Design and analysis issues in quantitative proteomics studies.

@article{citeulike:2801097, abstract = {Quantitative proteomics is the comparison of distinct proteomes which enables the identification of protein species which exhibit changes in expression or post-translational state in response to a given stimulus. Many different quantitative techniques are being utilized and generate large datasets. Independent of the technique used, these large datasets need robust data analysis to ensure valid conclusions are drawn from such studies. Approaches to address the problems that arise with large datasets are discussed to give insight into the types of statistical analyses of data appropriate for the various experimental strategies that can be employed by quantitative proteomic studies. This review also highlights the importance of employing a robust experimental design and highlights various issues surrounding the design of experiments. The concepts and examples discussed within will show how robust design and analysis will lead to confident results that will ensure quantitative proteomics delivers.}, address = {Department of Biochemistry, Cambridge University, Cambridge, UK.}, author = {Karp, N. A. and Lilley, K. S. }, citeulike-article-id = {2801097}, doi = {10.1002/pmic.200700683}, issn = {1615-9861}, journal = {Proteomics}, month = {September}, pages = {42--50}, posted-at = {2008-05-15 10:16:49}, priority = {2}, title = {Design and analysis issues in quantitative proteomics studies.}, url = {http://dx.doi.org/10.1002/pmic.200700683}, volume = {7 Suppl 1}, year = {2007} }

TY - JOUR ID - citeulike:2801097 L3 - citeulike-article-id:2801097 TI - Design and analysis issues in quantitative proteomics studies. JF - Proteomics VL - 7 Suppl 1 SP - 42 EP - 50 AD - Department of Biochemistry, Cambridge University, Cambridge, UK. SN - 1615-9861 N2 - Quantitative proteomics is the comparison of distinct proteomes which enables the identification of protein species which exhibit changes in expression or post-translational state in response to a given stimulus. Many different quantitative techniques are being utilized and generate large datasets. Independent of the technique used, these large datasets need robust data analysis to ensure valid conclusions are drawn from such studies. Approaches to address the problems that arise with large datasets are discussed to give insight into the types of statistical analyses of data appropriate for the various experimental strategies that can be employed by quantitative proteomic studies. This review also highlights the importance of employing a robust experimental design and highlights various issues surrounding the design of experiments. The concepts and examples discussed within will show how robust design and analysis will lead to confident results that will ensure quantitative proteomics delivers. AU - Karp, NA AU - Lilley, KS PY - 2007/09// UR - http://dx.doi.org/10.1002/pmic.200700683 ER -