Event box

Course will run 2.5 days in the week of the 19th February and 2.5 days the week of the 5th March.  Times/days to be confirmed.

This module will give the students a working knowledge of recent statistical approaches for analyzing modern genomic and genetic datasets. Students will learn about significance testing for genetic variants using logistic regression, multiple testing correction (using strategies such as Bonferroni and False discovery rate control), quantification of gene expression in RNA-seq data using expectation-maximization to determine ambiguous isoforms, differential expression testing using negative binomial model, and Bayesian network models for gene regulation.

Important please note:

If the course is fully booked please do complete the registration, as then you will be placed on a waiting list. You will be allocated a place if more places become available or if people cancel.

We will make every attempt to accommodate Lancaster University staff and postgraduate research students on our courses. However, if a course becomes fully booked we reserve the right to give priority to students on the MSc in Statistics, MSc in Data Science, and external participants.

Details of course fees.

Cancellation Policy

Registrations are transferable to another course or individual at any time. Full refunds will be given for cancellation 10 or more working days before the course start date. Otherwise the full course fee will be charged.

Accommodation Details 

Monday, February 19, 2018
All Day Event
Postgraduate Statistics Centre
Dr Jo Knight and Dr Frank Dondelinger
Taught Session - For Externals, Staff & Students

Topics covered will include:

Introduction to Molecular Biology.
Introduction to Human Genetics Studies.
Genome wide associations studies (QC, analysis, multiple testing correction, population stratification).
RNA-Seq gene expression analysis.
Differential Gene Expression.
Statistical Models for gene regulation.


Registration is now closed. See the events page for details of future sessions.