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1 Large-scale temporal gene expression mapping of central nervous system development Fluctuations in mrna expression of 2 genes during rat central nervous system development, focusing on the cervical spinal cord QuickTime?and a Photo - JPEG decompressor are needed to see this picture. Fig. 3. Gene expression waves. (a) Normalized gene expression trajectories (b) Euclidean distance tree of all gene expression patterns. (c) Plots of all normalized time series, highlighting wave 3 (Left, white lines) and a subcluster of wave 3 (d) Principal component analysis. Waves are clusters of genes grouped by tree bracnches with Euclidean metric in b Wen et al. (998) PNAS, 95:

Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation Fig.. Principle of SOMs.

2 Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation Fig.. Principle of SOMs. Initial geometry of nodes in 3 * 2 rectangular grid is indicated by solid lines connecting the nodes. Hypothetical trajectories of nodes as they migrate to fit data during successive iterations of SOM algorithm are shown. Data points are represented by black dots, six nodes of SOM by large circles, and trajectories by arrows. Fig. 2. Yeast Cell Cycle SOM. (a) 6 * 5 SOM. The 828 genes were grouped into 3 clusters. Each cluster is represented by the centroid (average pattern) for genes in the cluster dimensional k-mean cluster with MDS? (b) Cluster 29 detail. Cluster 29 contains 76 genes exhibiting periodic behavior with peak expression in late G. (c) Centroids for clusters 29, 4,, and 5, corresponding to G, S, G2 and M phases of the cell cycle, are shown. (d) Centroids for groups of genes identified by Cho et al. as having peak expression in G, S, G2, or M phase of the cell cycle are shown. By GENECLUSTER Tamayo et al. (Eric S. Lander) (999) PNAS 96:

The 567 genes passing the variation filter were grouped by a 4 3 SOM. Tamayo et al. (Eric S. Lander) (999) PNAS 96: 297-292. Fig. 4. Hematopoietic-Differentiation SOM.

3 Interpreting patterns of gene expression with SOM: Methods and application to hematopoietic differentiation Fig. 3. HL-6 SOM. HL-6 cells were treated with PMA for,.5, 4, or 24 hours, and expression levels of more than 6, genes were measured at each time point. The 567 genes passing the variation filter were grouped by a 4 3 SOM. Tamayo et al. (Eric S. Lander) (999) PNAS 96: Fig. 4. Hematopoietic-Differentiation SOM. The,36 genes varying in at least one of four cell lines were used to generate a 6 4 SOM. Time courses for four cell lines are shown (Left to Right): HL-6 + PMA, U937 + PMA, NB4 + ATRA, Jurkat + PMA. 3

4 Caution:. Data normalization / scaling is important 2. Possible parameters used in SOM analyses: * Grid dimension: D, 2D, 3D, * Grid shape: in 2D (Hexagon, Rectangle, ) * Number of nodes: in 2D_Rectangle (4 * 6, 5 * 5, 3 * 8, ) * Kernel type: Gaussian, biweight, triangular, * Kernel width * Learning rate * Neighborhood size: radius of N c (t) * Initial locations of reference vectors: random, use input vector * Order of input vectors * Ways of learning * Number of iterations SOM References: * Kohonen T. (99) The Self-Organizing Map. Proceedings of the IEEE, 78: * Kohonen T. (997) Self-Organizing Maps, New York: Springer-Verlag. * GeneCluster Molecular Pattern Recognition, Whitehead/MIT Center for Genome Research * Samuel Kaski. SOM-based exploratory analysis of gene expression data. In N. Allinson, H. Yin, L. Allinson, and J. Slack, editors, Advances in Self-Organizing Maps, pages Springer, London, 2. * Tamayo et al. (Eric S. Lander) (999) Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. PNAS 96:

Cluster analysis and display of genome-wide expression patterns Fig.. Clustered display of data from time course of serum stimulation of primary human fibroblasts.

Clustered before and after random permutation within rows (random ), within columns (random 2), and both (random 3). Figure 2.

