The document discusses building a global map of human gene expression by integrating data from thousands of gene expression experiments deposited in public databases. It describes two approaches: 1) Integrating over 9,000 samples on a quantitative level to identify major expression classes and differentially expressed genes. 2) A meta-analysis approach to identify condition-specific differentially expressed genes across many studies. Combining both approaches could provide a comprehensive global map of human gene expression.

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

2. From one genome to many biological states While there is only one genome sequence, different genes are expressed in many different cell types and tissues, different developmental or disease states The size and structure of this “expression space” is still largely unknown Most individual experiments are looking at small regions We would like to build a map of the global human gene expression space

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

4. How to build such a global map This space is huge - There are thousands of potentially different states – cell types, tissue types, developmental stages, disease states, systems under various treatments (drugs, radiation, stress, …) – It is not feasible to study them all in a single laboratory experiment (costs, rare samples, …) However thousands of gene expression experiments are performed every year (microarrays, new generation sequencing) Can we use the published data to build the global expression map?

9. ArrayExpress www.ebi.ac.uk/arrayexpress Data from over 280,000 assays and over 10,000 independent studies (microarrays, sequencing, …) Gene expression and other functional genomics assays Over 200 species Data collection and exchange from GEO

10. Can we integrate these data to answer questions that go beyond what was done in the individual studies? On a quantitative level - data on only the same microarray platform can be integrated

11. A global map of human gene expression Angela Gonzales (EBI) Misha Kapushesky (EBI) Janne Nikkila (Helsinki University of Technology) Helen Parkinson (EBI), Wolfgang Huber (EMBL) Esko Ukkonen (University of Helsinki) Margus Lukk et al, Nature Biotechnology , 28, p322-324 (April, 2010)

12. We collected over 9000 raw data files from Affymetrix U133A from GEO and ArrayExpress Applying strict quality controls, removing the duplicates Data on 5372 samples remained from 206 different studies generated in 163 different laboratories grouped in 369 different biological ‘conditions’ (tissue types, diseases, various cell lines, etc) The 369 conditions grouped in different larger ‘metagroups’ The most popular gene expression microarray platform: Affymetrix U133A

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

24. First 3 (5) principal components Hematopoietic axis – blood, ‘solid tissues’, ‘incompletely differentiated cells and connective tissues’ Malignancy axis - Cell lines – cancer – normals and other diseases Neurological axis – nervous system / the rest RNA degradation Samples seem to ‘cluster’ by the tissues of origin

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

28. Conclusions so far We have identified 6 major transcription profile classes in these data: cell lines incompletely differentiated cells and connective tissues neoplasms blood brain muscle Cell lines cluster together!

31. Gene expression across the 5372 samples The expression of most genes is relatively constant There are only 1034 probesets (mapping to less than 900) genes where normalised signal variability has standard deviation > 2

32. Clustering of 97 sample groups and 1000 most variable probesets (about 900 genes) Immune repsonse Nervous system development Lipid raft Mitosis Neurotransmitter uptake Cytoskeletal protein binding Extracellular matrix Extracellular regions Extracellular matirx Extracellular region Mitosis Defence response Nervous system development Actin cytoskeleton organisation and biogenesis Protein carrier activity No significant resout Antigen presentation, exogenous antigen Trans – 1,2-dyhydrobenzene, 1,2-dyhydrogenase activity S100 alpha binding 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

33. Clustering based on subset of these genes produce similar results Clustering based on 350 most variable probesets gives almost the same result Even clustering based on 30 most variable probesets is very close

36. 24 most variable genes

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

38. Can we go beyond the 6 major classes?

39. Hierarchical clustering of all 369 sample groups Human gene expression map 17/08/10 Some finer groups: Cancer: Sarcomas Carcinomas Neuroblastomas Normal: Liver and gut

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

41. Identifying condition specific genes by supervised analysis Using linear models to find condition specific genes, multiple testing correction, differential expression cut-offs Example - 174 leukemia specific genes include most well known markers (e.g, BCR, ETV6, FLT3, HOXA9, MUST3, PRDM2, RUNX1, and TAL1 ) Many confirmed as associated with leukemia

