I have used this chunk of code on numerous occasions to plot GWAS data, so I thought I'd share!The variables needed are a log p-value (or some other statistic) and the absolute genomic position of each SNP (distance from the beginning of chromosome 1). If you need the offsets to compute this absolute position, they are listed in MB in the xline(---) portion of the plot.

It took several months after learning about ggplot2 before I gave it a try myself.  I was apprehensive about learning a new graphics system with a new set of commands.  Thing is, if you've ever used plot() in R, you already know how to use much of the functionality in ggplot2!  In this tutorial I want to show you just how easy it is to start producing high-quality graphics using ggplot2.

One of R's biggest strengths is its unparalleled graphing capabilities.  Just see any of our previous posts on ggplot2, visualization, or other posts tagged with R. R has several fundamentally different systems for plotting, including base graphics, lattice, and ggplot2.  Furthermore, many add-on packages come with their own functions for producing problem-domain specific graphics.

Do you use SSH to connect to a remote Linux machine from your local Windows computer?  Ever needed to run a program on that Linux machine that displays graphical output, or uses a GUI? I was in this position last week trying to make figures using ggplot2 in R of results from an analysis of GWAS data which required using a 64-bit Linux machine with more RAM than my 32-bit windows machine can see.

Way back will wrote on this topic.  See his previous post for Stata code for doing this.  Unfortunately the R package that was used to create QQ-plots here has been removed from CRAN, so I wrote my own using ggplot2 and some code I received from Daniel Shriner at NHGRI. Of course you can use R's built-in qqplot() function, but I could never figure out a way to add the diagonal using base graphics.

Often when presenting statistics from a candidate gene study, or a region of interest from a genome-wide association study, it is useful to see various SNP-wise values in the context of linkage disequilibrium patterns.

Genome Browsers are nothing new, but JBrowse is a new JavaScript based genome browser that uses information from the UCSC genome browser and has the look and feel of Google Maps.  It's extremely easy to zoom in and out and scroll around because all the "work" is being done by your computer rather than some server farm thousands of miles away.

One of the nicer things about many statistics packages is the extremely granular control you get over your graphical output.  But I lack the patience to set dozens of command line flags in R, and I'd rather not power the computer by pumping the mouse trying to set all the clicky-box options in Stata's graphics editor.  I want something that just looks nice, using the out-of-the-box defaults.

While not directly related to genetics, this is an excellent example of well-designed data representation. The New York Times reports the results of a survey of average time spent on various activities through the day by different groups of people. The graphic is essentially a stacked density plot with time (24 hours) on the X-axis.

The Systems Biology Graphical Notation (SBGN) project is an effort to standardize the graphical notation used in diagrams of pathways, biochemical processes, and cellular processes studied in systems biology.SBGN defines a comprehensive set of symbols with precise semantics, together with detailed syntactic rules defining their use and how diagrams are to be interpreted.