JPLaverdure

About Jean-Philippe

Originally trained in molecular biology, I quickly realized my heart lied with bioinformatics ! (How can anyone be presented an HMM and not fall in love ?). While I spend most of my days writing Python code, I must admit I am starting to enjoy my occasional dip in R.
Rockin' out at the platform !

Let it roam free ! Releasing your code into the wild…

Today, I thought I'd do something a little different and talk about what one might expect from publicly releasing some code. I figured it might be nice to interview someone from our group which has lots of experience doing so, Tariq Daouda, to gain some of his insights. So without further ado, here we go ! JP: Hi Tariq, glad to have you with us. I thought I might ask you a few questions regarding what happens when one decides [...]

By | 2017-10-16T15:59:03+00:00 October 16, 2017|Categories: Computer science|Tags: , |0 Comments

Let Your Data Flow: Streams and Reactive Programming

What's all this about ? ReactiveX is a combination of the best ideas from the Observer pattern, the Iterator pattern, and functional programming. Using Rx, you can easily: - Create event or data emitting streams from sources such as a file or a web service - Compose and transform streams with query-like operators - Subscribe to any observable stream and "react" to its emissions to perform side effects Reactive programming has been gaining traction these past few years. Maybe you've [...]

By | 2017-05-03T09:19:14+00:00 May 2, 2017|Categories: Bioinformatics, Computer science, Data Analysis|Tags: , |2 Comments

SNP Filtering with pyGeno

Looking over the contents of our growing blog (good job guys !), it occured to me that we had not yet posted an article pertaining to the fantastic (and homegrown !) bioinformatics resource that is pyGeno. It turns out I need to use pyGeno to generate data and it's also my turn to write a blog post, how convenient ! I'll focus the article on writing a SNP filter, which can be a bit surprising the first time you try [...]

By | 2017-04-29T17:57:51+00:00 December 9, 2016|Categories: Bioinformatics, Python|Tags: , |0 Comments

Simple multiprocessing in R (2nd edition)

The last time I spoke about this subject, I presented a really simple way to change an lapply call into its multicore sibling mclapply. Now while this is an extremely easy modification to implement in your code to gain substantial performance benefits, it kinda required you to be making use of the lapply function in the first place. So let's look at another way to introduce multiprocessing into your existing codebase with the use of the foreach and doMC packages. [...]

By | 2017-04-29T16:24:45+00:00 September 19, 2016|Categories: Performance, R|Tags: , |0 Comments

Simple multiprocessing in R

Continuing my effort to help you get the most out of your CPUs, I figured we could look into using some multiprocessing functionality available for your R scripts. While there are a few different options for running multi-core treatments on your data, we'll focus on something really simple to put in place. A while back, I was putting together a script to run a large series of logistic regressions (using the glm package) in an attempt to model some data. [...]

By | 2017-04-29T15:33:26+00:00 March 14, 2016|Categories: Performance, R|Tags: |0 Comments