This work was supported by NIH 1U01CA235487 grant.

1. CorrelationCalculator
<ol>
	<li>Download a sample comma-delimited input file containing a list of metabolites with experimental measurements at&#160;http://metscape.med.umich.edu/kora_data_240.csv.</li>
	<li>Double-click on the downloaded sample file to open it.
	<ol>
		<li>Ensure that the file contains labels for both the samples and the metabolites.</li>
		<li>Since samples are in rows, confirm that the first column is the sample names and the first row is the metabolite names.</li>
	</ol>
	</li>
	<li>Download the CorrelationCalculator Java application (http://metscape.med.umich.edu/calculator.html). Double-click on the downloaded .jar file to launch the application.</li>
	<li>On the Input tab, click the Browse button to upload the input file.</li>
	<li>Under Specify File Format, use the dropdown arrow to select the appropriate input file format. Select Samples in Rows (Supplementary Figure 1).</li>
	<li>Move to the Data Normalization tab by clicking the Next &#62;&#62; button at the bottom right of the window.</li>
	<li>Under Select Method(s), check the box next to Log2-Transform Data. Check the box next to Autoscale Data.</li>
	<li>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).</li>
	<li>Under Normalize Data, click the Save button and save the new data file.</li>
	<li>Move to the Data Analysis tab by clicking the Next &#62;&#62; button at the bottom right of the window.</li>
	<li>Under Calculate Pearson&#39;s Correlation, click Run. Determine the best Pearson&#39;s Correlation range for the data.
	<ol>
		<li>Click the View Histogram button. Review the frequency of the maximal Pearson&#39;s correlation scores per feature.</li>
		<li>Click the View Heatmap button. Review the representation of Pearson&#39;s correlation matrix.</li>
	</ol>
	</li>
	<li>Under Filter by Pearson&#39;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.</li>
	<li>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.</li>
	<li>Under Calculate Partial Correlations, click the Run button (Supplementary Figure 3).</li>
	<li>Click the View CSV File and view the results. Click the Save button and save the results.</li>
	<li>Click the View in MetScape button to launch an interactive correlation network. 
	See Karnovsky, A. et al.6 for more information on using MetScape. 
	&#8203;NOTE: MetScape is a Cytoscape application that allows for the creation and exploration of correlation networks.</li>
</ol>
2. Filigree
<ol>
	<li>Download a sample comma-delimited input file containing metabolite measurements at&#160;http://metscape.med.umich.edu/T1D_primaryMetabolites_noIS_log_scaled_sorted.csv.</li>
	<li>Double-click on the downloaded sample file to open it.
	<ol>
		<li>Ensure the file contains sample names in column 1 and group assignations in column 2. Confirm that the remaining columns contain metabolites/lipids.</li>
		<li>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.</li>
	</ol>
	</li>
	<li>Download the Filigree Java application (http://metscape.med.umich.edu/filigree.html). 
	NOTE: A detailed user manual is available at&#160;http://metscape.ncibi.org/v0.1.2Filigree_UserManual.pdf.&#160;</li>
	<li>Double-click on the downloaded .jar file to launch the application.</li>
	<li>On the Data tab, click the Browse button to upload the input file.</li>
	<li>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.</li>
	<li>Under Specify Columns/Rows, click on the dropdown arrow next to &#34;Group&#34; to select the corresponding column/row from the input file. Select Group.</li>
	<li>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.</li>
	<li>Under Feature Grouping, check the box next to the desired method, Calculate Feature Groups.</li>
	<li>Click the View Heatmaps button. View the heatmap and determine a desired percent reduction.</li>
	<li>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).</li>
	<li>Move to the Analysis tab by clicking the Next &#62;&#62; button at the bottom right of the window.</li>
	<li>Under Select Output Directory, click the Browse button and select the desired directory location for storing the generated output files.</li>
	<li>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.</li>
	<li>On the Analysis tab, click the Browse Networks button to open the interactive Filigree subnetworks in a browser tab.</li>
	<li>Click on the Subnetwork 1 link under the Subnetwork Name column.</li>
	<li>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).</li>
	<li>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.</li>
	<li>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.</li>
	<li>Click the Collapse Features button at the top right of the page to collapse the recently expanded group nodes.</li>
	<li>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).</li>
	<li>Click the All Samples button to go back to the single subnetwork view.</li>
	<li>View the next subnetwork by clicking the Next button at the top right of the page.</li>
	<li>Repeat steps 2.19-2.23 for each subnetwork.</li>
	<li>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. 
	&#8203;NOTE: Import the edge and/or node output files in a different software tool, such as Cytoscape23, to create additional network visualizations.</li>
</ol>
3. Additional considerations
<ol>
	<li>For Mac computers running Big Sur (OSX 11.2) or later, approve the tool in Apple Menu &#62; System Preferences &#62; Security &#38; Privacy &#62; General and select Allow at the bottom of the tab.</li>
	<li>In addition, allow Filigree access to the files in Apple Menu &#62; System Preferences &#62; Security &#38; Privacy &#62; Privacy by selecting Files and Folders in the menu on the left and then selecting Filigree in the menu on the right.</li>
</ol>

