each data point on the scatter plot represents a sample, with one metabolite intensity plotted against another or against a quantitative trait. Scatter plots can reveal correlations, trends, clusters, or outliers in the data. They are valuable for identifying potential biomarkers or exploring associations between metabolites and biological variables.

Volcano Plots

Volcano plots are commonly used to visualize the results of differential expression analysis in metabolomics studies. In a volcano plot, each metabolite is represented by a point, with statistical significance (e.g., p-value) plotted on the y-axis and fold change (or effect size) plotted on the x-axis. Significant metabolites that exhibit both high fold change and statistical significance are typically located towards the upper right or left corners of the plot, resembling a volcano shape. Volcano plots help researchers prioritize metabolites for further investigation based on their biological relevance and statistical significance.

Proteomics analysis. Volcano plots showing the relative abundances of proteins (BCGDDosR vs. WT BCG), P-VALUE < 0.05 and FOLD CHANGE < 0.83 or FOLD CHANGE > 1.2., FDR ≤ 0.05 & |log 2 (FC)| ≥1 (Cui et al., 2023).

Multivariate Analysis Graphs

Multivariate analysis techniques simultaneously consider multiple variables (metabolites) to uncover patterns, relationships, and clusters within complex datasets.

Principal Component Analysis (PCA) Plots

Principal Component Analysis (PCA) is a dimensionality reduction technique commonly used in metabolomics data analysis. PCA plots visualize the reduced-dimensional representation of metabolomics data, where each point represents a sample projected onto principal components (PCs). PCs are orthogonal axes that capture the maximum variance in the data. PCA plots allow researchers to observe sample clustering, identify outliers, and explore patterns of similarity or dissimilarity between samples. They provide insights into underlying sources of variation and potential sample groupings based on overall metabolic profiles.

Principal component analysis of (A) the ratio of four water masses (CDW, YSCW, KSW, and KIW) across (B) seasons (red: winter; yellow: spring; blue: summer; and green: autumn) and depths (squares = surface and circles = water column) and (C) sampling stations (Song et al., 2023).

Partial Least Squares-Discriminant Analysis (PLS-DA) Plots

Partial Least Squares-Discriminant Analysis (PLS-DA) is a supervised multivariate analysis method used for classification and discrimination tasks in metabolomics. PLS-DA plots visualize the separation between sample groups or classes based on their metabolic profiles. The plot typically shows the first two or three latent variables, with each point representing a sample and color-coding indicating sample group membership. PLS-DA plots help identify metabolites contributing to sample discrimination and facilitate biomarker discovery for distinguishing between experimental conditions or phenotypic traits.

Hierarchical Clustering Heatmaps

Hierarchical clustering heatmaps display the similarity between samples or metabolites using color-coded intensity values. In sample clustering heatmaps, samples are arranged along both axes, with the intensity of the heatmap cells indicating the degree of similarity between samples based on their metabolic profiles. Similarly, in metabolite clustering heatmaps, metabolites are arranged along both axes, with the intensity of the heatmap cells representing the correlation or similarity between metabolite intensities across samples. Hierarchical clustering heatmaps help identify sample clusters, metabolic patterns, and potential correlations between metabolites.

Score Plots and Loading Plots

Score plots and loading plots are commonly used in multivariate analysis techniques such as PCA and PLS-DA. Score plots visualize sample clustering or separation in the reduced-dimensional space defined by principal components or latent variables. Each point on the score plot represents a sample, and the plot reveals patterns of similarity or dissimilarity between samples. Loading plots depict the contribution of individual variables (metabolites) to the observed clustering or separation in the score plot. Loading plots help identify metabolites driving sample differentiation and provide insights into the metabolic pathways or biological processes associated with sample grouping.

Pathway Analysis Graphs

Pathway analysis is essential in metabolomics to understand the biological context of metabolite changes and identify perturbed metabolic pathways associated with different experimental conditions or phenotypic traits.

Pathway Enrichment Analysis Plots

Pathway enrichment analysis identifies metabolic pathways enriched with differentially expressed metabolites or metabolites of interest. Enrichment plots visualize the results of pathway analysis, showing the significance and relevance of specific metabolic pathways to the experimental context. Each pathway is represented by a bar or dot on the plot, with the height or size indicating the level of enrichment significance (e.g., p-value or false discovery rate). Enrichment plots help prioritize relevant pathways for further investigation and provide a global view of metabolic dysregulation in biological systems.

Metabolic Pathway Diagrams with Highlighted Metabolites

Metabolic pathway diagrams illustrate the flow of metabolites through biochemical pathways and facilitate the interpretation of metabolomics data in a biological context. These diagrams often include graphical representations of metabolic reactions, enzymes, and metabolite intermediates. In pathway analysis graphs, specific metabolites of interest or differentially expressed metabolites are highlighted within the pathway diagram, indicating their significance in the context of the study. Highlighted metabolites help researchers identify key nodes or regulators within metabolic pathways and understand their roles in biological processes.

