Arthritis and Clinical Immunology (ACI) Cores

Oklahoma Medical Research Foundation

Data Analytics

Data Analytics/Data Visualization

Data analysts in the Translational Informatics Group support the statistical analysis of clinical research and  biostatistical/computational analysis of basic research project from investigators in the Arthritis and Clinical Immunology group. They also assist with the visualization of data analyzed to better present these results in publications/presentations/reports.  Some of the staff in this group have more experience in clinical trials support and clinical data analysis .  Others in the group have more biostatistics, bioinformatics and computational background to support the statistical analysis of basic research datasets, as well as the development of analysis pipelines for high-throughput, high-dimensional data.  Both types of Data Scientists in this group can help with the consultation and development of data analysis plans, power analyses for research projects.  Because they are tightly integrated with the Informatics groups, they are able to work in conjunction with those groups as well as the cores (clinics, biorepository, human phenotyping) to effectively curate the data types and actionable data to address specific questions using advanced statistical methods.

Statistical Analysis Platforms  and Visualization Tools Supported:

  1. R  (bioconductor packages of all types, basis statistics, graphics, clustering/classification, machine learning, )
  2. SPSS
  3. SAS
  4. SpotFire (interactive multidimensional visualization and advanced statistics, including machine learning)
  5. CytoBank (high-dimensional data analysis of cytometry and other single cell data) on Amazon Web Services Computational Cluster.
  6. Ingenuity Pathway Analysis (IPA)

Computational Resources:

  1. OMRF Computational Cluster,  (storage array and object storage for small and NGS focused analyses)
  2. Google Cloud –Compute Platform  (GCP),  flexible customizable  compute and storage configurations for ultra-high dimensional data, all forms of Python and R-based single-cell transcriptomics, genomics and machine learning  based analyses supported.