A Computational Framework For Identifying Relevant Cell Types And Specific Regulatory Mechanisms In Schizophrenia Using Data Integration Methods
Abstract
Combining multiple data types can help researchers gain deeper insight into the subject of the study compared to analyzing only one dataset in many cases. Biological researchers can also benefit from these methods of integration. For instance, GWAS data that gives information about variations in the DNA cannot provide us with much information about the specific biological components that are significant in the trait of interest. However, when combined with sequencing data such as chromatin accessibility data or gene expression data, they can help us find the significant biological elements in the trait of interest. In this study, I perform multiple statistical and machine learning-based integration methods on GWAS and sequencing data and find the relevant tissues and cell types in schizophrenia and specific regulatory elements affected by this complex mental disease.