CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Summary

We present CorrelationCalculator and Filigree, two tools for data-driven network construction and analysis of metabolomics data. CorrelationCalculator supports building a single interaction network of metabolites based on expression data, while Filigree allows building a differential network, followed by network clustering and enrichment analysis.

Abstract

A significant challenge in the analysis of omics data is extracting actionable biological knowledge. Metabolomics is no exception. The general problem of relating changes in levels of individual metabolites to specific biological processes is compounded by the large number of unknown metabolites present in untargeted liquid chromatography-mass spectrometry (LC-MS) studies. Further, secondary metabolism and lipid metabolism are poorly represented in existing pathway databases. To overcome these limitations, our group has developed several tools for data-driven network construction and analysis. These include CorrelationCalculator and Filigree. Both tools allow users to build partial correlation-based networks from experimental metabolomics data when the number of metabolites exceeds the number of samples. CorrelationCalculator supports the construction of a single network, while Filigree allows building a differential network utilizing data from two groups of samples, followed by network clustering and enrichment analysis. We will describe the utility and application of both tools for the analysis of real-life metabolomics data.

Introduction

In the last decade, metabolomics has emerged as an omics science due to advances in analytical technologies such as Gas Chromatography-Mass Spectrometry (GC-MS) and Liquid Chromatography-Mass Spectrometry (LC-MS). These techniques allow simultaneous measurement of hundreds to thousands of small molecule metabolites, creating complex multidimensional datasets. Metabolomics experiments can be performed in targeted or untargeted modes. Targeted metabolomics experiments measure specific classes of metabolites. They are usually hypothesis-driven, while untargeted approaches attempt to measure as many metabolites as possible and are hypothesis-generating in nature. Targeted assays usually include internal standards and thus allow for absolute quantification of metabolites of interest. In contrast, untargeted assays allow relative quantification and include many unknown metabolites¹.

Analysis of metabolomics data is a multi-step process that leverages many specialized software tools¹. It can be divided into the following three major steps: (1) data processing and quality control, (2) statistical analysis, and (3) biological data interpretation. The tools described here are designed to enable the latter step of the analysis.

An intuitive and popular way to interpret metabolomics data is to map the experimental measurements onto metabolic pathways. Numerous tools have been designed to achieve this^2,3,4,5, including Metscape, developed by our group⁶. Pathway mapping is often combined with enrichment analysis, which helps identify the most significant pathways^7,8. These techniques first gained prominence in the analysis of gene expression data and have been successfully applied for the analysis of proteomics and epigenomics data^{9,10,11,12,13}. However, the analysis of metabolomics data presents a number of challenges for knowledge-based approaches. First, in addition to the endogenous metabolites, metabolomics assays measure exogenous compounds, including those that come from nutrition and other environmental sources. These compounds, as well as metabolites produced by bacteria, cannot be mapped onto human or metabolic pathways of other eukaryotic organisms. Further, pathway coverage of secondary metabolism and lipid metabolism currently does not allow high-resolution mapping at the level that would easily support the biological interpretation of the data^14,15.

Data-driven network analysis techniques can help overcome these challenges. For example, correlation-based networks can help derive relationships among both known and unknown metabolites and facilitate the annotation of the unknowns¹⁶. While computing Pearson's correlation coefficients is the most straightforward approach to establishing the linear relationships between metabolites, the disadvantage is that it captures both direct and indirect associations^17,18,19. An alternative is to compute partial correlation coefficients that can distinguish between direct and indirect associations. Gaussian graphical modeling (GGM) can be used to estimate partial correlation networks. However, GGM requires that the sample size and the number of features be comparable. This condition is rarely met in untargeted LC-MS data that contains measurements for thousands of metabolic features. Regularization techniques can be utilized to overcome this limitation. Graphical lasso (Glasso) and nodewise regression are popular methods for regularized estimation of the partial correlation network^16,20.

The first of the bioinformatics tools presented here, CorrelationCalculator¹⁶, is based on the debiased sparse partial correlation (DSPC) algorithm. DSPC relies on de-sparsified graphical lasso modeling. The underlying assumption of the algorithm is that the number of connections among the metabolites is considerably smaller than the number of samples, i.e., the partial correlation network of metabolites is sparse. This assumption allows DSPC to discover the connectivity among large numbers of metabolites using fewer samples, leveraging regularized regression techniques. Further, using a debiasing step for the regularized regression estimates, it obtains sampling distributions for the edge parameters that can be used to construct confidence intervals and test hypotheses of interest (e.g., presence/absence of a single or a group of edges). The presence or absence of an edge in the partial correlation network can thus be formally tested using the computed p-values.

