Cluste Analysis of聚类分析.ppt

Cluster Analysis ofMicroarray Data Clustering Group objects that are similar to one another together in a cluster. Separate objects that are dissimilar from each other into different clusters. The similarity or dissimilarity of two objects is determined by comparing the objects with respect to one or more attributes that can be measured for each object. Data for Clustering Microarray Data for Clustering Microarray Data for Clustering Microarray Data for Clustering Microarray Data for Clustering Clustering: An Example Experiment Researchers were interested in studying gene expression patterns in developing soybean seeds. Seeds were harvested from soybean plants at 25, 30, 40, 45, and 50 days after flowering (daf). One RNA sample was obtained for each level of daf. An Example Experiment (continued) Each of the 5 samples was measured on two two-color cDNA microarray slides using a loop design. The entire process we repeated on a second occasion to obtain a total of two independent biological replications. Diagram Illustrating the Experimental Design The daf means estimated for each gene from a mixed linear model analysis provide a useful summary of the data for cluster analysis. 400 genes exhibited significant evidence of differential expression across time (p-value0.01, FDR=3.2%). We will focus on clustering their estimated mean profiles. We build clusters based on the most significant genes rather than on all genes because... Much of the variation in expression is noise rather than biological signal, and we would rather not build clusters on the basis of noise. Some clustering algorithms will become computationally expensive if there are a large number of objects (gene expression profiles in this case) to cluster. Estimated Mean Profiles for Top 36 Genes Dissimilarity Measures When clustering objects, we try to put similar objects in the same cluster and dissimilar objects in different clusters. We must define what we mean by dissimilar. There are many choices. Le