Pathway analysis graphs play a crucial role in elucidating the biological significance of metabolomics data and identifying potential targets for further experimental validation. By integrating metabolite changes with known metabolic pathways, these graphical representations provide a comprehensive understanding of metabolic dysregulation in health and disease.

Time Series Analysis Graphs

Time series analysis is essential for studying dynamic changes in metabolite levels over time. Time series analysis graphs visualize temporal patterns, trends, and fluctuations in metabolomics data, enabling researchers to identify temporal dynamics and understand the underlying biological processes.

Line Plots

Line plots are commonly used to visualize changes in metabolite abundance over time. In a time series line plot, time points are plotted on the x-axis, while metabolite intensities or concentrations are plotted on the y-axis. Each line represents the trajectory of a metabolite across different time points, allowing researchers to observe trends, periodicity, or irregular patterns in metabolite dynamics. Line plots provide a straightforward representation of temporal changes and are useful for identifying patterns such as diurnal rhythms or response to experimental perturbations.

Heatmaps

Heatmaps are effective for visualizing dynamic changes in metabolite levels across multiple time points or experimental conditions. In a time series heatmap, metabolite intensities are color-coded and arranged along one axis (e.g., rows), while time points are arranged along the other axis (e.g., columns). The intensity of each heatmap cell represents the abundance of a metabolite at a specific time point, allowing researchers to identify temporal patterns, clusters, or trends in metabolite dynamics. Heatmaps facilitate the comparison of metabolite profiles between different experimental conditions or biological states.

Metabolomic analysis of four periods of fruit after shading treatment. (A) PCA analysis of metabolites in different treatment groups at each stage; (B) KEGG pathway annotation of metabolites; (C) Cluster heat maps of all DEMs in the four periods, with relative levels of metabolites ranging from low (blue) to high (red); (D) Numbers of DEMs in the upper (purple) and lower (orange) tones during the four periods (Zhang et al., 2023).

Clustered Heatmaps

Clustered heatmaps group metabolites or samples based on similarity in temporal profiles. Hierarchical clustering algorithms are applied to both rows and columns of the heatmap, rearranging metabolites or time points to reveal patterns of co-regulation or coordinated changes in metabolite levels. Clustered heatmaps help identify modules of metabolites with similar temporal patterns and uncover underlying regulatory mechanisms or biological processes driving temporal dynamics.

Circular Plots for Cyclic Metabolites

Circular plots are specialized visualizations used to represent cyclic metabolic pathways, such as the citric acid cycle or circadian rhythms. In a circular plot, metabolites are arranged in a circular layout, with connections (edges) indicating metabolic reactions or interactions. Temporal changes in metabolite levels are represented as arcs or curves along the circumference of the circle, illustrating the progression of metabolic cycles over time. Circular plots provide an intuitive representation of cyclic metabolic processes and help researchers visualize the interplay between metabolites within these pathways.

Time series analysis graphs enable researchers to explore temporal dynamics in metabolomics data and uncover patterns of regulation, synchronization, and adaptation in biological systems. By visualizing changes over time, these graphical representations facilitate the interpretation of dynamic metabolic processes and provide insights into biological rhythms, responses to stimuli, and regulatory mechanisms.

Network Analysis Graphs

Network analysis is a powerful approach for studying the interactions and relationships between metabolites in biological systems. Network analysis graphs visualize metabolic networks, highlighting metabolic pathways, interactions, and regulatory mechanisms, thereby providing insights into the structure and function of biological networks.

Metabolic Network Visualization

Metabolic network visualization represents the interconnectedness of metabolites within biochemical pathways. In a metabolic network graph, metabolites are depicted as nodes (vertices), while metabolic reactions or interactions are represented as edges (lines or arrows) connecting the nodes. The layout of the network graph may be based on various algorithms, such as force-directed layouts or hierarchical layouts, to emphasize certain structural properties or clustering within the network. Metabolic network visualization facilitates the exploration of metabolic pathways, identification of key metabolites or enzymes, and understanding of metabolic regulation and fluxes.

Network Topology Plots

Network topology plots analyze the structural properties of metabolic networks, providing insights into network connectivity, centrality, and modularity. Common network topology measures include degree centrality (number of connections per node), betweenness centrality (importance of a node in connecting other nodes), and clustering coefficient (degree of clustering within a network). Network topology plots visualize these measures across nodes or edges, highlighting hubs (highly connected nodes), bridges (nodes connecting different network components), and network modules (clusters of densely interconnected nodes). Network topology analysis helps identify key metabolites or pathways critical for network integrity and function.

Node-Edge Diagrams

Node-edge diagrams visualize the interactions between metabolites as nodes connected by edges representing metabolic reactions or relationships. In a node-edge diagram, nodes represent metabolites, while edges represent biochemical reactions, conversions, or regulatory interactions between metabolites. The directionality and type of edges (e.g., metabolic reactions, enzyme-substrate relationships) provide additional information about the nature of interactions within the network. Node-edge diagrams help researchers understand the flow of metabolites through metabolic pathways, identify key regulatory points, and explore metabolic crosstalk and coordination.

