Getting Things Done in Genetics & Bioinformatics Research

Stephen Turner

Ben's paper was published today in the 2010 proceedings of the Pacific Symposium in Biocomputing.  You can download the PDF here.

Finding unique filter sets in PLATO: a precursor to efficient interaction analysis in GWAS data

I direct the Bioinformatics Core at the University of Virginia, and I'm hiring. Visit this link on the UVA Jobs website for more information. Here's the description: I'm Hiring - Bioinformatics Analyst in the UVA Bioinformatics CoreGetting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution (CC BY) License.

I'm Hiring!

A few months ago I showed you how to syntax-highlight R code using Github Gists for displaying R code on your blog or other online medium. The idea's really simple if you use blogger - head over to gist.github.com, paste in your R code, create a public "gist", hit "embed", then copy the javascript onto your blog.

Syntax Highlighting R Code, Revisited

The first ever RStudio conference was held January 11-14, 2017 in Orlando, FL. For anyone else like me who spends hours each working day staring into an RStudio session, the conference was truly

 excellent

. The speaker lineup was diverse and covered lots of areas related to development in R, including the tidyverse, the RStudio IDE, Shiny, htmlwidgets, and authoring with RMarkdown.

RStudio Conference 2017 Recap

A while back I wrote this post about how I stay current in bioinformatics &amp; genomics. That was

 nearly five years ago

. A lot has changed since then. A few links are dead.

Staying Current in Bioinformatics &amp; Genomics: 2017 Edition

I came across this awesome gist explaining how to syntax highlight code in Keynote. The same trick works for Powerpoint. Mac only. Install homebrew if you don’t have it already and brew install highlight. highlight -O rtf myfile.ext | pbcopy to highlight code to a formatted text converter in RTF output format, and copy the result to the system clipboard. Paste into Keynote or Powerpoint.

Syntax Highlight Code in Keynote or Powerpoint

How many reads do I need? What's my sequencing depth?

These are common questions I get all the time. Calculating how much sequence data you need to hit a target depth of coverage, or the inverse, what's the coverage depth given a set amount of sequencing, are both easy to answer with some basic algebra. Given one or the other, plus the genome size and read length/configuration, you can calculate either.

Covcalc: Shiny App for Calculating Coverage Depth or Read Counts for Sequencing Experiments

This is a guest post from VP Nagraj, a data scientist embedded within UVA’s Health Sciences Library, who runs our Data Analysis Support Hub (DASH) service.

Last weekend I was fortunate enough to be able to participate in the first ever Shiny Developer Conference hosted by RStudio at Stanford University. I’ve built a handful of apps, and have taught an introductory workshop on Shiny.

Shiny Developer Conference 2016 Recap

I just returned from the Genome Informatics meeting at Cold Spring Harbor. This was, hands down, the best scientific conference I've been to in years. The quality of the talks and posters was excellent, and it was great meeting in person many of the scientists and developers whose tools and software I use on a daily basis.

Software from CSHL Genome Informatics 2015

Unknown

Per tradition, Russ Altman gave his "Translational Bioinformatics: The Year in Review" presentation at the close of the AMIA Joint Summit on Translational Bioinformatics in San Francisco on March 26th.  This year, papers came from six key areas (and a final Odds and Ends category).  His full slide deck is available here.

Translational Bioinformatics Year In Review

Current Opinion in Microbiology

recently published a special issue in genomics. In an excellent editorial overview, “Genomics: The era of genomically-enabled microbiology”, Neil Hall and Jay Hinton give an overview of the state of the field in microbial genomics, summarize recent contributions, and give a great synopsis of each of the reviews in this issue.

Microbial Genomics: the State of the Art in 2015

I have a noteworthy blogs tag on this blog that I sort of forgot about, and haven't used in years. But I started reading one recently that's definitely qualified for the distinction. The Microbiome Digest is written by Elisabeth Bik, a scientist studying the microbiome at Stanford.

Microbiome Digest Blog

Last week I ran a one-day workshop on RNA-seq data analysis in the UVA Health Sciences Library. I set up an AWS public EC2 image with all the necessary software installed.

RNA-seq Data Analysis Course Materials

Sometimes you need to run some UNIX command on a file but only want to operate on the body of the file, not the header. Create a file called body somewhere in your $PATH, make it executable, and add this to it: #!/bin/bash IFS= read -r header printf '%s\n' "$header" eval $@ Now, when you need to run something but ignore the header, use the body command first.

