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

<ol><li>Sas, K. M., Karnovsky, A., Michailidis, G., Pennathur, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Metabolomics+and+diabetes%3A+analytical+and+computational+approaches%5BTitle%5D)+AND+%22Diabetes%22%5BJournal%5D)">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/pubmed?term=((MetExplore%3A+Collaborative+edition+and+exploration+of+metabolic+networks%5BTitle%5D)+AND+%22Nucleic+Acids+Research%22%5BJournal%5D)">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/pubmed?term=((Paintomics%3A+A+web+based+tool+for+the+joint+visualization+of+transcriptomics+and+metabolomics+data%5BTitle%5D)+AND+%22Bioinformatics%22%5BJournal%5D)">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/pubmed?term=((3Omics%3A+A+web-based+systems+biology+tool+for+analysis%2C+integration+and+visualization+of+human+transcriptomic%2C+proteomic+and+metabolomic+data%5BTitle%5D)+AND+%22BMC+Systems+Biology%22%5BJournal%5D)">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/pubmed?term=((The+pathway+tools+cellular+overview+diagram+and+Omics+Viewer%5BTitle%5D)+AND+%22Nucleic+Acids+Research%22%5BJournal%5D)">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/pubmed?term=((Metscape+2+bioinformatics+tool+for+the+analysis+and+visualization+of+metabolomics+and+gene+expression+data%5BTitle%5D)+AND+%22Bioinformatics%22%5BJournal%5D)">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/pubmed?term=((Using+MetaboAnalyst+4.0+for+metabolomics+data+analysis%2C+interpretation%2C+and+integration+with+other+omics+data%5BTitle%5D)+AND+%22Methods+in+Molecular+Biology%22%5BJournal%5D)">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/pubmed?term=((MBROLE+2.0-functional+enrichment+of+chemical+compounds%5BTitle%5D)+AND+%22Nucleic+Acids+Research%22%5BJournal%5D)">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/pubmed?term=((Broad-Enrich%3A+Functional+interpretation+of+large+sets+of+broad+genomic+regions%5BTitle%5D)+AND+%22Bioinformatics%22%5BJournal%5D)">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/pubmed?term=((DAVID+bioinformatics+resources%3A+Expanded+annotation+database+and+novel+algorithms+to+better+extract+biology+from+large+gene+lists%5BTitle%5D)+AND+%22Nucleic+Acids+Research%22%5BJournal%5D)">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. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((INRICH%3A+interval-based+enrichment+analysis+for+genome-wide+association+studies%5BTitle%5D)+AND+%22Bioinformatics%22%5BJournal%5D)">INRICH: interval-based enrichment analysis for genome-wide association studies.</a> Bioinformatics. 28 (13), 1797-1799 (2012).</li>
<li>Segre, A. V., Groop, L., Mootha, V. K., Daly, M. J., Altshuler, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Common+inherited+variation+in+mitochondrial+genes+is+not+enriched+for+associations+with+type+2+diabetes+or+related+glycemic+traits%5BTitle%5D)+AND+%22PLoS+Genetics%22%5BJournal%5D)">Common inherited variation in mitochondrial genes is not enriched for associations with type 2 diabetes or related glycemic traits.</a> PLoS Genetics. 6 (8), e1001058(2010).</li>
<li>Subramanian, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Gene+set+enrichment+analysis%3A+a+knowledge-based+approach+for+interpreting+genome-wide+expression+profiles%5BTitle%5D)+AND+%22Proceedings+of+the+National+Academy+of+Sciences+of+the+United+States+of+America%22%5BJournal%5D)">Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.</a> Proceedings of the National Academy of Sciences of the United States of America. 102 (43), 15545-15550 (2005).</li>
