Male Speaker: So we have Timo Erkkila.
And the -- we'll be hearing about the RF-ACE [spelled phonetically] method for uncovering
nonlinear associations from heterogeneous cancer data.
Thank you.
Timo.
Timo Erkkila: Can you hear me okay?
I also want to thank the organizers for holding such a great symposium.
There have been some fabulous talks that everybody has heard.
And I also want to thank the organizers for the opportunity to give this presentation
in this symposium.
Already looking forward to the next symposium.
So as you all know, the TCGA is about collecting large collection of data from different sources,
with which to interrogate the molecular mechanism driving the progression of cancer.
Gene expression data, microRNA data, methylation data, copy number data, protein domain mutation
information, clinical reports.
But not only that, also outputs from analysis groups.
For example, paradigm activities produced by the UCSC, various algorithm outputs built
inside Firehose [spelled phonetically] that you can take as yet another input to consider
as features with which to explain the progress of cancer.
So my interest has been to focus on association studies with all these heterogeneous collections
of data that has been given to us.
But before one can even think of doing that, there needs to be some kind of mechanism system,
automated way to integrate all this data together into a feature matrix that then, with which
it's then easier to automatically uncover associations from the data.
So as an example, per cancer type, if one collects all the data for them, there are
usually samples ranging from 100 close to 1,000.
And there may be tens of thousands of features, where each feature can be a gene expression
profile, microRNA profile, copy-number variation profile, et cetera.
And all the data is very heterogeneous in the sense that there may be some categorical
variables per sample, numerical variables, binary variables, string-literals [spelled
phonetically], as for example in clinical reports.
But also some missing values.
So not for all samples do you have data represented from all those sources.
So there is an obvious problem that we need an algorithm with which to uncover associations,
given this heterogeneous collection of data.
And actually, to my mind, I don't know, except for the random first [spelled phonetically]
algorithm that can handle negatively [spelled phonetically] that kind of heterogeneous data
sets.
And there are some apparent pros with the random first algorithm besides supporting
the mixed type data and missing values.
You don't need to do that much data transformations, and it also supports multivariate, uncovering
multivariate and nonlinear associations.
So it's not just pairwise [spelled phonetically] association analysis, but one can think of
having the data suggesting that there are some multivariate relationships within.
And it would be nice to uncover those as well.
And then some cons [spelled phonetically].
The original random first algorithm may not be that good for our problems, because there
is no way to, for example, assess the statistical significance of those associations that one
get.
Rather, mere ranking of associations is provided with the importance score.
Moreover, if one wants to furthermore consider prediction of new data, given these associations,
build predictors for new data, for example.
Given some new data, what would the survival be for a patient.
So prediction tasks are not that easy to handle with the initial random first algorithm.
And also, the current implementations lack flexibility, more or less.
So with that, we thought of implementing an improved random first algorithm based on already-established
variants of random forest [spelled phonetically].
Take these shortcomings into account.
For example, implementation with added flexibility, so that one could feed in string-literals
and give data in various formats.
For example, that annotated feature matrix form is really handy for our analysis.
So supports for that data is really important, that type of data.
And in order to construct large association maps, meaning that if one wants to see that
how all these features are related to each other, there needs to be a way to put all
the individual association analysis runs together.
So there needs to be a metric that is comparable to other analysis runs.
And also, a statistical significance assessments are important, so that you can provide a cutoff
at which association is meaningful and which is not.
And also, to deal with prediction, given new data.
There is also that taken into account in the RF-ACE algorithm.
So we have implemented a gradient boosting tree algorithm to take into account prediction.
So if one considers an example with the colorectal data, the gene expression of PIRAC [spelled
phonetically], which is the prostate tangent susceptibility candidate known to be expressed
in colorectal data, and see what comes out when RF-ACE is being run, targeting that particular
gene expression profile.
Here is just a snapshot of the algorithm being run.
It's a command line algorithm.
And you can see as an output here, basically if the fund [spelled phonetically] is not
too small, that there are some information regarding the data dimensions which feature
has been selected as target.
Here is the feature header for PIRAC, how much data is missing.
So approximately 50 percent of the data was missing.
But that doesn't matter, because the algorithm can handle missing values.
And then the algorithm automatically selects suitable parameter values to be utilized in
this particular run.
So if you don't have any experience of decision tree-based analysis, you can just simply feed
in the data matrix, provide a target and run the analysis.
But if you have better experience on the algorithm, you can manually fine-tune to the algorithm.
And in this particular run, it was executed in less than 200 seconds, and it could find
19 candidate associations with -- that exceed the significance threshold.
And if you look at the associations that come out in this particular example, out of 19
the top three associations were Huxby-13 [spelled phonetically], which is a proximal gene to
PIRAC, anatomic organ subdivision, which is basically indicating that PIRAC expression
varies as a function of location, between colon and rectum.
And also, it's promoter methylation is related to the expression, meaning that it's basically
silencing the PIRAC when methylation goes up.
