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Use of web-based tools for quantitative metabolomics

Use of web-based tools for quantitative metabolomics. Jianguo (Jeff) Xia Wishart Research Group University of Alberta, Canada. Metabolomics in the University of Alberta. Outline. Introduction Omics data overview Lessons from other omics research Project goals

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Use of web-based tools for quantitative metabolomics

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  1. Use of web-based tools for quantitative metabolomics Jianguo (Jeff) Xia Wishart Research Group University of Alberta, Canada METABOLOMICS 2010

  2. Metabolomics in the University of Alberta METABOLOMICS 2010

  3. Outline • Introduction • Omics data overview • Lessons from other omics research • Project goals • Web-based metabolomics tools • General data processing & statistical analysis • MetaboAnalyst (http://www.metaboanalyst.ca) • Identify functionally interesting patterns • MSEA (http://www.msea.ca) • Metabolic Pathway Analysis • MetPA (http://metpa.metabolomics.ca) • Public databases • Summary METABOLOMICS 2010

  4. The ‘-omics’ data overview METABOLOMICS 2010

  5. Common questions • Are there some interesting patterns present in my data? • What are the most important features associated with different phenotypes? • Is there a real difference between the groups? • Can I use this data to predict a phenotype? • How to interpret these features / patterns? • How does my result compared withpublished data? METABOLOMICS 2010

  6. Common approaches METABOLOMICS 2010

  7. Project Goals • Provide well-established methods proven highly successful in other ‘omics’ studies; • Do not re-invent the wheel! • Support traditional approaches • Cheminformatics approaches • Data processing & normalization procedures • Easy-t0-use • Not command-line • Target users – bench biologists METABOLOMICS 2010

  8. Indentify influential algorithms \\\\\\\\\\ METABOLOMICS 2010

  9. Identify the best practices Nature Genetics - 38, 500 - 501 (2006) METABOLOMICS 2010

  10. Metabolomics web applications • General data processing & analysis • MetaboAnalyst • http://www.metaboanalyst.ca • Metabolite Set Enrichment Analysis • MSEA • http://www.msea.ca • Metabolomic Pathway Analysis • MetPA • http://metpa.metabolomics.ca METABOLOMICS 2010

  11. MetaboAnalyst • http://www.metaboanalyst.ca • General metabolomics data processing, normalization, and statistical analysis • Support two-group and multi-group analysis • 20+ well-established methods • Dynamic graphical presentation • Automatic report generation METABOLOMICS 2010

  12. What MetaboAnalyst can: • Basic data processing: • Peak picking, Peak alignment, Baseline filtering, etc. • Data normalization • probabilistic quotient normalization, scaling, etc. • Data overview • PCA, Heatmaps, etc. • Identify important features • t-tests, ANOVA, SAM, etc. • Classification • PLS-DA, random Forest, SVM, etc. METABOLOMICS 2010

  13. Download • Processed data • PDF report • Images GC/LC-MS raw spectra MS / NMR peak lists MS / NMR spectra bins Metabolite concentrations • Peak detection • Retention time correction Baseline filtering Peak alignment • Data integrity check • Missing value imputation • Data normalization • Row-wise normalization (4) • Column-wise normalization (4) • Data analysis • Univariate analysis (4) • Dimension reduction (2) • Feature selection (2) • Cluster analysis (4) • Classification (2) • Data annotation • Peak searching (3) • Pathway mapping METABOLOMICS 2010

  14. MetaboAnalyst METABOLOMICS 2010

  15. Data Upload METABOLOMICS 2010

  16. Data processing and integrity check METABOLOMICS 2010

  17. Data normalization (1) METABOLOMICS 2010

  18. Data normalization (2) METABOLOMICS 2010

  19. Data Analysis METABOLOMICS 2010

  20. Clustering with PCA METABOLOMICS 2010

  21. Hierarchical clustering METABOLOMICS 2010

  22. Supervised approach – PLS-DA METABOLOMICS 2010

  23. Feature selection - ANOVA METABOLOMICS 2010

  24. Feature selection - SAM METABOLOMICS 2010

  25. Data Download METABOLOMICS 2010

  26. METABOLOMICS 2010

  27. Updates & Forecast • Recently upgraded • Support for multiple group analysis • One-way ANOVA & post-hoc analysis • To be added • To add some advanced methods for • Association analysis / ROC / OPLS • To enhance web interfacing with XCMS • Allow local installation • To be released by the end of this summer METABOLOMICS 2010

  28. Data Interpretation • Manual approach • Background knowledge plus literature search • Basic & Intuitive • Can be very accurate • Issues • Time-consuming • Subjective • Lack of statistical strength METABOLOMICS 2010

  29. Introducing MSEA • http://www.msea.ca • Metabolite Set Enrichment Analysis • Identify biological meaningful patterns from quantitative metabolomics data METABOLOMICS 2010

  30. Biologically meaningful • Associated to the same biological process (i.e. pathway) • Associated with the same genetic traits (i.e. SNP) • Co-regulated in the same pathological conditions (i.e. disease) • Located in the same cellular compartment • Enriched in certain tissues/ organs METABOLOMICS 2010

  31. The MSEA approach Over-representation Analysis Single Sample Profiling Quantitative Enrichment Analysis Compound concentrations Compound concentrations Compound concentrations Compare to normal references Compound selection (t-tests, ANOVA, PLS-DA) Assess metabolite set directly (GlobalTest) Important compound lists Abnormal compounds Find enriched biological themes Metabolite set libraries Biological interpretation METABOLOMICS 2010

  32. MSEA @ www.msea.ca METABOLOMICS 2010

  33. Over-representation analysis METABOLOMICS 2010

  34. Compound label standardization METABOLOMICS 2010

  35. Identify abnormal concentration METABOLOMICS 2010

  36. Library selection METABOLOMICS 2010

  37. Result METABOLOMICS 2010

  38. Download METABOLOMICS 2010

  39. MSEA summary • More biologically–motivated • Simultaneously biomarker identification and interpretation • Automatic comparison with published data • Important patterns • Reference concentrations • Potential issues • Limited by the size and quality of the knowledge database • For pathway-based metabolite sets • Does not consider the pathway topology METABOLOMICS 2010

  40. Topology matters Glycine, serine and theonine metabolism Galactose metabolism p = 1e-5 p = 1e-7 METABOLOMICS 2010

  41. Introducing MetPA • http://metpa.metabolomics.ca • Pathway Analysis Tool • 884 pathways covering 11 model organisms • Enrichment Analysis • Global Test • Global ANCOVA • Topology Analysis • Degree Centrality • Betweenness Centrality • Google-map style visualization METABOLOMICS 2010

  42. MetPA METABOLOMICS 2010

  43. 11 pathway libraries (KEGG) METABOLOMICS 2010

  44. Combining Enrichment analysis & Topology analysis METABOLOMICS 2010

  45. Node importance measure: centrality • Degree Centrality • Local structure; • Highly connected (hub) • The Red nodes • Betweeness Centrality • Global structures; • Sits on many shortest paths between other nodes • The Blue node Junker et al.BMC Bioinformatics 2006 METABOLOMICS 2010

  46. Point and Click METABOLOMICS 2010

  47. Lossless zooming METABOLOMICS 2010

  48. Result table METABOLOMICS 2010

  49. Downloads METABOLOMICS 2010

  50. MetPA summary • Combine statistical analysis and topological analysis • Results are more close to manual identification • Highly interactive visualization system • allows easy hierarchical navigation within a large amount of information METABOLOMICS 2010

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