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MetaboAnalyst 2.0 & ROCCET

Data A nalysis & B iomarker D iscovery. MetaboAnalyst 2.0 & ROCCET. Jianguo Xia, PhD University of Alberta, Canada. Outline. Introduction (updates) of two free web application MetaboAnalyst 2.0 ROCCET Background & basic concepts Screenshot tutorials Live demo (if we have time).

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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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