STAT 5810: Analysis of Gene Expression Data for the Biological Sciences
Credits: 3. Contact Hours: Lecture 3.
Introduction to high-throughput technologies for gene expression studies (especially R-sequencing technology): the role of blocking, randomization, and biological and technical replication in the design of gene expression experiments; normalization methods; methods for identifying differentially expressed genes including mixed linear model analysis, generalized linear model analysis, generalized linear mixed model analysis, quasi-likelihood methods, empirical Bayes analysis, and resampling based approaches; procedures for controlling false discovery rate for multiple testing; clustering and classification problems for gene expression data; testing gene categories; emphasis on practical use of methods. Graduation Restriction: May not be used for graduate credit in the Statistics MS and PhD degree programs. Credit in STAT 4160 or STAT 5810, but not both, may be applied toward graduation. (Typically Offered: Spring)