Richard Kreisberg: My name is Dick [spelled phonetically] Kreisberg
and I'm from Institute for Systems Biology in Seattle, I work for Ilya.
And in particular I'm a software engineer, so you won't be hearing any interesting cancer
results from me.
This talk is more about our approaches to tool building, especially in the face of the
challenges that TCGA data presents.
So, in this case I want to talk specifically about visual analytics and how it pertains
to the sorts of problems that we face in our work.
We have these large heterogeneous data sets as Ilya talked about earlier and we look for
ways -- advantageous ways to explore this data.
The goal is to allow a person who uses our tool to quickly formulate testable hypotheses
about the relationships between different parts of the data or even external parts of
the data such as pathways and outcome.
And I'd like to say right away that you know, there's many excellent tools aside from ours
and especially within the TCGA community, you have tools from places like UCSC and Broad
and MSKCC.
When we talk about visual analytics, there's a lot of things to keep in mind.
A couple of them are, for instance, the first thing would be how we encode the information.
So you might see in this slide, you know, there's a lot of encoding going on.
There's color and shape and size and annotation, but the goal is that we want the user to be
able to easily reason on what they're looking at.
They want -- it needs to be as close to their mental model as possible so that they can
-- when they're performing these complex analyses and operations in the tool, the abstraction
isn't so high that they're distracted by it.
We also, in these tools, we want to manage to scale so that when we're viewing the data
it's cognitively and visually manageable.
We don't want to clutter it or overwhelm the user.
And hopefully we can make it interactive so that the manipulation of the data allows the
user to focus on the useful abstraction rather than how they're actually manipulating the
interface.
We've liked to annotate so provide additional information on demand, not up front.
Information like metadata, research literature, error estimates and so forth.
And then sort of the bigger goals are we'd like to, you know, connect the data to other
sources and tools like the ones I had mentioned before and place them nearby in the analysis
and the visualization so that the user can gather, you know, additional evidence in support
of their hypothesis.
And of course, at the end of the day what you want to be able to do is share this insight
with others.
So you want to be able to collaborate and distribute and collect, you know, the different
analyses and link them out to everyone else so that you can gather more and more sort
of evidence.
So quickly, sorts of technologies that allow us to do this, you might call them emergent
technologies, for us the biggest one is the first one, the browser based graphical rendering.
So in the tools that I'll be showing again much like the one -- the demonstration Ilya
gave, we use SVG where the browser's rendering the image directly, but there's also Canvas
and WebGL which is a forthcoming standard that has -- allows you to accelerate the rendering
on the graphical card while still being in the browser experience.
There's also cloud services that allow you to scale up your computation and your data
management.
And then new types of databases called NoSQL and those allow us to have adaptive data models.
So often in TCGA a new analysis is born and from it a new data model is created and it's
hard in the classical sense with SQL to integrate those models quickly the new SQL, NoSQL technology
allow us to do that on the fly.
Also graph databases, they allow us in the same manner to be adaptive about the types
of relationships that we declare exist between the data.
And finally, graph computation is a new direction to go in where you can then reason upon the
data, especially in our case that's structured like a graph.
And you can do this in a distributed manner across large clusters of computers.
So in this case, we talked about integrating data sources and then performing analysis,
storing associations, bringing in other types of data and in the end visualizing it with
tools so in this one that I've highlighted that's a screenshot of the circular view from
Regulome Explorer, the genome level view.
And in my mind there's another arrow that sort of points from number five, the highlighted
one, back to number one because it's really an iterative process where, you know, the
results are understood and internalized and then the user proposes a new data matrix to
run the analysis on or a new analysis to run.
So I'm not as brave as Ilya, so I won't be doing a live demonstration.
Instead, I've just got some screenshots.
So in this case it's like I said before, the genome level view and this analysis is done
with random force created by Timo ErkkilÑ who'll be speaking tomorrow and it portrays
around the periphery the human chromosomes and each node represents different feature
types of the colors, describe whether it's a gene expression or methylation or copying
of a variation and so on.