And as I mentioned, now that there are the significant associations, you can also build
a predictor based on these and see, for example, that how good these features are in explaining
the PIRAC expression.
So, going one step further, this was actually computed also.
So you can take that as an alternative output, that what is the prediction accuracy given
that you have found these significantly-associated features to PIRAC.
And then you can build a predictor with gradient boosting trees and see that how well the data
points for which there is non-missing value were predicted.
So on that X-axis you see the measured data.
And on the Y-axis you see the predicted data.
And the gradient boosting tree algorithm is inherently applying bootstrapping, so as to
avoid overfeeding.
And as I mentioned, about 50 percent of the data were missing.
So one can then see that what the predictions would be for those missing data points.
And this plot essentially shows those predictions.
So they're on the same range.
But I haven't applied any further analysis to see what is the relevance of those predictions.
So that was just one example at how you can apply RF-ACE.
And let's repeat the analysis for tumor stage.
So there is one clinical feature tumor stage.
And you can rerun the analysis, runs in less than two minutes or so.
And you can see that you get lymph node spread, number of lymph nodes and, for example, paradigm
activity, which we used as a candidate input for our association studies.
The paradigm activity [spelled phonetically] ERCC4 was found to be significant feature.
And then some other features as well.
And you can directly see -- well on X-axis you see the tumor stage, so bearing [spelled
phonetically] from one to nine, that the lymph node features aren't very powerful in explaining
tumor stage at lower tumor stage levels.
So it's kind of flat, whereas the paradigm activity has some variation on the lower range.
So you would assume that when you combine these different features together, so as to
better predict tumor stage, that these features would complement each other.
And that's what seems to be happening in this case, that on X-axis, again, you have the
measured tumor stages.
And on Y-axis, the predicted ones.
And again, about 50 percent of the data was missing.
So the leftover data were predicted here on the left.
And we have taken a step further.
Now that there is an algorithm that can handle the TCGA data, the heterogeneous data set
with missing values, we have developed a pipeline.
Most of the credit goes to Smulevitz' [spelled phonetically] lab.
There are many people involved in this, in this developing this pipeline, where we, in
automated fashion, generate these feature matrices, analyze, make a complete association
maps with RF-ACE, store all the associations for all the cancer types that we have analyzed
in a queryable [spelled phonetically] database, and complement dot data-driven association
information with literature, pathways and that kind of things, and project everything
onto a Web service that can be used for exploring the massive amount of association information
that has been generated with our approach.
So as a summary, the core of my presentation was to introduce you to RF-ACE, which essentially
combines good parts from various established algorithms, like the random first algorithm,
gradient boosting trees and, most importantly, ACE, which has been published a few years
ago.
So there are not that many new parts that we introduced.
Rather, we put them into a easily-usable package.
It's generic and fast implementation of decision tree-based learning.
And it seems to suit pretty well to TCGA data.
And as novel aspects, there are P values assigned for associations.
And you can do pretty accurate prediction with gradient boosting trees.
And this is an open source project.
And you can look that webpage up.
And it's also a work in progress.
So there are some things that need to be improved, and so on.
But I think we're pretty close in finishing implementing the algorithm.
Many thanks go to Smulevitz' lab.
So most of the work has been done by members of Smulevitz' lab.
But also some people from Tampere [spelled phonetically] University of Technology and
yeah.
So thank you for your attention.
[applause]
Male Speaker: Questions?
Male Speaker: Thank you.
Great talk.
One question about all these associations.
We know that when you're taking the genomic data and putting it as an input to your algorithm,
there's a lot of correlations and really a lot of redundancy in that genomic data.
So the output you get, the associations, there's a lot of redundant information.
Timo Erkkila: Exactly.
Male Speaker: What approaches are you taking to condense
that redundant information and give a clear answer to the questions you ask of the data?
Timo Erkkila: That's an excellent question.
On the roadmap, in developing this algorithm, we are going to implement methods that take,
in particular, those redundancies into account, so as to make compact sets of good predictors
of whatever target you have chosen.
It is not yet implemented in there.
So if you have many correlated features, one can imagine that, for example, methylation
probes, if you use them as individual features, they are highly correlated with each other.
So it's going to create some associations that are basically spanning across hundreds
of methylation features.
And they all seem significant, whereas they are highly redundant.
So that is still a bit of an issue, depending on what data you are focusing on.
But we are certainly going to address that in future versions of RF-ACE.
Male Speaker: Andre [spelled phonetically].
Male Speaker: Thank you.
It's a very, very interesting work.
I was just wondering.
Maybe I missed it.
But on your plot, which I interpreted as a fitting curve.
And it carried the heading saying "training data."
So, did you really try to split your data set into a training and test set, and evaluate
the performance on the test set that you never used for training?
Timo Erkkila: Excellent question.
No, not in this example.
So, I didn't do that.
But I should have done it.
I know.
Male Speaker: Okay, let's thank Timo one more time and --
[applause]