Typical Experiments. One-color experiment. Two-color experiment. RNA extraction. RNA labeling

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1 Microarray analysis

2 Outline Define the common question The methods Supervised analysis of two groups How Multiple comparisons correction Primary vs. Metastasis tumor cells (An example) Caveats Unsupervised analysis What is clustering How we clustering Sample clustering (an example) Gene Clustering (an example) Coupling the two clustering Genes Samples

3 Typical Experiments Two-color experiment One-color experiment condition control condition control RNA extraction RNA labeling

4 DNA Array Technologies (A) Affymetrix (B) Spotting

5 Expression Data Matrix (a result of quantification) Experiments set Samples Sample annotations Genes c Gene expression matrix Gene annotations Gene expression levels

6 Analysis flow Linearization Normalization Filtering Data analysis Pre-processing Two-color experiment One-color experiment

7 Normalization

8 Applications of microarrays Evolution Most of gene expression differences between chimpanzees human have been detected in their brain. Development Associating gene expression with metamorphosis stages in Drosophila Regulation Finding novel regulatory motifs by coupling motif search with co- expression Behavior Can predict the behavior of honey-bees workers by their brain gene expression. Functional annotation Annotating unknown genes based on co expression (guilty by association) Tissue Molecular signature specific to subtype of cancer tissues

9 Biological questions Sample classification What are the set of genes that differentiate between two or more groups of Treatments (Supervised methods) What is the set of samples that have the same expression profile in the detected cell(s). (Unsupervised methods) Gene classification What is the set of genes that have the same expression profile along a set of treatments. (Unsupervised methods)

10 Data analysis methods Supervised methods Analysis of variance (ANOVA/T-test) Discriminate analysis K-nearest neighbors Unsupervised methods Partition methods K-means SOM (Self-Organization Maps) Hierarchical methods

11 Supervised classification

12 Genes Classifying normal and cancer Classifying normal and cancer group of patients Samples N (Normal) ALL (Cancer) Expression profile of a gene Patients Expression Intensity N1 N2 N3 N4 N5 N6 N7 N8 N9 N10 ALL1 ALL2 ALL3 ALL4 ALL5 ALL6 ALL7 ALL8 ALL9 ALL10

13 Multiple comparison correction rejecting the null hypothesis for 10,000 tests with a p -value of test are expected to be falsely significant results. Random matrix Group A Group B 10,000 rows

14 Multiple comparison correction methods Family-wise error rate (FWER) Adjust the type I error (p-value) in a way it ensures no more than one false positive False Discovery Rate (FDR) - Adjust the type I error (p-value) in a way it ensures expected proportion of false positive

15 Classification of primary and metastatic tumors by t-testt test Color code low High Ramaswamy S, Ross KN, Lander ES & Golub TD Nat Genet Jan;33(1):49-54.

16 Note About Supervision Note About Supervision Expression profile of a gene N (Normal) ALL (Cancer) outlier Patients Expression Intensity N1 N2 N3 N4 N5 N6 N7 N8 N9 N10 ALL1 ALL2 ALL3 ALL4 ALL5 ALL6 ALL7 ALL8 ALL9 ALL10

17 Note About Supervision Color code low High Ramaswamy S, Ross KN, Lander ES & Golub TD Nat Genet Jan;33(1):49-54.

18 t-test test vs. Discriminant analysis Discriminant analysis Group A Group B T-test Gene 2 Gene 2 Gene 1 Gene 1

19 Unsupervised classification

20 Hair length Chin length What is clustering?

21 People in n-dimentional characteristics space Characters People Person1 Chin Hair Hat Nose Glasses Neck Person2 Person3 Person4 Person5 Person6 Person7 Person8 Person9

22 Genes in n-dimentional experimental conditions space RNA samples Genes Gene1 Gene2 Gene3 Gene4 Gene5 Gene6 Gene7 Gene8 Gene9 Heart Uterus Liver Kidney Pancreas Muscle

23 Expression cluster (2D)

24 Expression profile (n-d)

25 Finding similar patterns in expression matrix

26 Reordered Gene Matrix

27 Distances Manhattan (blocks) Euclidian Pearson correlation

28 Distance Manhattan distance Manhattan distance x y Treatment Control Expression Intensity Treatments

29 Distance Euclidian distance Euclidian distance ( ) 2 y xi i Treatment Treatments Control Expression Intensity

30 Pearson correlation Intensity r = ( x x)( y y) 2 ( x x) ( y y) 2 Treatments

31 Constructing clustering (an example) Method: Agglomerative clustering Steps: Comparing all pairwise distances Define the relationship among samples Data: Six RNA samples from three Tissues with duplicated

32 Distance matrix Single linkage = minimum distance Average linkage = average distance Complete linkage = maximum distance

33 Create a Tissues Dendrogram

34 Sample classification Unsupervised classified the primary tumor samples into two groups Horizontal color bar shows recurrent (red) vs. non-recurrent (black) patient Vertical color bar shows Metastatic over- expressed (red) vs. primary over-expressed expressed genes (black) genes Color code low High Ramaswamy S, Ross KN, Lander ES & Golub TD Nat Genet Jan;33(1):49-54.

35 Genes related to the same function are clustered together Human Fibroblast Growth Down regulated 1:1 Up regulated 0h 24h A - Cholesterol biosynthesis B Cell cycle C Immediate and early response D Signaling and Angiogenesis E Wound healing Eisen MB, Proc. Natl. Acad. Sci. USA Vol. 95, pp ,

36 Co-expression may imply for co-regulation Genes that were clustered together found to be have the URS1 motif 5 -DSGGCGGCND-3 in their upstream region. For metabolic genes it was found in 15 out of 52. Genes that were clustered together found to be have the MSE motif 5 - DNCRCAAAW-3 in their upstream region, which is suggested to be recognized by Ndt80 transcription factor. S. Chu, J. et al., The Transcriptional Program of Sporulation in Budding Yeast Science 282:

37 Notes About Hierarchical Giving a dendrogram (or a tree) we may browse the level of coherent expression considering functionality or regulation. Flip nodes Clustering

38 Validation of clustering Random1 shuffling within rows Random2 shuffling within columns Random3 shuffling both rows and columns What are the number of valid cluster? How many genes should be cluster together by chance? Eisen MB, Proc. Natl. Acad. Sci. USA Vol. 95, pp

39 Coupling classification

40 Gene that classifies the sample into two expected groups 1:1 Down regulated Up regulated Normal Cancer Normal Cancer

41 Gene that classifies the sample into two unexpected groups Normal Cancer

42 Classifiers comparison Gene relationships t-testtest Each gene separately Discriminant analysis/k- nearest neighbors Combination of genes Hierarchical clustering Combination of genes A-priori knowledge yes yes no

43 Thank you