CorrelationCalculator proved to be very useful for single-group analysis¹⁶; however, the objective of many metabolomics experiments is the differential analysis of two or more conditions. While CorrelationCalculator can be employed on each of the groups separately to generate partial correlation networks for each condition, this approach limits the number of samples that can be used for network generation. Since a sufficiently large sample size is one of the biggest considerations in data-driven analysis, methods that can leverage all available samples in the data to construct networks are highly desirable. This approach is implemented in the second tool presented here, called Filigree²¹. Filigree relies on the previously published Differential Network Enrichment Analysis (DNEA) algorithm²². Table 1 shows the applications and the workflow of both tools.

Number of experimental conditions (k)	k = 1	k = 2
Software tool	CorrelationCalculator	Filigree
Input data	• Metabolites x Samples data matrix	• Metabolites x Samples data matrix • Experimental groups
Workflow • Pretreatment • Network estimation • Network clustering • Enrichment analysis	• Log transformation; autoscaling • DSPC • Via external apps • No	• Log transformation; autoscaling • Joint network estimation • Consensus clustering • NetGSA
Data visualization	Via external app, e.g., Cytoscape	Via external app, e.g., Cytoscape
Testing metabolic modules for the association with outcome of interest (optional)	Via external apps	Via external apps

Table 1: The scope of application and the workflow of CorrelationCalculator and Filigree.

Protocol

1. CorrelationCalculator

1. Download a sample comma-delimited input file containing a list of metabolites with experimental measurements at http://metscape.med.umich.edu/kora_data_240.csv.
2. Double-click on the downloaded sample file to open it.
   1. Ensure that the file contains labels for both the samples and the metabolites.
   2. Since samples are in rows, confirm that the first column is the sample names and the first row is the metabolite names.
3. Download the CorrelationCalculator Java application (http://metscape.med.umich.edu/calculator.html). Double-click on the downloaded .jar file to launch the application.
4. On the Input tab, click the Browse button to upload the input file.
5. Under Specify File Format, use the dropdown arrow to select the appropriate input file format. Select Samples in Rows (Supplementary Figure 1).
6. Move to the Data Normalization tab by clicking the Next >> button at the bottom right of the window.
7. Under Select Method(s), check the box next to Log2-Transform Data. Check the box next to Autoscale Data.
8. Under Normalize Data, click the Run button.
   NOTE: Once the normalization is complete, click the View Normalized Data button, located under Normalize Data, and review the updated dataset (Supplementary Figure 2).
9. Under Normalize Data, click the Save button and save the new data file.
10. Move to the Data Analysis tab by clicking the Next >> button at the bottom right of the window.
11. Under Calculate Pearson's Correlation, click Run. Determine the best Pearson's Correlation range for the data.
    1. Click the View Histogram button. Review the frequency of the maximal Pearson's correlation scores per feature.
    2. Click the View Heatmap button. Review the representation of Pearson's correlation matrix.
12. Under Filter by Pearson's Correlations, leave the default numbers to filter by a range of 0.00 to 1.00
    NOTE: Slide the small blue arrow at the right end from 1 and the small blue arrow at the left from 0 to change the filter. Entering specific numbers in the text boxes is also an option.
13. Under Select Partial Correlation Method, select the desired method, DSPC Method.
    NOTE: If the number of metabolites is smaller than the number of samples in the dataset, only the DSPC method can be used.
14. Under Calculate Partial Correlations, click the Run button (Supplementary Figure 3).
15. Click the View CSV File and view the results. Click the Save button and save the results.
16. Click the View in MetScape button to launch an interactive correlation network.
    See Karnovsky, A. et al.⁶ for more information on using MetScape.
    ​NOTE: MetScape is a Cytoscape application that allows for the creation and exploration of correlation networks.

