Ahmad, N. and Zhang, J. and Brown, P.J. and James, D. and Birch, J.R. and Racher, A.J. and Smales, C.M. (2006) On the statistical analysis of the GS-NS0 cell proteome: Imputation, clustering and variability testing. Biochimica Et Biophysica Acta-Proteins and Proteomics, 1764 (7). pp. 1179-1187. ISSN 1570-9639.
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We have undertaken two-dimensional gel electrophoresis proteomic profiling on a series of cell lines with different recombinant antibody production rates. Due to the nature of gel-based experiments not all protein spots are detected across all samples in an experiment, and hence datasets are invariably incomplete. New approaches are therefore required for the analysis of such graduated datasets. We approached this problem in two ways. Firstly, we applied a missing value imputation technique to calculate missing data points. Secondly, we combined a singular value decomposition based hierarchical clustering with the expression variability test to identify protein spots whose expression correlates with increased antibody production. The results have shown that while imputation of missing data was a useful method to improve the statistical analysis of such data sets, this was of limited use in differentiating between the samples investigated, and highlighted a small number of candidate proteins for further investigation.
|Uncontrolled keywords:||2D-PAGE; proteomic profiling; NS0 cells; imputed values; hierarchical clustering; rank correlation|
|Divisions:||Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics
Faculties > Science Technology and Medical Studies > School of Biosciences
|Depositing User:||Mark Smales|
|Date Deposited:||03 Sep 2008 07:07|
|Last Modified:||15 Oct 2012 12:36|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/6224 (The current URI for this page, for reference purposes)|
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