Network analysis graphs offer a holistic view of metabolic interactions and regulation within biological systems. By visualizing the structure and dynamics of metabolic networks, these graphical representations facilitate the interpretation of metabolomics data in the context of biochemical pathways, cellular metabolism, and organismal physiology.

Integration of Omics Data Graphs

Integration of omics data from multiple platforms, such as metabolomics, transcriptomics, and proteomics, offers a comprehensive view of biological processes and molecular interactions. Integration of omics data graphs visualize relationships between different types of molecular entities, uncovering cross-omic associations and providing insights into biological networks and regulatory mechanisms.

Correlation Plots

Correlation plots visualize relationships between metabolomics data and other omics data types, such as gene expression (transcriptomics) or protein abundance (proteomics). Each data point on the correlation plot represents a pair of variables (e.g., metabolite abundance and gene expression level), and the plot displays the strength and direction of correlation between these variables. Correlation coefficients (e.g., Pearson correlation coefficient) quantify the degree of linear association between variables, with positive coefficients indicating positive correlation and negative coefficients indicating negative correlation. Correlation plots help identify co-regulated genes and metabolites, uncover functional associations between molecular entities, and prioritize candidate biomarkers or regulatory elements.

Co-Expression Networks

Co-expression networks integrate omics data to identify modules of co-regulated genes and metabolites. In a co-expression network graph, nodes represent genes or metabolites, while edges represent co-expression relationships based on correlation coefficients or other similarity measures. Modular analysis of co-expression networks identifies clusters of genes and metabolites with similar expression profiles, revealing functional modules or pathways regulated by common regulatory mechanisms. Co-expression networks provide insights into the coordination and regulation of molecular processes across different omics layers and help uncover molecular interactions underlying complex biological phenotypes.

Integration of omics data graphs enables researchers to explore connections between different molecular layers, uncover regulatory relationships, and gain a deeper understanding of biological systems' complexity. By combining information from multiple omics platforms, these graphical representations facilitate systems-level analysis and provide comprehensive insights into molecular interactions and regulatory networks.

Advanced Visualization Techniques

Advanced visualization techniques provide sophisticated ways to explore and interpret complex omics data, including metabolomics, at a systems-level. These techniques leverage innovative approaches to represent high-dimensional data in meaningful and intuitive ways, facilitating data exploration, pattern recognition, and hypothesis generation.

3D Plots

3D plots extend traditional two-dimensional visualizations to include an additional dimension, enabling researchers to visualize complex relationships in three-dimensional space. In metabolomics, 3D plots can represent relationships between multiple metabolites or experimental conditions, providing insights into multidimensional patterns and interactions. Common types of 3D plots include scatter plots, surface plots, and contour plots. By visualizing data in three dimensions, researchers can uncover complex relationships that may not be apparent in traditional two-dimensional plots.

Interactive Visualization Tools

Interactive visualization tools allow users to manipulate and explore data dynamically, enhancing data exploration and analysis capabilities. These tools often include features such as zooming, panning, filtering, and dynamic linking between different visualizations. In metabolomics, interactive visualization tools enable researchers to interactively explore metabolite profiles, compare experimental conditions, and identify patterns or outliers in the data. By providing a user-friendly interface for data exploration, interactive visualization tools empower researchers to gain deeper insights into complex omics datasets.

Dimensionality Reduction Techniques (e.g., t-SNE)

Dimensionality reduction techniques transform high-dimensional data into lower-dimensional representations while preserving key data characteristics. t-Distributed Stochastic Neighbor Embedding (t-SNE) is a popular dimensionality reduction technique used in metabolomics to visualize high-dimensional data in two or three dimensions. t-SNE projects data points onto a lower-dimensional space, preserving local relationships between data points and revealing underlying clusters or structures in the data. t-SNE plots help researchers visualize complex metabolic profiles, identify sample clusters, and uncover subtle patterns or relationships in the data.

Advanced visualization techniques empower researchers to explore and interpret omics data in innovative ways, enabling deeper insights into biological systems' complexity and heterogeneity. By leveraging these techniques, researchers can uncover hidden patterns, identify novel associations, and generate hypotheses for further experimental investigation.

Best Practices for Graphical Representation

Effective data visualization relies on adhering to best practices:

• Choose appropriate graphs based on the nature of the data and research questions.
• Ensure clarity, simplicity, and consistency in graphical design.
• Provide clear labels, titles, and legends to aid interpretation.
• Use color schemes that are accessible to all viewers.
• Validate findings using statistical tests and biological knowledge.

References

1. Cui, Yingying, et al. "DosR's multifaceted role on Mycobacterium bovis BCG revealed through multi-omics." Frontiers in Cellular and Infection Microbiology 13 (2023): 1292864.
2. Song, Young Kyoung, et al. "Factors controlling the distribution of dissolved organic carbon and nitrogen in the coastal waters off Jeju Island." Frontiers in Marine Science 10 (2023): 1250601.
3. Zhang, Yao, et al. "Potential regulatory genes of light induced anthocyanin accumulation in sweet cherry identified by combining transcriptome and metabolome analysis." Frontiers in Plant Science 14 (2023): 1238624.