Weill Institute for Neurosciences, Brain and Spinal Injury Center (BASIC), University of California, San Francisco (UCSF), San Francisco, United States; Department of Neurological Surgery, University of California San Francisco (UCSF), San Francisco, United States; Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, United States
Austin Chou
Weill Institute for Neurosciences, Brain and Spinal Injury Center (BASIC), University of California, San Francisco (UCSF), San Francisco, United States; Department of Neurological Surgery, University of California San Francisco (UCSF), San Francisco, United States; Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, United States
J Russell Huie
Weill Institute for Neurosciences, Brain and Spinal Injury Center (BASIC), University of California, San Francisco (UCSF), San Francisco, United States; Department of Neurological Surgery, University of California San Francisco (UCSF), San Francisco, United States; Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, United States
Nikos Kyritsis
Weill Institute for Neurosciences, Brain and Spinal Injury Center (BASIC), University of California, San Francisco (UCSF), San Francisco, United States; Department of Neurological Surgery, University of California San Francisco (UCSF), San Francisco, United States; Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, United States
Pavan S Upadhyayula
School of Medicine, University of California San Diego (UCSD), San Diego, United States
Weill Institute for Neurosciences, Brain and Spinal Injury Center (BASIC), University of California, San Francisco (UCSF), San Francisco, United States; Department of Neurological Surgery, University of California San Francisco (UCSF), San Francisco, United States; Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, United States; San Francisco VA Health Care System, San Francisco, United States
Biomedical data are usually analyzed at the univariate level, focused on a single primary outcome measure to provide insight into systems biology, complex disease states, and precision medicine opportunities. More broadly, these complex biological and disease states can be detected as common factors emerging from the relationships among measured variables using multivariate approaches. ‘Syndromics’ refers to an analytical framework for measuring disease states using principal component analysis and related multivariate statistics as primary tools for extracting underlying disease patterns. A key part of the syndromic workflow is the interpretation, the visualization, and the study of robustness of the main components that characterize the disease space. We present a new software package, syndRomics, an open-source R package with utility for component visualization, interpretation, and stability for syndromic analysis. We document the implementation of syndRomics and illustrate the use of the package in case studies of neurological trauma data.
