Department of Marine Chemistry and Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 02543, United States.
MIT/WHOI Joint Program in Oceanography/Applied Ocean Science and Engineering, Department of Marine Chemistry and Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 02543, United States.
Anal Chem. 2020 Apr 21;92(8):5724-5732. doi: 10.1021/acs.analchem.9b04804. Epub 2020 Apr 8.
Untargeted metabolomics experiments provide a snapshot of cellular metabolism but remain challenging to interpret due to the computational complexity involved in data processing and analysis. Prior to any interpretation, raw data must be processed to remove noise and to align mass-spectral peaks across samples. This step requires selection of dataset-specific parameters, as erroneous parameters can result in noise inflation. While several algorithms exist to automate parameter selection, each depends on gradient descent optimization functions. In contrast, our new parameter optimization algorithm, AutoTuner, obtains parameter estimates from raw data in a single step as opposed to many iterations. Here, we tested the accuracy and the run-time of AutoTuner in comparison to isotopologue parameter optimization (IPO), the most commonly used parameter selection tool, and compared the resulting parameters' influence on the properties of feature tables after processing. We performed a Monte Carlo experiment to test the robustness of AutoTuner parameter selection and found that AutoTuner generated similar parameter estimates from random subsets of samples. We conclude that AutoTuner is a desirable alternative to existing tools, because it is scalable, highly robust, and very fast (∼100-1000× speed improvement from other algorithms going from days to minutes). AutoTuner is freely available as an R package through BioConductor.
非靶向代谢组学实验提供了细胞代谢的实时快照，但由于数据处理和分析涉及到的计算复杂性，仍然具有挑战性。在进行任何解释之前，必须对原始数据进行处理，以去除噪声并对齐样本之间的质谱峰。这一步需要选择特定于数据集的参数，因为错误的参数会导致噪声膨胀。虽然有几种算法可以自动选择参数，但每种算法都依赖于梯度下降优化函数。相比之下，我们新的参数优化算法 AutoTuner 可以在单个步骤中从原始数据中获取参数估计值，而不是经过多次迭代。在这里，我们测试了 AutoTuner 在与同位素参数优化 (IPO) 的准确性和运行时间的比较，IPO 是最常用的参数选择工具，并比较了处理后特征表参数的影响。我们进行了蒙特卡罗实验来测试 AutoTuner 参数选择的稳健性，发现 AutoTuner 可以从随机样本子集生成相似的参数估计值。我们得出结论，AutoTuner 是现有工具的理想替代方案，因为它具有可扩展性、高度稳健性和非常快的速度（从几天到几分钟，与其他算法相比，速度提高了约 100-1000 倍）。AutoTuner 可作为 BioConductor 中的 R 包免费获得。