<li>Afshinnia, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Lipidomic+signature+of+progression+of+chronic+kidney+disease+in+the+chronic+renal+insufficiency+cohort%5BTitle%5D)+AND+%22Kidney+International+Reports%22%5BJournal%5D)">Lipidomic signature of progression of chronic kidney disease in the chronic renal insufficiency cohort.</a> Kidney International Reports. 1 (4), 256-268 (2016).</li>
<li>Barupal, D. K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((MetaMapp%3A+Mapping+and+visualizing+metabolomic+data+by+integrating+information+from+biochemical+pathways+and+chemical+and+mass+spectral+similarity%5BTitle%5D)+AND+%22BMC+Bioinformatics%22%5BJournal%5D)">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/pubmed?term=((Sparse+network+modeling+and+Metscape-based+visualization+methods+for+the+analysis+of+large-scale+metabolomics+data%5BTitle%5D)+AND+%22Bioinformatics%22%5BJournal%5D)">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/pubmed?term=((Gaussian+graphical+modeling+reconstructs+pathway+reactions+from+high-throughput+metabolomics+data%5BTitle%5D)+AND+%22BMC+Systems+Biology%22%5BJournal%5D)">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/pubmed?term=((The+origin+of+correlations+in+metabolomics+data%5BTitle%5D)+AND+%22Metabolomics%22%5BJournal%5D)">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/pubmed?term=((Observing+and+interpreting+correlations+in+metabolomic+networks%5BTitle%5D)+AND+%22Bioinformatics%22%5BJournal%5D)">Observing and interpreting correlations in metabolomic networks.</a> Bioinformatics. 19 (8), 1019-1026 (2003).</li>
<li>Bühlmann, P., Van De Geer, S. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aB%C3%BChlmann%2C+P+%22Statistics+for+High-Dimensional+Data%3A+Methods%2C+Theory+and+Applications%22">Statistics for High-Dimensional Data: Methods, Theory and Applications</a>. , Springer Berlin, Heidelberg. (2011).</li>
<li>Iyer, G. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Application+of+differential+network+enrichment+analysis+for+deciphering+metabolic+alterations%5BTitle%5D)+AND+%22Metabolites%22%5BJournal%5D)">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/pubmed?term=((Differential+network+enrichment+analysis+reveals+novel+lipid+pathways+in+chronic+kidney+disease%5BTitle%5D)+AND+%22Bioinformatics%22%5BJournal%5D)">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/pubmed?term=((Cytoscape%3A+a+software+environment+for+integrated+models+of+biomolecular+interaction+networks%5BTitle%5D)+AND+%22Genome+Reserach%22%5BJournal%5D)">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/pubmed?term=((Mining+the+unknown%3A+a+systems+approach+to+metabolite+identification+combining+genetic+and+metabolic+information%5BTitle%5D)+AND+%22PLoS+Genetics%22%5BJournal%5D)">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/pubmed?term=((Systemic+alterations+in+the+metabolome+of+diabetic+NOD+mice+delineate+increased+oxidative+stress+accompanied+by+reduced+inflammation+and+hypertriglyceremia%5BTitle%5D)+AND+%22American+Journal+of+Physiology.+Endocrinology+and+Metabolism%22%5BJournal%5D)">Systemic alterations in the metabolome of diabetic NOD mice delineate increased oxidative stress accompanied by reduced inflammation and hypertriglyceremia.</a> American Journal of Physiology. Endocrinology and Metabolism. 308 (11), E978-E989 (2015).</li>
<li>Grapov, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Diabetes+associated+metabolomic+perturbations+in+NOD+mice%5BTitle%5D)+AND+%22Metabolomics%22%5BJournal%5D)">Diabetes associated metabolomic perturbations in NOD mice.</a> Metabolomics. 11 (2), 425-437 (2015).</li>
<li>Jin, Y., Bai, S., Huang, Z., You, L., Zhang, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Technology+characteristics+and+flavor+changes+of+traditional+green+wheat+product+nian+zhuan+in+Northern+China%5BTitle%5D)+AND+%22Frontiers+in+Nutrition%22%5BJournal%5D)">Technology characteristics and flavor changes of traditional green wheat product nian zhuan in Northern China.</a> Frontiers in Nutrition. 9, 996337(2022).</li>
