In this talk, I explain the need for basic visualization know-how in bioinformatics.

1. Humanizing bioinformatics
Jan Aerts
Assistant Professor - ESAT/SCD
BioData Analysis & Visualization
Faculty of Engineering
Leuven University
jan.aerts@esat.kuleuven.be
@jandot

2. whoami
Leuven

3. whoami
Wageningen

4. whoami
Roslin

5. whoami
Hinxton

6. whoami
Leuven

7. why “humanizing bioinformatics”?

8. bout
lk a
ta
at I’ll
wh scientific research paradigms
-
big & complex data
-
what about the user?
-
data visualization

9. scientific research throughout time

10. Science Paradigms
1st 1,000s years ago empirical
2nd 100s years ago theoretical
3rd last few decades computational
4rd today data exploration
Jim Gray

11. Science Paradigms
1st 1,000s years ago empirical
2nd 100s years ago theoretical
3rd computational biology
last few decades computational
4rd today bioinformatics
data exploration
Jim Gray

12. ever bigger datasets
ever more complicated mining algorithms

13. case in point:
genome sequencing

14. why do we sequence?

15. transcriptionally active sites
protein-DNA interactions
alternative splicing
gene expression
variation discovery
copy number variation
miRNA expression & discovery

16. single nucleotide polymorphisms
coverage
reads
polymorphisms
gene model

17. structural variation
Robberecht et al, 2010
Molecular Biology of the Cell, 4th Edition

20. Human Genome Project

21. automate, automate, automate

22. HGP:
15 years, $3 billion, tens of labs => 1 genome
now:
1 week, $5000, 1 technician => 1 genome

23. genome sequencing throughput
Mardis, 2010

24. genome sequencing throughput
“next-generation” sequencing platforms
Mardis, 2010

25. NHGRI

26. Metzker et al, 2010

27. big throughput => big data

28. advanced data structures

29. advanced data mining
support vector machine recursive feature e
limination
n ifold le arning
ma
adaptive cascade shar
ing trees

30. “Dammit Jim, I’m a doctor, not a bioinformatician!”
Christophe Lambert

31. “Dammit Jim, I’m a doctor, not a bioinformatician!”
We’re alienating the user...
too much data
blind trust (?) in bioinformatician

32. but...
what’s the question?
what parameters should I use?
can I trust this output?
I can’t wrap my head around this...

33. what’s the question?
4th paradigm
question -> hypothesis -> generate data

34. what’s the question?
4th paradigm
question -> hypothesis -> generate data
generate data -> see what we can do with it

35. Gene interaction data: “A regulates B”

36. what parameters should I use?

37. peak

38. but is this?

39. van de Wiel et al, 2010

40. T. Voet

41. can I trust this output?
data filtering
putative mutations
filter 1
filter 2
filter 3
A B C
different settings for
filters

42. B
A
C

43. B
A
C
State of the art: run many filter
pipelines and take intersection

44. What we should have found...
B
A
C

45. different algorithms for
finding the same thing

47. I can’t wrap my head around this...
too much (?) info

48. treatment plan for cancer patients
heterogeneous datasets
multiple abstraction levels
multiple sources
multiple formats
patient/clinical data
population/family data
tissue samples
MR/CT/X-ray
pathways gene expression data
collaborative data examination
pathologist
geneticist
biologist

49. researcher is lost...

50. data visualization

51. “... the use of computer-supported, interactive, visual representations of data to
amplify cognition” (S Card, J Mackinlay & B Schneiderman)
“... computer-based visualization systems providing visual representations of
datasets intended to help people carry out some task more effectively.” (T
Munzner)

53. cognitive task => perceptive task

55. I II III IV
x y x y x y x y
10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58
8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76
13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71
9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84
11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47
14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04
6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25
4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50
12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56
7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91
5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.80
mean x = 9.0 variance x = 11.0 correlation x & y = 0.816
n = 11
mean y = 7.5 variance y = 4.12 regression line: y = 3+0.5x

57. exploration explanation

58. exploration explanation
pictorial superiority effect
“information”
72hr
“informa” “i”
65% 1%

59. exploration explanation
J van Wijk

60. exploration explanation

61. some of the principles (taken from T Munzner)
know your visual encodings
power of the plane
danger of depth
eyes beat memory
overview, zoom and filter, details on demand
overview, zoom and filter, details on demand
overview, zoom and filter, details on demand
overview, zoom and filter, details on demand
overview, zoom and filter, details on demand
...

62. visual encoding channels
position on common scale
position on unaligned scale
2D size
3D size
Mackinlay

63. “power of the plane”
position on common scale
position on unaligned scale
2D size
3D size

64. examples of sub-optimal encoding

67. Florence Nightingale

68. Florence Nightingale

69. Don’t believe everything you see

71. networks... <sigh>

73. same network
Martin Krzewinsky

74. different networks!
Martin Krzewinsky

75. 3D, anyone?

76. 3D, anyone?
occlusion
interaction complexity
perspective distortion
text legibility

77. Gene interaction data: “A regulates B”

78. regulator
workhorse
manager

79. size of effect shown in graphic
“lie factor” =
size of effect in data

81. Humanizing bioinformatics

82. Humanizing bioinformatics
there and back again
put the user back in the loop!

83. Thank you

84. Acknowledgments
• graphics creators
• Tamara Munzner
• Martin Krzewinski

85. Image attributions
... got lost ...
If you find something that’s yours, let me know!