Operate on the body of a file but not the header

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.

R package to convert statistical analysis objects to tidy data frames

GNU datamash is a command-line utility that offers simple calculations (e.g. count, sum, min, max, mean, stdev, string coalescing) as well as a rich set of statistical functions, to quickly assess information in textual input files or from a UNIX pipe.

GNU datamash

TL;DR? We started an R Users group, awesome community, huge turnout at first meeting, lots of potential. --- I've sat through many hours of meetings where faculty lament the fact that their trainees (and the faculty themselves!) are woefully ill-prepared for our brave new world of computing- and data-intensive science.

UVA / Charlottesville R Meetup

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.

An Annotated Online Bioinformatics / Computational Biology Curriculum

Three years ago I wrote a blog post on how to create manhattan plots in R. After hundreds of comments pointing out bugs and other issues, I've finally cleaned up this code and turned it into an R package.

qqman: an R package for creating Q-Q and manhattan plots from GWAS results

I'm calling variants from exome sequencing data and I need to evaluate the efficiency of the capture and the coverage along the target regions. This sounds like a great use case for bedtools, your swiss-army knife for genomic arithmetic and interval manipulation.

Visualize coverage for targeted NGS (exome) experiments

A few weeks ago the 2014 AMIA Translational Bioinformatics Meeting (TBI) was held in beautiful San Francisco.  This meeting is full of great science that spans the divide between molecular and clinical research, but a true highlight of this meeting is the closing keynote, traditionally given by Russ Altman.

Russ Altman's Translational Bioinformatics Year in Review

The problem  I was looking for a way to compile an RMarkdown document and have the filename of the resulting PDF or HTML document contain the name of the input data that it processed. That is, if I compiled the analysis.Rmd file, where in that file it did some analysis and reporting on data001.txt, I’d want the resulting filename to look something like data001.txt.analysis.html.

Compiling RMarkdown from a Helper R Script

A colleague needed some help getting Illumina BeadArray gene expression data loaded into R for data analysis with limma. Hopefully whoever ran your arrays can export the data as text files formatted as described in the code below. If so, you can import those text files directly using the beadarray package.

Importing Illumina BeadArray data into R

For a few years now, my EvoSTAR colleague, Bill Langdon, has been exploring the degree to which Mycoplasma bacteria have contaminated experimental systems and even "infected" online databases with the contents of their genomes.

Mycoplasma Contamination in Cell-Line Based Experiments

Joanna Zhao’s and Jenny Bryan’s R graph catalog is meant to be a complement to the physical book, Creating More Effective Graphs, but it’s a really nice gallery in its own right. The catalog shows a series of different data visualizations, all made with R and ggplot2. Click on any of the plots and you get the R code necessary to generate the data and produce the plot.

R + ggplot2 Graph Catalog

This is a guest post from VP Nagraj, a data scientist embedded within UVA’s Health Sciences Library, who runs our Data Analysis Support Hub (DASH) service.

The What GRUPO (Gauging Research University Publication Output) is a Shiny app that provides side-by-side benchmarking of American research university publication activity.

GRUPO: Shiny App For Benchmarking Pubmed Publication Output

I archived and analyzed all Tweets containing #GI2013 from the recent Cold Spring Harbor Genome Informatics meeting, using my previously described code. Friday was the most Tweeted day.

Archival and analysis of #GI2013 Tweets

Last month I told you about Coursera's specializations in data science, systems biology, and computing. Today I was reading Jeff Leek's blog post defending p-values and found a link to HarvardX's Data Analysis for Genomics course, taught by Rafael Irizarry and Mike Love. Here's the course description: If you've ever wanted to get started with data analysis in genomics and you'd learn R along the way, this looks like a great place to start.

Data Analysis for Genomics MOOC

If you're doing any kind of scientific computing and not using version control, you're doing it wrong. The git version control system and GitHub, a web-based service for hosting and collaborating on git-controlled projects, have both become wildly popular over the last few years.

Collaborative lesson development with GitHub

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.

Scotty, We Need More Power! Power, Sample Size, and Coverage Estimation for RNA-Seq

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.

