GCC/BOSC & Bug Fixing

This week I spent the majority of my time at the GCC/BOSC Conference both volunteering at and attending talks. Most of the talks concerned the software suite Galaxy and how people were adapting it or extending it for different use cases and new applications.  Not having used Galaxy before nor intending to, these talks did not prove very useful to me. There were some talks that concerned more general biological problems and issues within the scientific community. These were accessible to a wider audience and I was able to learn some aspects of statistics and experimental design from them. This included talks on the importance of talking to the community of people who have the particular condition being studied as well as some concerning best practices on handling data.

I spent some portion of time fixing minor bugs in my code for the Bayesian Weighting Scheme. As a follow-up to a previous post, the run time of the code is now down to 2 or 3 minutes at the most. Using sets and tuples reduced the run time by several orders of magnitude and some other time saving measures also contributed to this much lower number. This is now sufficiently within the time frame of the CREU so the code will probably need much more speeding up.  Next steps involve changing the code to weight the PathLinker Interactome to check if the code works accurately as well as parsing through the math carefully.

Week 6: GCC/BOSC

This week we spent most of our time attending and volunteering at GCC/BOSC (Galaxy Community Conference/ Bioinformatics Open Source Conference). While the conference wasn’t extremely relevant to what we’re working on right now, we learned how to use software like Galaxy and Intermine, which might be useful to us in future projects. Also, this was the first conference I’ve ever attended so it was very interesting to learn how conferences work and to meet people.

Week 4: Cleaning up

With our big CNB-MAC paper deadline passed last Friday, my tasks this week have been relatively simple. My first goal this week was to take the bar chart figures in the paper and turn them into box and whisker plots.

For example, I took this figure:

and turned it into this:


The bar chart figure shows the mean AUC of each positive set with varying number of layers, while my figure is a little more descriptive. It shows the mean AUC as well as the interquartile range and outliers.

Next, we got some surprising news! We were notified that after a preliminary pass of the paper submissions, our paper was accepted to the CNB-MAC conference as either a talk or a poster (we’re still waiting to hear which one.) This gave us the opportunity to submit a two-page abstract by the end of next week, a more challenging task. I wrote up a quick rough draft that I will continue working on next week with Alex.

Finally, with our results generated and our paper submitted, it was time to clean up our github repo. This included deleting a lot of old code and useless output files as well as restructuring the code and writing README.md files. It’s looking a lot better now!

Week 5: Visualizing Data

This week I have continued trying to visualize and integrate gene expression data from GDC TCGA onto nodes in Graphspace. This is the graph I have so far:

Wnt Pathway with Gene Expression Data

The color of the nodes on the graph represent the mean foldchange of the gene. The foldchange is the ratio of gene expression of a cancerous tissue sample to a normal tissue sample from the same patient. There were 41 patients with colon adenocarcinoma who had gene expression data from both a tumor and a normal sample. The foldchange for each of these 41 patients was averaged to find the mean foldchange of each gene. Light blue indicates that the gene is slightly underexpressed in cancerous samples (less than 1 standard deviation), dark blue(not shown) indicates that the gene is underexpressed by 2 standard deviations, purple (not shown) indicates that the gene is underexpressed by greater than 2 standard deviations. Orange indicates that the gene is slightly overexpressed (within one standard deviation), dark orange indicates that the gene is overexpressed by 2 standard deviations, and red indicates that the gene is overexpressed by greater than 2 standard deviations.

One problem that came up was that some genes have a lot more variance in patient data than others. (Data on variance is available by clicking on the nodes in Graphspace). Additionally, patients that no gene expression or close to no gene expression in their normal samples had extremely high foldchanges that threw off the mean. It’s tempting to just throw away those samples as outliers, however several samples had multiple “outliers”. As an example I’ve included three sets of line graphs I made that show the gene expression data  and foldchange of each patient for three different genes:

CFTR Expression and Foldchange Line Graphs for 41 patients

In the first example, CFTR, one can see that cancerous samples tend to have lower gene regulation than healthy samples and most of the fold change values hover around 1.0, meaning that the change is fairly low.

