On the statistical analysis of the GS-NS0 cell proteome: Imputation, clustering and variability testing
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 invaria...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Published: |
Elsevier BV
2006
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| Online Access: | http://eprints.utm.my/9046/ |
| _version_ | 1848891799026794496 |
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| author | Ahmad, Norhaiza Zhang, Jian Brown, Phillip J. James, David C. Birch, John R. Racher, Andrew J. Smales, C. Mark |
| author_facet | Ahmad, Norhaiza Zhang, Jian Brown, Phillip J. James, David C. Birch, John R. Racher, Andrew J. Smales, C. Mark |
| author_sort | Ahmad, Norhaiza |
| building | UTeM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T21:03:42Z |
| format | Article |
| id | utm-9046 |
| institution | Universiti Teknologi Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T21:03:42Z |
| publishDate | 2006 |
| publisher | Elsevier BV |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utm-90462009-07-27T03:29:40Z http://eprints.utm.my/9046/ On the statistical analysis of the GS-NS0 cell proteome: Imputation, clustering and variability testing Ahmad, Norhaiza Zhang, Jian Brown, Phillip J. James, David C. Birch, John R. Racher, Andrew J. Smales, C. Mark 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. Elsevier BV 2006 Article PeerReviewed 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 http://dx.doi.org/10.1016/j.bbapap.2006.05.002 10.1016/j.bbapap.2006.05.002 |
| spellingShingle | Ahmad, Norhaiza Zhang, Jian Brown, Phillip J. James, David C. Birch, John R. Racher, Andrew J. Smales, C. Mark On the statistical analysis of the GS-NS0 cell proteome: Imputation, clustering and variability testing |
| title | On the statistical analysis of the GS-NS0 cell proteome: Imputation, clustering and variability testing |
| title_full | On the statistical analysis of the GS-NS0 cell proteome: Imputation, clustering and variability testing |
| title_fullStr | On the statistical analysis of the GS-NS0 cell proteome: Imputation, clustering and variability testing |
| title_full_unstemmed | On the statistical analysis of the GS-NS0 cell proteome: Imputation, clustering and variability testing |
| title_short | On the statistical analysis of the GS-NS0 cell proteome: Imputation, clustering and variability testing |
| title_sort | on the statistical analysis of the gs-ns0 cell proteome: imputation, clustering and variability testing |
| url | http://eprints.utm.my/9046/ http://eprints.utm.my/9046/ http://eprints.utm.my/9046/ |