I've covered plyr once before, showing you how to get means and variances for two quantitative traits across multilocus genotypes. JD Long over at Cerebral Mastication recently posted a nice screencast illustrating how plyr "just works" as an alternative to R's family of apply commands. There's a set of R functions (apply, sapply, lapply, tapply, eapply, and rapply) that can apply a command or function to your data and return a hopefully useful result. However, for the non-programmers among us, choosing which apply function to use and how to use it can be mind-bogglingly confusing. I've never gotten one of these functions to work as I wanted it to the first time around, and I often end up writing loops where the vectorized operation would be much faster.
Enter plyr.
As I mentioned previously, the plyr functions (ddply, in particular), are intuitive, usually returning the result that you wanted. The ddply function splits up your dataset based on one or more grouping variables, applies some function or statistic, and summarizes the results returning a dataframe.
Here's JD Long's screencast showing how plyr makes a task like this easy where the apply function fails.
Cerebral Mastication - Struggling with apply() in R
Capture system commands as R objects with system(..., intern=T)
Just discovered this very handy R command to capture the output from a system command as an R object. I wanted to use R to read in the output from another program (PLINK) and do some processing on each output file. Of course if the files are named sequentially (plink1.out, plink2.out, plink3.out, etc.) this would be simple with a for loop. This wasn't the case for me, but I could still list all the files I wanted to process with some pattern matching in Unix using wildcards. All the files had "_hdl" in the name, and ended with ".qassoc". Here's where the system() function is helpful.
This command issues "ls *_hdl*.qassoc" to the system, just as if you were typing it in at the terminal. The intern=TRUE tells the system command to treat the output as an R object. I can store this in myfiles, then do some processing on myfiles, which is a vector containing all the filenames of files I want to process.
[1] "0-all.01_hdl_modeled.qassoc"
[2] "0-all.02_hdl_modeled_polyresid.qassoc"
[3] "0-all.03_hdl_med.qassoc"
[4] "0-all.04_hdl_med_polyresid.qassoc"
[5] "0-all.05_hdl_med_smokage_polyresid.qassoc"
[6] "1-male.01_hdl_modeled.qassoc"
[7] "1-male.02_hdl_modeled_polyresid.qassoc"
[8] "1-male.03_hdl_med.qassoc"
[9] "1-male.04_hdl_med_polyresid.qassoc"
[10] "1-male.05_hdl_med_smokage_polyresid.qassoc"
This command issues "ls *_hdl*.qassoc" to the system, just as if you were typing it in at the terminal. The intern=TRUE tells the system command to treat the output as an R object. I can store this in myfiles, then do some processing on myfiles, which is a vector containing all the filenames of files I want to process.
myfiles = system("ls *_hdl*.qassoc", intern=TRUE)
myfiles[1] "0-all.01_hdl_modeled.qassoc"
[2] "0-all.02_hdl_modeled_polyresid.qassoc"
[3] "0-all.03_hdl_med.qassoc"
[4] "0-all.04_hdl_med_polyresid.qassoc"
[5] "0-all.05_hdl_med_smokage_polyresid.qassoc"
[6] "1-male.01_hdl_modeled.qassoc"
[7] "1-male.02_hdl_modeled_polyresid.qassoc"
[8] "1-male.03_hdl_med.qassoc"
[9] "1-male.04_hdl_med_polyresid.qassoc"
[10] "1-male.05_hdl_med_smokage_polyresid.qassoc"
Sync files across multiple computers with Dropbox (PC, Mac & Linux too!)
Do you ever find yourself switching back and forth between your work computer, your laptop, and your home computer? This happens to me all the time when I'm writing. Rather than carry all your files on a USB stick and risk losing it or corrupting your data, give Dropbox a try. It's dead simple, and works for PC, Mac, and Linux too.
Once you sign up and install on all your computers, you'll have a special folder, where if you save something there on one computer, it is automatically created and stays synchronized in the same folder on all your other computers. What's more, if you use someone else's computer, you can access all your files through a web interface because they're all securely backed up online. I've been using this for a while now to sync all the papers I'm working on, RefMan/EndNote databases, config files, and R functions I reuse all the time. You can also create "public" folders. Put something here, and you can get a direct link to the file online to share with other folks. For example, here's a link to some R code I wrote to use ggplot2 to make manhattan plots and QQ-plots for every PLINK output file in the current directory (I'm hoping to clean this code up and include this with some other functions I've written into a package on CRAN soon).