Addressing Metabolomics Data Analysis Using Advanced Bioinformatics Tools

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M., Karnovsky, A., Michailidis, G., Pennathur, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Metabolomics+and+diabetes:+analytical+and+computational+approaches.">Metabolomics and diabetes: analytical and computational approaches.</a> Diabetes. 64 (3), 718-732 (2015).</li><li>Cottret, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MetExplore:+Collaborative+edition+and+exploration+of+metabolic+networks.">MetExplore: Collaborative edition and exploration of metabolic networks.</a> Nucleic Acids Research. 46 (W1), W495-W502 (2018).</li><li>Garcia-Alcalde, F., Garcia-Lopez, F., Dopazo, J., Conesa, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Paintomics:+A+web+based+tool+for+the+joint+visualization+of+transcriptomics+and+metabolomics+data.">Paintomics: A web based tool for the joint visualization of transcriptomics and metabolomics data.</a> Bioinformatics. 27 (1), 137-139 (2011).</li><li>Kuo, T. C., Tian, T. F., Tseng, Y. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=3Omics:+A+web-based+systems+biology+tool+for+analysis,+integration+and+visualization+of+human+transcriptomic,+proteomic+and+metabolomic+data.">3Omics: A web-based systems biology tool for analysis, integration and visualization of human transcriptomic, proteomic and metabolomic data.</a> BMC Systems Biology. 7, 64 (2013).</li><li>Paley, S. M., Karp, P. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+pathway+tools+cellular+overview+diagram+and+Omics+Viewer.">The pathway tools cellular overview diagram and Omics Viewer.</a> Nucleic Acids Research. 34 (13), 3771-3778 (2006).</li><li>Karnovsky, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Metscape+2+bioinformatics+tool+for+the+analysis+and+visualization+of+metabolomics+and+gene+expression+data.">Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data.</a> Bioinformatics. 28 (3), 373-380 (2012).</li><li>Chong, J., Xia, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+MetaboAnalyst+4.0+for+metabolomics+data+analysis,+interpretation,+and+integration+with+other+omics+data.">Using MetaboAnalyst 4.0 for metabolomics data analysis, interpretation, and integration with other omics data.</a> Methods in Molecular Biology. 2104, 337-360 (2020).</li><li>Lopez-Ibanez, J., Pazos, F., Chagoyen, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MBROLE+2.0-functional+enrichment+of+chemical+compounds.">MBROLE 2.0-functional enrichment of chemical compounds.</a> Nucleic Acids Research. 44 (W1), W201-W204 (2016).</li><li>Cavalcante, R. G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Broad-Enrich:+Functional+interpretation+of+large+sets+of+broad+genomic+regions.">Broad-Enrich: Functional interpretation of large sets of broad genomic regions.</a> Bioinformatics. 30 (17), i393-i400 (2014).</li><li>Huang, D. W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=DAVID+bioinformatics+resources:+Expanded+annotation+database+and+novel+algorithms+to+better+extract+biology+from+large+gene+lists.">DAVID bioinformatics resources: Expanded annotation database and novel algorithms to better extract biology from large gene lists.</a> Nucleic Acids Research. 35 (Web Server issue), W169-W175 (2007).</li><li>Lee, P. H., O'Dushlaine, C., Thomas, B., Purcell, S. 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K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MetaMapp:+Mapping+and+visualizing+metabolomic+data+by+integrating+information+from+biochemical+pathways+and+chemical+and+mass+spectral+similarity.">MetaMapp: Mapping and visualizing metabolomic data by integrating information from biochemical pathways and chemical and mass spectral similarity.</a> BMC Bioinformatics. 13, 99 (2012).</li><li>Basu, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sparse+network+modeling+and+Metscape-based+visualization+methods+for+the+analysis+of+large-scale+metabolomics+data.">Sparse network modeling and Metscape-based visualization methods for the analysis of large-scale metabolomics data.</a> Bioinformatics. 33 (10), 1545-1553 (2017).</li><li>Krumsiek, J., Suhre, K., Illig, T., Adamski, J., Theis, F. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gaussian+graphical+modeling+reconstructs+pathway+reactions+from+high-throughput+metabolomics+data.">Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data.</a> BMC Systems Biology. 5, 21 (2011).</li><li>Camacho, D., de la Fuente, A., Mendes, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+origin+of+correlations+in+metabolomics+data.">The origin of correlations in metabolomics data.</a> Metabolomics. 1 (1), 53-63 (2005).</li><li>Steuer, R., Kurths, J., Fiehn, O., Weckwerth, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Observing+and+interpreting+correlations+in+metabolomic+networks.">Observing and interpreting correlations in metabolomic networks.</a> Bioinformatics. 19 (8), 1019-1026 (2003).</li><li>B&#252;hlmann, P., Van De Geer, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Statistics for High-Dimensional Data: Methods, Theory and Applications. , (2011).</li><li>Iyer, G. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Application+of+differential+network+enrichment+analysis+for+deciphering+metabolic+alterations.">Application of differential network enrichment analysis for deciphering metabolic alterations.</a> Metabolites. 10 (12), 479 (2020).</li><li>Ma, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Differential+network+enrichment+analysis+reveals+novel+lipid+pathways+in+chronic+kidney+disease.">Differential network enrichment analysis reveals novel lipid pathways in chronic kidney disease.</a> Bioinformatics. 35 (18), 3441-3452 (2019).</li><li>Shannon, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cytoscape:+a+software+environment+for+integrated+models+of+biomolecular+interaction+networks.">Cytoscape: a software environment for integrated models of biomolecular interaction networks.</a> Genome Reserach. 13 (11), 2498-2504 (2003).</li><li>Krumsiek, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mining+the+unknown:+a+systems+approach+to+metabolite+identification+combining+genetic+and+metabolic+information.">Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information.</a> PLoS Genetics. 8 (10), e1003005 (2012).</li><li>Fahrmann, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systemic+alterations+in+the+metabolome+of+diabetic+NOD+mice+delineate+increased+oxidative+stress+accompanied+by+reduced+inflammation+and+hypertriglyceremia.">Systemic alterations in the metabolome of diabetic NOD mice delineate increased oxidative stress accompanied by reduced inflammation and hypertriglyceremia.</a> American Journal of Physiology. 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The&#160;authors have no competing financial interests.

