Department of Biological Services, Weizmann Institute of Science, Rehovot, Israel.
Proteomics. 2011 Jun;11(12):2565-7. doi: 10.1002/pmic.201100033. Epub 2011 May 18.
Designing an experiment for quantitative proteomic analysis is not a trivial task. One of the key factors influencing the success of such studies is the number of biological replicates included in the analysis. This, along with the measured variation will determine the statistical power of the analysis. Presented is a simple yet powerful analysis to determine the appropriate sample size required for reliable and reproducible results, based on the total variation (technical and biological). This approach can also be applied retrospectively for the interpretation of results as it takes into account both significance (p value) and quantitative difference (fold change) of the results.
设计定量蛋白质组学分析实验并非易事。影响此类研究成功的关键因素之一是分析中包含的生物学重复数量。这与测量的变异性一起决定了分析的统计功效。本文提出了一种简单而强大的分析方法,可根据总变异(技术和生物学)来确定获得可靠和可重复结果所需的合适样本量。该方法还可以应用于结果的回顾性解释,因为它同时考虑了结果的显著性(p 值)和定量差异(倍数变化)。