5 Cluster analysis and display of genome-wide expression patterns Fig.. Clustered display of data from time course of serum stimulation of primary human fibroblasts. Original 86 genes Paired correlation of genes as prox Fig. 3. Clustered before and after random permutation within rows (random ), within columns (random 2), and both (random 3). Figure 2. Cluster image showing the different classes of gene expression profiles. 57 genes whose mrna levels changed in response to serum stimulation were selected. QuickTime?and a Photo - JPEG decompressor are needed to see this picture. Eisen et al. (998) PNAS 95: Iyer et al. (P. O. Brown) (999), Science, Jan 999:

6 Caution:. Tree architecture is not a natural ordination technique 2. Do not use random permutation as the initial order (gene, array) Different Seriations (Ordering of Terminal Nodes or Leaves) Generated from Identical Tree Structure Raw tree -dimensional variations 2-dimensional display Final Permutations from a Tree with k Intermediate Nodes ideal model flip 3 flips 5 flips many flips A B C D E A B C E D A B D E C A B E D C A B C E D B A C D E B A C E D B A D E C B A E D C A B C D E C D B A E C D E A B C E D A B D E C A B E D C A B A E B C D E B A C E D B A D E C B A E D C B A D C Cluster & TreeView, Michael Eisen's lab at the Lawrence Berkeley National Lab (LBNL) 34

DL DL6 DL8 AH2 AH3 AH DL7 DL E v TH3 TH4

NA5 NA6 NA7 NB NB2 NB3 NB4 NC NE NE2 NC2

7 A Standard GAP Procedure Corr. Rating - Scale 5 D a S a S d S S S v v2 v3 v4 v5 D d V a (a). Raw Data Map and Proximity Maps with Suitable Color Projection (d). Sufficient Graphs with Three Linkages for a Multivariate Data Set V d DL4 TH6 TH8 TH7 AH6 AH4 AH5 DL5 BE3 DL DL2 DL9 DL DL6 DL8 AH2 AH3 AH DL7 DL E v TH3 TH4 TH2 TH TH5 DL2 DL3 BE4 NA NA2 NA3 NA4 NA5 NA6 NA7 NB NB2 NB3 NB4 NC NE NE2 NC2 NC3 ND ND3 BE BE2 ND2 ND4 E s R ( 4) for symptoms V b D b (b). Sorted Data Map and Proximity Maps with Principle of Geometry S b (c). Partitioned Data Map and Proximity Maps with near Stationary Iterations S c R ( 3) for patients S S S v v2 v3 v4 v5 C. H. Chen (22), Statistica Sinica, Volume 2, Number, January 22 V c D c 35

8 Generalized Association Plots (GAP): An algorithm for identifying global clustering patterns and smoothing temporal expression profiles GAP Elliptical Seriation Michael Eisen Tree Seriation >8 >6 >4 >2 : >2 >4 >6 >8 - C. H. Chen (22), Statistica Sinica, Volume 2, Number, January 22 36

9 NCI6 Data A gene expression database for the molecular pharmacology of cancer 973gene/6cellline (Database A) with 7kcompound/6 cellline to 376gene/6cellline with 8compound/6 cellline Scherf et al. (2) Nature Genetics 24,

10 Figure 4: CIM relating activity patterns of 8 tested compounds to the expression patterns of,376 genes in the 6 cell lines. Scherf et al. (2) Nature Genetics 24,

NCI6 Data A gene expression database for

11 NCI6 Data A gene expression database for the molecular pharmacology of cancer to 376gene/6cellline with 8compound/6 cellline GAP Elliptical Seriation Michael Eisen Tree Seriation C. H. Chen (22), Statistica Sinica, Volume 2, Number, January 22 39

) Figure. Hierarchical clustering of gene expression data. Figure 2.

12 Distinct types of diffuse large B-cell lymphoma (DLBCL) identified by gene expression profiling 96 samples of normal and malignant lymphocytes (DLBCL + FL(follicular lymphoma) + CLL(chronic lymphocytic leukaemia) +.) Figure. Hierarchical clustering of gene expression data. Figure 2. Expanded view of biologically distinct gene expression signatures defined by hierarchical clustering. Alizadeh et al. (P. O. Brown) (2), Nature, 43, 3 Feb.,

Relationship of DLBCL subgroups to normal B-lymphocyte differentiation and activation.

13 Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling Figure 3. Discovery of DLBCL subtypes by gene expression profiling. Figure 4. Relationship of DLBCL subgroups to normal B-lymphocyte differentiation and activation. Germinal Centre blood B cell Activated peripheral Blood B cell Alizadeh et al. (P. O. Brown) (2), Nature, 43, 3 Feb.,

Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling Figure 5 Clinically distinct DLBCL subgroups defined by gene expression profiling.

