期刊
JOURNAL OF PROTEOME RESEARCH
卷 9, 期 7, 页码 3608-3620出版社
AMER CHEMICAL SOC
DOI: 10.1021/pr100142m
关键词
feature selection; biomarkers; disease classification; multivariate analysis; chemometrics; proteomics; mass spectrometry; MALDI-TOF; cerebrospinal fluid; multiple sclerosis
资金
- Norwegian Foundation for Health and Rehabilitation
- Kjell Almes Legacy
- Bergen MS Society
- Meltzer foundation
- Norwegian MS Society
- Norwegian Research Council
- Western Norway Regional Health Authority
Mass spectral profiles from cerebrospinal fluid (CSF) are used as input to a novel multivariate approach to select features responsible for the separation of patients with multiple sclerosis (MS) from control groups. Our targeted statistical approach makes it possible to systematically remove features in the spectral fingerprints masking the components expressing the disease pattern. The low molecular weight CSF proteome from 54 patients with MS and a range of other neurological diseases (OND), as well as neurological healthy controls (NHC), is analyzed in replicates using mass spectral profiling. Statistically validated partial least-squares discriminant analysis (PLS-DA) models are created as a first step to separate the groups. Using the group membership as a target, the most discriminatory projection in the multivariate space spanned by the spectral profiles is revealed. From the resulting target-projected component, the spectral regions most significantly contributing to group separation are identified using the nonparametric discriminating variable (DIVA) test together with the so-called selectivity ratio (SR) plot. Our approach is general and can be applied for other diseases and instrumental techniques as well.
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