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 data14,22,<s...

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.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>CorrelationCalculator</td>
			<td>JAVA</td>
			<td>http://metscape.med.umich.edu/calculator.html</td>
			<td></td>
		</tr>
		<tr>
			<td>clusterNet</td>
			<td></td>
			<td>https://github.com/Karnovsky-Lab/clusterNet</td>
			<td></td>
		</tr>
		<tr>
			<td>Cytoscape</td>
			<td>Cytoscape</td>
			<td>https://cytoscape.org/</td>
			<td></td>
		</tr>
		<tr>
			<td>Filigree</td>
			<td>JAVA</td>
			<td>http://metscape.med.umich.edu/filigree.html</td>
			<td></td>
		</tr>
		<tr>
			<td>MetScape</td>
			<td>Cytoscape</td>
			<td>https://apps.cytoscape.org/apps/metscape</td>
			<td>Cytoscape application that allows for the creation and exploration of correlation networks.</td>
		</tr>
	</tbody>
</table>

correlationcalculator and filigree: tools for data-driven network analysis of metabolomics data

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.

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...

Watch this Scientific Journal Video about Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis at JoVE.com

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

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.

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

A significant challenge in the analysis of omics data is extracting actionable biological knowledge. Metabolomics is no exception. The general problem of ...

correlationcalculator-filigree-tools-for-data-driven-network-analysis

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JoVE Journal

Biology

2.2K Views.  University of Michigan, Ann Arbor. 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. 

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

Using Micro-Electro-Mechanical Systems (MEMS) to Develop Diagnostic Tools

Using Micro-Electro-Mechanical Systems MEMS to Develop Diagnostic Tools

Counting and Determining the Viability of Cultured Cells

Determining the number of cells in culture is important in standardization of culture conditions and in performing accurate quantitation experiments. A hemacytometer is a thick glass slide with a central area designed as a counting chamber. Cell suspension is applied to a defined area and counted so cell density can be calculated. 

