BE577
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BE577 - Statistical Methods for Omics Data
Course ID
042620
Course Description
This 3-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 supervised and unsupervised learning, and pathway analysis) and their specific applications to different omics data types, including bulk RNA-seq, ChIP-seq, Epigenetics and DNA Methylation, SNP, single cell RNA sequencing, nanopore sequencing, metabolomics, and metagenomics.
Min Units
3
Max Units
3
Repeatable for Credit
No
Grading Basis
GRD - Regular Grades A, B, C, D, E
Career
Graduate
Course Attributes
CE - CL (Cross Listed)
Course Requisites
Basic statistical knowledge recommended.
Cross Listed Courses
May be convened with
Component
Laboratory
Optional Component
No
Component
Lecture
Optional Component
No