The document discusses potential approaches to visualizing large and complex genomic data sets. It describes some common challenges with analyzing big data in healthcare. Several existing and proposed visualization models are outlined, including representing data in polygons, n-dimensional illustrations, cell structures, helical structures, maps, exploding circles, 3D spheres, universe models, bar graphs, dot graphs, wave graphs, line graphs, and heat maps. The goal is to develop interactive visualizations that make complex genomic data more understandable and useful for researchers, medical practitioners, and patients.

1. Visual Analytics: An exploration
Presentation on Ideation of cancer informatics representation
By – Rupam and linu

2. Big Data
It is used to describe a massive
volume of both structured and
unstructured data that is so large that
it's difficult to process using
traditional database and software
techniques.

3. Big Data Analytics
Big data analytics refers to the process of collecting,
organizing and analyzing large sets of data to discover
patterns and other useful information. Enterprises are
increasingly looking to find actionable insights into their
data.
Many big data projects originate from the need to
answer specific business questions. With the right big
data analytics platforms in place, an enterprise can boost
sales, increase efficiency, and improve operations,
customer service and risk management.

4. Need for Informatics
As information technology becomes an integral part of
health care, it is important to collect and analyze data in
a way that makes the information understandable and
useful.
This big data surge presents many challenges around
data quality; moving/managing big data sets; reduction
of large sets of genes to find biomarkers; making sense
out of the data in a repeatable manner; integration of
clinical data with molecular/genomic data for the
purpose of analytics, and scalable analysis.

5. Current Model
These are the inferences we gathered from the
current model:
• Circle divided into arcs and colors.
• Bevier curves representing each arcs with color.
• Dot graph provides population density.
• Circle with big strokes filters the incoming and
outgoing curves.
• Outermost circle(one with speedometric lines)
dividing the circles into numbers.
• Bar histograms depicts SNP count.

6. What more can be done?
Why not show data relevant to the task at hand?
Why can’t the data be presented more realistically rather than in abstract format?
Why shouldn’t the screen layout change rather than keeping it in static circular format (layover, clouds, threads, n
dimension axis)?
Why not make it easier for even non researchers, practitioners, doctors and patients to understand?
Can we make the interaction more intuitive and natural?
Why not consider touch as mode of interaction ?
Can we build in ways to filter out specific chromosomes, DNA’s, and base pairs and remove rest of data?
Can we help convert data to information? Possible to add semantics to the relationships of base pairs?
Why not enable multimodal search (text, nos., zoom, click etc.) within the data for expert users?
Facilitate tools to dig data ?
Navigation and Walkthroughs?

7. Day dreaming about the setup?
• Dimensionality of the data:
• Chromosomal data = 12-15 dimensions
• Patient data = 5-10 dimensions
• Researchers data = 5-10 dimensions
• Diagnostic data = 4-10 dimensions
We are dealing with 30-50 dimensions of each data node (base pairs) to be presented to a user.
There has to be facility to search for correlations within 50 dimensions !
What’s the way? Human can at best see.. 3 dimensions… OK lets add sound and movement .. 5 dimensions!
How do we do that? Visualization beyond 3D is not possible.. We will have to be selective in displays at a time.

9. Data Visualization Patterns and Examples

10. Polygonal Structure
Data in polygon of n dimension.
Each side will indicate a pattern and they will be
interlinked with the help of straight and translucent
lines.
Application ex:
For Smaller sets of categories, we can change the layout
to something like no. of categories=no. sided polygon

12. N Dimensional Illustration
Data in n dimensional illustrations.
When vast amount of data is illustrated, each data has
many patterns passing through its location due to which
finding the that particular data is very tough. In that case
n-dimensional illustrations are very useful for
visualization.

13. FILTERS AGE
ETHNICITY
GENOLOGY
NATIONALITY
PATIENTS
DISEASE
GENDER
AGE
SETTINGS

14. AGE
AGE
SETTINGS

15. Cell Structure
Cell and all the constituents will be shown and the user
will zoom more into the pattern and upto the deepest
level to seek information.

16. This model is based on cell structure where we can have
animation and 3d visual graphics to make things
interesting, that way we can keep users engaged,
intrigued and absorbed.
Here the cell is bounded by a circle having all the
constituent chromosome. We can scroll towards the
nucleus to see the individual chromosome or a set of
chromosomes(like 2, 5 etc) in that boundary.

17. In this each of the chromosome is tagged with a
particular color and the same color is flanked by the arc
outside, which will show the information about it.
The particular chromosome will pop out of the screen
after selection where we can show various information
and data related to gene. In this way one can use the
scroll button to zoom more into the gene to see the area
of defect like the SNPs.

18. In this model the single chromosome having marked
with genes having high probability of SNPs are marked,
which will be connected with the arcs that are
protruding that particular portion of gene to the
chromosome where it has been linked.
Further one can find various information related to the
concerned cancer in the pop-up box near the marked
gene. Here the color of the chromosome that is in the
center will be same as the arc that has been colored to
show the correlation between the objects.

19. In this the chromosome will be towards the periphery
where the same chromosomal arc . From here the
bending arcs from the smaller circle will connect to
various other arcs which depicts other chromosome.

20. Helical structure
Actual DNA structure along with the corresponding
information can be shown to make user explore it while
scrolling and zooming.
Application ex:

21. In this model the whole circle can be inserted in the
chromosome in which we can provide with the levels of
information with the help scrolling, where we can zoom
deeper into the chromosome as well as move along it to
find high density SNP region.

22. Map
Data can be mapped as a physical map to make user
explore the given visual

24. Exploding circle , Sphere
This type of chart displays the contribution of each value
to a total while emphasizing individual values.
Application ex:
Here we could use selection as a way to explode the
circle and maybe to view info on it.

26. 3D circle
Sphere which will have various histograms and data
embedded on the surface of the sphere which will
explode further to showcase much deeper information.
Application ex:

28. Universe model
User will zoom into the different categories of elements
that will be provided to them and will explore it to its
limits.

30. Bar Graph
They can be used to make comparisons between
different variables effectively which will clearly show
trends in data. We can give various shapes to the bar like
Cylinder, Cone, or Pyramid charts.

31. Dot Graph
Intensity and probability information can be shown as
dot graph with visual graphics.

32. Wave graph
Wave will denote the information of the respective
pattern and will show the fluctuations among the
variables

33. Line Graph
This type of chart displays trends over time or
categories. It is also available with markers displayed at
each data value. With line graphs we can show the
variations of data along with Patterns unfolding
alongside.

34. Terrain and Heat Maps
Here data is represented by colors. It provides an
immediate visual summary of information. It will also
allow the viewer to understand complex data sets.

35. And Many Many More…….