Coursera Specializations: Data Science, Systems Biology, Python Programming

Jeff Leek, biostats professor at Johns Hopkins and instructor of the Coursera Data Analysis course, recently posted on Simly Statistics this list of awesome things other people accomplished in 2013 in genomics, statistics, and data science. At risk of sounding too

 meta

, I'll say that this list itself is one of the awesome things that was put together in 2013.

Jeff Leek's non-comprehensive list of awesome things other people did in 2013

Obi Griffith over at Biostar put together this excellent cheat sheet for dealing with one-based and zero-based genomic coordinate systems. The cheat sheet visually explains the difference between zero and one-based coordinate systems, as well as how to indicate a position, SNP, range, or indel using both coordinate systems.

Biostar Tutorial: Cheat sheet for one-based vs zero-based coordinate systems

One of the most powerful tools you can learn to use in genomics research is a relational database system, such as MySQL.  These systems are fairly easy to setup and use, and provide users the ability to organize and manipulate data and statistical results with simple commands.  As a graduate student (during the height of GWAS), this single skill quickly turned me into an “expert”.

Using Database Joins to Compare Results Sets

ENSEMBL is a frequently used resource for various genomics and transcriptomics tasks.  The ENSEMBL website and MART tools provide easy access to their rich database, but ENSEMBL also provides flat-file downloads of their entire database and a public MySQL portal.  You can access this using the MySQL Workbench using the following: Once inside, you can get a sense for what the ENSEMBL schema (or data model) is like.

Understanding the ENSEMBL Schema

I frequently get asked to recommend workshops or online learning resources for bioinformatics, genomics, statistics, and programming. I compiled a list of both online learning resources and in-person workshops (preferentially highlighting those where workshop materials are freely available online):

 List of Bioinformatics Workshops and Training Resources

I hope to keep the page above as up-to-date as possible.

List of Bioinformatics Workshops and Training Resources

Automatically Archiving Twitter Results

Ever since Twitter gamed its own API and killed off great services like IFTTT triggers, I've been looking for a way to automatically archive tweets containing certain search terms of interest to me. Twitter's built-in search is limited, and I wanted to archive interesting tweets for future reference and to start playing around with some basic text / trend analysis.

Automated Archival and Visual Analysis of Tweets Mentioning #bog13, Bioinformatics, #rstats, and Others

If you ever write code for scientific computing (chances are you do if you're here), stop what you're doing and spend 8 minutes reading this open-access paper: Wilson et al. Best Practices for Scientific Computing.

"Document Design and Purpose, Not Mechanics"

I saw this plot in the supplement of a recent paper comparing microarray results to RNA-seq results. Nothing earth-shattering in the paper - you've probably seen a similar comparison many times before - but I liked how they solved the overplotting problem using heat-colored contour lines to indicate density. I asked how to reproduce this figure using R on Stack Exchange, and my question was quickly answered by Christophe Lalanne.

Fix Overplotting with Colored Contour Lines

A few folks have asked me how I get my news and stay on top of what's going on in my field, so I thought I'd share my strategy.

How to Stay Current in Bioinformatics/Genomics

A few weeks ago I showed you how to convert gene IDs with BioMart. Yesterday I hosted a workshop on the Ensembl Genome Browser, given by Dr. Bert Overduin from EBI-EMBL. He gave several examples of very useful tasks that you can do very quickly and easily using BioMart. One, in particular, is something that I'm doing for a client in the core right now.

Video Tip: Use Ensembl BioMart to Quickly Get Ortholog Information

Farhad Manjoo at Slate has a good article on why you need to learn how to program. Chances are, if you're reading this post here you're already fairly adept at some form of programming. But if you're not, you should give it some serious thought.

New Year's Resolution: Learn How to Code

*Edit March 12*

Be sure to look at the comments, especially the commentary on Hacker News - you can supercharge the find|xargs idea by using find|parallel instead. --- Do you ever discover a trick to do something better, faster, or easier, and wish you could reclaim all the wasted time and effort before your discovery?

find | xargs ... Like a Boss

Kevin White from the University of Chicago will be giving a special guest lecture at NCI next week on systems biology approaches to mine genomics data for biomarkers and therapeutic targets. The lecture will be available online as a videocast.

Webinar: Genomic Networks - Resolving Biomarkers from a Cloud of Data

The winter Joint Techs meeting is next week in Baton Rouge. I'm not going, but I plan on participating via a netcast to see what's going on. Jim Bottum, Clemson's CIO, is moderating an entire day devoted to the topic Enhancing Infrastructure Support for Data Intensive Science. Of particular interest to me are the talks from 9:30-11am Tuesday January 24 from researchers and those supporting climatology, genomics, and the XSEDE projects.