14 Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling Figure 5 Clinically distinct DLBCL subgroups defined by gene expression profiling. a, Kaplan Meier plot of overall survival of DLBCL patients grouped on the basis of gene expression profiling. b, Kaplan Meier plot of overall survival of DLBCL patients grouped according to the International Prognostic Index (IPI). Low clinical risk patients (IPI score 2) and high clinical risk patients (IPI score 3 5) are plotted separately. c, Kaplan Meier plot of overall survival of low clinical risk DLBCL patients (IPI score 2) grouped on the basis of their gene expression profiles. Alizadeh et al. (P. O. Brown) (2), Nature, 43, 3 Feb.,

15 Biological problem: Time course & Cell Cycle analysis Microarray type: Time course - multiple arrays each corresponds to an expression profile for the experiment conducted at a certain time point Cell Cycle - multiple arrays each corresponds to an expression profile for a certain time point during one or more cell cycles Statistical method(s): Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Fourier Analysis, Major references: * Alter et al. (2) Singular value decomposition for genome-wide expression data processing and modeling. PNAS 97: -6. * Cho et al. (2) Transcriptional regulation and function during the human cell cycle, Nature Genetics, 27, Jan. 2, * Chu et al. (P. O. Brown) (998) The transcriptional program of sporulation in budding yeast, Science, 282, 23 Oct. 998, * Fellenberg et al. (2) Correspondence analysis applied to microarray data. PNAS USA 98: * Holter et al. (2) Fundamental patterns underlying gene expression profiles: Simplicity from complexity, PNAS 2 97: * Iyer et al. (P. O. Brown) (999) The Transcriptional Program in the Response of Human Fibroblasts to Serum, Science, Jan 999: * Spellman et al. (P. O. Brown) (998) Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Molecular Biology of the Cell 9: * Wall M (2) Singular value decomposition analysis of microarray data. Los Alamos National Labs. * Wen et al. (998) Large-scale temporal gene expression mapping of central nervous system development, PNAS, 95:

16 Comprehensive Identification of Cell Cycle-regulated Genes of the Yeast Saccharomyces cerevisiae by Microarray Hybridization Figure. Gene expression during the yeast cell cycle. (A). Gene expression patterns for cell cycle-regulated genes. The 8 genes are ordered by the times at which they reach peak expression. (B) Genes that share similar expression profiles are grouped by a clustering algorithm as described in the text.. Alpha factor arrest 2. Elutrination 3. Arrest of cdc5 temperature-sensitive mutant QuickTime?and a Photo - JPEG decompressor are needed to see this picture. QuickTime?and a Photo - JPEG decompressor are needed to see this picture. Spellman et al. (P. O. Brown) (998) Mol. Biol. Cell, 9:

Fundamental patterns underlying gene expression profiles: Simplicity from complexity singular value decomposition (SVD) Fig.

(a) Yeast cell cycle data (b) Yeast sporulation data (c) Human fibroblast data (d) Random data A? U? V T A j (t)?? U ji X i (t) r i?

A reconstruction of the expression profiles for the yeast cell cycle data set from the characteristic modes.

17 Fundamental patterns underlying gene expression profiles: Simplicity from complexity singular value decomposition (SVD) Fig.. Characteristic modes (Xi(t)) for the gene expression and random data sets. (a) Yeast cell cycle data (b) Yeast sporulation data (c) Human fibroblast data (d) Random data A? U? V T A j (t)?? U ji X i (t) r i? QuickTime?and a Photo - JPEG decompressor are needed to see this picture. Fig. 2. A reconstruction of the expression profiles for the yeast cell cycle data set from the characteristic modes. Panels -5 show the results of a hierarchical reconstruction of the expression profiles using only the first, 2, 3, 4, and 5 characteristic modes. The last panel uses all 4 characteristic modes and exactly reproduces the original data set. Holter et al. (2), PNAS 2 97:

18 singular value decomposition (SVD) Fig. 5. Plot of the coefficients for characteristic mode against the coefficients for characteristic mode 2. (a) cdc5 data (first 2 time points). (b) Sporulation data (7 time points). (c) Fibroblast data (3 time points). (d) random data (7 time points). QuickTime?and a Photo - JPEG decompressor are needed to see this picture. Holter et al. (2), PNAS 2 97:

