R in genomics @ SciLifeLab, Solna

Dear diary,

I went to the Stockholm R useR group meetup on R in genomics at the Stockholm node of SciLifeLab. It was nice. If I had worked a bit closer I would attend meetups all the time. I even got to be pretentious with my notebook while waiting for the train.

The speakers were:

Jakub Orzechowski Westholm on R and genomics in general. He demonstrated genome browser-style tracks with Gviz, some GenomicRanges, and a couple of common plots of gene expression data. I have been on the fence about what package I should use for drawing genes and variants along the genome. I should play with Gviz.

Daniel Klevebring on clinical sequencing and how he uses R (not that much) in sequencing pipelines aimed at targeting the right therapy to patients based on the mutations in their cancer cells. He mentioned some getopt snippets for getting R to play nicely on the command line, which is something I should definitely try more!

Finally, Arvind Singh Mer on predictive modelling for clinical genomics (like the abovementioned ClinSeq data). He showed the caret package for machine learning, with an elastic net regression.

I don’t know the rest of the audience, so maybe the choice to gear talks towards the non-bio* person was spot on, but that made things a bit less interesting for me. For instance, in Jakub’s talk about gene expression, I would’ve preferred more about the messy stuff: how to make that nice gene-by-sample matrix in the first place, and if R can be of any help in that process; also, in the other end, what models one would use after that first pass of visualisation. But this isn’t a criticism of the presenters — time and complexity constraints apply. (If I was asked to present how I use R any demos would be toy analyses of clean datasets. That is the way these things go.)

We also heard repeated praise for and recommendations of the hadleyverse and data.table. I’m not a data.tabler myself, but I probably should be. And I completely agree about the value of dplyr — there’s this one analysis where a couple of lines with dplyr changed it from ”argh, do I have to rewrite this in C?” to being workable. I think we also saw all the three plotting systems: base graphics, ggplot2 and lattice in action.