Thursday, June 20, 2019
Independent Component Analysis -- Combine all the data!
Possible winner for the most times I've written something about a paper, deleted it, and started over again:
This new paper in press at MCP.
What? Independent Component Analysis (ICA).
Great wikipedia article on it here. Good to read before you get further into the paper and are like -- "wait -- can't you do exactly the same thing with Perseus...? isn't that exactly what it's for..." And...honestly maybe you can do exactly the same thing. I'm not great with Perseus and I really really should do MaxQuant summer school next year , but once you realize that ICA is a real thing that you've just never heard of (you're very likely much smarter than me and know all about it already).
Wait. Is the MaxQuant summer school in Madison this year?
On topic. Come on. You can do it.
What these authors demonstrate is the use of ICA to combine both proteomic and genomic data in cancer cells. What is cool about this is the fact that, while ICA is independent (that's the I) it can be used in classifying combined signals against patient characteristics. If I write more about it I'll just embarrass myself, but considering I miscalculated the width of the isotopic envelope of a +1 small molecule and left that up on this blog for like 3 days, that probably doesn't seem to be something I really concern myself with.
The data is processed in R primarily using a package called FastICA (capitalization probably wrong). The data in question was pulled from TCGA (cancer genome atlas) and CPTAC (itraq 4-plex). I'm unclear as to whether this team developed the package, or if this is more of a proof-of-concept of the utilization of pre-existing packages to combine data in this manner, but its an interesting approach and all the tools are out there now for any of us to try them!
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