BIOST 2069 - STATISTICAL METHODS FOR OMICS DATA

Minimum Credits: 2
Maximum Credits: 2
This 2-credit course is a graduate level course to cover popular statistical and computational methods for high-throughput omics data analysis. With the rapid advances of many omics technologies, the course will focus on the fundamental concepts of various topics (e.g. data preprocessing, association analysis, causal mediation analysis, differential analysis, statistical learning, pathway analysis, etc.) and their specific applications to different omics data types (e.g. microarray, next-generation sequencing, single cell sequencing, mass spectrometry, microbiome, etc.). The major target audience is graduate students (master or PhD students) interested in omics data analysis and related research. Through homework problem sets, computer labs and a final project, students train with hands-on materials to understand the methods, implement the algorithms and interpret results in real omics applications.
Academic Career: Graduate
Course Component: Lecture
Grade Component: Grad Letter Grade
Course Requirements: PREQ: BIOST 2049; and BIOST 2037 and BIOST 2043; PLAN: BIOST-MS or BIOST-PHD Students are required to have basic R programming ability, which is provided through the three prerequisite courses.

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