A holistic statistical modelling approach to quantitative discovery proteomics and metabolomics for underpinning integrative systems medicine

医学研究人员越来越希望了解控制人体功能的基因、代谢物和蛋白质的组成部分之间的复杂相互作用，它们在疾病下如何分解，以及如何避免这种分解。系统生物学领域的出现是为了克服传统还原论方法的缺陷，这种方法已经确定了构建模块本身和许多个体相互作用，但无法推断出这些模块的系统如何一致地行动和反应。系统生物学的应用非常广泛，因为它有望彻底改变我们对植物，动物和人类健康过程的理解。来自生命科学研究的大量证据为转化为系统医学的广泛潜力提供了充分的理由，为医学研究，生物标志物发现和个性化医学提供了支持。通常，系统医学方法从特定生物样本的快照和支持读数或临床数据开始。质谱法是一种用于获得样品快照的普遍技术，它通过电离样品，然后基于产生的电荷测量每个组成化合物的质量和数量来实现。这通常不足以完全分离出样品，因此使用液相或气相色谱的前一阶段来提供初始分离。由于技术和生物学的差异，有必要分析多个样品以获得可靠的读数。此外，蛋白质和代谢物的类别需要不同的样品制备、不同的色谱设置和不同类型的质谱仪器。这些都增加了不同类型的偏差和变化。此外，在生物医学研究中，尽管在实验设计中严格控制了混杂因素，但在典型疾病模型和临床样本中，复杂性和变异性的阶跃变化是明显的。不幸的是，用于蛋白质和代谢物质谱的生物分析和生物信息学方法从根本上依赖于良好控制的系统生物学研究的简化特征，并且在复杂的生物医学样品上表现不佳。由于数据集如此之大，现有的计算技术往往过早地将来自质谱输出的丰富原始数据转换为化合物的符号表示。将来自生物医学样品的蛋白质和代谢物测量的补充整合到用于转化研究的严格统计模型中，临床试验设计以及临床诊断和预后预测依赖于其适当和准确的统计处理。不幸的是，这在当前的方法中是非常有问题的。相反，我们主张所有蛋白质，代谢物和基因表达的实验原始数据应该一起建模，因此在决定数据中是否真的存在化合物或化合物相互作用以及健康和疾病之间的置信度和相对数量时，可以在整个集合中借用统计“力量”。我们建议，通过一个整体模型精确评估整个实验设计中的所有统计变异和偏倚，我们可以显着提高我们对临床环境中质谱实验中潜在变异的理解，并提供一个改进数据分析和解释的途径，最终提高这些技术的灵敏度和稳健性，以利于转化和临床研究。

Medical researchers are increasing wishing to understand the complex interactions between the building blocks of genes, metabolites and proteins that control human function, how they break down under disease and how this breakdown can be averted. The field of systems biology has emerged to overcome the deficiencies of the traditional reductionist approach, which has identified the building blocks themselves and many of the individual interactions but has not been able to deduce how systems of these blocks act and react in unison. The application of systems biology is widespread, as it promises to revolutionise our understanding of healthy processes in plants, animals and humans. This huge body of evidence from life sciences research provides ample justification for the widespread potential in translation to systems medicine, for empowering medical research, biomarker discovery and personalised medicine. Often the systems medicine approach starts with snapshots of a particular biological sample and supporting readings or clinical data. Mass spectrometry is a pervasive technique for gaining a snapshot of a sample, and it does this by ionising the sample and then measuring each constituent compound's mass and quantity based on the resulting charge. This is often not enough to separate out the sample fully and therefore a preceding phase of liquid or gas chromatography is used to provide an initial separation. Due to technical and biological variations, it is necessary to analyse multiple samples to get reliable readings. Furthermore, classes of protein and metabolites require different sample preparation, different chromatography settings and different types of mass spectrometry instrumentation. These all add different kinds of biases and variation. Moreover, in biomedical research, despite stringent control of confounding factors in experimental design, a step-change in complexity and variation is evident within typical disease models and clinical samples. Unfortunately, bioanalytical and bioinformatics methodology for protein and metabolite mass spectrometry is fundamentally reliant on the simplifying characteristics of well-controlled systems biology studies, and performs poorly on complex biomedical samples. Since the datasets are so large, the existing computational techniques tend to convert the rich raw data from mass spectrometry output to a symbolic representation of compounds too early on. The integration of the complement of protein and metabolite measurements from biomedical samples into rigorous statistical models for translational research, clinical trial design and clinical diagnostic and prognostic prediction is reliant on their appropriate and accurate statistical handling. Unfortunately, this is exceptionally problematic with current approaches.We instead advocate all experimental raw data across proteins, metabolites and gene expression should be modelled together, so statistical 'strength' can be borrowed across the collection when making decisions about whether a compound or compound interaction truly exists in the data and at what level of confidence and relative quantity between health and disease. We propose that with a holistic model precisely evaluating all the statistical variation and bias across complete experimental designs, we can significantly increase our understanding of underlying variations in mass spectrometry experiments in the clinical setting and provide an enabling pathway to improving data analysis and interpretation, ultimately leading to enhanced sensitivity and robustness of these technologies to benefit translational and clinical research.