Title: Treatment of missing values for multivariate statistical analysis of gel-based proteomics data
Authors: Pedreschi Plasencia, Romina ×
Hertog, Maarten
Carpentier, Sebastien
Lammertyn, Jeroen
Robben, Johan
Noben, Jean-Paul
Panis, Bart
Swennen, Rony
Nicolai, Bart #
Issue Date: Apr-2008
Publisher: WILEY-VCH Verlag
Series Title: Proteomics vol:8 issue:7 pages: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
ISSN: 1615-9853
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Division of Mechatronics, Biostatistics and Sensors (MeBioS)
Biochemistry, Molecular and Structural Biology Section
Division of Crop Biotechnics
× corresponding author
# (joint) last author

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