Genomics in Psychoneuroimmunology
Overview
Introduces methods for analyzing transcriptome data, including GLM statistics, FDR and machine-learning approaaches to controlling analytic error rates, and bioinformatic interpretation of differentially expressed genes through Gene Ontology (GO) analysis, transcription control pathway analysis (TELIS), and cell differentiation analysis (TOA/TRA).
Objectives:
R programming language
Raw and normalized transcriptome profiling data results
General linear model statistics and differential gene expression analysis
False discovery rate (FDR) approach to controlling analytic error rates in genomic analysis
Effect size & machine learning approaches to controlling analytic error rates in genomic analysis
Bioinformatic interpretation of differentially expressed genes
Substantive findings in biobehavioral and social genomics
External Instructor(s):
Donald LamkinDates and Availability
Planned Days:
MondayPlanned Terms:
Spring, Fall, WinterCourse Code:
474
Last updated: May 16, 2016 - 13:55
