1. Unlocking the potential of public available gene expression data for large-scale analysis Jonatan Taminau PhD defense, November 2012

2. 22 Introduction In this thesis: Focus on data to information step. Focus on microarrays technology. Data KnowledgeInformation

3. 33 Introduction Data Information Data Repositories: + Massive amounts + Examples: GEO, ArrayExpress + Publicly available! Analysis Software: + Commercial: CLC Bio, Spotfire, etc. + Free: Bioconductor, Genepattern, Galaxy, etc. + A lot of existing research

4. 44 Introduction “Although hundreds of thousands of samples are publicly available, and several powerful analysis software solutions exist, the research community is facing a chasm between these two resources.” (Coletta et al, 2012) “One of the challenges for the future is how to integrate all the DNA microarray data that have been generated and deposited in public databases.” (Larsson et al, 2006) ?

5. 55 Introduction We identified two hurdles for large-scale microarray analysis: ① Consistent retrieval of individual datasets. ② Integrative analysis of multiple data sets.

6. 66 Outline Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6Chapter 7 Chapter 8 Chapter 9

7. 77 Outline Retrieval of data Integrative Analysis Problem Statement inSilico DB Problem Statement Meta-AnalysisMerging Application

8. 88 Outline Retrieval of data Problem Statement inSilico DB Problem Statement Meta-AnalysisMerging Application Integrative Analysis

9. 99 Retrieval of genomic data Data is online, freely available But: difficult to consistently retrieve the data (Example: Baggerly & Combes, 2011 ) What does it mean? Data retrieval is reproducible and tractable No manual intervention needed All data is preprocessed the same

10. 10 Retrieval of genomic data Typical microarray workflow: Image CEL file ScannerPrepro- cessing DNA microarray Image Analysis numerical (‘raw’) data Gene expression matrix

11. 11 Retrieval of genomic data CEL file Prepro- cessing numerical (‘raw’) data Gene expression matrix Complex + normalization/background correction + probe-to-gene mapping + versioning issues + etc. Not Documented! “only 48% of all data in GEO and ArrayExpress was submitted with raw data” (Larsson et al. 2006)

12. 12 Retrieval of genomic data + Features + Genes or probes + range: 20k-30k + Instances + Patients, tissues, etc. + range: 10-100 Gene Expression Value: + Expression of gene i in sample j + range between 2-14 + log2 scaled x ij

13. 13 Retrieval of genomic data What about phenotypical data or meta-data ? Extra information about the samples (age, gender, disease, etc.) No standard way of formatting this information MIAME / Ontologies / Free text / etc. Also still an open problem

14. 14 Retrieval of genomic data Why is consistent retrieval from public repositories so important? Reproducibility of results Comparison of new results with existing studies Combining different studies

15. 15 Outline Retrieval of data Problem Statement inSilico DB Problem Statement Meta-AnalysisMerging Application Integrative Analysis

16. 16 The inSilico Database Result of InSilico project Innoviris (2007-2012) 8 persons from VUB & ULB Provides consistently preprocessed and expert-curated genomic data Being commercialized

17. 17 The inSilico Database What makes the inSilico Database so valuable ? Not the fact that all data is precomputed But how it is precomputed What is the underlying engine ? Genomic Pipelines Backbone

18. 18 The inSilico DB | Genomic Pipelines For every data type there is a different pipeline Microarray pipeline: Jobs Dependencies Backbone

19. 19 The inSilico DB | Backbone Automatic Workflow System Barely manual intervention needed Control of intermediate results Pre-computation saves time (for the user) Streamlined Error management Automatic Monitoring

20. 20 The inSilico DB | Backbone How does it works? Java daemon (recently replaced by application server) Configuration Files

21. 21 inSilicoDb package One thing missing for large-scale analysis... Programmatic access via scripting Contains the basic functionality of InSilico DB Makes automatic retrieval of data possible! Seamlessly integrates with other bioconductor analysis tools Published in Bioinformatics, download > 2000 times

22. 22 Outline Retrieval of data Problem Statement inSilico DB Problem Statement Meta-AnalysisMerging Application Integrative Analysis

23. 23 Integrative Analysis “Combining the information of multiple, independent but related studies in order to extract more general and more reliable results” Problem: How to do it ? Two approaches: Meta-Analysis Merging

24. 24 Integrative Analysis MergingMeta-Analysis

25. 25 Outline Retrieval of data Problem Statement inSilico DB Problem Statement Meta-AnalysisMerging Application Integrative Analysis