<li>Lin, Y. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Probing+folate-responsive+and+stage-sensitive+metabolomics+and+transcriptional+co-expression+network+markers+to+predict+prognosis+of+non-small+cell+lung+cancer+patients%5BTitle%5D)+AND+%22Nutrients%22%5BJournal%5D)">Probing folate-responsive and stage-sensitive metabolomics and transcriptional co-expression network markers to predict prognosis of non-small cell lung cancer patients.</a> Nutrients. 15 (1), 3(2022).</li>
<li>Pan, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Metabolomics+study+identified+bile+acids+as+potential+biomarkers+for+gastric+cancer%3A+A+case+control+study%5BTitle%5D)+AND+%22Frontiers+in+Endocrinology+%28Lausanne%29%22%5BJournal%5D)">Metabolomics study identified bile acids as potential biomarkers for gastric cancer: A case control study.</a> Frontiers in Endocrinology (Lausanne). 13, 1039786(2022).</li>
<li>Pancoro, A., Karima, E., Apriyanto, A., Effendi, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((%281%29H+NMR+metabolomics+analysis+of+oil+palm+stem+tissue+infected+by+Ganoderma+boninense+based+on+field+severity+Indices%5BTitle%5D)+AND+%22Scientific+Reports%22%5BJournal%5D)">(1)H NMR metabolomics analysis of oil palm stem tissue infected by Ganoderma boninense based on field severity Indices.</a> Scientific Reports. 12 (1), 21087(2022).</li>
<li>Chele, K. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+global+metabolic+map+defines+the+effects+of+a+Si-based+biostimulant+on+tomato+plants+under+normal+and+saline+conditions%5BTitle%5D)+AND+%22Metabolites%22%5BJournal%5D)">A global metabolic map defines the effects of a Si-based biostimulant on tomato plants under normal and saline conditions.</a> Metabolites. 11 (12), 820(2021).</li>
<li>Hubert, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+effect+of+residual+pesticide+application+on+microbiomes+of+the+storage+mite+Tyrophagus+putrescentiae%5BTitle%5D)+AND+%22Microbial+Ecology%22%5BJournal%5D)">The effect of residual pesticide application on microbiomes of the storage mite Tyrophagus putrescentiae.</a> Microbial Ecology. 85 (4), 1527-1540 (2023).</li>
<li>Li, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Metabolomic+and+exposomic+biomarkers+of+risk+of+future+neurodevelopmental+delay+in+human+milk%5BTitle%5D)+AND+%22Pediatric+Research%22%5BJournal%5D)">Metabolomic and exposomic biomarkers of risk of future neurodevelopmental delay in human milk.</a> Pediatric Research. 93 (6), 1710-1720 (2023).</li>
<li>Marino, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+metabolomic+profile+in+amyotrophic+lateral+sclerosis+changes+according+to+the+progression+of+the+disease%3A+An+exploratory+study%5BTitle%5D)+AND+%22Metabolites%22%5BJournal%5D)">The metabolomic profile in amyotrophic lateral sclerosis changes according to the progression of the disease: An exploratory study.</a> Metabolites. 12 (9), 837(2022).</li>
<li>Ma, J., Shojaie, A., Michailidis, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Network-based+pathway+enrichment+analysis+with+incomplete+network+information%5BTitle%5D)+AND+%22Bioinformatics%22%5BJournal%5D)">Network-based pathway enrichment analysis with incomplete network information.</a> Bioinformatics. 32 (20), 3165-3174 (2016).</li>
<li>Mahieu, N. G., Patti, G. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Systems-level+annotation+of+a+metabolomics+data+set+reduces+25000+features+to+fewer+than+1000+unique+metabolites%5BTitle%5D)+AND+%22Analytical+Chemistry%22%5BJournal%5D)">Systems-level annotation of a metabolomics data set reduces 25000 features to fewer than 1000 unique metabolites.</a> Analytical Chemistry. 89 (19), 10397-10406 (2017).</li>
</ol>

