1. Statistics on big biomedical data
Methods and pitfalls when analyzing high-throughput
screens
Lars Juhl Jensen

2. Statistics on big biomedical data
Methods and pitfalls when analyzing high-throughput
screens
Lars Juhl Jensen

3. t-test

4. ANOVA

5. normal distribution

6. useful tests

7. counts

8. contingency table

9. Jensen et al., Nature Reviews Genetics, 2012

10. Fisher’s exact test

11. real numbers

12. no theoretical distribution

13. non-parametric statistics

14. do the medians differ?

15. Mann–Whitney U test

16. medians can mislead you

17. do the distributions differ?

18. Kolmogorov–Smirnov test

20. does not tell how they differ

21. resampling

22. Monte Carlo testing

24. always applicable

25. compute intensive

26. multiple testing

27. xkcd.com

28. xkcd.com

29. xkcd.com

30. xkcd.com

31. compare multiple condition

32. Gene Ontology enrichment

33. Bonferroni

34. avoid making any errors

35. too conservative

36. Benjamini–Hochberg

37. control false discovery rate

38. assumes independence

39. resampling

40. negative set

41. systematic biases

42. Huang et al., Journal of Proteome Research, 2014

43. studiedness bias

44. we study disease proteins

45. thus we know many PTMs

46. abundance bias

47. higher expressed

48. easier to detect in assays

49. better characterized

50. matched background

51. the big data effect

52. if you have enough data

53. any difference is significant

54. but maybe not relevant

55. “significant”

56. statistical significance

57. p-value

58. biological relevance

59. fold change

60. relative risk

61. significant and relevant

62. volcano plots

63. Lundby et al., Science Signaling, 2013

64. rather ad hoc

65. questions?