26. 26 Meta-Analysis + Combining p-values + Combining effect sizes + Combining Ranks + Vote Counting + etc. + Depends on goal + Much focus on finding DEGs + Defines what the results look like + Consistent Retrieval is essential ! + inSilicoDb package

27. 27 Meta-Analysis | Stable Genes 365 studies were screened for stable genes Motivation: Interested in reference genes Currently used genes (housekeeping genes) are not ideal Need a compact and diverse list of genes that are stable under most conditions In collaboration with Dr Bram de Craene (VIB-UGent)

28. 28 Meta-Analysis | Stable Genes (1) Retrieve Data + inSilicoDb package + All 365 datasets downloaded in less than 100 min (2) Calculate Stability Scores + For each gene: + Coefficient of Variation (CV) sd / mean + avoid lowly expressed genes (3) Combine Stability Scores + For each gene take median of CVs + Rank and take top 100 (4) Semantic Similarity Filtering + Exclude genes that are related + Uses gene annotation from GO + Innovative Step! + From 100 to 10 genes

29. 29 Meta-Analysis | Stable Genes Status: August 2012 | waiting for results… September 2012 | first positive results! November 2012 | second test case, positive feedback from NAR, manuscript in preparation…

30. 30 Outline Retrieval of data Problem Statement inSilico DB Problem Statement Meta-AnalysisMerging Application Integrative Analysis

31. 31 Merging + Consistent Retrieval is essential ! + inSilicoDb package + Batch effects + Methods to remove - Location-scale - Matrix Factorization - Discretization + Makes data compatible + Preprocessing not sufficient + Same as with single studies + Increased sample size !

32. 32 Merging | Batch Effects Illustrative Example what batch effects can cause: We merged 4 different studies with thyroid samples All studies contained normal and tumor samples In collaboration with Wilma Van Staveren (IRIBHM, ULB) Samples are plotted in MDS space We expect two clusters

33. 33 Merging | Batch Effects Merging without batch effect removalMerging with batch effect removal Legend: + symbol for study + color for normal/tumor

34. 34 inSilicoMerging package R/Bioconductor package combining: 6 different merging methods 5 visual inspection tools 6 quantitative measures Only resource so far combining all this functionality ! Seamlessly integrates with inSilicoDb package

35. 35 Outline Retrieval of data Problem Statement inSilico DB Problem Statement Meta-AnalysisMerging Application Integrative Analysis

36. 36 Identification of DEGs in Lung Cancer Idea: compare meta-analysis and merging approaches for integrative analysis We used lung cancer as case based on the content of inSilico DB. Ignore subtypes: DEGs can be seen as playing a role in the basic mechanisms of lung cancer

37. 37 Identification of DEGs in Lung Cancer What is our hypothesis ? Due to the small sample sizes of individual studies there are a lot or False Negatives when using meta- analysis Can we avoid this by using merging as an alternative approach?

38. 38 Identification of DEGs in Lung Cancer MergingMeta-Analysis Constraints: + fRMA preprocessed + > 30 samples + both normal and tumor + GPL96 or GPL570 Methodology: + apply limma - p-value 2 + robustness test - 100 iterations with 90% of data - resampling + inSilicoMerging package + take intersection

39. 39 Identification of DEGs in Lung Cancer Meta-Analysis:

40. 40 Identification of DEGs in Lung Cancer Merging:

41. 41 Identification of DEGs in Lung Cancer Findings: Resampling helps to remove false positives Relatively low impact of batch effect removal methods More DEGs identified through merging (102) than via meta-analysis (25) “Deriving separate statistics and then averaging is often less powerful than directly computing statistics from aggregated data.” (Xu et al, 2008) no False Positives? + checked literature + initial pathway analysis

42. 42 Outline Retrieval of data Problem Statement inSilico DB Problem Statement Meta-AnalysisMerging Application Integrative Analysis + Contributions + Conclusions

43. 43 Contributions Genomic pipelines / backbone (Ch 4) Release of 2 publicly available R/Bioconductor packages (Ch 4 & 7) Survey of batch effect removal methods (Ch 7) Two applications Identification of stable genes via meta-analysis (Ch 6) Screening of potential biomarkers via integrative analysis (Ch 8)

44. 44 Conclusions We identified two hurdles for large-scale microarray analysis: ① Consistent retrieval of individual datasets. ② Integration of multiple data sets for integrative analysis.

45. 45 Conclusions ① Consistent retrieval of individual datasets.  inSilicoDb package ② Integration of multiple data sets for integrative analysis.  inSilicoMerging package Paving the road towards unlocking the potential of public available gene expression studies

46. 46 Thanks! + InSilico Team! + Jury! + Audience! + Yann-Michaël!