Big data and reproducibility

Transcript

1. Big data and reproducibility

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3. N = SAMPLE SIZE

4. N = ($ YOU HAVE) ($ PER SAMPLE)

5. Year $ per (human) Genome

6. rna-seq 2008 N≈2 2010 N≈70 2013 N≈900 PMIDS: 19056941, 20220758,

   24092820

7. www.geni.com

8. http://erlichlab.wi.mit.edu/familinx/index.html

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13. what went wrong? 2 things

14. what went wrong? transparency The data/code weren’t reproducible

15. what went wrong? transparency There was a lack of cooperation

16. what went wrong? expertise They used silly prediction rules (Pr(FEC)

     =  5/8[Pr(F)  +  Pr(E)  +  Pr(C)]  –  ¼)  

17. what went wrong? expertise They had study design problems (Batch

     effects)  

18. what went wrong? expertise Their predictions weren’t locked down Today:

     Pr(FEC)  =  0.8   Tomorrow:  Pr(FEC)  =  0.1    

19. At the end of the day the Potti analysis was

    fully reproducible The problem is that the analysis was wrong

20. 1st Discussion Point: What is reproducibility?

21. The goal: a result that is reproducible (the code and

    data can be used to recreate the results) and replicable (you can perform the experiment again and get the same answer)

22. The goal: a result that is reproducible (the code and

    data can be used to recreate the results) and replicable (you can perform the experiment again and get the same answer)

23. Who  Reproduces  Research?   The  truth  is  A   I

     don’t   care   The  truth  is  B   The  truth  is  not  A   Original  InvesRgator   Reproducers   The  truth  is  A   ScienRsts   General   Public   ???   Slide courtesy R. Peng

24. hVps://github.com/jtleek/datasharing  

25. 2nd Discussion Point: Statistical modeling is only part of the

    process

26. What  is  Data  Analysis?   Raw  Data   Cleaning  /

      ValidaRon   Pre-­‐processing   Exploratory   data  analysis   StaRsRcal  model   development   SensiRvity   analysis   Finalize   results  /  report   StaRsRcs!   Slide courtesy R. Peng

27. 3rd Discussion Point: Analysis is (often) an afterthought

28. hVp://bit.ly/OgW3xv  

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30. 4th Discussion Point: Traditional statistics & epidemiology ideas still matter

    for big data

31. association between shoe size and literacy

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35. 1. Reproducibility by data sharing 2. Big data is not

    just statistics   3. Analysis is often an afterthought   4. Traditional ideas still matter  

36. jhudatascience.org

37. 9 classes 1 month long Every month

38. Cumulative Enrollment

39. jtleek.com/talks