Question: Help with creating tumor cell line network
gravatar for torgeirous
16 months ago by
torgeirous10 wrote:

I'm developing and testing some algorithms that scores drug combinations based on how they disrupt the topology of a network. To be able to compare results and look for correlation I will compare my algorithms with data from the NCI Almanac that has data on approximately 5000 drug combinations on the 60 cell lines in NCI-60. The problem is I'm not 100% sure how to create the networks that would represent a specific tumor cell line. I have a couple of articles that has shown correlation in cancer 5-y survival and the degree of complexity that I want to test the same methods to see if I can get similar results. the articles are: Degree-entropy and persistent homology, but they dont explain how the networks was constructed.

I've used a lot of time trying to figure this out where I ended up creating a ppi network selecting the most expressed genes (top 20%) then building a network using iRefIndex in R, but I only have 3 samples for each cell line from here. I know CellMiner has a lot of data on the NCI-60, but I don't know how to fully utilize it and would love to get some pointers from you guys. I even know there exists an R package to access CellMiner so maybe its best to use this? Would the solution be to use a mix of samples from different platforms (Affymetrix U133 Plus 2.0, RNA: Affy HG-U95(A-E), ... ) and somehow retrieve the most expressed genes from them, or something completely different?

I appreciate all help I can get. Then I can finally run the application I have created on correctly generated input networks so I can validate the algorithms.

R gene • 363 views
ADD COMMENTlink written 16 months ago by torgeirous10

...but, is low expressing not also important to consider? Why did you just pick the most expressed genes?

Creating an individual network on just 3 samples is not great but, if that's all that you can get for each cell line, then you could at least make a start. Merging data across different platforms is definitely not the best approach; however, if you normalise each independently and then convert the normalised values to Z-scores (separately for each platform), then I believe you have a better chance of merging them and creating a network from the Z-scores.

ADD REPLYlink written 16 months ago by Kevin Blighe50k
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