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Close to the launch of JMP9 with it’s R integration comes the announcement of JMP Genomics 5 released. The product brief is available here http://jmp.com/software/genomics/pdf/103112_jmpg5_prodbrief.pdf and it has an interesting mix of features. If you want to try out the features you can see http://jmp.com/software/license.shtml
As per me, I snagged some “new”stuff in this release-
- Perform enrichment analysis using functional information from Ingenuity Pathways Analysis.+
- New bar chart track allows summarization of reads or intensities.
- New color map track displays heat plots of information for individual subjects.
- Use a variety of continuous measures for summarization.
- Using a common identifier, compare list membership for up tofive groups and display overlaps with Venn diagrams.
- Filter or shade segments by mean intensity, with an optionto display segment mean intensity and set a reference valuefor shading.
- Adjust intensities or counts for experimental samples using paired or grouped control samples.
- Screen paired DNA and RNA intensities for allele-specific expression.
- Standardize using a shifting factor and perform log2transformation after standardization.
- Use kernel density information in loess and quantile normalization.
- Depict partition tree information graphically for standard models with new Tree Viewer
- Predictive modeling for survival analysis with Harrell’s assessment method and integration with Cross-Validation Model Comparison.
That’s right- that is incorporating the work of our favorite professor from R Project himself- http://biostat.mc.vanderbilt.edu/wiki/Main/FrankHarrell
Apparently Prof Frank E was quite a SAS coder himself (see http://biostat.mc.vanderbilt.edu/wiki/Main/SasMacros)
Back to JMP Genomics 5-
The JMP software platform provides:
• New integration capabilities let R users leverage JMP’s interactivegraphics to display analytic results.
• Tools for R programmers to build and package user interfaces that let them share customized R analytics with a broader audience.•
A new add-in infrastructure that simplifies the integration of external analytics into JMP.
+ For people in life sciences who like new stats software you can also download a trial version of IPA here at http://www.ingenuity.com/products/IPA/Free-Trial-Software.html
Read rest of the new software here http://jmp.com/software/genomics/pdf/103112_jmpg5_prodbrief.pdf
- JMP 9 releasing on Oct 12 (r-bloggers.com)
- New JMP Software Version Extends Analytic Options (eon.businesswire.com)
- Dan Ariely Headlines JMP Analytics Conference (eon.businesswire.com)
- Whole Genome Sequencing of Japanese Individual Reveals Wealth of Undiscovered Genetic Variation (prweb.com)
- Blog – Ozzy Osbourne’s Genome (technologyreview.com)
- SAS Continues to Expand Analytics Options with Additional R Integration (eon.businesswire.com)
- Human Genome Sciences Invites Investors to Listen to Webcast of Presentation at JMP Securities Healthcare Conference (eon.businesswire.com)
- SAS, JMP Mix Simulation and Analytics to Foster Innovation (eon.businesswire.com)
- Using JMP 9 and R together (r-bloggers.com)
- Japanese flower has the biggest genome in the world [Mad Genomics] (io9.com)
- JMP Customer Herzenberg Lab Wins Computerworld Honor (eon.businesswire.com)
Not only is the contest useful, it is likely to teach R Users some data hacking skills, as well as the basics of creating a GitHub Project.
For that reason, we’ve settled on the more manageable question, “which packages are most often installed by normal R users?”
This last question could potentially be answered in a variety of ways. Our current approach uses a convenience sample of installation data that we’ve collected from volunteers in the R community, who kindly agreed to send us a list of the packages they have on their systems. We’ve anonymized this data and compiled a set of metadata-based predictors that allow us to predict the installation probabilities quite well. We’re releasing all of our current work, including the data we have and all of the code we’ve used so far for our exploratory analyses. The contest itself will go live on Kaggle on Sunday and will end four months from Sunday on February 10, 2011. The rules, prizes and official data sets are all described below.
Rules and Prizes
To win the contest, you need to predict the probability that a user U has a package P installed on their system for every pair, (U, P). We’ll assess your performance using ROC methods, which will be evaluated against a held out test data set. The winning team will receive 3 UseR! books of their choosing. In order to win the contest, you’ll have to provide your analysis code to us by creating a fork of our GitHub repository. You’ll also be required to provide a written description of your approach. We’re asking for so much openness from the winning team because we want this contest to serve as a stepping stone for the R community. We’re also hoping that enterprising data hackers will extend the lessons learned through this contest to other programming languages.
Read the full article there
From the monthly newsletter- which I consider quite useful for keeping updated on application of R
Every month, we’ll bring you the latest news about Revolution’s products and events in this section. Follow us on Twitter at @RevolutionR for up-to-the-minute news and updates from Revolution Analytics!
Revolution R Enterprise 4.0 for Windows now available. Based on the latest R 2.11.1 and including the RevoScaleR package for big-data analysis in R, Revolution R Enterprise is now available for download for Windows 32-bit and 64-bit systems. Click here to subscribe, or available free to academia.
New! Integrate R with web applications, BI dashboards and more with web services. RevoDeployR is a new Web Services framework that integrates dynamic R-based computations into applications for business users. It will be available September 30 with Revolution R Enterprise Server on RHEL 5. Click here to learn more.
Free Webinar, September 22: In a joint webinar from Revolution Analytics and Jaspersoft, learn how to use RevoDeployR to integrate advanced analytics on-demand in applications, BI dashboards, and on the web. Register here.
Revolution in the News: SearchBusinessAnalytics.com previews the forthcoming Revolution R GUI; Channel Register introduces RevoDeployR, while IT Business Edge shows off the Web Services architecture; and ReadWriteWeb.com looks at how RevoScaleR tackles the Big Data explosion.
Inside-R: A new site for the R Community. At www.inside-R.org you’ll find the latest information about R from around the Web, searchable R documentation and packages, hints and tips about R, and more. You can even add a “Download R” badge to your own web-page to help spread the word about R.
R News, Tips and Tricks from the Revolutions blog
The Revolutions blog brings you daily news and tips about R, statistics and open source. Here are some highlights from Revolutions from the past month.
R’s key role in the oil spill response: Read how NIST’s Division Chief of Statistical Engineering used R to provide critical analysis in real time to the Secretaries of Energy and the Interior, and helped coordinate the government’s response.
Animating data with R and Google Earth: Learn how to use R to create animated visualizations of geographical data with Google Earth, such as this video showing how tuna migrations intersect with the location of the Gulf oil spill.
Are baseball games getting longer? Or is it just Red Sox games? Ryan Elmore uses nonparametric regression in R to find out.
Keynote presentations from useR! 2010: the worldwide R user’s conference was a great success, and there’s a wealth of useful tips and information in the presentations. Video of the keynote presentations are available too: check out in particular Frank Harrell’s talk Information Allergy, and Friedrich Leisch’s talk on reproducible statistical research.
Looking for more R tips and tricks? Check out the monthly round-ups at the Revolutions blog.
Every month, we’ll highlight some upcoming events from R Community Calendar.
September 23: The San Diego R User Group has a meetup on BioConductor and microarray data analysis.
September 28: The Sydney Users of R Forum has a meetup on building world-class predictive models in R (with dinner to follow).
September 28: The Los Angeles R User Group presents an introduction to statistical finance with R.
September 28: The Seattle R User Group meets to discuss, “What are you doing with R?”
September 29: The Raleigh-Durham-Chapel Hill R Users Group has its first meeting.
October 7: The NYC R User Group features a presentation by Prof. Andrew Gelman.
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