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><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

Research

JoVE Journal

Biology

2.2K Views.  University of Michigan, Ann Arbor. Metabolomics-gegevensanalyse is een proces dat uit meerdere stappen bestaat en dat gebruikmaakt van veel gespecialiseerde softwaretools. Deze data-analyse kan worden onderverdeeld in drie hoofdstappen: gegevensverwerking en kwaliteitscontrole, statistische analyse en interpretatie van biologische gegevens. De tools die in dit protocol worden beschreven, zijn ontworpen om de laatste stap in de analyse mogelijk te maken.In het afgelopen decennium is metabolomics naar voren gekomen als an...

Video: Auteur in de schijnwerpers: opkomende technologieën en geavanceerde tools voor het decoderen van metabolomics-gegevensanalyse 

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)

Non-targeted metabolite profiling by ultra performance liquid chromatography coupled with mass spectrometry (UPLC-MS) is a powerful technique to investigate metabolism. The approach offers an unbiased and in-depth analysis that can enable the development of diagnostic tests, novel therapies, and further our understanding of disease processes. The inherent chemical diversity of the metabolome creates significant analytical challenges and there is no single experimental approach that can detect all metabolites. Additionally, the biological variation in individual metabolism and the dependence of metabolism on environmental factors necessitates large sample numbers to achieve the appropriate statistical power required for meaningful biological interpretation. To address these challenges, this tutorial outlines an analytical workflow for large scale non-targeted metabolite profiling of serum by UPLC-MS. The procedure includes guidelines for sample organization and preparation, data acquisition, quality control, and metabolite identification and will enable reliable acquisition of data for large experiments and provide a starting point for laboratories new to non-targeted metabolite profiling by UPLC-MS.

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Non-targeted metabolite profiling by ultra performance liquid chromatography coupled with mass spectrometry (UPLC-MS) is a powerful technique to investigate metabolism. This article outlines a typical workflow utilized for non-targeted metabolite profiling of serum including sample organization and preparation, data acquisition, data analysis, quality control, and metabolite identification.

Non-targeted metabolite profiling by ultra performance liquid chromatography coupled with mass spectrometry (UPLC-MS) is a powerful technique to ...

Chemistry

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

Terroir refers to the combination of environmental factors that affect the characteristics of crops such as grapevine (Vitis vinifera) according to particular habitats and management practices. This article shows how certain terroir signatures can be detected in the berry metabolome and transcriptome of the grapevine cultivar Corvina using multivariate statistical analysis. The method first requires an appropriate sampling plan. In this case study, a specific clone of the Corvina cultivar was selected to minimize genetic differences, and samples were collected from seven vineyards representing three different macro-zones during three different growing seasons. An untargeted LC-MS metabolomics approach is recommended due to its high sensitivity, accompanied by efficient data processing using MZmine software and a metabolite identification strategy based on fragmentation tree analysis. Comprehensive transcriptome analysis can be achieved using microarrays containing probes covering ~99% of all predicted grapevine genes, allowing the simultaneous analysis of all differentially expressed genes in the context of different terroirs. Finally, multivariate data analysis based on projection methods can be used to overcome the strong vintage-specific effect, allowing the metabolomics and transcriptomics data to be integrated and analyzed in detail to identify informative correlations.

This article describes the application of untargeted metabolomics, transcriptomics and multivariate statistical analysis to grape berry transcripts and metabolites in order to gain insight into the terroir concept, i.e., the impact of the environment on berry quality traits.

Terroir refers to the combination of environmental factors that affect the characteristics of crops such as grapevine (Vitis vinifera) according to ...

Genetics

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Recently, a new implementation of a previously described method for interpreting genome-wide association study (GWAS) data using metabolic pathway analysis has been developed and released. The Pathway Association Study Tool (PAST) was developed to address concerns with user-friendliness and slow-running analyses. This new user-friendly tool has been released on Bioconductor and Github. In testing, PAST ran analyses in less than one hour that previously required twenty-four or more hours. In this article, we present the protocol for using either the Shiny application or the R console to run PAST.

By running the Pathway Association Study Tool (PAST), either through the Shiny application or through the R console, researchers can gain a deeper understanding of the biological meaning of their genome-wide association study (GWAS) results by investigating the metabolic pathways involved.

Recently, a new implementation of a previously described method for interpreting genome-wide association study (GWAS) data using metabolic pathway ...

