Moving to blog.stephenturner.us (Paired Ends)

My new blog/newsletter ("Paired Ends") is now at blog.stephenturner.us. I'll be posting semi-regular updates and literature highlights in bioinformatics, computational biology, and data science, along with the occasional post on programming. Head over to blog.stephenturner.us to subscribe by email, or add the RSS feed to your favorite reader app.

List of Bioinformatics Workshops and Training Resources

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. Below is a snapshop of what I have listed as of today. Please leave a comment if you're aware of any egregious omissions, and I'll update the page above as appropriate.

From http://stephenturner.us/p/edu, April 4, 2013

In-Person Workshops:

Cold Spring Harbor Courses: meetings.cshl.edu/courses.html

Cold Spring Harbor has been offering advanced workshops and short courses in the life sciences for years. Relevant workshops include Advanced Sequencing Technologies & Applications, Computational & Comparative Genomics, Programming for Biology, Statistical Methods for Functional Genomics, the Genome Access Course, and others. Unlike most of the others below, you won't find material from past years' CSHL courses available online.

Canadian Bioinformatics Workshops: bioinformatics.ca/workshops
Bioinformatics.ca through its Canadian Bioinformatics Workshops (CBW) series began offering one and two week short courses in bioinformatics, genomics and proteomics in 1999. The more recent workshops focus on training researchers using advanced high-throughput technologies on the latest approaches being used in computational biology to deal with the new data. Course material from past workshops is freely available online, including both audio/video lectures and slideshows. Topics include microarray analysis, RNA-seq analysis, genome rearrangements, copy number alteration,network/pathway analysis, genome visualization, gene function prediction, functional annotation, data analysis using R, statistics for metabolomics, and much more.

UC Davis Bioinformatics Training Program: training.bioinformatics.ucdavis.edu
The UC Davis Bioinformatics Training program offers several intensive short bootcamp workshops on RNA-seq, data analysis and visualization, and cloud computing with a focus on Amazon's computing resources. They also offer a week-long Bioinformatics Short Course, covering in-depth the practical theory and application of cutting-edge next-generation sequencing techniques. Every course's documentation is freely available online, even if you didn't take the course.

MSU NGS Summer Course: bioinformatics.msu.edu/ngs-summer-course-2013
This intensive two week summer course will introduce attendees with a strong biology background to the practice of analyzing short-read sequencing data from Illumina and other next-gen platforms. The first week will introduce students to computational thinking and large-scale data analysis on UNIX platforms. The second week will focus on mapping, assembly, and analysis of short-read data for resequencing, ChIP-seq, and RNAseq. Materials from previous courses are freely available online under a CC-by-SA license.

Genetic Analysis of Complex Human Diseases: hihg.med.miami.edu/edu...
The Genetic Analysis of Complex Human Diseases is a comprehensive four-day course directed toward physician-scientists and other medical researchers. The course will introduce state-of-the-art approaches for the mapping and characterization of human inherited disorders with an emphasis on the mapping of genes involved in common and genetically complex disease phenotypes. The primary goal of this course is to provide participants with an overview of approaches to identifying genes involved in complex human diseases. At the end of the course, participants should be able to identify the key components of a study team, and communicate effectively with specialists in various areas to design and execute a study. The course is in Miami Beach, FL. (Full Disclosure: I teach a section in this course.) Most of the course material from previous years is not available online, but my RNA-seq & methylation lectures are on Figshare.

UAB Short Course on Statistical Genetics and Genomics: soph.uab.edu/ssg/...
Focusing on the state-of-art methodology to analyze complex traits, this five-day course will offer an interactive program to enhance researchers' ability to understand & use statistical genetic methods, as well as implement & interpret sophisticated genetic analyses. Topics include GWAS Design/Analysis/Imputation/Interpretation; Non-Mendelian Disorders Analysis; Pharmacogenetics/Pharmacogenomics; ELSI; Rare Variants & Exome Sequencing; Whole Genome Prediction; Analysis of DNA Methylation Microarray Data; Variant Calling from NGS Data; RNAseq: Experimental Design and Data Analysis; Analysis of ChIP-seq Data; Statistical Methods for NGS Data; Discovering new drugs & diagnostics from 300 billion points of data. Video recording from the 2012 course are available online.