Joint Techs Netcast: Enhancing Infrastructure Support for Data Intensive Science

I found the slides below on the education page from Bioinformatics &amp; Research Computing at the Whitehead Institute. The first set (PDF) gives an overview of the methods and software available for quality assessment of microarray and RNA-seq experiments using the FastX toolkit and FastQC. The second set (PDF)  gives an example RNA-seq workflow using TopHat, SAMtools, Python/HTseq, and R/DEseq.

An example RNA-Seq Quality Control and Analysis Workflow

This week, I'm off to Montreal for the International Congress on Human Genetics and I hope to see you there! If you are attending and are already a part of the Twitterverse, bring a tablet or phone and tweet away about the meeting using the official hashtag,  #ICHG2011.

Find me at ICHG!

There are several tools available for conducting a post-hoc analysis of GWAS data looking for enrichment of significant SNPs using literature or pathway based resources. Examples include  GRAIL, ALLIGATOR, and WebGestalt among others (see SNPath R Package).  Since gene enrichment and pathway analysis essentially evolved from methods for analyzing gene expression data, many of these tools require specific gene identifiers as input.

Mapping SNPs to Genes for GWAS Enrichment Analysis

I just read a helpful paper on pathway analysis and interactome reconstruction: Tieri, P., Fuente, A. D., Termanini, A., &amp; Franceschi, C. (2011). Integrating Omics Data for Signaling Pathways, Interactome Reconstruction, and Functional Analysis. In

 Bioinformatics for Omics Data, Methods in Molecular Biology, vol.

Resources for Pathway Analysis, Functional Annotation, and Interactome Reconstruction with Omics Data

Agilent Technologies is fostering integrated, whole-systems approaches to biological research through two $75,000 grants. The application deadline is August 12, 2011. Funds will support academic or nonprofit research projects covering the development of open source Agilent-compatible software tools for integrating data from different omics platforms—genomics, transcriptomics, proteomics, and metabolomics.

Agilent Integrated Biology Grant Program

After evaluating an unnamed bioinformatics core facility, a group of bioinformaticians in Europe wrote up a short list of basic guidelines for organizing a bioinformatics core facility in large research institutes.

How to Run a Bioinformatics Core

Recently I tried compiling Eigensoft on my Ubuntu 10.10 Linux system running in Virtualbox and had no success. From comments on this blog post, it looks like the newer Ubuntu distros don't have the libg2c0 and related libraries (which were a part of the gcc3) and gcc4 uses gfortran instead.

Shellfish for Parallel PCA on GWAS data (Alternative to Eigenstrat)

This looks like an interesting and relevant webinar for anyone receiving federal funding for genetics projects.

Sharing Genomics Data: NIH Data Sharing Policies Past, Present, and Future

I'm working on a project using next-gen sequencing to fine-map a genetic association in a gene region. Now that I've sequenced the region in a small sample, I'm picking SNPs to genotype in a larger sample. When designing the genotyping assay the lab will need flanking sequence. This is easy to get for SNPs in dbSNP, but what about novel SNPs?

Using R + Bioconductor to Get Flanking Sequence Given Genomic Coordinates

I found a really nice tip on Postersession.com's blog about putting QR codes in your research posters (via @gw_dailyscan). QR codes, short for quick response, is a two-dimensional bar code that can be read by most smartphones with a camera.

QR Code on Conference Posters

Thanks to links from Sarah Pendergrass, I stumbled upon this awesome program for graph visualization and analysis called Gephi. It seems rather feature rich, with built in connectors to database systems, extensive graph coloring, layout, and rendering features, and several analysis tools.

Its like Photoshop for Graphs

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.

Splitting a Dataset Revisited: Keeping Covariates Balanced Between Splits

I recently analyzed some data trying to find a model that would explain body fat distribution as predicted by several blood biomarkers. I had more predictors than samples (p&gt;n), and I didn't have a clue which variables, interactions, or quadratic terms made biological sense to put into a model.

