Several challenges remain in data-independent acquisition (DIA) data analysis, such as to confidently identify peptides, define integration boundaries, remove interferences, and control false discovery rates. In practice, a visual inspection of the signals is still required, which is impractical with large datasets. We present Avant-garde as a tool to refine DIA (and parallel reaction monitoring) data. Avant-garde uses a novel data-driven scoring strategy: signals are refined by learning from the dataset itself, using all measurements in all samples to achieve the best optimization. We evaluate the performance of Avant-garde using benchmark DIA datasets and show that it can determine the quantitative suitability of a peptide peak, and reach the same levels of selectivity, accuracy, and reproducibility as manual validation. Avant-garde is complementary to existing DIA analysis engines and aims to establish a strong foundation for subsequent analysis of quantitative mass spectrometry data.

数据独立采集 (DIA) 数据分析仍然存在一些挑战，例如自信地识别肽、定义集成边界、消除干扰和控制错误发现率。在实践中，仍然需要对信号进行目视检查，这对于大型数据集来说是不切实际的。我们将 Avant-garde 作为提炼 DIA（和平行反应监测）数据的工具。Avant-garde 采用了一种新颖的数据驱动评分策略：通过从数据集本身学习来细化信号，使用所有样本中的所有测量值来实现最佳优化。我们使用基准 DIA 数据集评估了 Avant-garde 的性能，并表明它可以确定肽峰的定量适用性，并达到与手动验证相同水平的选择性、准确性和重现性。Avant-garde是对现有DIA分析引擎的补充，旨在为后续定量质谱数据分析奠定坚实的基础。