利用半监督功能聚类发现亚细胞蛋白质组尼奇
Subcellular proteome niche discovery using semi-supervised functional clustering
AI总结：
FSPmix通过半监督功能聚类方法，利用部分标记蛋白注释预测亚细胞蛋白质定位，提升对非模式生物亚细胞蛋白质组数据的分析与解读能力。
摘要：
细胞内蛋白质的分室化决定了其功能和所维持的代谢过程。各种基于质谱的蛋白质组学方法（亚细胞空间蛋白质组学）现在允许高通量的亚细胞蛋白定位。然而，这些数据的整理、分析和解释仍然具有挑战性，尤其是在非模式生物中，建立可靠的标记蛋白很困难，而且在实验重复和亚细胞分数化受限的背景下。在这里，我们开发了FSPmix，一种半监督功能聚类方法，作为开源的R包实现，它利用部分标记蛋白的注释来从亚细胞空间蛋白质组学数据中预测蛋白质亚细胞定位。该方法明确假设蛋白质特征在亚细胞分数中平滑变化，从而在低信噪比数据情况下实现更稳健的推断。我们将FSPmix应用于一种海洋硅藻的亚细胞蛋白质组学数据集，使我们能够为蛋白质分配概率定位，并发现可能的新蛋白质功能。总的来说，这项工作为更稳健地分析和解释亚细胞蛋白质组学数据奠定了基础，特别是在研究较少的生物体中。
英文摘要：
Intracellular compartmentalization of proteins underpins their function and the metabolic processes they sustain. Various mass spectrometry-based proteomics methods (subcellular spatial proteomics) now allow high throughput subcellular protein localization. Yet, the curation, analysis and interpretation of these data remain challenging, particularly in non-model organisms where establishing reliable marker proteins is difficult, and in contexts where experimental replication and subcellular fractionation are constrained. Here, we develop FSPmix, a semi-supervised functional clustering method implemented as an open-source R package, which leverages partial annotations from a subset of marker proteins to predict protein subcellular localization from subcellular spatial proteomics data. This method explicitly assumes that protein signatures vary smoothly across subcellular fractions, enabling more robust inference under low signal-to-noise data regimes. We applied FSPmix to a subcellular proteomics dataset from a marine diatom, allowing us to assign probabilistic localizations to proteins and uncover potentially new protein functions. Altogether, this work lays the foundation for more robust statistical analysis and interpretation of subcellular proteomics datasets, particularly in understudied organisms.
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