UVA Research Computing

Research Computing

Creating innovative solutions for researchers

/tag/r

  • Data Analysis

    Research Computing can help with accessing, preparing, visualizing and analyzing data. We can assist you by implementing your analysis strategy in an appropriate computing language, including R, Python and Matlab. We will work with you to prepare scripts that are are reproducible, efficient and flexible. Manipulation Data analysis generally involves a significant effort to transform, aggregate, subset or otherwise prepare a dataset. That could include dealing with missing values as well as merging or joining multiple datasets. We can help you wrangle your data into a “tidy” format in order to analyze the features and observations relevant to your question.
  • Workshops

    UVA Research Computing provides training opportunities covering a variety of data analysis, basic programming and computational topics. All of the classes listed below are taught by experts and are freely available to UVa faculty, staff and students. Upcoming Workshops DATE WORKSHOP INSTRUCTOR 01/30/20 Using Rivanna from the Command Line Gladys Andino 01/31/20 Fundamentals of Matlab Ed Hall 02/06/20 Parallel Computing with Matlab Ed Hall 02/11/20 Shiny Web Apps in R Christina Gancayco 02/13/20 Software Containers for HPC Environments Ruoshi Sun 02/13/20 Statistical Methods in Matlab Ed Hall 02/18/20 Intro to Image Processing with Fiji/ImageJ Karsten Siller 02/19/20 Moving R to HPC Jackie Huband 02/20/20 High Performance Python Karsten Siller 02/20/20 Optimization Methods in Matlab Ed Hall 02/25/20 Automation of Image Processing with Fiji/ImageJ Karsten Siller 02/26/20 Optimizing R Code Jackie Huband 02/27/20 C/C++ and Fortran on Rivanna Ruoshi Sun 02/27/20 Deep Learning in Matlab Christina Gancayco 03/04/20 Parallelizing R Jackie Huband 03/05/20 Image Processing in Matlab Christina Gancayco 03/18/20 Parallel R with MPI Jackie Huband 03/26/20 Julia on Rivanna Ed Hall View All Upcoming Workshops on CADRE Academy ↗]() – View All Upcoming Workshops
  • epihet

    RC is working with researchers in the Center for Public Health Genomics to write an R package to calculate Relative Proportion of Sites with Intermediate Methylation (RPIM) scores, which represent the epigenetic heterogeneity in a bisulfite sequencing sample. https://github.com/databio/epihet PI: Nathan Sheffield (Center for Public Health Genomics)
  • LOLAweb

    The past few years have seen an explosion of interest in understanding the role of regulatory DNA. This interest has driven large-scale production of functional genomics data resources and analytical methods. One popular analysis is to test for enrichment of overlaps between a query set of genomic regions and a database of region sets. In this way, annotations from external data sources can be easily connected to new genomic data. SOM Research Computing is working with faculty in the UVA Center for Public Health Genomics to implement LOLAweb, an online tool for performing genomic locus overlap annotations and analyses. This project, written in the statistical programming language R, allows users to specify region set data in BED format for automated enrichment analysis.
  • PHACTR1 and Smooth Muscle Cell Behavior

    Coronary artery disease (CAD) is the major cause of morbidity and mortality worldwide. Recent genome wide association studies (GWAS) have revealed more than 50 genomic loci that are associated with increased risk for CAD. However, the pathological mechanisms for majority of the GWAS loci leading to increased susceptibility to this complex disorder are still unclear. RC is working with Redouane Aherrahrou (CPHG) who aims to study the impact of the CAD-associated genetic factors on the cellular and molecular SMC phenotypes. Support for this project has included preparation of scripts for programmatic data analyses, data visualization, statistical modeling, and assistance with use of the Rivanna high-performance computing cluster.
  • simpleCache

    In partnership with researchers in the Center for Public Health Genomics, School of Medicine Research Computing has contributed to the development of a novel package for computationally efficient caching and loading of data in R. simpleCache provides an interface to a series of functions to store and retrieve cached objects, including in the context batch processing or HPC environments. The package further extends base R functionality of saving and loading external representations of objects by enabling caching to pre-defined directories and timed cache operations. RC helped document and develop new functions for the package ahead of its release to the Comprehensive R Archive Network (CRAN).
  • Preinstalled R on Ivy Linux VM

    R Overview R is an open source programming language, used by Data Miners, Scientists, Data Analysts, and Statisticians. It is available under the GNU GPL V2 license from the Comprehensive R Archive Network R can be used for many statistical, modeling, and graphical solutions. It is very Object Oriented in nature and is easily extensible. Running the command line R console Type R at the terminal to launch the R console. Installing packages Our Linux VMs come equipped with R preinstalled. Most major R packages are also installed and further could be installed from CRAN using (from within the R console)
  • Preinstalled R on Ivy Windows VM

    R Overview R is an open source programming language, used by Data Miners, Scientists, Data Analysts, and Statisticians. It is available under the GNU GPL V2 license from the Comprehensive R Archive Network R can be used for many statistical, modeling, and graphical solutions. It is very Object Oriented in nature and is easily extensible. Running Rstudio from the desktop You can start R in a Graphical interface using the RStudio application from the desktop Running the command line R console Type R at the command prompt to launch the R console. Installing packages Our Windows VMs come equipped with R preinstalled.
  • Courses

    In addition to providing free, in-person workshop training, UVA Research Computing staff teach for-credit courses. Below is a selection of courses that members of our group have taught, co-taught or provided guest lectures: BIMS 8382: Introduction to Biomedical Data Science Spring 2017, Spring 2018 This course introduces methods, tools, and software for reproducibly managing, manipulating, analyzing, and visualizing large-scale biomedical data. Specifically, the course introduces the R statistical computing environment and packages for manipulating and visualizing high-dimensional data, covers strategies for reproducible research, and culminates with analysis of data from a real RNA-seq experiment using R and Bioconductor packages.