MBL Molecular Evolution Workshop: hermes.mbl.edu/education/...
One of the longest-running courses listed here (est. 1988), the Workshop on Molecular Evolution at Woods Hole presents a series of lectures, discussions, and bioinformatic exercises that span contemporary topics in molecular evolution. The course addresses phylogenetic analysis, population genetics, database and sequence matching, molecular evolution and development, and comparative genomics, using software packages including AWTY, BEAST, BEST, Clustal W/X, FASTA, FigTree, GARLI, MIGRATE, LAMARC, MAFFT, MP-EST, MrBayes, PAML, PAUP*, PHYLIP, STEM, STEM-hy, and SeaView. Some of the course materials can be found by digging around the course wiki.


Online Material:

Canadian Bioinformatics Workshops: bioinformatics.ca/workshops
(In person workshop described above). Course material from past workshops is freely available online, including both audio/video lectures and slideshows. Topics include microarray analysis, RNA-seq analysis, genome rearrangements, copy number alteration, network/pathway analysis, genome visualization, gene function prediction, functional annotation, data analysis using R, statistics for metabolomics, andmuch more.

UC Davis Bioinformatics Training Program: training.bioinformatics.ucdavis.edu
(In person workshop described above). Every course's documentation is freely available online, even if you didn't take the course. Past topics include Galaxy, Bioinformatics for NGS, cloud computing, and RNA-seq.

MSU NGS Summer Course: bioinformatics.msu.edu/ngs-summer-course-2013
(In person workshop described above). Materials from previous courses are freely available online under a CC-by-SA license, which cover mapping, assembly, and analysis of short-read data for resequencing, ChIP-seq, and RNAseq.

EMBL-EBI Train Online: www.ebi.ac.uk/training/online
Train online provides free courses on Europe's most widely used data resources, created by experts at EMBL-EBI and collaborating institutes. Topics include Genes and Genomes, Gene Expression,Interactions, Pathways, and Networks, and others. Of particular interest may be the Practical Course on Analysis of High-Throughput Sequencing Data, which covers Bioconductor packages for short read analysis, ChIP-Seq, RNA-seq, and allele-specific expression & eQTLs.

UC Riverside Bioinformatics Manuals: manuals.bioinformatics.ucr.edu
This is an excellent collection of manuals and code snippets. Topics include Programming in R, R+Bioconductor, Sequence Analysis with R and Bioconductor, NGS analysis with Galaxy and IGV, basicLinux skills, and others.

Software Carpentry: software-carpentry.org
Software Carpentry helps researchers be more productive by teaching them basic computing skills. We recently ran a 2-day Software Carpentry Bootcamp here at UVA. Check out the online lectures for some introductory material on Unix, Python, Version Control, Databases, Automation, and many other topics.

Coursera: coursera.org/courses
Coursera partners with top universities to offer courses online for anytone to take, for free. Courses are usually 4-6 weeks, and consist of video lectures, quizzes, assignments, and exams. Joining a course gives you access to the course's forum where you can interact with the instructor and other participants. Relevant courses include Data Analysis, Computing for Data Analysis using R, and Bioinformatics Algorithms, among others. You can also view all of Jeff Leek's Data Analysis lectures on Youtube.
Rosalind: http://rosalind.info
Quite different from the others listed here, Rosalind is a platform for learning bioinformatics through gaming-like problem solving. Visit the Python Village to learn the basics of Python. Arm yourself at theBioinformatics Armory, equipping yourself with existing ready-to-use bioinformatics software tools. Or storm the Bioinformatics Stronghold, implementing your own algorithms for computational mass spectrometry, alignment, dynamic programming, genome assembly, genome rearrangements, phylogeny, probability, string algorithms and others.


