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On the statistical analysis of the GS-NS0 cell proteome: Imputation, clustering and variability testing

Ahmad, Norhaiza and Zhang, Jian and Brown, Phillip J. and James, David C. and Birch, John R. and Racher, Andrew J. and Smales, C. Mark (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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Official URL: http://dx.doi.org/10.1016/j.bbapap.2006.05.002


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.

Item Type:Article
Uncontrolled Keywords:2D-PAGE, hierarchical clustering, imputed values, NS0 cells, proteomic profiling, rank correlation
ID Code:9046
Deposited By: Nurunnadiah Baharum
Deposited On:27 Jul 2009 03:29
Last Modified:27 Jul 2009 03:29

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