A multivariate approach to reveal biomarker signatures for disease classification: application to mass spectral profiles of cerebrospinal fluid from patients with multiple sclerosis.
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.
Rajalahti
Tarja
T
Department of Clinical Medicine, University of Bergen, Bergen, Norway. Tarja.Rajalahti@kj.uib.no
Kroksveen
Ann C
AC
Arneberg
Reidar
R
Berven
Frode S
FS
Vedeler
Christian A
CA
Myhr
Kjell-Morten
KM
Kvalheim
Olav M
OM
D015415:Biomarkers / Q000134:cerebrospinal fluid*; D019937:Diagnostic Techniques and Procedures; D006801:Humans; D009103:Multiple Sclerosis / Q000175:diagnosis*; D015999:Multivariate Analysis; D025341:Principal Component Analysis; D040901:Proteomics; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*; D018709:Statistics, Nonparametric
D015415:Biomarkers