Signal-based image registration and mixed modelling for differential analysis of large scale cross-omics datasets

生物学家越来越希望了解基因、代谢物和蛋白质之间的复杂相互作用，这些基因、代谢物和蛋白质控制着每一个活着的有机体的功能。系统生物学领域的出现是为了克服传统简化论方法的缺陷，传统的简化论方法已经确定了构成要素本身和许多个体相互作用，但无法推断这些要素的系统如何协调行动和反应。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、动物和人类的健康过程的理解，以及它们如何在疾病中分解以及如何避免这种分解。系统生物学的方法通常从特定生物样本的“快照”开始。质谱学是一种普遍存在的获取样品快照的技术，它通过电离样品，然后根据产生的电荷测量每个组成化合物的质量和数量来实现这一点。这通常不足以将样品完全分离出来，因此使用前一阶段的液体或气相色谱来提供初始分离。由于技术和生物上的差异，有必要对样本进行多次分析，以获得可靠的读数。此外，蛋白质、代谢物和金属的类别需要不同的样品制备、不同的层析方法和不同类型的质谱仪。所有这些都增加了不同种类的偏见和变异，使得推断化合物之间的联系变得极其困难，特别是如果化合物来自不同的类别。更糟糕的是，需要许多快照来捕捉正在研究的生物过程的不同角度，而仪器条件本身并不能随着时间的推移而完全重现。所有这些都导致系统生物学成为一门逐步计算的学科。然而，由于数据集如此之大，现有的计算技术往往过早地将丰富的原始数据从质谱学输出转换为化合物的符号表示。相反，我们主张将样本中的所有数据一起建模为原始数据，以便在决定数据中是否真的存在化合物或化合物交互作用以及置信度水平时，可以在整个集合中借用统计“强度”。不幸的是，层析步骤特别多变，因此相应的化合物必须在分析前或分析过程中相互匹配。我们建议直接对原始数据进行这一操作，以便通过试图在每个数据集中孤立地检测化合物来减少遗漏。此外，我们建议，在正确的“混合模型”和对齐的原始数据上，我们可以分离出数据中的系统性偏差，尽管它们的混合相关性令人困惑。这将为样本类之间的相互作用和系统生物学领域的燃料进步提供高质量的证据。

Biologists are increasing wishing to understand the complex interactions between the building blocks of genes, metabolites and proteins that control the function of every living organism. 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, as well as how they break down under disease and how this breakdown can be averted.Often the systems biology approach starts with a 'snapshot' of a particular biological sample. 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 will be necessary to analyse the sample a number of times to get reliable readings. Furthermore, classes of protein, metabolite and metals require different sample preparation, different chromatography approaches and different types of mass spectrometry instrumentation. These all add different kinds of biases and variation which make it extremely challenging to infer links between compounds, especially if the compounds are from different classes. To make matters worse, many snapshots are needed to capture different 'angles' of the biological process under investigation, and the instrumental conditions themselves are not entirely reproducible over time.All this has led systems biology to become a progressively computational discipline. Since the datasets are so large, however, the existing computational techniques tend to convert the rich raw data from mass spectrometry output to a symbolic representation of compounds too early on. We instead advocate all the data across the samples should be modelled together as raw data, 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. Unfortunately, the chromatographic step is particularly variable, so corresponding compounds have to be matched to each other before or during analysis. We propose to do this directly on the raw data so that far less compounds are missed by trying to detect them on each dataset in isolation. Furthermore, we propose that with the right 'mixed model' and on the aligned raw data, we can separate out the systematic biases in the data despite being confounded by their intermixed correlations. This will provide high quality evidence for interactions across sample classes and fuel advancements in the systems biology field.