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MetaboAnalyst 2.0 & ROCCET

This presentation introduces two free web applications, MetaboAnalyst 2.0 and ROCCET, for metabolomic data analysis and biomarker discovery. It covers the background, basic concepts, and provides screenshot tutorials and live demos.

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MetaboAnalyst 2.0 & ROCCET

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  1. Data Analysis & Biomarker Discovery MetaboAnalyst 2.0 & ROCCET Jianguo Xia, PhD University of Alberta, Canada

  2. Outline • Introduction (updates) of two free web application • MetaboAnalyst 2.0 • ROCCET • Background & basic concepts • Screenshot tutorials • Live demo (if we have time) Metabolomics 2012

  3. Metabolomic Data Analysis

  4. MetaboAnalyst (www.metaboanalyst.ca)

  5. GC/LC-MS raw spectra • Peak lists • Spectral bins • Concentration table • Spectra processing • Peak processing • Noise filtering • Missing value estimation • Row-wise normalization • Column-wise normalization • Combined approach Data integrity check Data input Data processing Data normalization Statistical Exploration Functional Interpretation Enrichment analysis Pathway analysis Time-series analysis Two/multi-group analysis • Over representation analysis • Single sample profiling • Quantitative enrichment • analysis • Enrichment analysis • Topology analysis • Interactive visualization • Data overview • Two-way ANOVA • ANOVA - SCA • Time-course analysis • Univariate analysis • Correlation analysis • Chemometric analysis • Feature selection • Cluster analysis • Classification Outputs Image Center Quality checking Other utilities • Resolution: 150/300/600 dpi • Format: png, tiff, pdf, svg, ps • Methods comparision • Temporal drift • Batch effect • Biolgoical checking • Peak searching • Pathway mapping • Name/ID conversion • Lipidomics • Processed data • Result tables • Analysis report • Images

  6. MetaboAnalyst Overview • Raw data processing • Data reduction & statistical analysis • Functional enrichment analysis • Metabolic pathway analysis • Quality control analysis

  7. Data processing overview • Supported data formats • Concentration tables • Peak lists • Spectral bins • Raw spectra (* not recommended)

  8. Example Datasets

  9. Data Processing Purpose: to convert various raw data forms into data matrices suitable for statistical analysis Metabolomics 2012

  10. Data Upload

  11. Alternatively …

  12. Data Integrity Check

  13. Data Normalization

  14. Normalization Result

  15. Quality Control • Dealing with outliers • Detected mainly by visual inspection • May be corrected by normalization • May be excluded • Dealing with missing values • Noise reduction

  16. Visual Inspection • What does an outlier look like? Finding outliers via PCA Finding outliers via Heatmap

  17. Metabolomics 2012

  18. Quality Check Module

  19. Outlier Removal

  20. Data Filtering • Characteristics of noise & uninformative features • Low intensities • Low variances (default)

  21. Noise Reduction

  22. Missing values Metabolomics 2012

  23. Dimension Reduction & Statistical Analysis

  24. Common tasks • To identify important features; • To detect interesting patterns; • To assess difference between the phenotypes • To facilitate classification / prediction

  25. ANOVA

  26. View Individual Compounds

  27. Overall correlation pattern

  28. High resolution image Specify format Specify resolution Specify size

  29. Template Matching • Looking for compounds showing interesting patterns of change • Essentially a method to look for linear trends or periodic trends in the data • Best for data that has 3 or more groups

  30. Template Matching (cont.) Strong linear + correlation to grain % Strong linear - correlation to grain %

  31. PCA Scores Plot

  32. PCA Loading Plot Compounds most responsible for separation

  33. PLS-DA Score Plot

  34. Evaluation of PLS-DA Model • PLS-DA Model evaluated by cross validation of Q2 and R2 • More components to model improves quality of fit, but try to minimize this value • 3 Component model seems to be a good compromise here • Good R2/Q2 (>0.7)

  35. Important Compounds

  36. Model Validation

  37. Heatmap Visualization

  38. Heatmap Visualization (cont.)

  39. Download Results

  40. Analysis Report

  41. Metabolite Set Enrichment Analysis (MSEA)

  42. Enrichment Analysis • Purpose: To test if there are some biologicallymeaningful groups of metabolites that are significantly enriched in your data • Biological meaningful groups • Pathways • Disease • Localization • Currently, only supports human metabolomic data

  43. MSEA • Accepts 3 kinds of input files • 1) list of metabolite names only (ORA) • 2) list of metabolite names + concentration data from a single sample (SSP) • 3) a concentration table with a list of metabolite names + concentrations for multiple samples/patients (QEA)

  44. 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, clustering) Assess metabolite set directly Important compound lists Abnormal compounds Find enriched biological themes ORA input For MSEA Metabolite set libraries Biological interpretation

  45. Start with a compound List

  46. Upload Compound List

  47. Compound Name Standardization

  48. Name Standardization (cont.)

  49. Select a Metabolite Set Library

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