42. Beyond the major 6 classes the ‘signal’ becomes weak The problem may be lab effects The large biological effects are stronger than the lab effects However, when we zoom into particular subclasses, the lab effects may be taking precedence

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

45. Second approach Integrating data on statistics level

46. Gene Expression Atlas Ele Holloway Ibrahim Emam Pavel Kurnosov Helen Parkinson Anrey Zorin Tony Burdett Gabriella Rustici Eleanor William Andrew Tikhonov

47. Global Differential Expression Analysis Selected ~ 10% of the data from ArrayExpress (including GEO imports), manually curated for quality and mapped to a custom-built ontology of experimental factors , EFO: http://www.ebi.ac.uk/efo Data on differential expression of genes in 1000+ studies, comprising ~30000 assays, in over 5000 conditions For each experiment, differentially expressed genes have been identified computationally via moderated t-tests and statistical meta-analysis Meta-Analysis Approaches Vote counting: number of independent studies supporting an observation for a particular gene Effect size integration: compute effect size statistics in each study, assess relevant statistical model and compute combined z-score, for each gene/condition/study combination (extension of Choi et al, 2003)

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

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

50. Effect size-based meta-analysis We have for each gene in each experiment/condition: p -value for significance simulaneous t -statistics & confidence intervals d.e. label (“up” or “down”) However, we would like to: Measure of strength of d.e. effect size Ability to combine d.e. findings statistically Effect Size Standardized mean difference or similar (e.g., correlation coef.)

51. Meta-analysis Procedure For each gene-experiment-condition combination Compute effect size from simultaneous d.e. t-statistics Combine effect sizes across multiple studies Using fixed-effects or random- effects models Obtain for each gene-condition combination: Mean effect size estimate Combined z -score Overall p -value

52. Long tail of annotations…

53. Annotating data with ontologies Diverse nature of annotations on data Need to support complex queries which contain semantic information E.g. which genes are under-expressed in brain samples in human or mouse If we annotate with do we get this data? cancer adenocarcinoma James Malone

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

63. Integrating both approaches First approach gives the global view, but obsucres the detail The second approach gives detail, but doesn’t allow easily to integrate everything in one map Can we combine both approaches?

64. Other data RNAseq data Proteomics data – Human Proteome Atlas from KTH in Stockholm (collaboration with Mathias Uhlen) Time series – what states a cell goes through to become from an ESC to a mature cell?

67. Two ways of integrating the data On a quantitative level – normalise all data together Advantages – results easier to interpret Disadvantages – lab effects On a statistics level – analyse each dataset separately first Advantages – less lab effects Disadvantages – combined data difficult to interpret (in each experiment each conditions is compared to something else) How to combine the two approaches?

68. Acknowledgements Margus Lukk Misha Kapushesky Angela Gonzales Helen Parkinson Gabriela Rustici Ugis Sarkans Ele Holloway Roby Mani Mohammadreza Shojatalab Nikolay Kolesnikov Niran Abeygunawardena Anjan Sharma Miroslaw Dylag Ekaterina Pilicheva Ibrahim Emam Pavel Kurnosov Andrew Tikhonov Andrey Zorin Collaborators Audrey Kaufman (EBI) Wolfgang Huber (EBI) Sami Kaski (Helsinki) Morris Swertz (Groningen) … Funding European Commision FELICS MolPAGE ENGAGE MuGEN SLING DIAMONDS EMERALD NIH (NHGRI) EMBL Anna Farne Eleanor Williams Tony Burdett James Malone Holly Zheng Tomasz Adamusiak Susanna-Assunta Sansone Philippe Rocca-Serra Natalija Sklyar Marco Brandizi Chris Taylor Eamonn Maguire Maria Krestyaninova Mikhail Gostev Johan Rung Natalja Kurbatova Katherine Lawler Nils Gehlenborg Lynn French