The course is full, with a long waiting list.

Overview

Several statistical methods are available to determine whether a set of genes shows statistically significant differences between two classes (two biological states, two phenotype states, two experimental conditions, etc). Once differentially expressed genes are identified, enrichment analysis (EA) methods can be conducted to identify groups of genes (e.g. particular pathways) that are differentially expressed. One example of such method is the Gene set enrichment analysis (GSEA), which is very popular and frequently used for high-throughput biological data analysis.

This course will cover GSEA and alternative enrichment tools. Since most of their implementations are directly linked to databases that annotate the function of genes in the cell, the course will introduce also GO enrichment analysis.

Audience

Biologists eager to identify a statistically reliable set of genes that are differentially expressed.

Learning objectives

At the end of the course, the participants are expected to be able to:

• identify statistical methods that could be used to pinpoint differentially expressed genes
• determine whether a set of genes shows statistically significant differences between two classes
• apply GSEA using R
• distinguish available enrichment analysis methods
• apply enrichment analysis implementations using R
• do an Ab initio exploration of transcript data
• determine whether the genes of a GO term have a statistically significant difference in expression.

Prerequisites

Knowledge / Competencies

• statistics beginner level (T-test, multiple testing methods)
• R beginner level (Rstudio, install a library, matrix manipulation, read files)

Technical

• a Wi-Fi enabled laptop with latest R and RStudio versions installed. There will be access to the eduroam and guest-unil networks.

Application

The course is full, with a long waiting list.

Registration fees are 50 CHF for academics and 250 CHF for for-profit companies. This includes course content material and coffee breaks.

Deadline for registration and free-of-charge cancellation is set is set to 09/03/2017. Cancellation after this date will not be reimbursed. Please note that participation to SIB courses is subject to our general conditions.

You will be informed by email wether or not you are eligible for this training, based on the result of the survey.

Venue and Time

University of Lausanne, Génopode building, classroom 2020 (Metro M1 line, Sorge station).

The course will start at 9:00 and end around 17:00. Precise information will be provided to the participants on due time.

Additional information

Coordination: Diana Marek, SIB training group

We will recommend 0.25 ECTS credits for this course (given a passed exam at the end of the course).

You are welcome to register to the SIB courses mailing list to be informed of all future courses and workshops, as well as all important deadlines using the form here.

For more information, please contact training@sib.swiss.