06-Microarray_data_analysis(生物信息学国外教程2010版).ppt

Data matrix (20 genes and 3 time points from Chu et al., 1998) Software: S-PLUS package Fig. 7.8 Page 205 genes samples (time points) 3D plot (using S-PLUS software) t=0 t=0.5 t=2.0 Fig. 7.8 Page 205 Descriptive statistics: clustering Clustering algorithms offer useful visual descriptions of microarray data. Genes may be clustered, or samples, or both. We will next describe hierarchical clustering. This may be agglomerative (building up the branches of a tree, beginning with the two most closely related objects) or divisive (building the tree by finding the most dissimilar objects first). In each case, we end up with a tree having branches and nodes. Page 204 Agglomerative clustering a b c d e a,b 4 3 2 1 0 Fig. 7.9 Page 206 Adapted from Kaufman and Rousseeuw (1990) a b c d e a,b d,e 4 3 2 1 0 Agglomerative clustering Fig. 7.9 Page 206 a b c d e a,b d,e c,d,e 4 3 2 1 0 Agglomerative clustering Fig. 7.9 Page 206 a b c d e a,b d,e c,d,e a,b,c,d,e 4 3 2 1 0 Agglomerative clustering …tree is constructed Fig. 7.9 Page 206 Divisive clustering a,b,c,d,e 4 3 2 1 0 Fig. 7.9 Page 206 Divisive clustering c,d,e a,b,c,d,e 4 3 2 1 0 Fig. 7.9 Page 206 Divisive clustering d,e c,d,e a,b,c,d,e 4 3 2 1 0 Fig. 7.9 Page 206 Divisive clustering a,b d,e c,d,e a,b,c,d,e 4 3 2 1 0 Fig. 7.9 Page 206 Divisive clustering a b c d e a,b d,e c,d,e a,b,c,d,e 4 3 2 1 0 …tree is constructed Fig. 7.9 Page 206 divisive agglomerative a b c d e a,b d,e c,d,e a,b,c,d,e 4 3 2 1 0 4 3 2 1 0 Fig. 7.9 Page 206 Adapted from Kaufman and Rousseeuw (1990) Fig. 7.8 Page 205 Fig. 7.10 Page 207 1 1 12 12 Agglomerative and divisive clustering sometimes give conflicting results, as shown here Fig. 7.10 Page 207 Cluster and TreeView Fig. 7.11 Page 208 Cluster and TreeView clustering PCA SOM K means Fig. 7.11 Page 208 Cluster and TreeView Fig. 7.12 Page 208 Two-way clustering of genes (y-axis) and cell lines (x-axis) (Alizadeh et al., 2000) Fig. 7.13 Page 209 An exploratory technique used to reduce the dimensio