FZD9 Gene expression and foldchange for 41 patients

The second example, FZD9, is slightly different. The FZD9 expression data is much less uniform. In some patients, FZD9 is largely overregulated in cancerous samples. In other, it’s largely under regulated. The foldchange data shows foldchanges that range from zero to extremely high values (greater than 30,0000). This occurs because those patients had normal sample values of 0.0. In this case, it looks like dismissing the two extremely high foldchanges as outliers would yield a more realistic data set.

ZSCAN4 Gene Expression and Foldchanges

However, in ZSCAN4, there are many patients with extremely high or low foldchanges. Instead of ignoring these patients, I think I need to find a way to normalize the data so that a few large foldchanges don’t completely throw off the data.

The next step in this project is to work with Usman and Kathy to integrate the gene expression data onto the edge weights so that PathLinker can calculate the most disregulated protein pathways. To begin this process, I’ve written a program that produces a text file that contains for each gene the ID, common name, mean foldchange, standard deviation, and either a +1, 0, or -1, which represents whether it’s under-regulated, unchanged, or over-regulated in cancerous samples. Right now the +1, 0, and -1 are really just placeholder values. I need to meet with Ibrahim and Anna to discuss how to better weight the nodes.

Week 4: Obtaining and Processing Data

This week our main goal has been to find a pipeline to obtain TCGA data in a neat form. We discovered UCSC’s Xena Browser, which has files from the TCGA and a number of other databases.

Last week, we used the data from FireBrowse to make a graph of the genes that have patients with abnormally high or low levels of expression.

Number of patients that have abnormally high or low levels of expression

This week we changed that graph slightly by showing the difference between the number of patients with high expression and the number of patients with low expression by gene.

Number of patients by gene that have abnormally high or abnormally low levels of gene expression.

It is interesting to me that there are generally more patients with severe under-expression rather than severe-overexpression. I wonder if this is because these genes play a role in suppressing tumors, and that therefore maybe under-expression is more likely to cause cancer than overexpression?

I also worked on integrating gene expression data from Xena into our graph of the Wnt pathway.

Wnt Pathway from PathLinker with Gene Expression Data. Red = high, orange = medium-high, yellow= medium, green = low, blue(not shown) = very low, white(not shown) = no expression. Triangles are transcription factors, squares are receptors, circles are intermediate proteins.

Kathy figured out what was wrong with PathLinker the first time we ran it and re-ran it. I am working on turning it into a graph, but the input data is very different because it’s coming from a different version of NetPath so I need to change the program to be able to process the new data.

I also noticed while I was processing the expression data from Xena that there was a large amount of variability in gene expression between patients. I’m currently working on several things. Instead of just averaging gene expression for genes I’m comparing gene expression patient-by-patient so I’m comparing a tumor sample to a normal tissue sample for every patient. I also want to come up with a way to visualize the variance of expression among patients, because the more variance there is the less significant differences in expression between cancerous and normal tissue are. Anna suggested I do this by making the borders on nodes with high variance thicker. I am also going back and checking my math on gene expression to make sure that it is actually statistically significant and is conducted in a way that is similar to how other researchers have done similar research in the past.

Week 3 – Implementing Bayesian Weighting Schemes

The achievements from last week as well as the goals were laid out in this progress report. Accordingly, I spent the majority of this week working on an implementation of the Bayesian Weighting Scheme I discussed last week. This involved carefully fleshing out all the minutiae of the relevant paper (which I laid out and explained in this document from last week). I then applied the code onto a small toy-network that I made myself and then hand-calculated a few edges to verify my implementation.

Toy Network from Bayesian Weighting Scheme Implementation

I am currently working to implement the weighting scheme onto the HIPPIE Interactome. Thus far this has meant simply writing code to read the relevant text files and format the data efficiently in a way that meshes well with code I wrote for the toy example. Eventually, I will compare the scores of edges from this weighting scheme to the ones on the actual HIPPIE Interactome and further analysis on the differences between the two weighting schemes will probably be among the next steps. Besides implementing the Bayesian Weighting Scheme I have also been working to understand the underlying statistics behind the methods used to weight the HIPPIE Interactome. I will try to have a document that explains the mathematics for this in similar fashion to the one for the Bayesian Weighting Scheme.