95065 - Statistical Methods for Genomics

Learning outcomes

At the end of the course, the student is able to use the most important statistical methods for analyzing high-throughput data in genomics and molecular biology. Also, the student will be able to answer important biology questions by analyzing multiple types of genomic data or aggregating data across multiple studies (meta-analysis).

Course contents

Part I: Methods for Statistical inference
Likelihood methods:Likelihood function and related quantities; Maximum likelihood estimation; Inference (tests and confidence intervals/regions) based on the likelihood

Part II: Statistical models
Linear regression models (LM)
Estimation of LM models
Use of dummy variables: analysis of variance and beyond
Hypothesis testing on regression coefficients
Model construction, variable selection, model diagnostic
Generalised linear regression models: binary and count data

The narrative in both parts will be driven by pertinent examples and applications.

Readings/Bibliography

Lavine, M., Introduction to Statistical Thought. 2013. http://people.math.umass.edu/~lavine/Book/book.html

Teaching methods

Lectures complemented with practical sessions.  As concerns the teaching methods of this course unit, all students must attend Module 1, 2  on Health and Safety online.

Assessment methods

Written exam

Teaching tools

Statistical software will be used as a pedagogical tool. That is, instead of viewing the computer merely as a convenient calculating device, computer calculation and simulation will be used as another way of explaining and helping students understand the underlying concepts. The chosen software is R (R development Core Team, 2006).

Students with disability or specific learning disabilities (DSA) are required to make their condition known to find the best possibile accomodation to their needs.

Office hours

See the website of Monica Chiogna