Interpreting a list of differentially regulated genes can take many forms. One of the most widely used method is looking for enrichment of functional group of genes compared to a random sampling of gene from the same universe, namely an over-representation analysis (ORA).
The point I want to explore today is what is the best way to interpret the results...
I mentioned in a previous post that our team at the recent Hack/Reduce hackathon had some fun with a data set which consisted of Bixi station states at minute level temporal resolution. In addition to pulling out and plotting the flux at each station on an hourly basis, we also plotted the system state (number 
Inspired by Margintale’s post “ggplot2 Time Series Heatmaps” and Google Flu Trends I decided to use a heat map to visualize sick days logged by HeiaHeia.com Finnish users. I got the data from our database, filtering results by country (Finnish users only) in a tab separated form with the first line as the header. Three columns
A recent post on the Junkcharts blog looked at US weather dataand the importance of explaining scales (which in this case went up to 118). Ultimately, it turns out that 118 is the rank of the data compared to the previous 117 years of data (in ascending order, so that 118 is the highest). At … Continue reading...
How do you easily get beautiful calendar heatmaps of time series in ggplot2? E.g:From MarginTaleI was impressed by the lattice-based implementation from Paul Bleicher of Humedica, which you can find referenced in http://blog.revolutionanalytics.c...
Building Optimization Functions for Julia In hopes of adding enough statistical functionality to Julia to make it usable for my day-to-day modeling projects, I’ve written a very basic implementation of the simulated annealing (SA) algorithm, which I’ve placed in the same JuliaVsR GitHub repository that I used for the code for my previous post about
Bellow is the announcement made by Peter Dalgaard: The build system rolled up R-2.15.0.tar.gz (codename “Easter Beagle”) at 9:00 this morning. This is the first release of the 2.15 series and contains several new features and changes; see the list below for details. You can get the source code from http://cran.r-project.org/src/base/R-2/R-2.15.0.tar.gz or wait for it to be mirrored at...
I’ve largely avoided “time” in R to date, but following a chat with @mhawksey at #dev8d yesterday, I went down a rathole last night exploring a few ways of visualising a Twitter user timeline and as a result also had a quick initial play with some time handling features of R, such as timeseries objects, 
A pint of R plotted an interesting dataset: intentional homicides in South America. I thought the graphs were pretty but I was unhappy about the way information was conveyed in the plots; relative risk should be very important but number … Continue reading →
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
