ECS124_Lecture11

ECS124_Lecture11 - ECS 124 Theory and Practice of...

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ecture 11 : Regression and ECS 124 Theory and Practice of Bioinformatics Lecture 11 : Regression and Classification Instructor: Ilias Tagkopoulos itagkopoulos@ucdavis.edu Office: Kemper 3063 and GBSF 5313 5/11/2010 1 UC Davis
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LAST TIME: Biclustering c Sometimes patterns hold only for a subset of conditions/genes. c Biclustering is the simultaneous clustering of oth row and columns in a data matrix 5/11/2010 UC Davis 2 both row and columns in a data matrix (Hartigan72, Mirkin96) c A bicluster is a cluster of a subset of genes (rows) in a subset of conditions (columns)
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LAST TIME: Biclustering .2 1.4 5/11/2010 UC Davis 3 1 1.5 2 2.5 3 3.5 4 4.5 5 0 0.2 0.4 0.6 0.8 1 1.2
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Principal Component Analysis (PCA) c Goal: Make data more separable c Technique: Project the data to the coordinates (vectors) that maximize variance 5/11/2010 UC Davis 4 c Formal definition: an orthogonal linear transform that decomposes the data to uncorrelated variables that maximize the variance of each projection c It is based on Singular Value Decomposition
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xpression in Exp 2
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This note was uploaded on 05/30/2010 for the course ECS ECS 124 taught by Professor Tagkopoulos during the Spring '10 term at UC Davis.

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ECS124_Lecture11 - ECS 124 Theory and Practice of...

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