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Dynamic protein-protein interactions control cellular behavior, from motility to DNA replication to signal transduction. However, monitoring dynamic interactions among multiple proteins in a protein interaction network is technically difficult. Here, we present a protocol for Quantitative Multiplex Immunoprecipitation (QMI), which allows quantitative assessment of fold changes in protein interactions based on relative fluorescence measurements of Proteins in Shared Complexes detected by Exposed Surface epitopes (PiSCES). In QMI, protein complexes from cell lysates are immunoprecipitated onto microspheres, and then probed with a labeled antibody for a different protein in order to quantify the abundance of PiSCES. Immunoprecipitation antibodies are conjugated to different MagBead spectral regions, which allows a flow cytometer to differentiate multiple parallel immunoprecipitations and simultaneously quantify the amount of probe antibody associated with each. QMI does not require genetic tagging and can be performed using minimal biomaterial compared to other immunoprecipitation methods. QMI can be adapted for any defined group of interacting proteins, and has thus far been used to characterize signaling networks in T cells and neuronal glutamate synapses. Results have led to new hypothesis generation with potential diagnostic and therapeutic applications. This protocol includes instructions to perform QMI, from the initial antibody panel selection through to running assays and analyzing data. The initial assembly of a QMI assay involves screening antibodies to generate a panel, and empirically determining an appropriate lysis buffer. The subsequent reagent preparation includes covalently coupling immunoprecipitation antibodies to MagBeads, and biotinylating probe antibodies so they can be labeled by a streptavidin-conjugated fluorophore. To run the assay, lysate is mixed with MagBeads overnight, and then beads are divided and incubated with different probe antibodies, and then a fluorophore label, and read by flow cytometry. Two statistical tests are performed to identify PiSCES that differ significantly between experimental conditions, and results are visualized using heatmaps or node-edge diagrams.

Quantitative Multiplex Immunoprecipitation (QMI) uses flow cytometry for sensitive detection of differences in the abundance of targeted protein-protein interactions between two samples. QMI can be performed using a small amount of biomaterial, does not require genetically engineered tags, and can be adapted for any previously defined protein interaction network.

Dynamic protein-protein interactions control cellular behavior, from motility to DNA replication to signal transduction. However, monitoring dynamic ...

Biochemistry

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling

Immunoaffinity purification mass spectrometry (IP-MS) has emerged as a robust quantitative method of identifying protein-protein interactions. This publication presents a complete interaction proteomics workflow designed for identifying low abundance protein-protein interactions from the nucleus that could also be applied to other subcellular compartments. This workflow includes subcellular fractionation, immunoprecipitation, sample preparation, offline cleanup, single-shot label-free mass spectrometry, and downstream computational analysis and data visualization. Our protocol is optimized for detecting compartmentalized, low abundance interactions that are difficult to identify from whole cell lysates (e.g., transcription factor interactions in the nucleus) by immunoprecipitation of endogenous proteins from fractionated subcellular compartments. The sample preparation pipeline outlined here provides detailed instructions for the preparation of HeLa cell nuclear extract, immunoaffinity purification of endogenous bait protein, and quantitative mass spectrometry analysis. We also discuss methodological considerations for performing large-scale immunoprecipitation in mass spectrometry-based interaction profiling experiments and provide guidelines for evaluating data quality to distinguish true positive protein interactions from nonspecific interactions. This approach is demonstrated here by investigating the nuclear interactome of the CMGC kinase, DYRK1A, a low abundance protein kinase with poorly defined interactions within the nucleus.

Described is a proteomics workflow for identifying protein interaction partners from a nuclear subcellular fraction using immunoaffinity enrichment of a given protein of interest and label-free mass spectrometry. The workflow includes subcellular fractionation, immunoprecipitation, filter aided sample preparation, offline cleanup, mass spectrometry, and a downstream bioinformatics pipeline.

Immunoaffinity purification mass spectrometry (IP-MS) has emerged as a robust quantitative method of identifying protein-protein interactions. This ...

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

The proper use of statistical modeling in NGS data analysis requires an advanced level of expertise. There has recently been a growing consensus on using generalized linear models for differential analysis of RNA-Seq data and the advantage of mixture models to perform co-expression analysis. To offer a managed setting to use these modeling approaches, we developed DiCoExpress that provides a standardized R pipeline to perform an RNA-Seq analysis. Without any particular knowledge in statistics or R programming, beginners can perform a complete RNA-Seq analysis from quality controls to co-expression through differential analysis based on contrasts inside a generalized linear model. An enrichment analysis is proposed both on the lists of differentially expressed genes, and the co-expressed gene clusters. This video tutorial is conceived as a step-by-step protocol to help users take full advantage of DiCoExpress and its potential in empowering the biological interpretation of an RNA-Seq experiment.

