1. Expression profiling & functional genomics Exercises

2. Differential expression

3. Use the normalized data to find statistically differentially expressed genes: CyberT software oefnbaldi.xls http://visitor.ics.uci.edu/genex/cybert/ The file contain the 4 normalised ratios (see SNOMAD) T test on the ratios Condition 1 Dye1 Replica L Condition 1 dye1 Replica R Condition 2 dye2 Replica L Condition 2 dye2 Replica R Condition 2 dye1 Replica L Condition 2 dye1 Replica R Condition 1 dye2 Replica L Condition 1 dye2 Replica R Array 1 Array 2 Per gene, per condition 4 measurements available Paired samples CyberT

5. Results CyberT Mn: mean ratio # obs: number of ratios available to calculate the statistics SD: standard deviation on the ratio estimates T, p calculated t and p value that indicate the significance of the measurement

6. Results CyberT

7. SAM

8. MARAN ANOVA based Filtering Linearisation Bootstrapping Log transformation

9. Two typical cDNA designs Reference design (Spellman data set) Reference: unsynchronized cells Condition: synchronized cells during cell cycle at distinct time intervals (18) Condition 1 Dye1 Replica L Condition 2 Dye1 Replica L Condition 3 Dye1 Replica L Condition 4 Dye1 Replica L. … Condition 19 Dye2 Replica L Condition 19 Dye2 Replica L Condition 19 Dye2 Replica L Condition 19 Dye2 Replica L Array 1 Experimental design Exercises

10. Data were precalculated Login: username userGGS Password: Njoedel Uploaded data: Spellman: test cell cycle (reference design) Mouse: latin sqaure design (log transformed) MARAN

13. Spellman non log transformed

14. MARAN

16. Complex cDNA design Latin Square (mouse data set) Reference: normal mouse Condition: pygmee mouse Two experiments T=1, T=2 reflects two sample time points 2 batches: not all genes of the genome on one array A 1, T 1 B1 Test = R Ref = G A 2, T 1 B1 Test = G Ref = R A 5, T 2 B1 Test = R Ref = G A 6, T 2 B1 Test = G Ref = R A 3, T 1 B2 Test = R Ref = G A 4, T 1 B2 Test = R Ref = G A 7, T 2 B2 Test = R Ref = G A 8, T 2 B2 Test = G Ref = R Exercises

17. Clustering of expression profiling experiments

18. Complex cDNA design Latin Square (mouse data set) Reference: normal mouse Condition: pygmee mouse Two experiments T=1, T=2 reflects two sample time points 2 batches: not all genes of the genome on one array A 1, T 1 B1 Test = R Ref = G A 2, T 1 B1 Test = G Ref = R A 5, T 2 B1 Test = R Ref = G A 6, T 2 B1 Test = G Ref = R A 3, T 1 B2 Test = R Ref = G A 4, T 1 B2 Test = R Ref = G A 7, T 2 B2 Test = R Ref = G A 8, T 2 B2 Test = G Ref = R Experimental design 8 Arrays 2 Batches 2 Dyes 2 Conditions Exercises

19. Dataset Yeast cell cycle data set –Data set is preprocessed (slide by slide) –Expression level of each gene is expressed as the log of the ratio –15 experiments, 7000 genes –Filtering based on variance => retain 3000 genes –Rescaling (mean variance) –Cluster the experiment using Kmeans (EPCLUST) Hierarchical clustering (EPCLUST) AQBC (INCLUsive)

20. Exercises Clustering INCLUsive

21. Exercises Clustering INCLUsive http://www.esat.kuleuven.ac.be/~thijs/AQBC/testkat_157088/testkat_157088.html

22. Exercises Clustering INCLUsive Average profile

23. Exercises EPCLUST

24. Exercises EPCLUST Remember the ID of the file

25. Check if your data were uploaded Go back and refresh the page to return to the original page Exercises EPCLUST Continue here

26. Exercises EPCLUST

27. Exercises EPCLUST Make a selection of the most interesting genes, because a filtering was already performed select all data

28. Exercises EPCLUST Try hierarchical clustering and K- means clustering

29. K-means 30 clusters, Euclidea n distance Exercises EPCLUST: result Kmeans

30. Exercises EPCLUST Try hierarchical clustering and K- means clustering

31. The comparison between the content of these two clusters can be seen in the file vergelijkingcluster.xls

32. Exercises EPCLUST: hierarchical clustering Analyze the tree Try to detect the number of clusters in the dataset Click on a node and view the profile of a subcluster

33. Exercises EPCLUST: automatic linking to other tools

34. Exercises EPCLUST: automatic linking to other tools

35. Exercises EPCLUST: automatic linking to other tools

39. FATIGO: calculating statistical overrepresentation using GO