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STAT 992

Multi-Omics Data Analysis

UW Madison, Spring 2023

Instructor: Kris Sankaran (ksankaran@wisc.edu)

Office Hours will be set using this poll.

Time and Location: Tuesdays 11 - 11:50am, 222 Ingraham Hall

Official Syllabus

Course Description

Modern biology increasingly relies on high-throughput data from many sources, often called multi-omics data, to solve complex scientific problems. For example, multi-omics data is central in current efforts to improve cancer treatment [1, 2], understand the factors underlying mental health [3, 4], and respond to climate change [5, 6]. However, managing, exploring, and drawing inferences from these data is notoriously complex. Each modality reflects a different view (e.g., transcriptional, taxonomic, or chemical) of the samples under study, and simply analyzing each source separately fails to support the cross-modality comparisons needed for scientific progress.

By drawing wisely from the statistical toolbox, it is possible to build more useful, integrated portraits of complex systems from multi-omics data. Moreover, with their experience comparing and critiquing data analysis procedures, statisticians have the potential to support the design of accessible and effective multi-omics workflows. This course will help you navigate the literature, giving opportunities to gain hands-on experience with the central data sources and statistical methods in the field. You will develop skill in reading, implementing, and evaluating the types of methods being actively developed for multi-omics data analysis.

Learning Outcomes

By the conclusion of the course, you will be able to,

  1. Design and implement benchmarking studies (using both simulated and real data) to clarify the properties of existing multi-omics data analysis workflows.
  2. Compare and contrast sequencing technologies and study designs widely used in modern multi-omics studies.
  3. Write accessible technical reviews and prepare minimal code demos that illustrate how theoretical advances in statistics can inform practical multi-omics data analysis.
  4. Apply and critique R packages for visualization and modeling of multi-omics data.
  5. Navigate the multi-omics literature, prepare academic peer reviews, and plan well-motivated research projects in the area.