伯恩:提高液体染色学质谱数据的分类,使用批量效应去除神经网络
Simon J Pelletier1, Mickaël Leclercq1, Florence Roux-Dalvai1,2
1Computational Biology Laboratory, CHU de Québec - Université Laval Research Center, Québec City, QC, Canada.
Nature communications
|May 6, 2024
概括
液体染色学质谱学 (LC-MS) 的批量效应阻碍了欧米学研究. 我们开发了批量效果删除神经网络 (BERNN),以改进批量校正和保存生物变异,以便更好地解释数据.
科学领域:
- 蛋白质组学和代谢组学
- 生物信息学和计算生物学
- 分析化学 分析化学
背景情况:
- 液体染色学质谱 (LC-MS) 对于生物样本分析至关重要.
- 来自实验变化的批量效应损害了数据的可复制性和可解释性.
- 现有的批量校正方法往往会减少生物变异性.
研究的目的:
- 引入批量效果删除神经网络 (BERNN) 以在大型LC-MS数据集中进行有效的批量效果校正.
- 为了提高条件之间的样本分类性能,同时确保模型对未见批次的概括.
- 根据现有方法对BERNN进行评估,并评估对生物变异的影响.
主要方法:
- 开发一套批量效果删除神经网络 (BERNN).
- 在不同的LC-MS数据集上培训和测试BERNN模型.
- 对BERNN与其他批量效应校正技术进行比较分析.
- 对分类性能和生物变异性保护的评估.
主要成果:
- 伯恩模型在多个数据集中显示出优异的样本分类性能.
- 最好的分类模型并不总是产生最有效的批量效应去除.
- 批量效应的过度校正导致关键生物信息的丢失.
结论:
- 伯恩提供了一种有希望的方法来减轻LC-MS数据中的批量效应.
- 消除批量效应与保护生物变异性的平衡对于强大的奥米克研究至关重要.
- 优化批量校正策略对于最大限度地提高大规模LC-MS实验的效用至关重要.
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