Analysis of variance–principal component analysis: A soft tool for proteomic discovery

A soft tool for detection of biomarkers in high dimensional data sets has been developed. The tool combines analysis of variance (ANOVA) and principal component analysis (PCA). Covariations are separated using ANOVA into main effects and interaction. The covariances for each effect are combined with...

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Veröffentlicht in:Analytica chimica acta 2005-07, Vol.544 (1), p.118-127
Hauptverfasser: Harrington, Peter de B., Vieira, Nancy E., Espinoza, Jimmy, Nien, Jyh Kae, Romero, Roberto, Yergey, Alfred L.
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container_end_page 127
container_issue 1
container_start_page 118
container_title Analytica chimica acta
container_volume 544
creator Harrington, Peter de B.
Vieira, Nancy E.
Espinoza, Jimmy
Nien, Jyh Kae
Romero, Roberto
Yergey, Alfred L.
description A soft tool for detection of biomarkers in high dimensional data sets has been developed. The tool combines analysis of variance (ANOVA) and principal component analysis (PCA). Covariations are separated using ANOVA into main effects and interaction. The covariances for each effect are combined with the pure error and subjected to PCA. If the main effect is significant compared to the residual error, the first principal component will span this source of variation. This technique avoids rotation of the principal components and when significant the variable loadings are amenable to interpretation. ANOVA–PCA is demonstrated as a tool for optimization of a proteomic assay for biomarkers. Two independent sets of matrix assisted laser desorption/ionization-mass spectra (MALDI-MS) were collected from amniotic fluids. These studies gave consistent biomarkers for premature delivery.
doi_str_mv 10.1016/j.aca.2005.02.042
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subjects Amniotic fluid
Analysis of variance–principal component analysis
Analytical chemistry
ANOVA–PCA
Chemistry
Exact sciences and technology
Hotelling T 2
MALDI-MS
Mass spectrometry
Matrix-assisted laser desorption/ionization
Premature delivery
Proteomic biomarker
Spectrometric and optical methods
title Analysis of variance–principal component analysis: A soft tool for proteomic discovery
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