Comparison of Statistical Tests and Power Analysis for Phosphoproteomics Data.

Advances in protein tagging and mass spectrometry (MS) have enabled generation of large quantitative proteome and phosphoproteome data sets, for identifying differentially expressed targets in case-control studies. Power study of statistical tests is critical for designing strategies for effective target identification and control of experimental cost. Here, we develop a simulation framework to generate realistic phospho-peptide data with known changes between cases and controls. Using this framework, we quantify the performance of traditional t-tests, Bayesian tests, and the ranking-by-fold-change test. Bayesian tests, which share variance information among peptides, outperform the traditional t-tests. Although ranking-by-fold-change has similar power as the Bayesian tests, its type I error rate cannot be properly controlled without proper permutation analysis; therefore, simply relying on the ranking likely brings false positives. Two-sample Bayesian tests considering dependencies between intensity and variance are superior for data sets with complex variance. While increasing the sample size enhances the statistical tests’ performance, balanced controls and cases are recommended over a one-side weighted group. Further, higher peptide standard deviations require higher fold changes to achieve the same statistical power. Together, these results highlight the importance of model-informed experimental design and principled statistical analyses when working with large-scale proteomic and phosphoproteomic data.

蛋白质标记和质谱（MS）技术的进步使得能够生成大量的定量蛋白质组和磷酸化蛋白质组数据集，用于在病例对照研究中识别差异表达的靶点。统计检验的功效研究对于设计有效靶点识别策略和控制实验成本至关重要。在此，我们开发了一个模拟框架，以生成具有病例和对照之间已知变化的真实磷酸肽数据。利用该框架，我们量化了传统t检验、贝叶斯检验和倍数变化排序检验的性能。贝叶斯检验在肽段之间共享方差信息，其性能优于传统t检验。尽管倍数变化排序检验与贝叶斯检验具有相似的功效，但如果不进行适当的置换分析，其I型错误率无法得到恰当控制；因此，仅仅依赖排序可能会带来假阳性结果。考虑强度和方差之间相关性的两样本贝叶斯检验对于具有复杂方差的数据集更为优越。虽然增加样本量可提高统计检验的性能，但建议对照组和病例组保持平衡，而不是一侧加权。此外，较高的肽段标准差需要更高的倍数变化才能达到相同的统计功效。总之，这些结果凸显了在处理大规模蛋白质组学和磷酸化蛋白质组学数据时，基于模型的实验设计和合理的统计分析的重要性。