I talked a little bit about tidy data my recent post about dplyr, but you should really go check out Hadley’s paper on the subject. R expects inputs to data analysis procedures to be in a tidy format, but the model output objects that you get back aren’t always tidy. The reshape2, tidyr, and dplyr are meant to take data frames, munge them around, and return a data frame.
Two years ago David Searls published an article in PLoS Comp Bio describing a series of online courses in bioinformatics. Yesterday, the same author published an updated version, "A New Online Computational Biology Curriculum," (PLoS Comput Biol 10(6): e1003662.
I first mentioned Coursera about a year ago, when I hired a new analyst in my core. This new hire came in as a very competent Python programmer with a molecular biology and microbial ecology background, but with very little experience in statistics.
Since the near beginning of genome-wide association studies, the PLINK software package (developed by Shaun Purcell’s group at the Broad Institute and MGH) has been the standard for manipulating the large-scale data produced by these studies.
Two of the most common questions at the beginning of an RNA-seq experiments are "how many reads do I need?" and "how many replicates do I need?". This paper describes a web application for designing RNA-seq applications that calculates an appropriate sample size and read depth to satisfy user-defined criteria such as cost, maximum number of reads or replicates attainable, etc.
About a year ago I wrote a post about producing scatterplot matrices in R. These are handy for quickly getting a sense of the correlations that exist in your data. Recently someone asked me to pull out some relevant statistics (correlation coefficient and p-value) into tabular format to publish beside a scatterplot matrix.
I haven't posted much here recently, but here is a roundup of a few of the links I've shared on Twitter (@genetics_blog) over the last two weeks. Here is a nice tutorial on accessing high-throughput public data (from NCBI) using R and Bioconductor. Cloudnumbers.com , a startup that allows you to run high-performance computing (HPC) applications in the cloud, now supports the previously mentioned R IDE, RStudio.
In my previous post I showed you how to randomly split up a dataset into training and testing datasets. (Thanks to all those who emailed me or left comments letting me know that this could be done using other means.
When I have a question I usually ask the internet before bugging my neighbor.
I thought it would be trivial to extract the p-value on the F-test of a linear regression model (testing the null hypothesis R²=0). If I fit the linear model: fit<-lm(y~x1+x2), I can't seem to find it in names(fit) or summary(fit). But summary(fit)$fstatistic does give you the F statistic, and both degrees of freedom, so I wrote this function to quickly pull out the p-value from this F-test on a lm object, and added it to my R profile.