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

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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.