Other Resources:

• Titus Brown's list bioinformatics courses: Includes a few others not listed here (also see the comments).
• GMOD Training and Outreach: GMOD is the Generic Model Organism Database project, a collection of open source software tools for creating and managing genome-scale biological databases. This page links out to tutorials on GMOD Components such as Apollo, BioMart, Galaxy, GBrowse, MAKER, and others.
• Seqanswers.com: A discussion forum for anything related to Bioinformatics, including Q&A, paper discussions, new software announcements, protocols, and more.
• Biostars.org: Similar to SEQanswers, but more strictly a Q&A site.
• BioConductor Mailing list: A very active mailing list for getting help with Bioconductor packages. Make sure you do some Google searching yourself first before posting to this list.
• Bioconductor Events: List of upcoming and prior Bioconductor training and events worldwide.
• Learn Galaxy: Screencasts and tutorials for learning to use Galaxy.
• Galaxy Event Horizon: Worldwide Galaxy-related events (workshops, training, user meetings) are listed here.
• Galaxy RNA-Seq Exercise: Run through a small RNA-seq study from start to finish using Galaxy.
• Rafael Irizarry's Youtube Channel: Several statistics and bioinformatics video lectures.
• PLoS Comp Bio Online Bioinformatics Curriculum: A perspective paper by David B Searls outlining a series of free online learning initiatives for beginning to advanced training in biology, biochemistry, genetics, computational biology, genomics, math, statistics, computer science, programming, web development, databases, parallel computing, image processing, AI, NLP, and more.
• Getting Genetics Done: Shameless plug – I write a blog highlighting literature of interest, new tools, and occasionally tutorials in genetics, statistics, and bioinformatics. I recently wrote this post about how to stay current in bioinformatics & genomics.

Software Carpentry Bootcamp at University of Virginia

A couple of weeks ago I, with the help of others here at UVA, organized a Software Carpentry bootcamp, instructed by Steve Crouch, Carlos Anderson, and Ben Morris. The day before the course started, Charlottesville was racked by nearly a foot of snow, widespread power outages, and many cancelled incoming flights. Luckily our instructors arrived just in time, and power was (mostly) restored shortly before the boot camp started. Despite the conditions, the course was very well-attended.

Software Carpentry's aim is to teach researchers (usually graduate students) basic computing concepts and skills so that they can get more done in less time, and with less pain. They're a volunteer organization funded by Mozilla and the Sloan foundation, and led this two-day bootcamp completely free of charge to us.

The course started out with a head-first dive into Unix and Bash scripting, followed by a tutorial on automation with Make, concluding the first day with an introduction to Python. The second day covered version control with git, Python code testing, and wrapped up with an introduction to databases and SQL. At the conclusion of the course, participants offered near-universal positive feedback, with the git and Make tutorials being exceptionally popular.

Software Carpentry's approach to teaching these topics is unlike many others that I've seen. Rather than lecturing on for hours, the instructors inject very short (~5 minute) partnered exercises between every ~15 minutes of instruction in 1.5 hour sessions. With two full days of intensive instruction and your computer in front of you, it's all too easy to get distracted by an email, get lost in your everyday responsibilities, and zone out for the rest of the session.  The exercises keep participants paying attention and accountable to their partner.

All of the bootcamp's materials are freely available:

Unix and Bash: https://github.com/redcurry/bash_tutorial
Python Introduction: https://github.com/redcurry/python_tutorial
Git tutorial: https://github.com/redcurry/git_tutorial
Databases & SQL: https://github.com/bendmorris/swc_databases
Everything else: http://users.ecs.soton.ac.uk/stc/SWC/tutorial-materials-virginia.zip

Perhaps more relevant to a broader audience are the online lectures and materials available on the Software Carpentry Website, which include all the above topics, as well as many others.