19 Singular value decomposition for genome-wide expression data processing and modeling Fig.. Normalized elutriation eigengenes. (a) Raster display of NT, the expression of 4 eigengenes in 4 arrays. (b) Bar chart of the fractions of eigenexpression, showing that N and 2N capture about 2% of the overall normalized expression each, and a high entropy d =.88. (c) Line-joined graphs of the expression levels of N (red) and 2N (blue) in the 4 arrays fit dashed graphs of normalized sine (red) and cosine (blue) of period T = 39 min and phase = 2/3, respectively. QuickTime?and a Photo - JPEG decompressor are needed to see this picture. Fig. 2. Normalized elutriation expression in the subspace associated with the cell cycle. (a) Array correlation with N along the y-axis vs. that with 2N along the x-axis, color-coded according to the classification of the arrays into the five cell cycle stages, M/G (yellow), G (green), S (blue), S/G2 (red), and G2/M (orange). The dashed unit and half-unit circles outline % and 25% of overall normalized array expression in the N and 2N subspace. (b) Correlation of each gene with N vs. that with 2N, for 784 cell cycle regulated genes, color-coded according to the classification by Spellman et al. QuickTime?and a Photo - JPEG decompressor are needed to see this picture. Alter et al. (2) PNAS 97: -6 47

20 Singular value decomposition for genome-wide expression data processing and modeling Fig. 3. Genes sorted by relative correlation with N and 2N of normalized elutriation. (a) Normalized elutriation expression of the sorted 5,98 genes in the 4 arrays, showing traveling wave of expression. (b) Eigenarrays expression; the expression of N and 2N, the eigenarrays correspond to N and 2N, displays the sorting. (c) Expression levels of N (red) and 2N (green) fit normalized sine and cosine functions of period Z~N - = 5,98 and phase 2/3 (blue), respectively. QuickTime?and a Photo - JPEG decompressor are needed to see this picture. Don t get confused by the colors of green & red for ratios of expression Alter et al. (2) PNAS 97: -6 48

21 Transcriptional regulation and function during the human cell cycle 387 human cell-cycleregulated transcripts with known function Original Order Tree Order GAP Elliptical Order Nature Genetics Order M Phase G2 Phase S Phase G Phase Cho et al. (2), Nature Genetics, 27, Jan. 2,

22 Biological problem: Cancer type classification Microarray type: Multiple arrays each corresponds to a sample (patient, cell line) of two or more tumor types Statistical / Informatics method(s): Fisher discriminant analysis, Maximum likelihood discriminant rules, Classification tree, k-nearest neighbor classifier, Support Vector Machine (SVM) Aggregating classifier (Bagging, Boosting) Major references: * Brown et al. (2) Knowledge-based analysis of microarray gene expression data by using support vector machines. PNAS 97: * Dudoit et al. (2) Comparison of discrimination methods for the classification of tumors using gene expression data. TR, Statistics, UC-Berkeley. * Golub et al. (999) Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286: * Hastie et al. (2) Supervised harvesting of expression trees. TR, Statistics, Stanford U. * Ramaswamy et al. (E. Lander) (2) Multiclass cancer diagnosis using tumor gene expression signatures, PNAS, 98, 26, * Smith et al. (2) Class prediction for gene expression data. Nat Genet. * West et al. (2) Predicting the clinical status of human breast cancer using gene expression profiles. TR -, Inst of Stat and Deci Sci, Duke University. 5

23 Major references: continued * Corinna Cortes, Vladimir Vapnik, "Support-Vector Networks", Machine Learning, 2, pp (995) * Xiong et al. (2) Feature (Gene) Selection in Gene Expression-Based Tumor Classification, Molecular Genetics and Metabolism, 73, 3, July 2, * Zhang et al. (2) Recursive partitioning for tumor classification with gene expression microarray data, PNAS 98: SVM * (Chih-Jen Lin's Home Page) * Christopher J.C. Burges, "A Tutorial on Support Vector Machines for Pattern Recognition", 9/Nov/996. a draft to Data Mining and Knowlledge Discovery * M.O. Stitson, J.A.E. Weston, A Gammerman, V.Vork, V. Vapnik, "Theory of Support Vector Machines", Technical Report CSD-TR-96-7, Department of Computer Science, Royal Holloway College, University of London, Dec. 996 * M.O. Stitson, J.A.E. Weston, "Implementation Issues of Support Vector Machines", Technical Report CSD-TR-96-8, Department of Computer Science, Royal Holloway College, University of London, Feb. 997 * M.O. Stitson, J.A.E. Weston, A Gammerman, V.Vork, V. Vapnik, "Experiments with Support Vector Machines", Technical Report CSD- TR-96-9, Department of Computer Science, Royal Holloway College, University of London, Dec * Steve Gunn, "Support Vector Machiens for Classification and Regression", ISIS Technical Report ISIS--98, Image Speech & Intelligent Systems Research Group, University of Southapton, May. 998 * M. A. Hearst, B. Sch lkopf, S. Dumais, E. Osuna, and J. Platt. Trends and controversies - support vector machines. IEEE Intelligent Systems, 3(4):8-28,

