Functional Class Scoring (FCS) is a network-based approach previously demonstrated to be powerful in missing protein prediction (MPP). We update its performance evaluation using data derived from new proteomics technology (SWATH) and also checked for reproducibility using two independent datasets profiling kidney tissue proteome. We also evaluated the objectivity of the FCS p-value, and followed up on the value of MPP from predicted complexes. Our results suggest that (1) FCS [Formula: see text]-values are non-objective, and are confounded strongly by complex size, (2) best recovery performance do not necessarily lie at standard [Formula: see text]-value cutoffs, (3) while predicted complexes may be used for augmenting MPP, they are inferior to real complexes, and are further confounded by issues relating to network coverage and quality and (4) moderate sized complexes of size 5 to 10 still exhibit considerable instability, we find that FCS works best with big complexes. While FCS is a powerful approach, blind reliance on its non-objective [Formula: see text]-value is ill-advised.

中文翻译：

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更深入地研究功能类别评分在蛋白质组学数据中缺失蛋白质预测中的效用

功能类别评分 (FCS) 是一种基于网络的方法，先前已证明在缺失蛋白质预测 (MPP) 方面非常强大。我们使用来自新蛋白质组学技术 (SWATH) 的数据更新其性能评估，并使用两个独立的数据集分析肾组织蛋白质组检查可重复性。我们还评估了 FCS p 值的客观性，并从预测的配合物中跟踪 MPP 的值。我们的结果表明 (1) FCS [公式：见文本]-值是非客观的，并且会受到复杂大小的强烈混淆，(2) 最佳恢复性能不一定位于标准 [公式：见文本]-值截止值, (3) 虽然预测复合物可用于增强 MPP，但它们不如真实复合物，并且被与网络覆盖和质量相关的问题进一步混淆，并且 (4) 大小为 5 到 10 的中等规模的复合体仍然表现出相当大的不稳定性，我们发现 FCS 最适用于大型复合体。虽然 FCS 是一种强大的方法，但盲目依赖其非客观的 [公式：见文本] 值是不明智的。