We capped the course at 50, and had 95 register within a day of opening registration, so we'll likely do this again in the future. I sit in countless meetings where faculty lament how nearly all basic science researchers enter grad school or their postdoc woefully unprepared for this brave new world of data-rich high-throughput science. Self-paced online learning works well for some, but if you're in a department or other organization that could benefit from a free, on-site, intensive introduction to the topics listed above, I highly recommend contacting Software Carpentry and organizing your own bootcamp.

Finally, when organizing an optional section of the course, we let participants vote whether they preferred learning number crunching with NumPy, or SQL/databases; SQL won by a small margin. However, Katherine Holcomb in UVACSE has graciously volunteered to teach a two-hour introduction to NumPy this week, regardless of whether you participated in the boot camp (although some basic Python knowledge is recommended). This (free) short course is this Thursday, March 21, 2-4pm, in the same place as the bootcamp (Brown Library Classroom in Clark Hall). Sign up here.

Computing for Data Analysis, and Other Free Courses

Coursera's free Computing for Data Analysis course starts today. It's a four week long course, requiring about 3-5 hours/week. A bit about the course:

In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment, discuss generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, creating informative data graphics, accessing R packages, creating R packages with documentation, writing R functions, debugging, and organizing and commenting R code. Topics in statistical data analysis and optimization will provide working examples.

There are also hundreds of other free courses scheduled for this year. While the Computing for Data Analysis course is more about using R, the Data Analysis course is more about the methods and experimental designs you'll use, with a smaller emphasis on the R language. There are also courses on Scientific Computing, Algorithms, Health Informatics in the Cloud, Natural Language Processing, Introduction to Data Science, Scientific Writing, Neural Networks, Parallel Programming, Statistics 101, Systems Biology, Data Management for Clinical Research, and many, many others. See the link below for the full listing.

Free Courses on Coursera

NSF BIGDATA webinar

If you're doing any kind of big data analysis - genomics, transcriptomics, proteomics, bioinformatics - then unless you've been on vacation the last few weeks you've no doubt heard about the NSF/NIH BIGDATA  Initiative (here's the NSF solicitation and here's the New York Times article about the funding opportunity). The solicitation "aims to advance core scientific and technological means of managing, analyzing, visualizing, and extracting useful information from large, diverse, distributed and heterogeneous data sets so as to: accelerate the progress of scientific discovery and innovation; lead to new fields of inquiry that would not otherwise be possible; encourage the development of new data analytic tools and algorithms; facilitate scalable, accessible, and sustainable data infrastructure; increase understanding of human and social processes and interactions; and promote economic growth and improved health and quality of life."

NSF is holding a webinar to describe the goals and focus of the BIGDATA solicitation, help investigators understand its scope, and answer any questions potential PIs might have.

The Webinar will be held from 11am-noon EST on May 8, 2012. Register here. The webinar will also be archived here a few days later.

NSF BIGDATA Webinar - May 8 2012

Redesign by Subtraction

GGD has a new look. I was inspired by Gina Trapani (Smarterware, Lifehacker) to remove any extra lines, links, and other "ink" that doesn't serve any purpose, and I hope the site appears cleaner and easier to read. I also wanted the extra horizontal space for larger images and avoid the dreaded side-scrolling in posts with lots of code like this one. The space will also be handy for short instructional screencasts I've been recording for my clients here in the core, which I'll start posting here soon. Leave a comment if anything appears broken. I hope you like the new look!

Galaxy Project Group on CiteULike and Mendeley

The Galaxy Project started using CiteULike to organize papers that are about, use, or reference Galaxy. The Galaxy CiteULike group is open to any CUL user, and once you join, you can add papers to the group, assign tags, and rate papers.

While not a CUL user, I'm a big fan of Mendeley for managing references, PDFs, and creating bibliographies (and so are many of you). I'm happy to hear that the Galaxy folks also set up a Galaxy Mendeley Group, also open to the public for anyone to join.  If you join the Galaxy public Mendeley group, all of the groups references will show up in your Mendeley library (and these won't count against your personal quota).