I can't recommend this little app enough. If you're still not convinced, check out this short video that explains what Dropbox is all about and shows of just how simple it is to use.
You get a whopping 2GB for free, but if you use the registration link provided below, you'll get an extra free 1/4GB. Happy holidays from GGD, and I'll catch up with you all next week!
Dropbox - Secure online backup and synchronization
Once you sign up and install on all your computers, you'll have a special folder, where if you save something there on one computer, it is automatically created and stays synchronized in the same folder on all your other computers. What's more, if you use someone else's computer, you can access all your files through a web interface because they're all securely backed up online. I've been using this for a while now to sync all the papers I'm working on, RefMan/EndNote databases, config files, and R functions I reuse all the time. You can also create "public" folders. Put something here, and you can get a direct link to the file online to share with other folks. For example, here's a link to some R code I wrote to use ggplot2 to make manhattan plots and QQ-plots for every PLINK output file in the current directory (I'm hoping to clean this code up and include this with some other functions I've written into a package on CRAN soon).
I can't recommend this little app enough. If you're still not convinced, check out this short video that explains what Dropbox is all about and shows of just how simple it is to use.
You get a whopping 2GB for free, but if you use the registration link provided below, you'll get an extra free 1/4GB. Happy holidays from GGD, and I'll catch up with you all next week!
Dropbox - Secure online backup and synchronization
Review: The challenges of sequencing by synthesis
A tip of the hat to a commenter on my previous coverage of a next-gen sequencing paper for pointing out this detailed and perhaps more technically-oriented review on sequencing by synthesis recently published in Nature Biotechnology. Thanks, Clive.
Review: The challenges of sequencing by synthesis (Nature Biotechnology)
Review: The challenges of sequencing by synthesis (Nature Biotechnology)
Recent improvements to Pubget
1. Citation matching. Pubget's citation matcher seems to work better than Pubmed most of the time. Try going to Pubget and pasting any of these random citations into the search bar:
J Biol Chem 277: 30738-30745
Nucleic Acids Res 2004;32:4812-20.
Evol. Biol. 7, 214 (2007).
2. The PaperPlane bookmarklet. Go here and drag the link to your bookmark toolbar. Now, if you're searching from pubmed, click the bookmarklet for one-click access to the PDF.
3. If you have a long list of PMIDs, separate them with commas and you can paste them directly into the search bar.
Pubget (Vanderbilt institutional link)
Pubget (If you're anywhere else)
Seminar announcement: A Multivariate Methodology for Analyzing Genome-wide Association Studies
This looks interesting.
Department of Biostatistics Seminar/Workshop Series: A Multivariate Methodology for Analyzing Genome-wide Association Studies, by Janice Brodsky, PhD, UCLA.
Wednesday, December 16, 1:30-2:30pm, MRB III Conference Room 1220
Intended Audience: Persons interested in applied statistics, statistical theory, epidemiology, health services research, clinical trials methodology, statistical computing, statistical graphics, R users or potential users
In the last few years, high-dimensional genome-wide association (GWA) studies have become a common tool in genetics for investigating which genes are associated with physical traits. However, the results of many GWA studies have fewer genes than expected or even no genes at all. This does not necessarily indicate that there are no genetic associations in the data: genes with weaker associations or which only work in groups will be missed with the standard GWA statistical analysis. We present a multivariate methodology for analyzing GWA data which is designed to handle weaker signals, dependent data, and multicollinearity. We applied this method to a large GWA study, and the results were consistent with previously performed studies. We also discuss extensions of the methodology.
Department of Biostatistics Seminar/Workshop Series: A Multivariate Methodology for Analyzing Genome-wide Association Studies, by Janice Brodsky, PhD, UCLA.
Wednesday, December 16, 1:30-2:30pm, MRB III Conference Room 1220
Intended Audience: Persons interested in applied statistics, statistical theory, epidemiology, health services research, clinical trials methodology, statistical computing, statistical graphics, R users or potential users
In the last few years, high-dimensional genome-wide association (GWA) studies have become a common tool in genetics for investigating which genes are associated with physical traits. However, the results of many GWA studies have fewer genes than expected or even no genes at all. This does not necessarily indicate that there are no genetic associations in the data: genes with weaker associations or which only work in groups will be missed with the standard GWA statistical analysis. We present a multivariate methodology for analyzing GWA data which is designed to handle weaker signals, dependent data, and multicollinearity. We applied this method to a large GWA study, and the results were consistent with previously performed studies. We also discuss extensions of the methodology.
Follow GGD on Twitter @genetics_blog
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