M14 - Microbiome Data Analysis Boot Camp

Type of Course - Dates - Venue - Description - Target audience - Exam - IMPORTANT: Incorporation in DTP and reimbursement by DS
Course prerequisites - Teacher - Course material - Fees - Enrol

Type of course

 This is an on campus course, with blended learning options.

Dates

Two and a half days during the Easter Holiday 2022: Monday April 4 and Tuesday April 5, 2022, from 9 am to 12 pm and from 1 pm to 4 pm; Wednesday April 6, 2022, from 9 am to 12 pm.
Please note: The deadline for UGent PhD students who want a refund to open a dossier on the DS website (Application for Recognition) is March 4, 2022.

Venue

 To be confirmed

Description

High-throughput sequencing technologies allow easy characterisation of the microbiome, but the data analysis faces many particular issues and difficulties. The data analysis starts with the processing of the raw read counts to turn them into an OTU table. In this process, quality control, filtering and clustering into OTUs are essential steps. Once the OTU count table is ready, the choice of data analysis method depends on the research objectives, but very often a first visual data exploration is performed.

Ordination methods, which often originate from ecology, are well suited for this purpose, but new methods tailored to microbiome data behave better for the overdispersed, zero inflated sequencing data. Formal statistical data analysis methods are required for identifying species that are differentially abundant between several conditions; again there is a need for special methods that can deal with overdispersion, zero-inflation, library size variability and potentially with the compositional nature of microbiome data.

The data analysis becomes even more elaborated for longitudinal data when studying the evolution of the microbiome over time. These analyses may focus on either individual taxa or on diversity of the microbial community (richness, alpha and beta diversity, ...). We focus on 16S rRNA amplicon sequencing data.

The course starts with a brief overview of the processing of raw reads data into an OTU table (including filtering, trimming and clustering into OTUs). We continue with summarizing, exploring and plotting the high dimensional data with ordination and clustering methods. Next we focus on the estimation of diversity (including eveness, richness, beta diversity) and relative abundances, while spending attention on normalization issues. We will discuss several methods for testing for differential abundance and diversity, including methods for longitudinal data analysis.

During the practical exercises we will use R and several packages that will be provided later on.

Target audience

 Scientists who need statistical data analysis for microbiome studies (biologists, statisticians, data scientists, bioinformaticians, …).

Exam

Participants can, if they wish, take part in an exam. Upon succeeding in this test a certificate from Ghent University will be issued.
The exam consists of a take home project assignment. Students are required to write a report by a set deadline.

Incorporation in DTP and reimbursement from DS for UGent PhD students

As a UGent PhD student, to be able to incorporate this 'specialist course' in your Doctoral Training Program (DTP) and get a reimbursement of the registration fee from your Doctoral School (DS) you need to follow strict rules: please take the necessary action in time. The deadline to open a dossier on the DS website (Application for Recognition) for this course is March 4, 2022. Please note that opening a dossier does not mean that you are enrolled. You still need to enrol via the registration form on this site.

Please note: For UGent PhD students it is no longer necessary to participate/succeed in this exam to be able to incorporate the course in the DTP.

Course prerequisites

Participants must have experience with R and a basic knowledge of sequencing, the microbiome and statistics.

Teacher

Foto Olivier ThasProf. dr. Olivier Thas studied bio-engineering at Ghent University and biostatistics at Hasselt University. He obtained his PhD from Ghent University on nonparametric statistics. After his postdoc he was tenured in 2004 as a professor of biostatistics at the Department of Data Analysis and Mathematical Modelling of Ghent University. In 2018 he moved to Hasselt University as a full time professor of biostatistics at the Data Science Institute (DSI), but he is still guest professor at Ghent University. He is also Honorary Professor at the National Institute for Applied Statistics Research Australia (NIASRA) of the University of Wollongong (Australia). He has worked on nonparametric and semiparametric statistical methods for the life sciences. His applied research is particularly targeted to genomics and high-throughput technologies (sequencing, qPCR, dPCR, microbiome studies,… ). He has published more than 120 papers and two monographs.

Course material

Slides, R markdown files

Fees

A different price applies, depending on your main type of employment.

Employment Module 14 Exam
Industry/Private sector1 925 30
Non-profit, government, higher education staff2 695 30
(Doctoral) students, retired, unemployed2 310 30

1 If two or more employees from the same company enrol simultaneously for this course a reduction of 20% on the module price is taken into account starting from the second enrolment.

2 UGent-staff and UGent doctoral students who pay internally via SAP or internal transfer can participate at these special rates.

Enrol for this course