Just one important thing to note: The Mendeley group is a mirror of the CiteULike group, so if you want to add more publications to the Galaxy Group, add them on CiteULike, not Mendeley (it doesn't work the other way around - papers added to Mendeley won't make it to the CUL group).

Galaxy Project Group on CiteULike and Mendeley

I'm Starting a New Position at the University of Virginia

I just accepted an offer for a faculty position at the University of Virginia in the Center for Public Health Genomics / Department of Public Health Sciences. Starting in October I will be developing and directing a new centralized bioinformatics core in the UVA School of Medicine. Over the next few weeks I'm taking a much-needed vacation next door in Kauai and then packing up for the move to Charlottesville. Posts here may be sparse over the next few weeks, but once I start my new gig I'll be sure to make up for it. And if you're bioinformatics-savvy and in the job market keep an eye out here - once I figure out what I need I will soon be hiring, and will repost any job announcements here.

I've enjoyed my postdoc here at the University of Hawaii Cancer Center, and there is much I'll miss about island life out here in the Pacific. But I'm very seriously looking forward to getting started in this wonderful opportunity at UVA. Thank you all for your comments, suggestions, and help when I needed it. I'll be back online in a few weeks - until then, follow me on Twitter (@genetics_blog).

Aloha!

Personal Genomics and Data Sharing Survey

I was recently contacted by a couple of German biologists working on a project evaluating opinions on sharing raw data from DTC genetic testing companies like 23andme. A handful of people like the gang at Genomes Unzipped, the PGP-10, and others at SNPedia have released their own genotype or sequencing data into the public domain. As of now, data like this is scattered around the web and most of it is not attached to any phenotype data.

These three biologists are working on a website that collects genetic data as well as phenotypic data. The hope is to make it easy to find and access appropriate data and to become a resource for a kind of open-source GWAS - similar to the research 23andMe performs in its walled garden right now.

But because of privacy concerns, many people (myself included) hesitate to freely publish their genetic data for the world to see. These three biologists are conducting a survey to assess how willing people might be to participate in something like this, and for what reasons they would (or would not). The survey can be accessed at http://bit.ly/genotyping_survey. It took about 2 minutes for me to complete, and you can optionally sign up to receive an email with their results once they've completed the survey.

Although I'm still hesitant to participate in something like this myself, I like the idea, and I'm very interested to see the results of their survey. Hit the link below if you'd like to take the quick survey.

Personal Genomics and Data Sharing Survey

Steal This Blog!

I wanted to contribute any content and code I post here to the R Programming Wikibook so I made a slight change to the Creative Commons license on this blog. All the written content is now cc-by-sa and all the code here is still open source BSD. So feel free to wholesale copy, modify, share, or redistribute anything you find here, just include a link back to the site.

Agilent Integrated Biology Grant Program

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. Click here for full details on eligibility, submission, and the review process.

Grant 1: Validating Protein Pathway Information—Integrating Proteomic Data with Transcriptomic or Metabolomic Data Sets. The purpose of this initiative is to support the development or improvement of advanced mass spectrometry informatics tools that drive integration of gene expression, metabolomic, and targeted proteomics data. Specifically, we are looking for proposals that focus on automation of targeted mass spectrometry-based proteomics experiments (e.g., SRM, exact mass) aimed at hypothesis testing or validation of protein pathways and/or interaction networks generated by integrating existing transcriptomic and/or metabolomic datasets.

Grant 2: Modeling Disease Progression—Combining Gene Expression and Copy Number Variation Data. The purpose of this initiative is to support the development of advanced microarray and next-generation sequencing-based informatics tools to drive the integration of gene expression and genomic copy number data. Specifically, we are looking for proposals that focus on the correlation of copy number events and whole transcriptome measurements aimed at hypothesis testing or validation of disease progression models.