2. Filigree

1. Download a sample comma-delimited input file containing metabolite measurements at http://metscape.med.umich.edu/T1D_primaryMetabolites_noIS_log_scaled_sorted.csv.
2. Double-click on the downloaded sample file to open it.
   1. Ensure the file contains sample names in column 1 and group assignations in column 2. Confirm that the remaining columns contain metabolites/lipids.
   2. Ensure that each row represents a sample.
      NOTE: The metabolite measurements should be log-transformed and auto-scaled unless performing feature aggregation, in which case the measurements should only be log-transformed.
3. Download the Filigree Java application (http://metscape.med.umich.edu/filigree.html).
   NOTE: A detailed user manual is available at http://metscape.ncibi.org/v0.1.2Filigree_UserManual.pdf. 
4. Double-click on the downloaded .jar file to launch the application.
5. On the Data tab, click the Browse button to upload the input file.
6. Under Specify Columns/Rows, click on the dropdown arrow next to Sample ID to select the corresponding column/row name from the input file. Select Sample.
7. Under Specify Columns/Rows, click on the dropdown arrow next to "Group" to select the corresponding column/row from the input file. Select Group.
8. Under Specify Sample Groups, click on the dropdown arrows next to each Group to select the corresponding group column from the input file. For Group 1, select Diabetic. For Group 2, select Non-diabetic.
9. Under Feature Grouping, check the box next to the desired method, Calculate Feature Groups.
10. Click the View Heatmaps button. View the heatmap and determine a desired percent reduction.
11. Use the Feature Reduction slider to select the desired percent reduction of features. Slide the small circle until the percent reduction shows a feature-to-sample ratio of 1.25 (Supplementary Figure 4).
12. Move to the Analysis tab by clicking the Next >> button at the bottom right of the window.
13. Under Select Output Directory, click the Browse button and select the desired directory location for storing the generated output files.
14. Click the Run Analysis button located at the bottom left of the window. The progress bars update for each analysis component (Supplementary Figure 5). Click the OK button on the popup window displaying the message Analysis Completed Successfully.
15. On the Analysis tab, click the Browse Networks button to open the interactive Filigree subnetworks in a browser tab.
16. Click on the Subnetwork 1 link under the Subnetwork Name column.
17. Explore the interactive subnetwork using the various buttons. Click the + button and zoom in on the part of the network. Click the - button and zoom out (Supplementary Figure 6).
18. Click on a Group Node and drag it to reposition it within the subnetwork.
    NOTE: Node color represents up/down-regulation, and color opacity represents higher/lower fold change. The edge color represents the differential status between groups.
19. Click the Expand Features button at the top right of the page to expand all the group nodes. Review the specific compounds that make up the group nodes.
20. Click the Collapse Features button at the top right of the page to collapse the recently expanded group nodes.
21. Click the By Sample Group button at the top right of the page to change the view from a single subnetwork to multiple subnetworks split by a group. Explore and compare the groups using this view of the subnetworks (Supplementary Figure 7).
22. Click the All Samples button to go back to the single subnetwork view.
23. View the next subnetwork by clicking the Next button at the top right of the page.
24. Repeat steps 2.19-2.23 for each subnetwork.
25. Click the Differential Network Enrichment Analysis Results link at the top middle of the window to return to the summary table view listing all the subnetworks.
    ​NOTE: Import the edge and/or node output files in a different software tool, such as Cytoscape²³, to create additional network visualizations.

3. Additional considerations

1. For Mac computers running Big Sur (OSX 11.2) or later, approve the tool in Apple Menu > System Preferences > Security & Privacy > General and select Allow at the bottom of the tab.
2. In addition, allow Filigree access to the files in Apple Menu > System Preferences > Security & Privacy > Privacy by selecting Files and Folders in the menu on the left and then selecting Filigree in the menu on the right.

Results

To illustrate the use of CorrelationCalculator, we constructed a partial correlation network using a subset of the metabolomics data from the KORA population study described in Krumsiek et al.²⁴. The dataset contained 151 metabolites and 240 samples. Figure 1 shows the resulting partial correlation network that was visualized in Cytoscape. The network contains 148 nodes and 272 edges. The color of the nodes represents metabolites that belong to different chem...

Discussion

Partial correlation-based network analysis methods implemented in CorrelationCalculator and Filigree help overcome some of the limitations of knowledge-based metabolic pathway analyses, especially for the datasets with a high prevalence of unknown metabolites and limited coverage of metabolic pathways (e.g., lipidomics data). These tools have been widely used by the research community to analyze a broad range of metabolomics and lipidomics data^14,22,

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
CorrelationCalculator	JAVA	http://metscape.med.umich.edu/calculator.html
clusterNet		https://github.com/Karnovsky-Lab/clusterNet
Cytoscape	Cytoscape	https://cytoscape.org/
Filigree	JAVA	http://metscape.med.umich.edu/filigree.html
MetScape	Cytoscape	https://apps.cytoscape.org/apps/metscape	Cytoscape application that allows for the creation and exploration of correlation networks.