Setac 2008 Genespring Ppt.

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bioinformatics using GeneSpring GX10

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  • Setac 2008 Genespring Ppt.

    1. 1. Expertise in Gene Discovery, Microarray Development and Bioinformatics SETAC 29 th Annual Meeting November 16th-20th Tampa, FL EcoArray, Inc. www.ecoarray.com
    2. 2. EcoArray Bioinformatics <ul><li>Experimental grouping; </li></ul><ul><li>Quality control on samples (PCA); </li></ul><ul><li>Fold change and t-test/ANOVA </li></ul><ul><li>Differential expression complete lists and (GO) information. </li></ul><ul><li>Advanced/Custom Analysis ( optional ) </li></ul>
    3. 3. GeneSpring GX <ul><li>Robust and accessible statistical tools </li></ul><ul><li>Fast visualization and analysis of expression data </li></ul><ul><li>Place significantly results into biological context: </li></ul><ul><li>-Full GO info and browser </li></ul><ul><li>-Pathway analysis </li></ul><ul><li>-Gene enrichment (GSEA) </li></ul><ul><li>Cluster analyses </li></ul><ul><li>Classification algorithms </li></ul>
    4. 4. Boxplots
    5. 5. Principal components analysis (PCA) Quality control
    6. 6. Histograms
    7. 7. Profile and Scatter Plots
    8. 8. Filter by Expression
    9. 9. Multiple pair-wise comparison Volcano plot ANOVA Venn Diagram
    10. 10. Basic/Advanced Statistics Now what? We can do the analysis for you . Advanced Basic <ul><li>Filtering for outliers </li></ul><ul><li>Normalization </li></ul><ul><li>PCA </li></ul><ul><li>ANOVA </li></ul><ul><li>Significance (p < 0.05) </li></ul><ul><li>Fold change > 2.0 </li></ul><ul><li>Gene ontology </li></ul>(optional) <ul><li>Predictive modeling </li></ul><ul><li>Biological pathways </li></ul><ul><li>Cluster Analysis </li></ul><ul><li>Biological interpretation </li></ul>
    11. 11. Fold Changes >=2.0
    12. 12. Fold Changes >=2.0
    13. 13. Cluster Analysis <ul><li>K-means </li></ul><ul><li>Hierarchal </li></ul><ul><li>Self-organizing maps </li></ul><ul><li>PCA-based </li></ul>Uncover interesting patterns
    14. 15. Prediction Model Goal: To build a robust model to predict known phenotypic samples from gene expression data. unknown sample Predict gene expression characteristics
    15. 16. Building a Prediction Model 1) Validate , 2) Train, 3) Classify                 <ul><li>appropriate algorithm </li></ul><ul><li>select right set of features </li></ul><ul><li>avoid over-fitting models </li></ul><ul><li>training algorithm </li></ul><ul><li>output </li></ul><ul><li>visualize efficacy of </li></ul><ul><li>classification and </li></ul><ul><li>prediction </li></ul>
    16. 17. GeneSpring GX network builder 1) Right click on edge, Source of supporting literature (ie.PubMed) 2) Locate sentence(s),
    17. 18. Confusion matrix Prediction results
    18. 19. Plug-in databases
    19. 20. Expertise in Gene Discovery, Microarray Development and Bioinformatics EcoArray, Inc. www.ecoarray.com Heather R. Hammers (Bioinformatician/Scientist) [email_address]

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