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

Abstract
Data availability
Article and author information
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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.

Data availability

This work used already available data at the Open Data Commons for Spinal Cord Injury (odc-sci.org). The data comes from ODC-SCI:26 dataset 474 (https://scicrunch.org/odc-sci/about/odc-sci_26).

The following previously published data sets were used

1. Ferguson AR
2. Irvine K-A
3. Gensel JC
4. Nielson JL
5. Lin A
6. Ly J
7. Segal MR
8. Ratan RR
9. Bresnahan JC
10. Beattie MS
(2018) Cervical (C5), unilateral spinal cord injury with diverse 740 injury modalities, multiple behavioral outcomes, and histopathology
Open Data Common for Spinal Cord Injury (ODC-SCI:26.

https://scicrunch.org/odc-sci/about/odc-sci_26
1. Nielson JL
2. Cooper SR
3. Yue JK
4. Sorani MD
5. Inoue T
6. Yuh EL
7. Mukherjee P
8. Petrossian TC
9. Paquette J
10. Lum PY
11. Carlsson GE
12. Vassar MJ
13. Lingsma HF
14. Gordon WA
15. Valadka AB
16. Okonkwo DO
17. Manley GT
18. Ferguson AR
19. TRACK-TBI Investigators
(2017) Uncovering precision phenotype-biomarker associations in traumatic brain injury using topological data analysis
Plos One.

https://cdn.elifesciences.org/articles/61812/10.1371/journal.pone.0169490

Article and author information

Author details

Abel Torres Espín

Neurological Surgery, University of California, San Francisco, San Francisco, United States

For correspondence
abel.torresespin@ucsf.edu

Competing interests
The authors declare that no competing interests exist.
Austin Chou

Neurological Surgery, University of California, San Francisco, San Francisco, United States

Competing interests
The authors declare that no competing interests exist.
Russell Huie

Neurological Surgery, University of California, San Francisco, San Francisco, United States

Competing interests
The authors declare that no competing interests exist.
Nikolaos Kyritsis

Neurological Surgery, University of California, San Francisco, San Francsico, United States

Competing interests
The authors declare that no competing interests exist.
Pavan S Upadhyayula

School of Medicine, University of California, San Diego, San Diego, United States

Competing interests
The authors declare that no competing interests exist.
Adam Ferguson

Neurological Surgery, University of California, San Francisco, San Francisco, United States

For correspondence
adam.ferguson@ucsf.edu

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0001-7102-1608

Funding

National Institute of Health (NS106899)

Adam Ferguson

National Institute of Health (NS088475)

Adam Ferguson

Veterans Affairs (1I01RX002245)

Adam Ferguson

Veterans Affairs (I01RX002787)

Adam Ferguson

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.