Visual Analytics in omics - why, what, how?

Prof Jan AertsSTADIUS - ESAT, Faculty of Engineering, University of Leuven, BelgiumData Visualization Lab

jan.aerts@esat.kuleuven.bejan@datavislab.org

creativecommons.org/licenses/by-nc/3.0/

• What problem are we trying to solve?

• What is Visual Analytics and how can it help?

• How do we actually do this?

• Some examples

• Challenges

2

A. So what’s the problem?

3

hypothesis-driven -> data-driven

Scientific Research Paradigms (Jim Gray, Microsoft)

I have an hypothesis -> need to generate data to (dis)prove it.I have data -> need to find hypotheses that I can test.

1st 1,000s years ago empirical

2nd 100s years ago theoretical

3rd last few decades computational

4rd today data exploration

4

What does this mean?

• immense re-use of existing datasets

• much of initial analysis is exploratory in nature => what’s my hypothesis?

• biologically interesting signals may be too poorly understood to be analyzed in automated fashion

• visualization is very effective in facilitating human reasoning about complex data

• automated algorithms often act as black boxes => biologists must have blind faith in bioinformatician (and bioinformatician in his/her own skills)

5

inputfilter 1

filter 2

output A

filter 3

output B output

Opening the black box

6

A B

C

7

A B

C

8

A B

C

9

What’s my hypothesis?

10

Martin Krzywinski

11

Martin Krzywinski

12

Martin Krzywinski

B. What is Visual Analytics and how can it help?

13

14

What is visualization?

T. Munzner

15

What is visualization?

T. Munzner

cognition <=> perceptioncognitive task => perceptive task

16

• record information

• blueprints, photographs,seismographs, ...

• analyze data to support reasoning

• develop & assess hypotheses

• discover errors in data

• expand memory

• find patterns (see Snow’s cholera map)

• communicate information

• share & persuade

• collaborate & revise

Why do we visualize data?

17

pictorial superiority effect

“information”

“informa” “i”65% 1%

72hr

18

Steven’s psychophysical law

= proposed relationship between the magnitude of a physical stimulus and its perceived intensity or strength

19

Accuracy of quantitative perceptual tasks

McKinlay

what/where (qualitative)how much (quantitative)

20

Accuracy of quantitative perceptual tasks

McKinlay

what/where (qualitative)how much (quantitative)

21

Accuracy of quantitative perceptual tasks

McKinlay“power of the plane”

what/where (qualitative)how much (quantitative)

22

Pre-attentive vision

= ability of low-level human visual system to rapidly identify certain basic visual properties

• some features “pop out”

• used for:

• target detection

• boundary detection

• counting/estimation

• ...

• visual system takes over => all cognitive power available for interpreting the figure, rather than needing part of it for processing the figure

23

24

25

1. Combining pre-attentive features does not always work => would need to resort to “serial search” (most channel pairs; all channel triplets)e.g. is there a red square in this picture

Limitations of preattentive vision

2. Speed depends on which channel (use one that is good for categorical; see further (“accuracy”))

26

Gestalt laws - interplay between parts and the whole

27

Gestalt laws - interplay between parts and the whole

• simplicity

• proximity

• similarity

• connectedness

• good continuation

• common fate

• familiarity

• symmetry

28

Context affects perceptual tasks

C. How do we actually do this?

30

Talking to domain experts

31

Data visualization framework

32

Card sorting

33

Tools of the trade

34

Processing - http://processing.org

• java

35

D3 - http://d3js.org/

• javascript

36

Vega - https://github.com/trifacta/vega/wiki

• html + json

37

To use vega

• Create the json file

• Create the index.html

• Run “python -m SimpleHTTPServer”

• Go to http://127.0.0.1:8000/index.html

• Get help at https://github.com/trifacta/vega/wiki

38

D. Examples

39

HiTSeeBertini E et al. IEEE Symposium on Biological Data Visualization (2011)

40

Aracari

Ryo Sakai

Bartlett C et al. BMC Bioinformatics (2012)

41

MeanderPavlopoulos et al. Nucl Acids Res (2013)

42

Georgios Pavlopoulos

ParCoordBoogaerts T et al. IEEE International Conference on

Bioinformatics & Bioengineering (2012)

Thomas Boogaerts

Endeavour gene prioritization

43

Data filtering (visual parameter setting)

TrioVis

Ryo Sakai

Sakai R et al. Bioinformatics (2013)

44

User-guided analysis

SparkNielsen et al. Genome Research (2012)

clustering

chromatin modification

DNA methylationRNA-Seq

data samples

regions of interest

45

Bret Victor - Ladder of abstration

46

E. Challenges

47

Many challenges remain

• scalability (data processing + perception), uncertainty, “interestingness”, interaction, evaluation

• infrastructure & architecture

• fast imprecise answers with progressive refinement

• incremental re-computation

• steering computation towards data regions of interest

48

Thank you

• Georgios Pavlopoulos

• Ryo Sakai

• Thomas Boogaerts

• Data Visualization Lab (datavislab.org)

• Erik Duval

• Andrew Vande Moere

49