Agilent Integrated Biology Grant Program

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

This looks like an interesting and relevant webinar for anyone receiving federal funding for genetics projects. You can view it online by registering http://www.readytalk.com (Access Code 5345825) or listen by phone: 1-866-740-1260 (Access Code 5345825).

iDASH Webinar Series

University of California, San Diego

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

Laura Rodriguez, Ph.D.

Director for the Office of Policy, Communication, and Education

ABSTRACT: One of the key tenets of genomics research is rapid and broad access to basic genomic data.  The National Institutes of Health (NIH) is similarly committed to open data sharing practices in order to ensure access to federally-funded data and resources and promote maximum public benefit through the public's biomedical research investment.  In this webinar, the principles underlying NIH's genomic data sharing policies will be reviewed and an overview will be provided of the policy model and expectations for protecting the interests of research participants whose data is contained within primary genomic datasets.

SPEAKER BIO: Laura Lyman Rodriguez, Ph.D., is the Director for the Office of Policy, Communication, and Education and the Senior Advisor to the Director for Research Policy at the National Human Genome Research Institute (NHGRI), National Institutes of Health (NIH). Dr. Rodriguez works to develop and implement policy for research initiatives at the NHGRI, as well as trans-NIH programs.  She is particularly interested in the policy and ethics questions related to the inclusion of human research participants in genomics and genetics research.  Among other activities, Dr. Rodriguez has provided leadership for many of the policy development activities pertaining to genomic data sharing and the creation of the database for Genotypes and Phenotypes (dbGaP) at the NIH.
Dr. Rodriguez received her bachelor of science with honors in biology from Washington and Lee University in Virginia and earned a doctorate in cell biology from Baylor College of Medicine in Texas.

Date and Time

Friday, April 15, 2011

11:00AM-11:50AM (PST)

DETAILS ON HOW TO JOIN THE MEETING:

Web: http://www.readytalk.com/ (Join meeting with Access Code 5345825)
Audio: 1-866-740-1260 (Access Code 5345825) 

Genetic Alliance Celebrates its 25th Year

Genetic Alliance is a nonprofit health advocacy organization that improves health through the authentic engagement of communities and individuals. This year, they are celebrating their 25th anniversary, and they're hosting a variety of events throughout the year, including monthly salons around the country and the 25th Anniversary Annual Conference in June. If you cannot attend an event in person, you can still learn all about genetics, health, and advocacy with their webinar series. I've watched a few of these webinars in the past, including one on the Myriad gene patent case featuring John Conley from Genomics Law Report. In addition, they are honoring innovators in the genetics community and post new videos weekly. 

Read more to find out how to get involved: http://www.geneticalliance.org/25anniversary.

So long Vanderbilt, and thanks for all the fish!

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. Because I won't have the same level of statistical and bioinformatics support in Hawaii that I have now, I'll have much to figure out on my own, so I'll have even more to write about here. But for now, enjoy this Illustrated guide to a Ph.D., reproduced with permission from Matt Might, and follow me on Twitter (@genetics_blog).

...

Imagine a circle that contains all of human knowledge:

By the time you finish elementary school, you know a little:

By the time you finish high school, you know a bit more:

With a bachelor's degree, you gain a specialty:

A master's degree deepens that specialty:

Reading research papers takes you to the edge of human knowledge:

Once you're at the boundary, you focus:

You push at the boundary for a few years:

Until one day, the boundary gives way:

And, that dent you've made is called a Ph.D.:

Of course, the world looks different to you now:

So, don't forget the bigger picture:

Keep pushing!

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

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. It also integrates with MS Word and allows you to insert citations and format a bibliography using any of EndNote's styles. And it's free.

Before I make the switch and leave RefMan for good, I would love to see what everyone else here uses to manage references. I know many of you use social bookmarking sites like CiteULike, del.icio.us, FriendFeed and others to save and share literature, but I'm really interested to see what software you use while writing to manage references and format bibliographies, and how satisfied you are with what you use.

Thanks for responding! Check back in a few days and I'll summarize what you all said.