Determining the number of cells in culture is important in standardization of culture conditions and in performing accurate quantitation experiments. In this video, we demonstrate how cells are counted using a hemacytometer.

Determining the number of cells in culture is important in standardization of culture conditions and in performing accurate quantitation experiments. A ...

Trypsinizing and Subculturing Mammalian Cells

As cells reach confluency, they must be subcultured or passaged. Failure to subculture confluent cells results in reduced mitotic index and eventually in cell death. The first step in subculturing is to detach cells from the surface of the primary culture vessel by trypsinization or mechanical means. The resultant cell suspension is then subdivided, or reseeded, into fresh cultures. Secondary cultures are checked for growth and fed periodically, and may be subsequently subcultured to produce tertiary cultures. The time between passaging of cells varies with the cell line and depends on the growth rate.

As cells reach confluency, they must be subcultured or passaged. This video will demonstrate a procedure for subculturing both adherent and suspension cells.

As cells reach confluency, they must be subcultured or passaged. Failure to subculture confluent cells results in reduced mitotic index and eventually in ...

Freezing, Thawing, and Packaging Cells for Transport

Cultured mammalian cells are used extensively in cell biology studies. It requires a number of special skills in order to be able to preserve the structure, function, behavior, and biology of the cells in culture. This video describes the basic skills required to freeze and store cells and how to recover frozen stocks. 

Cultured mammalian cells are used extensively in cell biology studies. This video describes the basic skills required to freeze and store cells and how to recover frozen stocks.

Cultured mammalian cells are used extensively in cell biology studies. It requires a number of special skills in order to be able to preserve the ...

A Neuronal and Astrocyte Co-Culture Assay for High Content Analysis of Neurotoxicity

High Content Analysis (HCA) assays combine cells and detection reagents with automated imaging and powerful image analysis algorithms, allowing measurement of multiple cellular phenotypes within a single assay. In this study, we utilized HCA to develop a novel assay for neurotoxicity. Neurotoxicity assessment represents an important part of drug safety evaluation, as well as being a significant focus of environmental protection efforts. Additionally, neurotoxicity is also a well-accepted in vitro marker of the development of neurodegenerative diseases such as Alzheimer's and Parkinson's diseases. Recently, the application of HCA to neuronal screening has been reported. By labeling neuronal cells with &beta;III-tubulin, HCA assays can provide high-throughput, non-subjective, quantitative measurements of parameters such as neuronal number, neurite count and neurite length, all of which can indicate neurotoxic effects. However, the role of astrocytes remains unexplored in these models. Astrocytes have an integral role in the maintenance of central nervous system (CNS) homeostasis, and are associated with both neuroprotection and neurodegradation when they are activated in response to toxic substances or disease states. GFAP is an intermediate filament protein expressed predominantly in the astrocytes of the CNS. Astrocytic activation (gliosis) leads to the upregulation of GFAP, commonly accompanied by astrocyte proliferation and hypertrophy. This process of reactive gliosis has been proposed as an early marker of damage to the nervous system. The traditional method for GFAP quantitation is by immunoassay. This approach is limited by an inability to provide information on cellular localization, morphology and cell number. We determined that HCA could be used to overcome these limitations and to simultaneously measure multiple features associated with gliosis - changes in GFAP expression, astrocyte hypertrophy, and astrocyte proliferation - within a single assay. In co-culture studies, astrocytes have been shown to protect neurons against several types of toxic insult and to critically influence neuronal survival. Recent studies have suggested that the use of astrocytes in an in vitro neurotoxicity test system may prove more relevant to human CNS structure and function than neuronal cells alone. Accordingly, we have developed an HCA assay for co-culture of neurons and astrocytes, comprised of protocols and validated, target-specific detection reagents for profiling &beta;III-tubulin and glial fibrillary acidic protein (GFAP). This assay enables simultaneous analysis of neurotoxicity, neurite outgrowth, gliosis, neuronal and astrocytic morphology and neuronal and astrocytic development in a wide variety of cellular models, representing a novel, non-subjective, high-throughput assay for neurotoxicity assessment. The assay holds great potential for enhanced detection of neurotoxicity and improved productivity in neuroscience research and drug discovery.

This article describes a novel protocol and reagent set designed for sensitive measurement of neurotoxic effects of compounds and treatments on co-cultures of neurons and astrocytes using high content analysis. Results demonstrate that high content analysis represents an exciting novel technology for neurotoxicity assessment.

High Content Analysis (HCA) assays combine cells and detection reagents with automated imaging and powerful image analysis algorithms, allowing ...

