| AJTR Copyright © 2009-All rights reserved. Published by e-Century Publishing Corporation, Madison, WI 53711 |
Am J Translational Res 2011;3(2):180-196
Original Article
ROC-supervised principal component analysis in connection with the
diagnosis of diseases
Jason B. Nikas, Walter C. Low
Department of Neurosurgery, Pharmaco-Neuro-Immunology Program, Graduate Program in Neuroscience,
Department of Integrative Biology and Physiology, Institute for Translational Neuroscience, Center for
Neuroengineering, Medical School, University of Minnesota, Minneapolis, MN, USA.
Received January 12, 2011; Accepted February 1, 2011; Epub February 3, 2011; Published February 15, 2011
Abstract: Principal component analysis (PCA) is a data analysis method that can deal with large volumes of data.
Owing to the complexity and volume of the data generated by today’s advanced technologies in genomics,
proteomics, and metabolomics, PCA has become predominant in the medical sciences. Despite its popularity,
PCA leaves much to be desired in terms of accuracy and may not be suitable for certain medical applications,
such as diagnostics, where accuracy is paramount. In this study, we introduced a new PCA method, one that is
carefully supervised by receiver operating characteristic (ROC) curve analysis. In order to assess its performance
with respect to its ability to render an accurate differential diagnosis, and to compare its performance with that of
standard PCA, we studied the striatal metabolomic profile of R6/2 Huntington disease (HD) transgenic mice, as
well as that of wild type (WT) mice, using high field in vivo proton nuclear magnetic resonance (NMR)
spectroscopy (9.4-Tesla). We tested both the standard PCA and our ROC-supervised PCA (using in each case
both the covariance and the correlation matrix), 1) with the original R6/2 HD mice and WT mice, 2) with unknown
mice, whose status had been determined via genotyping, and 3) with the ability to separate the original R6/2 mice
into the two age subgroups (8 and 12 wks old). Only our ROC-supervised PCA (both with the covariance and the
correlation matrix) passed all tests with a total accuracy of 100%; thus, providing evidence that it may be used for
diagnostic purposes. (AJTR1101001).
Keywords: Diagnostic methods, principal Component Analysis; Receiver Operating Characteristic (ROC) Curve
Analysis; Metabolomics; Nuclear Magnetic Resonance Spectroscopy; Huntington disease
Full Text PDF
Address all correspondence to:
Dr. Jason B. Nikas
Department of Neurosurgery, Medical School
University of Minnesota
4-218 MTRF
2001 Sixth St., SE
Minneapolis, MN 55455, USA
T: 612-625-2868 / F: 612-626-9201
E-mail: nikas001@umn.edu
Original Article
ROC-supervised principal component analysis in connection with the
diagnosis of diseases
Jason B. Nikas, Walter C. Low
Department of Neurosurgery, Pharmaco-Neuro-Immunology Program, Graduate Program in Neuroscience,
Department of Integrative Biology and Physiology, Institute for Translational Neuroscience, Center for
Neuroengineering, Medical School, University of Minnesota, Minneapolis, MN, USA.
Received January 12, 2011; Accepted February 1, 2011; Epub February 3, 2011; Published February 15, 2011
Abstract: Principal component analysis (PCA) is a data analysis method that can deal with large volumes of data.
Owing to the complexity and volume of the data generated by today’s advanced technologies in genomics,
proteomics, and metabolomics, PCA has become predominant in the medical sciences. Despite its popularity,
PCA leaves much to be desired in terms of accuracy and may not be suitable for certain medical applications,
such as diagnostics, where accuracy is paramount. In this study, we introduced a new PCA method, one that is
carefully supervised by receiver operating characteristic (ROC) curve analysis. In order to assess its performance
with respect to its ability to render an accurate differential diagnosis, and to compare its performance with that of
standard PCA, we studied the striatal metabolomic profile of R6/2 Huntington disease (HD) transgenic mice, as
well as that of wild type (WT) mice, using high field in vivo proton nuclear magnetic resonance (NMR)
spectroscopy (9.4-Tesla). We tested both the standard PCA and our ROC-supervised PCA (using in each case
both the covariance and the correlation matrix), 1) with the original R6/2 HD mice and WT mice, 2) with unknown
mice, whose status had been determined via genotyping, and 3) with the ability to separate the original R6/2 mice
into the two age subgroups (8 and 12 wks old). Only our ROC-supervised PCA (both with the covariance and the
correlation matrix) passed all tests with a total accuracy of 100%; thus, providing evidence that it may be used for
diagnostic purposes. (AJTR1101001).
Keywords: Diagnostic methods, principal Component Analysis; Receiver Operating Characteristic (ROC) Curve
Analysis; Metabolomics; Nuclear Magnetic Resonance Spectroscopy; Huntington disease
Full Text PDF
Address all correspondence to:
Dr. Jason B. Nikas
Department of Neurosurgery, Medical School
University of Minnesota
4-218 MTRF
2001 Sixth St., SE
Minneapolis, MN 55455, USA
T: 612-625-2868 / F: 612-626-9201
E-mail: nikas001@umn.edu
 | |  | |  | |  | |  | |  | |  |