1. Building a Global Map of (Human) Gene Expression Misha Kapushesky European Bioinformatics Institute, EMBL St. Petersburg, Russia May, 2010

3. Mapping the human transcriptome Traditional research A microarray experiment Everest Lhasa Kathmandu The map we want to build

13. Different metagroupings (4 and 15):

14. 5372 samples (369 different conditions) ~18,000 genes After RMA normalisation we obtain:

15. Principal Component Analysis – each dot is one of the 5372 samples 1 st 2 nd

16. Human gene expression map 17/08/10 1 st 2 nd

17. Human gene expression map 17/08/10 Hematopoietic axis 2 nd

18. Human gene expression map 17/08/10 Hematopoietic axis 2 nd

19. Human gene expression map 17/08/10 Hematopoietic axis Malignancy

20. Hematopoietic and malignancy axes Lukk et al, Nature Biotechnology, 28: 322

21. 1 st 2 nd 3 rd

22. Coloured by tissues of origin 3 rd PC

23. Tissues of origin Neurological axis

26. Hierarchical clustering of 97 groups with at least 10 replicates each Human gene expression map 17/08/10

27. Comparison of the 97 larger sample groups to the rest Incompletely differentiated cell type and connective tissue group

36. 24 most variable genes

37. www.ebi.ac.uk/gxa/human/U133a

38. Can we go beyond the 6 major classes?

40. Leukemia Normal blood and blood non-neoplastic disease Other blood neoplasm Blood cell lines

43. Mapping the human transcriptome Traditional research A microarray experiment Everest Lhasa Kathmandu The map we want to build Our current view on global transcriptome

44. 97 groups – colours recycled Frontal cortex Muscular dystrophy Skeletal muscle Brain Heart and heart parts Cerebellum Caudate nucleus Hippocampal tissue Nervous system tumors Mono- nuclear cells AML

48. Analysing each contributing dataset separately: one-way ANOVA AML CML normal genes

49. Combining the datasets … Experiments 1, 2, 3, …, m

52. Long tail of annotations…

54. Decoupling knowledge from data Atlas/AE James Malone

55. Semantically-enriched Queries with EFO

56. We can use the ontology structure We can perform effect size meta-analysis on a hierarchy, if we follow several rules:

57. Increased statistical power

58. Condition-specificity through EFO

59. Condition-specific Gene Expression

60. Query for genes Query for conditions species The ‘advanced query’ option allows building more complex queries http://www.ebi.ac.uk/gxa www.ebi.ac.uk/gxa

61. Query results for gene ASPM ArrayExpress ASPM is downregulated in ‘normlal’ condition in comparison to a disease in 9 studies out of 10 Upregulated in ‘Glioblastoma’ in 3 indepnendent studies Zoom into one of the ‘Glioblastoma’ studies. Each bar represents an expression level in a particular sample

62. ‘ wnt pathway ’ genes in various cancers ArrayExpress