Microarray Analysis for Saccharomyces cerevisiae

In this protocol, gene expression in yeast (Saccharomyces cerevisiae) is changed after exposure to oxidative stress induced by the addition of hydrogen peroxide (H2O2), an oxidizing agent. In the experiment, yeast is grown for 48 hours in 1/2X YPD broth containing 3X glucose. The culture is split into a control and treated group. The experiment culture is treated with 0.5 mM H2O2 in Hanks Buffered Saline (HBSS) for 1 hour. The control culture is treated with HBSS only. Total RNA is extracted from both cultures and is converted to a biotin-labeled cRNA product through a multistep process. The final synthesis product is taken back to the UVM Microarray Core Facility and hybridized to the Affymetrix yeast GeneChips. The resulting gene expression data are uploaded into bioinformatics data analysis software.

Microarray Analysis for Saccharomyces cerevisiae

In this protocol, gene expression in yeast (Saccharomyces cerevisiae) is changed after exposure to oxidative stress induced by the addition of hydrogen peroxide (H2O2), an oxidizing agent.

In this protocol, gene expression in yeast (Saccharomyces cerevisiae) is changed after exposure to oxidative stress induced by the addition of hydrogen ...

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

ChIPseq is a widely used technique for investigating protein-DNA interactions. Read density profiles are generated by using next-sequencing of protein-bound DNA and aligning the short reads to a reference genome. Enriched regions are revealed as peaks, which often differ dramatically in shape, depending on the target protein1. For example, transcription factors often bind in a site- and sequence-specific manner and tend to produce punctate peaks, while histone modifications are more pervasive and are characterized by broad, diffuse islands of enrichment2. Reliably identifying these regions was the focus of our work.
Algorithms for analyzing ChIPseq data have employed various methodologies, from heuristics3-5 to more rigorous statistical models, e.g. Hidden Markov Models (HMMs)6-8. We sought a solution that minimized the necessity for difficult-to-define, ad hoc parameters that often compromise resolution and lessen the intuitive usability of the tool. With respect to HMM-based methods, we aimed to curtail parameter estimation procedures and simple, finite state classifications that are often utilized.
Additionally, conventional ChIPseq data analysis involves categorization of the expected read density profiles as either punctate or diffuse followed by subsequent application of the appropriate tool. We further aimed to replace the need for these two distinct models with a single, more versatile model, which can capably address the entire spectrum of data types.
To meet these objectives, we first constructed a statistical framework that naturally modeled ChIPseq data structures using a cutting edge advance in HMMs9, which utilizes only explicit formulas-an innovation crucial to its performance advantages. More sophisticated then heuristic models, our HMM accommodates infinite hidden states through a Bayesian model. We applied it to identifying reasonable change points in read density, which further define segments of enrichment. Our analysis revealed how our Bayesian Change Point (BCP) algorithm had a reduced computational complexity-evidenced by an abridged run time and memory footprint. The BCP algorithm was successfully applied to both punctate peak and diffuse island identification with robust accuracy and limited user-defined parameters. This illustrated both its versatility and ease of use. Consequently, we believe it can be implemented readily across broad ranges of data types and end users in a manner that is easily compared and contrasted, making it a great tool for ChIPseq data analysis that can aid in collaboration and corroboration between research groups. Here, we demonstrate the application of BCP to existing transcription factor10,11 and epigenetic data12 to illustrate its usefulness.

Our Bayesian Change Point (BCP) algorithm builds on state-of-the-art advances in modeling change-points via Hidden Markov Models and applies them to chromatin immunoprecipitation sequencing (ChIPseq) data analysis. BCP performs well in both broad and punctate data types, but excels in accurately identifying robust, reproducible islands of diffuse histone enrichment.

ChIPseq is a widely used technique for investigating protein-DNA interactions. Read density profiles are generated by using next-sequencing of ...

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Metabolite profiling has been a valuable asset in the study of metabolism in health and disease. However, current platforms have different limiting factors, such as labor intensive sample preparations, low detection limits, slow scan speeds, intensive method optimization for each metabolite, and the inability to measure both positively and negatively charged ions in single experiments. Therefore, a novel metabolomics protocol could advance metabolomics studies. Amide-based hydrophilic chromatography enables polar metabolite analysis without any chemical derivatization. High resolution MS using the Q-Exactive (QE-MS) has improved ion optics, increased scan speeds (256 msec at resolution 70,000), and has the capability of carrying out positive/negative switching. Using a cold methanol extraction strategy, and coupling an amide column with QE-MS enables robust detection of 168 targeted polar metabolites and thousands of additional features simultaneously.&#160; Data processing is carried out with commercially available software in a highly efficient way, and unknown features extracted from the mass spectra can be queried in databases.

