Statistical Analysis of Omics Data

Catalog description

GR This course provides a practical and conceptual introduction to the analysis of high-throughput omics data using R and Bioconductor. Students will learn how to work through the essential preprocessing, quality control, and normalization steps required to reach interpretable biological results. RNA-seq data will serve as the primary framework throughout the course, as many modern tools and statistical methods for omics analysis were originally developed and optimized in the context of transcriptomics. We will cover core statistical concepts behind count-based differential expression analysis, normalization strategies, batch-effect correction, and multiple-testing adjustment. Students will also be introduced to basic machine learning techniques and apply them to high-dimensional data for tasks such as dimension reduction, feature selection, classification, and clustering. In addition to transcriptomics, the course will provide an overview of epigenomic data analysis, including DNA methylation and ATAC-seq. We will then examine statistical approaches for integrating multiple layers of omics data. The course will end with a brief description of the basic steps for analyzing single cell datasets from preprocessing, normalization, and clustering.