DiCoExpress is a script-based tool implemented in R to perform an RNA-Seq analysis from quality control to co-expression. DiCoExpress handles complete and unbalanced design up to 2 biological factors. This video tutorial guides the user through the different features of DiCoExpress.

The proper use of statistical modeling in NGS data analysis requires an advanced level of expertise. There has recently been a growing consensus on using ...

Engineering

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Untargeted metabolomics techniques are being widely used in recent years. However, the rapidly increasing throughput and number of samples create an enormous amount of spectra, setting challenges for quality control of the mass spectrometry spectra. To reduce the false positives, false discovery rate (FDR) quality control is necessary. Recently, we developed a software for FDR control of untargeted metabolome identification that is based on a Target-Decoy strategy named XY-Meta. Here, we demonstrated a complete analysis pipeline that integrates XY-Meta and metaX together. This protocol shows how to use XY-meta to generate a decoy database from an existing reference database and perform FDR control using the Target-Decoy strategy for large-scale metabolome identification on an open-access dataset. The differential analysis and metabolites annotation were performed after running metaX for metabolites peaks detection and quantitation. In order to help more researchers, we also developed a user-friendly cloud-based analysis platform for these analyses, without the need for bioinformatics skills or any computer languages.

We constructed an untargeted metabolomic workflow that integrated XY-Meta and metaX together. In this protocol, we displayed how to use XY-Meta to generate a decoy spectral library from open access spectra reference, and then performed FDR control and used the metaX to quantitate the metabolites after identifying the metabolomics spectra.

Untargeted metabolomics techniques are being widely used in recent years. However, the rapidly increasing throughput and number of samples create an ...

Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer

With the advent of the -omics approaches our understanding of the chronic diseases like cancer and metabolic syndrome has improved. However, effective mining of the information in the large-scale datasets that are obtained from gene expression microarrays, deep sequencing experiments or metabolic profiling is essential to uncover and then effectively target the critical regulators of diseased cell phenotypes. Estrogen Receptor &#945; (ER&#945;) is one of the master transcription factors regulating the gene programs that are important for estrogen responsive breast cancers. In order to understand to role of ER&#945; signaling in breast cancer metabolism we utilized transcriptomic, cistromic and metabolomic data from MCF-7 cells treated with estradiol. In this report we described generation of samples for RNA-Seq, ChIP-Seq and metabolomics experiments and the integrative computational analysis of the obtained data. This approach is useful in delineating novel molecular mechanisms and gene regulatory circuits that are regulated by a particular transcription factor which impacts metabolism of normal or diseased cells.

Integration of data from genome-wide sequencing experiments and metabolomics experiments is a challenge. In this paper we report, for the first time, generation, analysis and integration of transcriptome, cistrome and metabolome data from breast cancer cells treated with estradiol.

With the advent of the -omics approaches our understanding of the chronic diseases like cancer and metabolic syndrome has improved. However, effective ...

Medicine

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 ...

Analyzing Signaling Modules in Alzheimer's Disease with Deep Learning

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Large omics datasets are becoming increasingly available for research into human health. This paper presents DeepOmicsAE, a workflow optimized for the analysis of multi-omics datasets, including&#160;proteomics, metabolomics, and clinical data. This workflow employs a type of neural network called autoencoder, to extract a concise set of features from the high-dimensional multi-omics input data. Furthermore, the workflow provides a method to optimize the key parameters needed to implement the autoencoder. To showcase this workflow, clinical data were analyzed from a cohort of 142 individuals who were either healthy or diagnosed with Alzheimer&#39;s disease, along with the proteome and metabolome of their postmortem brain samples. The features extracted from the latent layer of the autoencoder retain the biological information that separates healthy and diseased patients. In addition, the individual extracted features represent distinct molecular signaling modules, each of which interacts uniquely with the individuals&#39; clinical features,&#160;providing for a mean to integrate&#160;the proteomics, metabolomics, and clinical data.

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches

DeepOmicsAE is a workflow centered on the application of a deep learning method (i.e., an autoencoder) to reduce the dimensionality of multi-omics data, providing a foundation for predictive models and signaling modules representing multiple layers of omics data.

Large omics datasets are becoming increasingly available for research into human health. This paper presents DeepOmicsAE, a workflow optimized for the ...

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

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