CINECA IRIS Institutional Research Information System

Does the airborne pollen spectrum of Alpine sites reflect local vegetation?

Statistics: the essentials

A basic understanding of statistics is essential in analyzing metabolomics data. In particular, the reasons behind common assumptions made in statistical inference, such as normally distributed data, should be understood. Because of the highly multivariate nature of metabolomics data, multiple-testing issues arise and should be dealt with appropriately. Multivariate analysis and visualization methods are easily applied now that they are commonly available in many software packages, but the in...

Study design and preparation

The definition of the research question will also direct the choice of the most suitable experimental design and, consequently, will shape the data analysis plan, which is a critical part of metabolomics. A common type of research question consists of comparing groups of samples, where a group can be defined as a set of biological samples with an important characteristic in common. The last point is particularly critical, and it is also one of the weaknesses of many large-scale metabolomics r...

Mass Spectrometry Data Processing

This chapter focuses on the mass spectrometry data processing workflow. The first step consists of processing the raw MS data, leading to a feature matrix amenable for statistical analysis. The raw MS files contain the list of spectra acquired during the run, and each spectrum contains many pairs. In the field of metabolite fingerprinting, Direct Infusion Mass Spectrometry and Flow Injection Mass Spectrometry can be used to complement chromatography-based MS techniques. To reduce this complex...