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.
![](http://blogs.reed.edu/compbio/files/2018/06/patients_sum_per_genenum.png)
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.
![](http://blogs.reed.edu/compbio/files/2018/06/Genes.png)
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.
![](http://blogs.reed.edu/compbio/files/2018/06/Wnt-Pathway-from-Pathlinker-1-704x1024.png)
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.