As promised a few posts ago, another demonstration of the excellent biomaRt package, this time in conjunction with GenomeGraphs. Here’s what we’re going to do: Grab some public microarray data Normalise and get a list of the most differentially-expressed probesets Use biomaRt to fetch the genes associated with those probesets Plot the data using GenomeGraphs 
Just a short note concerning a “gotcha”. As I have many times before, I opened an R console on my newly-upgraded (to lucid 10.04) Ubuntu machine, typed source(“http://bioconductor.org/biocLite.R”) and began a Bioconductor install with biocLite(). Only this time, I saw this: Error in dyn.load(file, DLLpath = DLLpath, ...) : unable to load shared library '/home/sau103/R/i486-pc-linux-gnu-library/2.11/affyio/libs/affyio.so': 
Networks – social and biological – are all the rage, just now. Indeed, a recent entry at Duncan’s QOTD described the “hairball” network representation as the dominant cultural icon in molecular biology. I’ve not had occasion to explore networks “professionally”, but have always been fascinated by both networks and the tools used to analyse them. 
Here’s the situation. Web applications, built using a framework (e.g. Rails, Django) are great for fetching data from a database and rendering it. They’re not so great for crunching and charting the data. Conversely, R is great for crunching and charting, but doesn’t make for a great web application. The idea then, is to let 
Visit this URL and you’ll find a perfectly-formatted CSV file containing information about recent earthquakes. A nice feature of R is the ability to slurp such a URL straight into a data frame: quakes <- read.csv("http://neic.usgs.gov/neis/gis/qed.asc", header = T) colnames(quakes) # "Date" "TimeUTC" "Latitude" "Longitude" "Magnitude" "Depth" # number of recent quakes nrow(quakes) # 
William asks: How can I make a graph that looks like this, “tweet density” style, showing time intervals? He then helpfully describes his input data: a CSV file with headers “time started, time finished, date”. Here’s a simple CSV file, tasks.csv: task,date,start,end task1,2010-03-05,09:00:00,13:00:00 task2,2010-03-06,10:00:00,15:00:00 task3,2010-03-06,11:00:00,18:00:00 task4,2010-03-07,08:00:00,11:00:00 task5,2010-03-08,14:00:00,17:00:00 task6,2010-03-09,12:00:00,16:00:00 task7,2010-03-10,14:00:00,19:00:00 task8,2010-03-11,09:30:00,13:30:00 Read into R, calculate the 
I’ve been vaguely aware of BioMart for a few years. Inexplicably, I’ve only recently started to use it. It’s one of the most useful applications I’ve ever used. The concept is simple. You have a set of identifiers that describe a biological object, such as a gene. These are called filters. They have values – 
I generally skip over “From the Blogosphere”, a (mostly) weekly-summary of one or two blog posts in Nature’s “Authors” section (here is the latest). Why? Well, I’ve always suspected that the title is rather misleading. Now, I have the hard numbers to prove it. My feed reader contains an archive of 128 articles, dating back 
Yesterday, Deepak wrote about BridgeDB, a software package to deal with the “identifier mapping problem”. Put simply, biologists can name a biological entity in any way that they like, leading to multiple names for the same object. Easily solved, you might think, by choosing one identifier and sticking to it, but that’s apparently way too 
Andrew asks: I want to get an NCBI GEO report showing the number of samples per series or data set. Short of downloading all of GEO, anyone know how to do this? Is there a table of just metadata hidden somewhere? At work, we joke that GEO is the only database where data goes in, 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
