Sunday, December 3, 2017

MORPHEUS (not that one!) at the Broad for downstream analysis!

Have you been trying all weekend to successfully do some sweet clustering on a collaborator's data, but keep getting this when you try to run ClustVis? 

Have you already tried the popular American strategy of calling the person(s) responsible a name on Twitter to see if that helps?

..and it totally didn't help at all?

Are you also too lazy to do it in R yourself, despite the fact that Brett Phinney tipped you off to an awesome and super easy looking package that would do it?

Well -- do I ever have the solution for you!

Check out the MORPHEUS web interface hosted by the Broad Institute here!

1) Wow, is this thing ever easy! It was designed for genomics stuff -- wait, some of the example data is quantitative proteomics! You have a format to follow!  Just cut your data out into a Tab delimited text file and go. It looks like you can load your entire file and then determine what is important, but I found it simpler to cut it myself down to my protein accession numbers and my normalized protein abundances from each RAW file.
2) It has a lot of fancy stats things in it that you can use. Do they work? Sure probably!  My results seem to make a lot of sense....
3) It doesn't like enormously huge names in the Row and column titles. If you have, for example, clustered your RAW files 7 different ways in Proteome Discoverer 2.x and the title of your row has over 128 characters, it will compress the space for visualizing the clustering distance. Shorten the name and it's fine!

And it's got a bunch of cool stuff to choose from once your tables are loaded!

(This isn't patient confidential data or anything like that -- but unless I've gotten explicit permission to share someone's stuff I figure you can never be too careful. See what a good collaborator I am? Just don't expect analysis in a hurry...)

You have loads of power to take a first pass at your data here. All the normal clustering algorithms (that I have to go straight to WikiPedia to remember what is what) are here. Surprisingly, it appears to do all the analysis within your local browser! When I really loaded it with data (clustering proteins in one tab and all quantified peptides in another tab) it used up a good 10% of the processing power available on my PC for upwards of 10 minutes before wrapping it up. (This suggests to me it is limited by the amount of math my web browser can handle). Come on, Chrome! You can do it!

Are there a ton of ways to get your data clustered? Sure! But it never hurts to have another nice (and easy) tool to get there.

Is it a little confusing that it shares a name with another great tool? 

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