Work

Reproducible analysis of disease space via principal components using the novel R package syndRomics

Year: 2021

Type: article

Abstract: 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.

Source: eLife

DOI: 10.7554/elife.61812

Authors: Abel Torres-Espín, Austin Chou, J Russell Huie, Nikos Kyritsis, Pavan S Upadhyayula, Adam R Ferguson

Institutions: San Francisco General Hospital, University of California, San Francisco, University of California San Diego, San Francisco VA Health Care System

Language: English

Topic: Statistical Methods and Applications

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,500