Statistical Methods for Genomic Data

1.5 credits · Graduate

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.