And as Ilya showed, you can bring up the raw data which is critical for the user to be
able to confirm or possibly deny the association that the analysis gave.
And I want to emphasize that the circular view is very much a high level, 30,000 foot
view of the data, right.
There's no particular one little insight that's just going to jump out by bringing up all
these results.
But it does allow the user to cognitively manage the data using filtering tools like
the one that's on the right side of the screen and to bring up the data and iterate through
a paginate through it.
So here's another tool, in this case the analysis is a metric that measure aggressiveness of
tumors in colorectal cancer.
And you can see on the right side there's a linear view which is brought up whenever
you want to zoom in to the chromosome or sub-chromosome level and you're able to pan and zoom through
the data.
And then you can see on there, there's a pop-up and from there you can look at the data specifically
or link out to other tools like the UCSC genome browser.
And there's another one, in this case all pair significance.
And in this case, you can see the hovering bars would show more information.
They can actually be brought up and pinned to the screen, you know, set off to the side
so that you can sort of collect your data together and come back and look at it later.
And finally, we have the network view, which Brady has a poster in the hall speaking more
about.
But in this case we're talking about bringing together many different kinds of associations,
whether that's from protein domain mapping or the mining of Medline or random force and
many other things.
And this is going to be more closely integrated with the entire tool set very soon, but the
idea is that the circular view is not ideal for many types of data and often what you
really are trying to get at is the network, the topology of the graph itself.
And that will be built up along with, I mentioned, the graph computation earlier.
We're going to -- when our analyses on graph computation become more sophisticated this
tool is ideal for bringing in and reviewing those results.
So in the future, or in the near future, there's a couple other things.
I talked about the emergent technologies, graph databases and graph computation.
And then what I'm excited about is the network topologies and exploring the very abstract
topologies that can be created.
And you can begin to use them to possibly identify explanatory variables and drive further
analysis in the correct direction by overlaying these graphed outputs, whether they're cross
cancer or cross analysis and bringing in external information.
But in a more concrete way, we plan on soon adding explicit, retrievable state so that
for any given screen that you've brought up and you're at this point in your analysis
and you'd like to share that with a colleague, it's important to be able to just share that
to them, not send them directions about where to click and how.
And that will be just a URL-based link.
And also of course, user data import is very, very important.
Being able to bring in your own data and look at it in the context of other analyses or
just simply for the usage of the tool and the benefits that it provides.
So the website is up, the explorer.cancerregulome.org.
The tool is open source hosted on Google Code, both the Regulome Explorer itself and the
underlying rendering tools that create the visualizations that you saw.
And, you know, we're always looking for more and more people to use it and give us feedback
or even work on it with us and it's very exciting for us.
So, I'd like to thank or acknowledge Ilya and then myself and Jake worked on genome
view.
Network view was Andrea Eakin and Brady Bernard and the analysis is done by Vesteinn Thorsson,
Sheila Reynolds and Timo ErkkilÑ.
I won't go into all of the acknowledgements, but there's some of, lists of some of the
technologies that we've leveraged so far in creating these kinds of tools and our funding
and so forth is at the bottom.
Thank you.
[applause]
Male Speaker: Any questions?
Well, you probably are not expecting a question from me, Dick.
But I'd like to ask, and I get this asked a lot from people, people ask, can I load
my own data into Regulome Explorer?
So do you see that being -- how do you see that happening?
That somebody would upload a data set or would that be if it's a very large data set you
would -- presumably you would have to you know, not move it but do it close to the data
then.
Richard Kreisberg: Right, so that kind of comes back to concrete
and formalized data specification which we're pretty much at at this point.
And so if they wanted to match our format, than yes we could provide some sort of either
upload or be able to retrieve the data from them and display it.
But if it's a different abstraction on the data, a different data model, then it's also
possible to take the tool, the underlying code itself and modify it quickly and provide
it from their own side next to their data where it -- where it lives.
Male Speaker: Okay.
Richard Kreisberg: Yeah, okay.
Male Speaker: Other questions?
Okay if not, thanks again and I'd like to thank all the speakers of this session.
[applause]