Whole-genome Gene Expression Analysis
Microarrays are routinely used in genomic studies to detect changes in mRNA expression levels. These experiments have fundamental statistical and data processing challenges associated with them. This course covers: the statistical aspects of experimental design, biological and technical replicates, preprocessing, quality assessment, parametric and non-parametric statistical tests, multiple-hypothesis testing, P-value correction and false discovery rates, visualization techniques (e.g. heatmaps, volcano plots), and biological significance (e.g. functional annotation, pathways, hypergeometric tests, gene set enrichment).
At the end of the course, students will be able to:
Design, normalize and analyze single- and dual-channel microarray experiments from several platforms including Affymetrix and Agilent.
Gain a working experience for methods of analysis of microarray experiments, from the most basic preprocessing analysis to advanced machine learning, identifying genes or chromosomal regions of interests, and annotation of large genomic data.
Choose methods of analysis for various types of data.