Stats254_Ch2_Multivariate_3

Stats254_Ch2_Multivariate_3 - Stats M254 Statistical...

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1 1 Chapter 2 Multivariate Methods Stats M254 Statistical Methods in Computational Biology 2 Outline of this chapter 2.1 Gene selection by comparative study Hypothesis test, permutation test, multiple testing, false discovery rate. (See Lecture notes Ch2_1) 2.2 Clustering algorithms K-means, K-medoids, (See Lecture notes Ch2_2) and hierarchical clustering. 2.3 Liquid association
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2 3 2.2 Clustering algorithms Motivation Discover interesting/unexpected grouping of genes and samples – Visualization of overall gene expression patterns – Data checking purpose – Prediction of functions of unknown genes by known ones – Promoter analysis of commonly regulated genes 4 Iterative methods K-means: (1) update centers given clustering assignment; (2) assign points to the closest center; K-medoids: More robust version. Hierarchical clustering Bottom-up methods: recursively merge two closest groups of points
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3 5 Hierarchical Clustering algorithm 1. Similarity (or distance/dissimilarity) between all possible
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Stats254_Ch2_Multivariate_3 - Stats M254 Statistical...

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