24 Molecular classification of cancer: Neighborhood Analysis (AML/ALL) acute myeloid leukemia (AML, samples) acute lymphoblastic leukemia (ALL, 27 samples) st issue: to explore whether there were genes whose expression pattern was strongly correlated with the AML/ALL class (A) Neighborhood analysis. c : "idealized expression pattern. Gene g: is well correlated with c. Gene g2: is poorly correlated with c. Neighborhood analysis: count the number of genes having various levels of correlation with c. The results are compared to the randomized expression patterns c*, An unusually high density of genes indicates that there are many more genes correlated with the pattern than expected by chance. 2nd issue: to create a "class predictor" capable of assigning a new sample to one of two classes AML/ALL. (B) Class predictor. prediction of a new sample is based on "weighted votes" of a set of informative genes. Each such gene gi votes for either AML or ALL, depending on whether its expression level xi in the sample is closer to AML or ALL magnitude of the vote: wi*vi, (wi (weight) reflects how well the gene is correlated with the class distinction, vi = xi - (µ AML + µ ALL)/2 reflects the deviation of the expression level in the sample from the average of AML and ALL. The votes for each class are summed to obtain total votes VAML and VALL. The sample is assigned to the class with the higher vote total, provided that the prediction strength exceeds a predetermined threshold. Golub et al. (999), Science 286:

Molecular classification of cancer: Neighborhood Analysis (AML/ALL) 3rd issue: to test the validity of class predictors.? p(g,c)?? ALL(g)?? AML (g)? ALL (g)?? AML (g) Figure 2.

Number of genes within various "neighborhoods" of the ALL-AML class distinction together with curves showing the 5% and % significance levels for the number of genes within corresponding

25 Molecular classification of cancer: Neighborhood Analysis (AML/ALL) 3rd issue: to test the validity of class predictors.? p(g,c)?? ALL(g)?? AML (g)? ALL (g)?? AML (g) Figure 2. Neighborhood analysis: ALL vs AML. Number of genes within various "neighborhoods" of the ALL-AML class distinction together with curves showing the 5% and % significance levels for the number of genes within corresponding neighborhoods of the randomly permuted class distinctions. Golub et al. (999), Science 286: (B) Genes distinguishing ALL from AML. The 5 genes most highly correlated with the ALL-AML class distinction are shown. Figure 3. (A) Prediction strengths. prediction strengths (PSs): in cross-validation (left) on the independent sample (right). 53

Multiclass cancer diagnosis using tumor gene expression signatures Fig.. Clustering of tumor gene expression data and identification of tumor-specific molecular markers. (a).

26 Multiclass cancer diagnosis using tumor gene expression signatures Fig.. Clustering of tumor gene expression data and identification of tumor-specific molecular markers. (a). Hierarchical clustering 9 primary human tumor samples (b). self-organizing map (SOM) genes each class High corr. in OVA (c). 44 tumors spanning 4 tumor classes according to their gene expression patterns. Ramaswamy et al. (E. Lander) (2) PNAS, 98, 26,

The multiclass cancer classification problem is divided into a series of 4 OVA

27 Multiclass cancer diagnosis using tumor gene expression signatures Fig. 2. Multiclass classification scheme. The multiclass cancer classification problem is divided into a series of 4 OVA problems. Fig. 4. Multiclass classification error analysis. Ramaswamy et al. (E. Lander) (2) PNAS, 98, 26,

28 Recursive partitioning for tumor classification with gene expression microarray data (Classification Tree) Fig.. Classification trees for tissue types by using expression data from three genes (M26383, R5447, M2824). CT, number of cancer tissues; NT, number of normal tissues. Zhang et al. (2) PNAS 98:

Case Studies on High Throughput Gene Expression Data Kun Huang, PhD Raghu Machiraju, PhD

Case Studies on High Throughput Gene Expression Data Kun Huang, PhD Raghu Machiraju, PhD Department of Biomedical Informatics Department of Computer Science and Engineering The Ohio State University Review