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MeV: Joe White
MeV: Joe White
MeV: Joe White
MeV: Joe White
MeV: Joe White
MeV: Joe White
MeV: Joe White
MeV: Joe White
MeV: Joe White
MeV: Joe White
MeV: Joe White
MeV: Joe White
MeV: Joe White
MeV: Joe White
MeV: Joe White
MeV: Joe White
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MeV: Joe White

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    • 1. Analysis of Multiple Experiments TIGR Multiple Experiment Viewer (MeV) Joseph White DFCI January 24,2008
    • 2. MeV
      • Stand-alone java application for analysis
      • New version: 4.1
      • Not database centric; uses TDMS files
      • Writes TDMS files
      • Primarily for normalized data
      • MeV does not currently write MAGE-TAB
      • Download MeV from: tm4.org
    • 3. Outline
      • Description of MeV
      • How MeV treats expression
      • Some essential concepts
      • Demo: basic operations in MeV
        • New file loader
        • ANOVA example
      • Demo of MeV new features
        • Affymetrix file reader
        • Non-parametric tests
        • CGH
      • GCOD
    • 4. The Expression Matrix is a representation of data from multiple microarray experiments. Each element is a log ratio (usually log 2 (Cy5 / Cy3) ) Red indicates a positive log ratio, i.e, Cy5 > Cy3 Green indicates a negative log ratio , i.e., Cy5 < Cy3 Black indicates a log ratio of zero, i. e., Cy5 and Cy3 are very close in value Gray indicates missing data Exp 1 Exp 2 Exp 3 Exp 4 Exp 5 Exp 6 Gene 1 Gene 2 Gene 3 Gene 4 Gene 5 Gene 6
    • 5. Expression Vectors
      • -Gene Expression Vectors
      • encapsulate the expression of a gene over a set of experimental conditions or sample types.
      Log2(cy5/cy3) -0.8 0.8 1.5 1.8 0.5 -1.3 -0.4 1.5
    • 6. Expression Vectors As Points in ‘Expression Space’ Experiment 1 Experiment 2 Experiment 3 Similar Expression -0.8 -0.6 0.9 1.2 -0.3 1.3 -0.7 Exp 1 Exp 2 Exp 3 G1 G2 G3 G4 G5 -0.4 -0.4 -0.8 -0.8 -0.7 1.3 0.9 -0.6
    • 7. Distance and Similarity -the ability to calculate a distance (or similarity, it’s inverse) between two expression vectors is fundamental to clustering algorithms -distance between vectors is the basis upon which decisions are made when grouping similar patterns of expression -selection of a distance metric defines the concept of distance
    • 8. Distance: a measure of similarity between genes.
      • Some distances: (MeV provides 11 metrics)
      • Euclidean:  i = 1 (x iA - x iB ) 2
      3. Pearson correlation p 0 p 1 Exp 1 Exp 2 Exp 3 Exp 4 Exp 5 Exp 6 Gene A Gene B x 1A x 2A x 3A x 4A x 5A x 6A x 1B x 2B x 3B x 4B x 5B x 6B 6
      • Manhattan:  i = 1 |x iA – x iB |
      6
    • 9. Distance is Defined by a Metric 4.2 1.4 -1.00 -0.90 Euclidean Pearson(r*-1) Distance Metric : D D
    • 10. Normal distribution X = μ (mean of the distribution) σ = std. deviation of the distribution
    • 11. Current MeV Algorithms
      • Hierarchical Clustering
      • K Means clustering
      • Support Trees for HCL
      • EASE (annotation clustering
      • Self-organizing maps
      • K-Nearest Neighbors
      • Support Vector Machines
      • Relevance Networks
      • Template Matching
      • PCA
      • CGH
      • Bayesean Networks
      • T-test
      • ANOVA
        • One and two factor
      • SAM
      • Non-parametric tests
        • Wilcoxon
        • Fisher Exact Test
        • Mack-Skillings
        • Kruskat-Wallins
      • BRIDGE
    • 12. Demos
      • File loaders
      • HTA data: ANOVA
      • Affymetrix data: SAM
      • Non-Parametric tests
      • CGH
    • 13. GeneChip Oncology Database
    • 14. GeneChip Oncology Database
    • 15. GCOD statistics
      • Studies: 52
      • Hybridizations: 4591
      • Analysis Result sets: 12,637
      • Signal values: 204,296,195
      • Samples: 3644
      • Probesets: 160,817
            • eg. (HG-U133A: 22,293)
          • (HG_U133_Plus_2: 54,684)
      • Arraydesigns: 9
      • Accessions: 54,414
    • 16. MeV Team
      • Eleanor Howe
      • Sarita Nair
      • Raktim Sinha
      • [email_address]

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