solving DESeq2 installation issues

At work a colleague asked me to do a system-wide installation of the R module DESeq2 in one of our internal servers.
The installation procedure is quite straight-forward:


Unfortunately I had some issues on my system, in fact I got:

Warning in fun(libname, pkgname) :
couldn't connect to display "localhost:12.0"
* DONE (maSigPro)

The downloaded source packages are in
Warning messages:
1: In install.packages(pkgs = doing, lib = lib, ...) :
  installation of package ‘XML’ had non-zero exit status
2: In install.packages(pkgs = doing, lib = lib, ...) :
  installation of package ‘annotate’ had non-zero exit status
3: In install.packages(pkgs = doing, lib = lib, ...) :
  installation of package ‘genefilter’ had non-zero exit status
4: In install.packages(pkgs = doing, lib = lib, ...) :
  installation of package ‘geneplotter’ had non-zero exit status
5: In install.packages(pkgs = doing, lib = lib, ...) :
  installation of package ‘DESeq2’ had non-zero exit status

I then tried to install manually the various dependencies, like XML. Still no luck. After a quick Google search I found that I was missing a couple of -dev packages on my Ubuntu machine, so I installed them:

root@server:~# apt-get install libcurl4-openssl-dev libxml2-dev

… and then re-tried to install DESeq2.
This time everything was ok! Problem solved!

The homogenization of scientific computing, or why Python is steadily eating other languages’ lunch

Speaking only for myself, I’ve now arrived at the point where around 90 – 95% of what I do can be done comfortably in Python. So the major consideration for me, when determining what language to use for a new project, has shifted from what’s the best tool for the job that I’m willing to learn and/or tolerate using? to is there really no way to do this in Python? By and large, this mentality is a good thing, though I won’t deny that it occasionally has its downsides. For example, back when I did most of my data analysis in R, I would frequently play around with random statistics packages just to see what they did. I don’t do that much any more, because the pain of having to refresh my R knowledge and deal with that thing again usually outweighs the perceived benefits of aimless statistical exploration.

Conversely, sometimes I end up using Python packages that I don’t like quite as much as comparable packages in other languages, simply for the sake of preserving language purity. For example, I prefer Rails’ ActiveRecord ORM to the much more explicit SQLAlchemy ORM for Python–but I don’t prefer to it enough to justify mixing Ruby and Python objects in the same application. So, clearly, there are costs. But they’re pretty small costs, and for me personally, the scales have now clearly tipped in favor of using Python for almost everything. I know many other researchers who’ve had the same experience, and I don’t think it’s entirely unfair to suggest that, at this point, Python has become the de facto language of scientific computing in many domains. If you’re reading this and haven’t had much prior exposure to Python, now’s a great time to come on board!

Tal Yarkoni ☞ [citation needed]

how to install RMySQL on CentOS

A quick note for who have the need of installing RMySQL on CentOS (or RHEL).

On my system R is installed via the EPEL repository (as I’ve added in the comments of this post of this very blog).

Given this I went to the RMySQL project page and downloaded both the RMySQL_0.7-5.tar.gz and the (required) DBI_0.2-5.tar.gz packages.

This because installing the R-DBI package provided by the activated repository on my system gave me errors during the actual RMySQL installation.

So, as root – since the installation was needed system-wide – i gave those two following commands:

[root@testing ~]# R CMD INSTALL DBI_0.2-5.tar.gz
[root@testing ~]# R CMD INSTALL RMySQL_0.7-5.tar.gz

Then all the user needed to do was loading into his R environment the new modules!
Happy coding!

For future reference:

  • CRAN – RMySQL package
  • CRAN – DBI package

How-to install R on CentOS

CentOS, logo A not so christmas-connected topid today as I’m going to write down a few lines on how-to install the open source statistical tool and relative developement libraries R on CentOS release 4 and 5.

Personally I’ve tried it successfully on CentOS 5.

CentOS today does not provide any build of this tool in it’s official and semi-official repositories but, luckily the R-project itselfs provides the binaries for the most common GNU/Linux distributions (and for Windows or Mac OS X, alongside with the sources) on it’s mirror network.

There you can find binary rpms and also yum metadata, so you can create a .repo file for and organic and integrated use of those inside the YUM package manager.

So here’s the R.repo file I’ve created for myself:

name=R project for Statistical Computing repository

Maybe it’s necessary a quick note on gpgcheck=0 since I was not able to find the GPG key of the rpms (maybe they’re not available ???) and on priority=15 since i use yum-priorities to protect the official core of the distro.

If you have suggestions or enhancements to propose the comments are always open!!