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Microarrays 2

Comparing Gene Expression. Compare the responses of two genes through different conditions (yes)Directly compare the expression of two genes in the same condition (no)Absolute versus relative changes. Gene Clustering. Condition Clustering. . . . . . . . . . . Condition 1. . Gene 2. Gene 1. Gene 3.

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Microarrays 2

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    1. Microarrays 2 Metrics for determining coexpression Unsupervised learning (clustering)

    2. Comparing Gene Expression Compare the responses of two genes through different conditions (yes) Directly compare the expression of two genes in the same condition (no) Absolute versus relative changes

    3. Gene Clustering

    4. Condition Clustering

    5. Clustering Gene Expression Data Choose a distance metric Pearson Correlation Spearman Correlation Euclidean Distance Mutual Information

    6. Normalization of Expression Profiles

    7. Pearson Correlation Coefficient Compares scaled profiles! Can detect inverse relationships Most commonly used Spearman rank correlation technically more correct

    8. Correlation Examples

    9. Correlation Pitfalls 1

    10. Correlation Pitfalls 2

    11. Avoid Pitfalls By Filtering The Data Remove Genes that do not reach some threshold level in at least one (or more) conditions For Affymetrix, remove genes whose stdev/mean ratio does not reach some threshold For spotted arrays, remove genes whose stdev does not reach some threshold

    12. Euclidean Distance Based on Pythagoras Scaled versus unscaled Cannot detect inverse relation ships

    13. Mutual Information Must bin expression values (discreet). Can detect inverse and non-linear relationships (for example y=x3)

    14. Mutual Information Example

    15. Clustering Algorithms Hierarchical Agglomerative Neural Networks Principal Component Analysis And many others

    16. Clustering: Example 1, Step 1

    17. Clustering: Example 1, Step 2

    18. Clustering: Example 1, Step 3

    19. Tree View Eisen et al. (1998) PNAS 95: 14863-14868

    20. Hierarchical Clustering Summary Advantages Easy Very Visual Flexible (mean, median, etc.) Disadvantages Unrelated Genes Are Eventually Joined Hard To Define Clusters Manual Interpretation Often Required

    21. Clustering: Example 2, Step 1

    22. Clustering: Example 2, Step 2

    23. Clustering: Example 2, Step 3

    24. Clustering: Example 2, Step 4

    25. Clustering: Example 2, Step 5

    26. K-means algorithm Pick a number (k) of cluster centers Assign every gene to its nearest cluster center Move each cluster center to the mean of its assigned genes Repeat 2-3 until convergence

    27. K-means clustering summary Advantages Genes automatically assigned to clusters Can vary starting locations of cluster centers to determine initial condition dependence Disadvantages Must pick number of clusters before hand All genes forced into a cluster

    28. Which Clustering Method Should I Use? What is the biological question? Do I have a preconceived notion of how many clusters there should be? How strict do I want to be? Spilt or Join? Can a gene be in multiple clusters? Hard or soft boundaries between clusters

    29. Keep in Mind. Clustering is NOT an analysis in itself. Clustering cannot NOT work.

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