filigree: a bioinformatics tool for building differential network clustering and enrichment analysis

Addressing Metabolomics Data Analysis Using Advanced Bioinformatics Tools

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 this2,3,4,5, including Metscape, developed by our group6. Pathway mapping is often combined with enrichment analysis, which helps identify the most significant pathways7,8. These techniques first gained prominence in the analysis of gene expression data and have been successfully applied for the analysis of proteomics and epigenomics data9,10,11,12,13. 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 data14,15.
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&#39;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 associations17,18,19. 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 network16,20.
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&#160;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.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Number of experimental conditions (k)</td>
			<td>k = 1</td>
			<td>k = 2</td>
		</tr>
		<tr>
			<td>Software tool</td>
			<td>CorrelationCalculator</td>
			<td>Filigree</td>
		</tr>
		<tr>
			<td>Input data</td>
			<td>&#8226; Metabolites x Samples data matrix</td>
			<td>&#8226; Metabolites x Samples data matrix 
			&#8226; Experimental groups</td>
		</tr>
		<tr>
			<td>Workflow 
			&#8226; Pretreatment 
			&#8226; Network estimation 
			&#8226; Network clustering 
			&#8226; Enrichment analysis</td>
			<td> 
			&#8226; Log transformation; autoscaling 
			&#8226; DSPC 
			&#8226; Via external apps 
			&#8226; No</td>
			<td> 
			&#8226; Log transformation; autoscaling 
			&#8226; Joint network estimation 
			&#8226; Consensus clustering 
			&#8226; NetGSA</td>
		</tr>
		<tr>
			<td>Data visualization</td>
			<td>Via external app, e.g., Cytoscape</td>
			<td>Via external app, e.g., Cytoscape</td>
		</tr>
		<tr>
			<td>Testing metabolic modules for the association with outcome of interest (optional)</td>
			<td>Via external apps</td>
			<td>Via external apps</td>
		</tr>
	</tbody>
</table>
Table 1: The scope of application and the workflow of CorrelationCalculator and Filigree.

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis 

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

Metabolomics data analysis is a multi-step process that leverages many specialized software tools. This data analysis can be divided into three major steps, data processing and quality control, statistical analysis, and biological data interpretation. The tools described in this protocol are designed to enable the latter step in the analysis.In the last decade, metabolomics has emerged as anomic science due to advances in analytical technologies such as gas chromatography-mass spectrometry, and liquid chromatography-mass spectrometry. These techniques allow the simultaneous measurements of hundreds to thousands of small molecule metabolites creating complex multidimensional data sets. Metabolomics data analysis presents several challenges for pathway based enrichment approaches.First, a significant number of metabolites cannot be mapped onto metabolic pathways. Further, pathway coverage of secondary and lipid metabolism are not sufficient. Therefore, alternative approaches are needed for the biological interpretation of the data.Data-driven network analysis techniques can help overcome challenges associated with knowledge-based pathway enrichment analysis for metabolomics data. For example, correlation networks can help derive relationships among both known and unknown metabolites, and can thus facilitate annotations of the unknowns.

Watch this Scientific Journal Video about Filigree: A Bioinformatics Tool for Building Differential Network Clustering and Enrichment Analysis at JoVE.com

Filigree: A Bioinformatics Tool for Building Differential Network Clustering and Enrichment Analysis  

To begin, download a sample comma delimited input file containing metabolite measurements. Double click on the downloaded sample file to open it and ensure it contains sample names in column one and group assignations in column two. The remaining columns contain metabolites and each row represents a sample.Next, download the Filigree Java application and double click on the downloaded. jar file to launch the application. On the Data tab, click the Browse button to upload the input file.Under Specify Columns or Rows, click the dropdown arrow next to sample ID to select the corresponding column or row name from the input file. Then select Sample. Next, click on the dropdown arrow next to Group to select the corresponding column or row from the input file and select Group.Under Specify Sample Groups, click the dropdown arrows next to each group to select the corresponding group column from the input file. Under Feature Grouping, check the box next to the desired method, calculate feature groups. Click the view heat maps to view the heat map and determine a desired percent reduction.Next, using the feature reduction slider, select the desired percent reduction of features and slide the small circle until the percent reduction shows a feature-to-sample ratio of 1.25. Click the next button to move to the Analysis tab. Under select output directory, click the browse button and select the desired directory location for storing the generated output files.Click on the Run Analysis button. Upon displaying the message, analysis completed successfully, click the Okay button on the popup window. Next, on the Analysis tab, click the Browse Networks button to open the interactive filigree subnetworks in a browser tab.Under the subnetwork name column, click on the Subnetwork 1 link. Then explore the interactive subnetwork by clicking the plus button and zoom in on the part of the network and click the minus button to zoom out. Click on a group node and drag it to reposition it within the subnetwork.Then click the Expand Features button to expand all the group nodes and review the specific compounds that make up the group nodes. Then click the Collapse Features button to collapse the recently expanded group nodes. Next, click the By Sample Group button to change the view from a single subnetwork to multiple subnetworks split by a group.Then using the view of the subnetworks, explore and compare the groups. Click the All Samples button to go back to the single subnetwork view and click the Next button to view the next subnetwork. The differential network constructed using the plasma metabolite measurements from Type 1 diabetes and non-diabetic mice revealed a higher degree of network connectivity in the non-diabetic group.Enrichment analysis results showed that nine of the 12 metabolic modules identified were significantly different between Type 1 diabetes and non-diabetic mice.

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Research

JoVE Journal

Biology

316 vues.  University of Michigan, Ann Arbor. Pour commencer, téléchargez un exemple de fichier d’entrée délimité par des virgules contenant des mesures de métabolites. Double-cliquez sur le fichier d’exemple téléchargé pour l’ouvrir et assurez-vous qu’il contient les noms des exemples dans la première colonne et les affectations de groupe dans la deuxième colonne. Les colonnes restantes contiennent des métabolites et chaque ligne représente un échantillon.Ensuite, téléchargez l’application Java en filigr...