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

被引:9
作者
Ahmad, Norhaiza
Zhang, Jian
Brown, Phillip J.
James, David C.
Birch, John R.
Racher, Andrew J.
Smales, C. Mark [1 ]
机构
[1] Univ Kent, Res Sch Biosci, Canterbury CT2 7NJ, Kent, England
[2] Univ Kent, Inst Math & Stat, Canterbury, Kent, England
[3] Univ Queensland, Sch Engn, Brisbane, Qld 4072, Australia
[4] Lonza Biol Plc, Slough SL1 4DY, Berks, England
来源
BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS | 2006年 / 1764卷 / 07期
关键词
2D-PAGE; proteomic profiling; NS0; cells; imputed values; hierarchical clustering; rank correlation;
D O I
10.1016/j.bbapap.2006.05.002
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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. (c) 2006 Elsevier B.V. All rights reserved.
引用
收藏
页码:1179 / 1187
页数:9
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