Graduate School of health and Medical Sciences

Statistical methods in bioinformatics
Provider: Faculty of Health and Medical Sciences

Activity no.: 3322-19-00-00
	Enrollment deadline: 08/03/2019

Date and time	08.04.2019, at: 08:00 - 12.04.2019, at: 15:00
Regular seats	40
Course fee	3,600.00 kr.
Lecturers	Claus Thorn Ekstrøm
ECTS credits	3.50

Contact person	Susanne Kragskov Laupstad E-mail address: skl@sund.ku.dk

Enrolment Handling/Course Organiser	PhD administration E-mail address: phdkursus@sund.ku.dk

Aim and content This is a generic course. This means that the course is reserved for PhD students at the Graduate School of Health and Medical Sciences at UCPH. Anyone can apply for the course, but if you are not a PhD student at the Graduate School, you will be placed on the waiting list until enrollment deadline. After the enrolment deadline, available seats will be allocated to the waiting list. The course is free of charge for PhD students at Danish universities (except Copenhagen Business School). All other participants must pay the course fee. Course title Statistical methods in bioinformatics Learning objectives A student who has met the objectives of the course will be able to: Bioinformatics is concerned with the study of inherent structure of biological information and statistical methods are the workhorses in many of these studies. Some of this inherent structure is very obvious and can be observed directly through correlations of patterns in high-dimensional data, while other patterns arise through more complicated underlying relationships. This course covers some of the basic and novel statistical models and methods suitable for analysing high dimensional data - in particular high dimensional data that rely heavily on statistical methods. The course will contain of equal parts theory and applications and consists of six full days of teaching and computer lab exercises. It is the intention that the participants will have a thorough understanding of the statistical methods and are able to apply them in practice after having followed this course. A student who has met the objectives of the course will be able to: 1.Analyse data from a bioinformatics experiment using the methods described below and draw valid conclusions based on the results obtained. 2.Understand the advantages/disadvantages of the methods presents and be able to discuss potential pitfalls from using these methods. 3.Develop new methods that can be used to analyse novel types of bioinformatics data. Content 1.Brief overview of molecular data. Introduction to statistical methods for high-dimensional data, linear models and regularization methods -Big-p small-n problems -Multiple testing techniques (inference correction, false discovery rates, q-values) -The correlation vs. causation and prediction vs. hypothesis differences -Partial least squares, principal component regression 2.Analysis of mapped reads from mRNA data -General assembly -Alignment methods for mRNA data -Poisson methods for expression quantification and transcript distribution 3.Genome-wide association studies -Multiple testing problems -Imputation -Common variants vs rare variants. Sequence Kernel Association Test -Regularization methods, SVM -Enrichment approaches, gene-set analyses, 4.Text and data mining -Unsupervised data mining approaches -Logistic regression models and neural nets 5.Analysis of array data and integrative data analysis -DNA variant calling -Gene expression analyses -Weighted analyses of microarray experiments -Matrix factorization -Combining data from multiple platforms and experiments -Inference methods for combined (and simultaneous) data Participants The course is tailored for Ph.D.-students with experience in mathematics, statistics or bioinformatics, who wish to have more knowledge about the statistical methods underlying the approaches used for common problems in bioinformatics. A basic knowledge of statistics including a little exposure to calculus is expected. However, little or no previous exposure to the topics covered is expected. Students from applied fields are welcome on the course but should expect extra focus on the statistical methodology. Relevance to graduate programmes The course is relevant to PhD students from the following graduate programmes at the Graduate School of Health and Medical Sciences, UCPH: All graduate programmes Language English Form The course will consist of 5 full days with lectures before lunch and hands-on computer exercises after lunch each day. Course director Claus Thorn Ekstrøm, Professor, Section of Biostatistics, Department of Public Health, University of Copenhagen, ekstrom@sund.ku.dk Teachers Claus Thorn Ekstrøm, Professor, Section of Biostatistics, University of Copenhagen. Jan Gorodkin, Professor, Animal Genetics, Bioinformatics and Breeding, University of Copenhagen. Lars Juhl, Professor, Novo Nordic Foundation Center for Protein Research, Disease Systems Biology, University of Copenhagen. Dates 8, 9, 10, 11, 12 April 2019, all days 8-15 Course location CSS Registration Please register before 8 March 2019 Seats to PhD students from other Danish universities will be allocated on a first-come, first-served basis and according to the applicable rules. Applications from other participants will be considered after the last day of enrolment. Note: All applicants are asked to submit invoice details in case of no-show, late cancellation or obligation to pay the course fee (typically non-PhD students). If you are a PhD student, your participation in the course must be in agreement with your principal supervisor.

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