Split a Data Frame into Testing and Training Sets in R

Had a mental block today trying to figure out how to get the indices of columns in a data frame given their names. Simple task but difficult to search Google for an answer. Thanks to jashapiro, Matt, and Vince for giving me a heads up on the which() function. The which() function returns the indices of TRUE values in a logical vector. If you're looking at the iris data: data(iris) head(iris)

R: Given column name in a Data Frame, Get the Index

After finishing the final revisions on my dissertation I was reminded of this spot-on graphical guide to what a Ph.D. is really all about. Now that I'm finished, I'm leaving Vanderbilt to start a postdoc in genetic epidemiology with Dr. Loic Le Marchand at the University of Hawaii Cancer Center. Posts may be sparse over the next few weeks, but I plan on blogging as usual once I'm set up at my postdoc.

So long Vanderbilt, and thanks for all the fish!

A few months ago I covered an algorithm called EMMA (Efficient Mixed-Model Association) implemented in R for simultaneously correct for both population stratification and relatedness in an association study.

Efficient Mixed-Model Association eXpedited (EMMAX) to Simutaneously Account for Relatedness and Stratification in Genome-Wide Association Studies

When I started grad school I started using Reference Manager (RefMan), similar to EndNote, to manage my references and bibliographies. It's a real pain, and I often feel like I'm powering my computer with the endless pumping and clicking of the mouse that it takes to import a reference into my library. Recently I've started using Zotero because of how easy it is to import references, store PDFs, and sync between computers.

Which Reference Management Software do you use? (Reader Poll)

I was recently reading a bit about logistic regression in Gelman and Hill's book on hierarchical/multilevel modeling when I first learned about the "divide by 4 rule" for quickly interpreting coefficients in a logistic regression model in terms of the predicted probabilities of the outcome. The idea is pretty simple.

Using the "Divide by 4 Rule" to Interpret Logistic Regression Coefficients

Designer Jesse Dee has an entertaining presentation on Slideshare about how to use Powerpoint effectively (although Edward Tufte may assert that such a thing is impossible). These are all things we probably know, but just don't take into consideration enough when we're giving a presentation. According to Dee, the number one most common mistake is lack of preparation.

How to Not Suck at Powerpoint, and the Secrets of Malcolm Gladwell

If you missed the tutorial on the 1000 genomes project data last week at ASHG, you can now watch the tutorials on youtube and download the slides online at http://genome.gov/27542240.

Video and slides from ASHG 1000 Genomes tutorials

Seminar announcement for Vanderbilt folks:

 Vanderbilt-Ingram Cancer Center


 
 Quantitative Sciences Seminar Series
 

Presents

 Reproducible Research in the Omics Era:


 A Presentation and Panel Discussion


 Kevin R. Coombes, PhD

Deputy Chair, Bioinformatics, and Professor of Bioinformatics and Computational BiologyM.D. Anderson Cancer Center and

 Keith Baggerly, PhD

Associate Professor, Dept.

Reproducible Research in the Omics Era:  A Presentation and Panel Discussion

While writing my thesis I came across this nice review by Rita Cantor, Kenneth Lange, and Janet Sinsheimer at UCLA, "Prioritizing GWAS Results: A Review of Statistical Methods and Recommendations for Their Application." Skip the introduction unless you're new to GWAS, in which case you'll probably want to start with this more recent review by Teri Manolio.

Prioritizing GWAS Results: A Review of Statistical Methods and Recommendations for Their Application

Note 2010-11-17: there's more than one way to do this.

Embed R Code with Syntax Highlighting on your Blog

Last year I linked to a series of perspectives in NEJM with contrasting views on the success or failure of GWAS - David Goldstein's paper and Nick Wade's synopsis that soon followed in the New York Times being particularly pessimistic. Earlier this year I was swayed by an essay in Cell by Jon McClellan and Mary-Claire King condemning the common disease common variant hypothesis and chalking up most GWAS hits to population stratification.

Success of GWAS Approach Demonstrated by Latest Lipids Meta-Analysis

A live webcast of the House Committee on Energy and Commerce hearing on “Direct-to-Consumer Genetic Testing and the Consequences to the Public Health" is available at this link. I had trouble viewing the webcast in firefox - had to save the link and open it with VLC media player to get it working. You can also follow the #HouseDTC hastag on Twitter.