Metabolite profiling has been a valuable asset in the study of metabolism in health and disease. Utilizing normal-phased liquid chromatography coupled to high-resolution mass spectrometry with polarity switching and a rapid duty cycle, we describe a protocol to analyze the polar metabolic composition of biological material with high sensitivity, accuracy, and resolution.

Metabolite profiling has been a valuable asset in the study of metabolism in health and disease. However, current platforms have different limiting ...

Tracking and Quantifying Developmental Processes in C. elegans Using Open-source Tools

Quantitatively capturing developmental processes is crucial to derive mechanistic models and key to identify and describe mutant phenotypes. Here protocols are presented for preparing embryos and adult C. elegans animals for short- and long-term time-lapse microscopy and methods for tracking and quantification of developmental processes. The methods presented are all based on C. elegans strains available from the Caenorhabditis Genetics Center and on open-source software that can be easily implemented in any laboratory independently of the microscopy system used. A reconstruction of a 3D cell-shape model using the modelling software IMOD, manual tracking of fluorescently-labeled subcellular structures using the multi-purpose image analysis program Endrov, and an analysis of cortical contractile flow using PIVlab (Time-Resolved Digital Particle Image Velocimetry Tool for MATLAB) are shown. It is discussed how these methods can also be deployed to quantitatively capture other developmental processes in different models, e.g., cell tracking and lineage tracing, tracking of vesicle flow.

Tracking and Quantifying Developmental Processes in C. elegans Using Open-source Tools

Here it is shown how to track and quantify developmental processes in C. elegans. The methods presented are based on open-source tools that can be easily implemented. It is demonstrated how to reconstruct 3D cell-shape models, how to manually track subcellular structures, and how to analyze cortical contractile flow.

Quantitatively capturing developmental processes is crucial to derive mechanistic models and key to identify and describe mutant phenotypes. Here ...

Complete Workflow for Analysis of Histone Post-translational Modifications Using Bottom-up Mass Spectrometry: From Histone Extraction to Data Analysis

Nucleosomes are the smallest structural unit of chromatin, composed of 147 base pairs of DNA wrapped around an octamer of histone proteins. Histone function is mediated by extensive post-translational modification by a myriad of nuclear proteins. These modifications are critical for nuclear integrity as they regulate chromatin structure and recruit enzymes involved in gene regulation, DNA repair and chromosome condensation. Even though a large part of the scientific community adopts antibody-based techniques to characterize histone PTM abundance, these approaches are low throughput and biased against hypermodified proteins, as the epitope might be obstructed by nearby modifications. This protocol describes the use of nano liquid chromatography (nLC) and mass spectrometry (MS) for accurate quantification of histone modifications. This method is designed to characterize a large variety of histone PTMs and the relative abundance of several histone variants within single analyses. In this protocol, histones are derivatized with propionic anhydride followed by digestion with trypsin to generate peptides of 5 - 20 aa in length. After digestion, the newly exposed N-termini of the histone peptides are derivatized to improve chromatographic retention during nLC-MS. This method allows for the relative quantification of histone PTMs spanning four orders of magnitude.

This protocol outlines a fully integrated workflow for characterizing histone post-translational modifications using mass spectrometry (MS). The workflow includes histone purification from cell cultures or tissues, histone derivatization and digestion, MS analysis using nano-flow liquid chromatography and instructions for data analysis. The protocol is designed for completion within 2 - 3 days.

Nucleosomes are the smallest structural unit of chromatin, composed of 147 base pairs of DNA wrapped around an octamer of histone proteins. Histone ...

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

Summary

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Chapters in this video

Using Micro-Electro-Mechanical Systems (MEMS) to Develop Diagnostic Tools

Counting and Determining the Viability of Cultured Cells

Trypsinizing and Subculturing Mammalian Cells

Freezing, Thawing, and Packaging Cells for Transport

A Neuronal and Astrocyte Co-Culture Assay for High Content Analysis of Neurotoxicity

Microarray Analysis for Saccharomyces cerevisiae

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Tracking and Quantifying Developmental Processes in C. elegans Using Open-source Tools

Complete Workflow for Analysis of Histone Post-translational Modifications Using Bottom-up Mass Spectrometry: From Histone Extraction to Data Analysis