Omics Data Analysis

Objectives

Explain how an organism functions at the genome level (DNA, RNA, protein, cell).
Identify omics approaches in epidemiology and clinical research.
Analyze associations between a genotype and a trait via a generalized linear model or via measures of allelic association.
Apply multiple testing or post-hoc inference to genomic data.
Analyze genomic data using supervised and unsupervised learning procedures.

Course outline

Data presentation in genetic epidemiology, and omics data in clinical research.
(1) Class Comparison: GWAS, Differential Analysis for expression data – post hoc methods,
(2) Class Discovery: Analysis of DNA copy number data – segmentation, Evaluation indices, Mixture model, Gene network
(3) Class Prediction.

Prerequisites

Probability, inferential statistics, multivariate exploratory statistics, R (1A)
Linear regression, GLM, Supervised learning, Markov chain, Resampling methods (2A)
Process Statistics, Machine Learning (3A)