Webcast this Morning: House Committee on Energy and Commerce hearing on DTC Genetic Testing

Update

Tuesday, September 14, 2010: Fixed the ylim issue, now it sets the y axis limit based on the smallest observed p-value. A while back Will showed you how to create QQ plots of p-values in Stata and in R using the now-deprecated

 sma

package. A bit later on I showed you how to do the same thing in R using ggplot2.

QQ plot of p-values in R using base graphics

I've covered a few topics in the past including the plyr package, which is kind of like "GROUP BY" for R, and the merge function for merging datasets. I only recently found the sqldf package for R, and it's already one of the most useful packages I've ever installed. The main function in the package is sqldf(), which takes a quoted string as an argument. You can treat data frames as tables as if they were in a relational database.

Use SQL queries to manipulate data frames in R with sqldf package

A few weeks ago I covered an R package for efficient mixed model regression that is capable of simultaneously accounting for both population stratification and relatedness to compute unbiased estimates of standard errors and p-values for genetic association studies.

Mixed linear model approach adapted for genome-wide association studies

I recently did an analysis for the eMERGE network where I had lots of individuals from a small town in central Wisconsin where many of the subjects were related to one another. The subjects could not be treated as independent, but I could not use a family-based design either. I ended up using a mixed model approach using previously mentioned GenABEL.

Efficient Mixed-Model Association in GWAS using R

Our Program  in  Computation Genomics Journal Club is starting again, now the 3rd   Friday of each month. The next meeting is this Friday, April 16, at 3pm  in the CHGR conference room. As usual, please bring in any articles  you've  found recently and give a brief overview of why you thought it  was  interesting.

Journal Club 4/16/2010

A short announcement - my friend, colleague, running partner, and GGD contributor Will Bush is now an assistant professor in the Department of Biomedical Informatics, and investigator in the Center for Human Genetics Research here at Vanderbilt.Getting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution (CC BY) License.

Professor Bush

A new paper in Nature by the Wellcome Trust Case Control Consortium examining 16,000 cases of 8 common diseases and 3000 shared controls finds that common CNVs probed on existing arrays are well tagged by SNPs and are unlikely to contribute much to common human disease.

Wellcome Trust: Common CNVs Unlikely to Influence Common Disease

VICC's retreat this year is Tuesday, May 11, 2010. The theme of the retreat looks interesting: "Genomic Approaches for Personalized Medicine." You can register free here. VICC retreat: Genomic Approaches for Personalized MedicineGetting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution (CC BY) License.

Vanderbilt-Ingram Cancer Center Retreat

Just caught this on the OpenHelix Blog. In an interview with Charlie Rose, NIH director Francis Collins said Computational biologists will be the "breakthrough" artists of the future. Getting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution (CC BY) License.

Francis Collins: Computational biologists are "breakthrough artists"

Thomas Lasko, MD, PhD, from Google Inc., Mountain View CA, will be giving a seminar next week entitled: "Spectral Anonymization Data" March 3, 2010 12:00 - 1:00 p.m. 214 Light Hall Abstract:  The great challenge of data anonymization is to condition a dataset for public release such that it remains scientifically useful, but it does not disclose sensitive information about the individuals it describes.

Seminar: Spectral Anonymization Data

Department of Biostatistics Seminar/Workshop Series:

 Statistical Methods for DNA Resequencing Analysis in Disease-Gene Studies

Wenyi Wang, Ph.D., Faculty Candidate Stanford Genome Technology Center, UC Berkeley Statistics 2:00-3:00pm Monday, February 15, 2010 MRB III Room 1220 Intended Audience: Persons interested in applied statistics, statistical theory, epidemiology, health services research, clinical trials methodology, statistical

Seminar: Statistical Methods for DNA Resequencing Analysis in Disease-Gene Studies

Here are the papers we talked about in yesterday's Journal Club: PLoS Biol. 2010 Jan 26;8(1):e1000294. Rare variants create synthetic genome-wide associations. See my previous coverage of this paper and the comments. Dickson SP, Wang K, Krantz I, Hakonarson H, Goldstein DB. PLoS Genet. 2010 Jan;6(1):e1000798.

Papers from Feb 8 2010 Journal Club

Found this nice way to visually summarize contingency tables using ggplot2 in R on Hadley Wickham's ggplot2 cheat sheet. Using the same data in my previous post on making scatterplots in small multiples, I'll demonstrate how to use ggfluctuation() to make a fluctuation plot.

