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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/8262

Title: Treatment of missing values for multivariate statistical analysis of gel-based proteomics data
Authors: Pedreschi, Romina
Hertog, Maarten L. A. T. M.
Carpentier, Sebastien C.
Lammertyn, Jeroen
Robben, Johan
Noben, Jean-Paul
Panis, B.
Swennen, R.
Nicolai, B.M.
Issue Date: 2008
Publisher: WILEY-VCH VERLAG GMBH
Citation: PROTEOMICS, 8(7). p. 1371-1383
Abstract: The presence of missing values in gel-based proteomics data represents a real challenge if an objective statistical analysis is pursued. Different methods to handle missing values were evaluated and their influence is discussed on the selection of important proteins through multivariate techniques. The evaluated methods consisted of directly dealing with them during the multivariate analysis with the nonlinear estimation by iterative partial least squares (NIPALS) algorithm or imputing them by using either k-nearest neighbor or Bayesian principal component analysis (BPCA) before carrying out the multivariate analysis. These techniques were applied to data obtained from gels stained with classical postrunning dyes and from DIGE gels. Before applying the multivariate techniques, the normality and homoscedasticity assumptions on which parametric tests are based on were tested in order to perform a sound statistical analysis. From the three tested methods to handle missing values in our datasets, BPCA imputation of missing values showed to be the most consistent method.
Notes: Katholieke Univ Leuven, BIOSYST MeBioS Div, B-3001 Heverlee, Belgium. Katholieke Univ Leuven, Div Crop Biotech, Louvain, Belgium. Transnatl Univ Limburg, Hasselt Univ, Biomed Res Inst, Diepenbeek, Belgium. Transnatl Univ Limburg, Sch Life Sci, Biomed Res Inst, Diepenbeek, Belgium.Pedreschi, R, Katholieke Univ Leuven, BIOSYST MeBioS Div, Willem Croylaan 42, B-3001 Heverlee, Belgium.romina.pedreschiplasencia@biw.kuleuven.be
URI: http://hdl.handle.net/1942/8262
DOI: 10.1002/pmic.200700975
ISI #: 000254986200004
ISSN: 1615-9853
Category: A1
Type: Journal Contribution
Validation: ecoom, 2009
Appears in Collections: Research publications

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