Using statistics and machine learning to create a new metabolomics fragmentation spectra resolver

非靶向代谢组学实验旨在鉴定组成特定样品的小分子，例如，血液尿液样品通过质谱仪以多种方式扫描样品，以帮助我们确定可以找到哪些代谢物。识别这些代谢物可用于临床试验，疾病诊断和进展以及各种其他应用。有多种选择扫描的方法，以最佳地收集有关样品的信息，但在一种特定的方法中，DIA，我们经常在一次扫描中看到来自多种代谢物的离子碎片。为了将单个碎片光谱分配给样品中的代谢物，我们必须计算出每次扫描中的哪个碎片属于每个观察到的代谢物。该项目将利用最近创建的虚拟质谱仪ViMMS，结合机器学习和统计学的数据分析方法，例如，LASSO，随机森林，预测扫描和代谢物之间的关系。将对这些方法进行评估，并对数据提取方法进行优化，以测试这些方法是否优于当前最先进的方法。在项目中，该方法将被扩展到处理多个相同然后不同样本的情况

Untargeted metabolomics experiments aim to identify the small molecules that make up a particular sample, e.g., blood, urine. The sample is put through a mass spectrometer which scans the sample in multiple ways to help us work out what metabolites can be found. Identifying these metabolites can be useful for clinical trials, disease diagnosis and progression, and various other applications. There are various ways of choosing the scans in order to optimally collect information about the sample, but in one particular method, DIA, we often see ion fragments from multiple metabolites in a single scan. In order to assign individual fragmentation spectra to metabolites within the sample we must work out which of the fragments in each of the scans belongs to each observed metabolite. This project will make use of a recently created virtual mass spectrometer, ViMMS, combined with data analytics methods from machine learning and statistics, e.g., LASSO, random forest, to predict the relationship between the scans and the metabolites. The methods will be evaluated, and data extraction methods optimised in order to test whether these methods can outperform current state of the art methods. Over the project, the method will be extended to deal with case of multiple identical and then different samples