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Ready to dive into ML analysis on Depmap data?

The team put together an example code file, which you can play with on Google Colab without logging in or setting anything up. Click the button. 

The video below introduces the concepts in the code, and is useful for both scientists and data scientists.  Enjoy!

What did we learn?

  1. We identified a known relationship between MTAP deficiency and the MAT2A/PRMT5/RIOK1 axis by correlating how dependent a cell line is on MTAP with abundance of the genes from the MAT2A/PRMT5/RIOK1 axis. The lower the abundance of MAT2A/PRMT5/RIOK1, the more dependent the cell line is on MTAP for its survival.

  2. By looking at the correlations in different tissues, we also identify in which tissues this relationship is stronger, which would inform our hypothesis on which tissues and eventually for which patients targeting the MAT2A/PRMT5/RIOK1 axis would be effective.

  3. We were also able to quantify that relationship using observational data through ML - for example, using linear regression we see how much a change in for example MTAP abundance corresponds to a change in PRMT5 dependency. This alludes to how much MTAP would need to be reduced to observe a significant dependency on PRMT5.

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