Statistical Methods for Genomic Data
Introduction to genomic data and statistical methodology for its analysis; examples of data may include single-nucleotide polymorphisms or gene expression levels, generated from microarrays or next-generation sequencing. Statistical techniques may include data preprocessing, filtering, normalization, exploratory methods, visualization, dimension reduction, differential expression, generalized linear models, corrections for multiple comparisons, clustering, gene ontology analyses, genome-wide association studies.
- Credit will be granted for only one of STAT 565, STAT 556 (if taken in the same topic).
- May be offered as a joint undergraduate and graduate class.
Graduate course in the Statistics program offered by the Faculty of Graduate Studies.