Revolution R in Life Sciences
The bioinformatics field was among the first to widely embrace the R statistics language for data analysis and vizualization—and now across the entire Life Sciences industry, R is at the cutting-edge of analysis and research. With ever-increasing data volumes and model complexity, practitioners are finding that traditional statistics software packages and lower-level languages are often too costly to develop, slow to deploy, and more error-prone than R.
Revolution R excels for certain applications:
- Where high-performance computation and custom graphics are important;
- Where "validation" is required within a regulated industry -- such as clinical trials
- Where custom statistical models offer a competitive advantage; or
- Where “canned” statistical methods must be cutting edge
Revolution Analytics Life Sciences Customers
Bioinformatics, Pharmacometrics & Biostatistics
Revolution R Enterprise from Revolution Analytics enhances the R language for both rapid prototyping and production-grade analysis in large-scale research. It is designed for corporations, government agencies and academic researchers that require the highest levels of performance, reliability and computational power. It includes access to expert technical support -- as well as groundbreaking, easy-to-use tools for parallel and distributed computing with R that can scale out many algorithms across multiple workstations, clusters and grids.
Many of the advanced parallel libraries and 'Big Data' features in Revolution R Enterprise were developed specifically for bioinformatics users.
Applications of R in Life Sciences
Chemometrics: Linear and nonlinear regression; Curve resolution; Clustering; Self-Organizing Maps; Calibration; Cellular Automata; Thermodynamics; Spectroscopy; Mass Spectrometry.
Medical Image Analysis: Data IO: DICOM, ANALYZE and NIfTI; Magnetic Resonance Imaging: DTI, DCE-MRI; General image processing; Positron Emission Tomography
Pharmacokinetics: non-compartmental analysis; compartment models; other nonlinear models; nonlinear mixed-effects models; Bayesian estimation; dose response curves; panel charts.
Statistical Genetics: Population genetics; Phylogenetics; Linkage; QTL mapping; Association; Linkage disequilibrium and haplotype mapping; Genome-Wide Association Studies; Multiple testing and Importing Sequence data.
Survival Analysis: Left, right, and interval censoring; Kaplan-Meier estimation; hazard estimation; survival curve comparison; Cox models; Parametric proportional hazard models; Multistate models; Relative survival; Multivariate survival; Bayesian methods.