20%20HierarchiClustering%204_10_08

20%20HierarchiClustering%204_10_08 - Clustering...

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1 1 Introduction to Clustering Analysis Peng Liu 4/10/2008 2 Clustering ± Clustering analysis is the process of “grouping similar entities”. ± Cluster analysis search for groups ( clusters ) in the data, in such a way that objects belonging to the same cluster resemble each other whereas objects in different clusters are dissimilar. 3 Data for Clustering attribute object 1 2 3 ... m 1 4.7 3.8 5.9 . .. 1.3 2 5.2 6.9 3.8 . .. 2.9 3 5.8 4.2 3.9 . .. 4.4 . . . . . . . . . . . . . . . . . . n 6.3 1.6 4.7 . .. 2.0 4 Microarray Data for Clustering attribute object 1 2 3 ... m 1 4.7 3.8 5.9 . .. 1.3 2 5.2 6.9 3.8 . .. 2.9 3 5.8 4.2 3.9 . .. 4.4 . . . . . . . . . . . . . . . . . . n 6.3 1.6 4.7 . .. 2.0 genes time points estimated expression levels
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2 5 Microarray Data for Clustering attribute object 1 2 3 ... m 1 4.7 3.8 5.9 . .. 1.3 2 5.2 6.9 3.8 . .. 2.9 3 5.8 4.2 3.9 . .. 4.4 . . . . . . . . . . . . . . . . . . n 6.3 1.6 4.7 . .. 2.0 genes treatment conditions estimated expression levels 6 Microarray Data for Clustering attribute object 1 2 3 ... m 1 4.7 3.8 5.9 . .. 1.3 2 5.2 6.9 3.8 . .. 2.9 3 5.8 4.2 3.9 . .. 4.4 . . . . . . . . . . . . . . . . . . n 6.3 1.6 4.7 . .. 2.0 samples genes estimated expression levels 7 Classification ± Start with a collection of objects and a given number of known classes . ± Assume that each object belongs to exactly one of the classes and that the class of each object is known. ± Further suppose that measures of each of several attributes are available for each object . ± Develop a rule that can be used to predict the class of a new object given its attributes . 8 Data for Classification attribute class object 1 2 3 ... m 1 4.7 3.8 5.9 . .. 1.3 A 2 5.2 6.9 3.8 . .. 2.9 B 3 5.8 4.2 3.9 . .. 4.4 B . . . . . . . . . . . . . . . . . . . . . n 6.3 1.6 4.7 . .. 2.0 A n+1 5.7 4.3 3.8 . .. 4.6 ?
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3 9 Example Microarray Data for Classification attribute class object 1 2 3 ... m 1 4.7 3.8 5.9 . .. 1.3 benign 2 5.2 6.9 3.8 . .. 2.9 malignant 3 5.8 4.2 3.9 . .. 4.4 malignant . . . . . . . . . . . . . . . . . . . . . n 6.3 1.6 4.7 . .. 2.0 benign n+1 5.7 4.3 3.8 . .. 4.6 ? tumor gene estimated expression levels 10 References for Classification Analysis Classification analysis is a huge area involving computer scientists, statisticians, mathematicians, physicists, biologists, chemists, etc. ± Dudoit, S., Fridlyand, J., Speed, T. P. (2002). Comparison of discrimination methods for the classification of tumors using gene expression data. JASA , 97, 77-87. ± Tibshirani, R. Hastie, T. Narasimhan, B. and Chu, G. (2002) Diagnosis of multiple cancer types by shrunken centroids of gene expression, PNAS , 99, 6567–6572 PAM: Prediction Analysis for Microarrays ± Hastie, T., Tibshirani, R., Friedman, J. (2001). The Elements of Statistical Learning: Data Mining, Inference, and Prediction . Springer, New York. 11 Example II: Jesch et al , 2006, J. Biol. Chem. ± Goal : To identify metabolic signaling networks that coordinate phospholipid metabolism with gene expression ± Take samples induced by inositol over time and perform microarray experiments to profile the sequential and temporal changes in genome- wide expression 12 Clustering ± Grouping genes into different “clusters” based on their expression profile Æ Clustering
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4 13 We build clusters based on the most significant genes rather than on all genes because.
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This note was uploaded on 08/27/2009 for the course STAT 447 taught by Professor Staff during the Spring '08 term at Iowa State.

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20%20HierarchiClustering%204_10_08 - Clustering...

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