Karp Natasha A, Lilley Kathryn S
Department of Biochemistry, Cambridge University, Cambridge, UK.
Proteomics. 2009 Jan;9(2):388-97. doi: 10.1002/pmic.200800485.
If biological questions are to be answered using quantitative proteomics, it is essential to design experiments which have sufficient power to be able to detect changes in expression. Sample subpooling is a strategy that can be used to reduce the variance but still allow studies to encompass biological variation. Underlying sample pooling strategies is the biological averaging assumption that the measurements taken on the pool are equal to the average of the measurements taken on the individuals. This study finds no evidence of a systematic bias triggered by sample pooling for DIGE and that pooling can be useful in reducing biological variation. For the first time in quantitative proteomics, the two sources of variance were decoupled and it was found that technical variance predominates for mouse brain, while biological variance predominates for human brain. A power analysis found that as the number of individuals pooled increased, then the number of replicates needed declined but the number of biological samples increased. Repeat measures of biological samples decreased the numbers of samples required but increased the number of gels needed. An example cost benefit analysis demonstrates how researchers can optimise their experiments while taking into account the available resources.
如果要使用定量蛋白质组学来回答生物学问题，设计出有足够效力以检测表达变化的实验至关重要。样本分组是一种可用于减少方差但仍能使研究涵盖生物变异的策略。样本合并策略的基础是生物平均假设，即对样本池进行的测量等于对个体进行的测量的平均值。本研究未发现样本合并引发的系统偏差的证据，且合并样本有助于减少生物变异。在定量蛋白质组学中，首次将两种方差来源解耦，结果发现小鼠脑以技术方差为主，而人脑以生物方差为主。功效分析表明，随着合并个体数量的增加，所需重复次数减少，但生物样本数量增加。对生物样本的重复测量减少了所需样本数量，但增加了所需凝胶数量。一个成本效益分析示例展示了研究人员如何在考虑可用资源的同时优化实验。