Defense!

On Friday, December 3rd, at 8:00 AM, after copious amounts of coffee, my friend, colleague, and perpetual workout buddy Stephen Turner will defend his thesis.

Knowledge-Driven Genome-wide Analysis of Multigenic Interactions Impacting HDL Cholesterol Level

Join us in room 206 of the Preston Research Building at Vanderbilt Medical Center for the auspicious occasion!

Seminar: A New Measure of Coefficient of Determination for Regression Models

Human Genetics / Biostatistics Associate Professor (and my first statistics teacher) Dr. Chun Li will be giving a talk Wednesday on a new measure of R² for continuous, binary, ordinal, and survival outcomes. Here are the details:

Department of Biostatistics Seminar/Workshop Series

A New Measure of Coefficient of Determination for Regression Models

 
Chun Li, PhD
 

Associate Professor, Department of Biostatistics, Vanderbilt University School of Medicine

Wednesday, November 17, 1:30-2:30pm, MRBIII Conference Room 1220
http://biostat.mc.vanderbilt.edu/CLiNov17

Summary: Coefficient of determination is a measure of the goodness of fit for a model. It is best known as R² in ordinary least squares (OLS) for continuous outcomes. However, as a ratio of values on the squared outcome scale, R² is often not intuitive to think of. In addition, extensions of the definition to other outcome types often have unsatisfactory properties. One approach is to define a ratio of two quantities, but often such a definition does not have an underlying decomposition property that is enjoyed by R². Another approach is to employ the connection of R² with the likelihood ratio statistic in linear regression where the residuals follow normal distributions, but for discrete outcomes, this will result in a value less than one even for a perfect model fit. For regression models, we propose a new measure of coefficient of determination as the correlation coefficient between the observed values and the fitted distributions. As a correlation coefficient, this new measure is intuitive and easy to interpret. It takes into account the variation in fitted distributions, and can be readily extended to other outcome types. For OLS, it is numerically the same as R²! We present the new measure for continuous, binary, ordinal, and time-to-event outcomes.

Keep up with what's happening at ASHG 2010

As this year's ASHG meeting starts to wind down be sure to check out Variable Genome where Larry Parnell is summarizing what's going on at the talks he's been to. Also see the commentary on Genetic Inference by Luke Jostins. The 1000 Genomes tutorial from Wednesday night will be made available on genome.gov soon, and the presidential address, plenary sessions, and distinguished speaker symposium talks were recorded and will also soon be online. You can keep up with what's going on in real time by following the #ASHG2010 tag on Twitter.

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

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 Biology

M.D. Anderson Cancer Center

and

Keith Baggerly, PhD

Associate Professor, Dept. of Bioinformatics and Computational Biology

M.D. Anderson Cancer Center

Panel Discussion at 1 p.m., following presentations:

Featuring Drs. Baggerly and Coombes, along with
Vanderbilt University School of Medicine’s
Dr. William Pao, Dr. Frank Harrell, and Dr. Yu Shyr

Friday, November 19, 2010

12 noon – 2 PM

214 Light Hall

PacBio Film, Discussion & Reception/Dinner at ASHG 2010

Pacific Biosciences is hosting a reception and dinner, and is screening their film The New Biology at this year's ASHG meeting. According to a flyer the mailed me, the film will showcase their SMRT sequencing technology and how it can be used to "create predictive models of living systems and gain wisdom about the fundamental nature of life itself." While the last bit is perhaps an overstatement, the event should nonetheless be an event worth attending. The event includes a reception, dinner, and a moderated discussion featuring individuals from the film. Unfortunately this conflicts with the previously mentioned 1000 Genomes Tutorial, but if you get waitlisted at the tutorial, sign up for this event at the link below!

Date
Wednesday, November 3 2010

Time
7-10pm

Location
Smithsonian National Air and Space Museum
Independence Ave at 6th St SW
Washington, DC 20560

RSVP here - pacificbiosciences.com/newbiology