DNA – DECISION STATS

An interesting use case of technology for better health is HANA Oncolyzer at http://epic.hpi.uni-potsdam.de/Home/HanaOncolyzer

“Build on the newest in-memory technology the HANA Oncolyzer is able to analyze even huge amounts of medical data in shortest time”, says Dr. Alexander Zeier, Deputy Chair of EPIC. Research institutes and university hospital support from HANA Oncolyzer by building the basis for a flexible exchange of information about efficiency of medicines and treatments.

In near future, the tumor’s DNA of all cancer patients needs to be analyzed to support specific patient therapies. These analyses result in medical data in amount of multiple terabytes. “These data need to be analyzed regarding mutations and anomalies in real-time”, says Matthias Steinbrecher at SAP’s Innovation Center in Potsdam. As one of the aims the research prototype HANA Oncolyzer was developed at our chair in cooperation with SAP’s Innovation Center in Potsdam. “The ‘heart’ of our development builds the in-memory technology that supports the parallel analysis of million of data within seconds in main memory”, saysMatthieu Schapranow, Ph.D. cand. at the HPI.

and

research activities result in 500.000 or more data points per patient.

and

With the help of a dedicated iPad application medical doctors can access all data mobile at any location anytime.

Animation of the structure of a section of DNA... — Image via Wikipedia

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