Fluctuation plot using ggplot2 in R

Illumina just unveiled their latest sequencing machine, the HiSeq 2000, capable of sequencing two human genomes to 30x coverage in a single run for less than $10,000 each. Using reversible terminator-based sequencing by synthesis chemistry, the HiSeq 2000 is capable of quickly generating a flood of data: 200 Gb per run, 2 x 100 bp read length, up to 25 Gb per day, two billion paired-end reads/run.

Illumina: $10,000 genome sequence with HiSeq 2000

A Beautiful WWW

put together a great set of resources for getting started with machine learning in R.  First, they recommend the previously mentioned free book, The Elements of Statistical Learning.  Then there's a link to a list of dozens of machine learning and statistical learning packages for R.  Next, you'll need data.  Hundreds of free real datasets are available at the UCI machine learning repository.

Get Started with Machine Learning in R

Learning R blog details some of the new features in the latest update to ggplot2. The latest version includes functions to make it easier to change axis and legend labels, as well as a function to easily set the limits of the plot display outside the range of the data. Be sure to check back next week - I'm putting together a short introductory ggplot2 tutorial.

New Features in ggplot2 0.8.5

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.

Use plyr instead of _apply() in R

If you've never heard of it before, check out my previous coverage on Pubget. It's like PubMed, but you get the PDFs right away.  Pubget has recently implemented a number of improvements. 1. Citation matching.  Pubget's citation matcher seems to work better than Pubmed most of the time.

Recent improvements to Pubget

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.

Browse R Graphics with the R Graph Gallery and the R Graphical Manual

While flirting around with previously mentioned ggplot2 I came across an incredibly useful set of functions in the plyr package, made by Hadley Wickham, the same guy behind ggplot2.  If you've ever used MySQL before, think of "GROUP BY", but here you can arbitrarily apply any R function to splits of the data, or write one yourself.

Split, apply, and combine in R using PLYR

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.

QQ plots of p-values in R using ggplot2

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.

Annotated LD-Plots

Any R user no matter what level of experience has had trouble finding the package or the function to do what you want to do and then figuring out how to use it.  The sos package in R just made that a lot easier. First, fire up R, then install the sos package (don't omit the quotes): install.packages("sos") It'll ask you to choose a mirror.  Choose the closest one.

Find the function you're looking for in R

Vanderbilt ACCRE users with PCs only... If you use ACCRE to run multi-processor jobs you'll be glad to know that they now allow you to map your home directory to your local desktop using Samba (so you can access your files through My Computer as you normally would with local files).

Sync your home directories on ACCRE and the local Linux servers (a.k.a. "the cheeses")

Nice review on the genetics of quantitative traits with good figures and references published this morning in Nature Reviews Genetics. Abstract:The genetics of quantitative traits: challenges and prospects (NRG AOP)Getting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution (CC BY) License.

The genetics of quantitative traits: challenges and prospects

A quick announcement about a formatting fix here - you can now print posts from GGD a little more cleanly.  When you print it should no longer include the title and sidebar, so most posts should now only use a page or two.Getting Genetics Done by Stephen Turner is licensed under a Creative Commons Attribution (CC BY) License.

GGD posts now printer-friendly

As far as I know there are 3 different systems for producing figures in R: (1) base graphics, included with R, (2) the lattice package, and (3) ggplot2, one of the newer plotting systems which is, according to the creator Hadley Wickham, "based on the grammar of graphics, which tries to take the good parts of base and lattice graphics and none of the bad parts.

ggplot2: more wicked-cool plots in R

Hadley Wickham, creator of the previously mentioned R plotting system ggplot2 and author of a forthcoming book from Springer, is teaching a workshop in data visualization using R, ggplot2, and GGobi. Unfortunately this workshop conflicts with IGES and ASHG this year, but he mentioned the possibility of holding a workshop here at Vanderbilt if there is enough interest.

ggplot2 workshop at Vanderbilt

Here are citations for the articles discussed at our most recent meeting (July 31). Our next meeting is scheduled for August 14.~JuliaBogdanowicz W, Allen M, Branicki W, Lembring M, Gajewska M, Kupiec T. Genetic identification of putative remains of the famous astronomer Nicolaus Copernicus.

Getting Genetics Done

GWAS Manhattan plots and QQ plots using ggplot2 in R

Visualizing sample relatedness in a GWAS using PLINK and R

Convert PLINK output to CSV