查看文章

Technological advances have transformed the scientific landscape by enabling comprehensive quantitative measurements, thereby increasingly facilitating data-driven research. This includes genome biology, where many data sets nowadays comprise a collection of heterogeneous high-dimensional data modalities, collected from different assays, tissues, organisms, time points or conditions. An important example are multi-omics data, i.e. data combining measurements from multiple biological layers. Jointly, such data promise to provide a better and more comprehensive understanding of biological processes and complex traits. A critical step to realize these promises is the development of statistical and computational methods that facilitate moving from the data to sound conclusions and biological insights. For this purpose, an integrative analysis that combines information from different data modalities is essential. In this thesis, we propose novel methods that provide a multivariate approach to data integration, and we apply them in the context of multi-omics studies in precision medicine and single cell biology. Given a collection of different data modalities on a set of samples, we aim at addressing two main questions: First, how can we obtain an (unbiased) overview of the main structures that are present in the data, both within and across data modalities? And second, how can we use all data to predict a response of interest and identify relevant features, whilst taking the heterogeneity of the features into account? The first question is important in all exploratory data analysis and leads us to unsupervised methods for data integration. Finding …