Abstract
Principal component analysis (PCA) is much used in exploring tithe-course biological data sets, but does not distinguish variation between time and subjects. This study proposes a new integrated approach by combining analysis of variance (ANOVA) and three component modeling methods. The former was used to separate the between- and within-subject variation, and the latter represent modeling strategies on a scale moving from commonality to individuality. The proposed approach was applied to a surface-enhanced laser desorption and ionization time of flight mass spectrometry (SELDI-TOF-MS) data set of a serum protein expression time course before and after colon resection. Two common biological processes are identified and individual differences among patients were also detected, and the biological relevance of both is discussed. (C) 2009 Elsevier B.V. All rights reserved.
Original language | English |
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Pages (from-to) | 20-27 |
Number of pages | 8 |
Journal | Analytica Chimica Acta |
Volume | 661 |
Issue number | 1 |
DOIs | |
Publication status | Published - 19-Feb-2010 |
Keywords
- Colon cancer
- Modeling
- Surface-enhanced laser desorption and ionization time of flight mass spectrometry
- Surgery
- MULTILEVEL COMPONENT ANALYSIS
- METABOLIC FINGERPRINTING DATA
- TIME-COURSE ANALYSIS
- ASCA
- PARAFAC2
- TOOL