DNA Microarray Analysis

Transcription

1 DNA Microarray Analysis Course # January 2011 DTU Center for Biological Sequence analysis (CBS)

2 Teachers Carsten Friis Assistant professor Chemical Engineer and PhD from DTU Worked with bioinformatics since 1997 and microarray data since 2000 Teaching on this course: R and Exercises in Statistics.

3 Teachers Agnieszka Junker Graduate Engineer and PhD in Bioinformatics from DTU Worked with bioinformatics and microarray data since 2000 Teaching on this course: Dimension reduction, several exercises & assistant course coordinator.

4 Teachers Nils Weinhold & Tune Pers PhD students Specializes in Human Disease Genetics Teaching on this course: Genome Wide Association Nils Tune

5 Additional Teachers Hanne Jarmer 5

6 Additional Teachers Hanne Jarmer Aron C. Eklund 5

7 Additional Teachers Hanne Jarmer Aron C. Eklund Chris Workman 5

8 Additional Teachers Hanne Jarmer Aron C. Eklund Chris Workman Simon Rasmussen 5

9 Additional Teachers Hanne Jarmer Aron C. Eklund Chris Workman Simon Rasmussen Ramneek Gupta 5

10 Additional Teachers Hanne Jarmer Aron C. Eklund Chris Workman Simon Rasmussen Ramneek Gupta Kirstine Belling 5

11 Additional Teachers Hanne Jarmer Aron C. Eklund Chris Workman Simon Rasmussen Ramneek Gupta Kirstine Belling Agata Wesolowska 5

12 Additional Teachers Hanne Jarmer Aron C. Eklund Chris Workman Simon Rasmussen Ramneek Gupta Kirstine Belling Agata Wesolowska Ulrik Plate 5

13 Teachers H. Bjørn Nielsen Associate professor MC in Biology (KU), PhD from DTU Worked with microarray data since 1999 Teaching on this course:, Preprocessing, Statistics, Project work and Course organizer

14 Who are you the students The course has 36 students signed up 2 Bachelor students (2 bioteknologi) 22 Master students (11 System Biology, 11 bioteknologi) 10 PhD students 2 guest students

15 Learning Objectives (L.O.) for the course Account for the individual steps in a general microarray analysis Explain the overall theoretical principles behind the individual steps in the analysis Account for the preconditions and assumptions in the individual steps of the analysis Independently apply a basal microarray analysis Read into and manipulate simple data structures in the statistical computing environment R Formulate problem formulations that may be answered by microarray analysis Answer the problem formulated by the students Collaborate in a group about the project work

16 Learning Objectives (L.O.) for the course That the student learn A) To apply the methods for microarray analysis on real data C) To manipulate simple data structures in the statistical computing environment R B) To formulate interesting hypotheses that can be addressed by microarray data analysis

17 Content Tree main tracks A) Lectures, discussions and exercises on microarray analysis B) Lectures and exercises on R C) Problem formulation and project

18 The Course Tracks time schedule Intro The Pipeline Advanced Analysis Project Period R Array exercises Learning by doing Poster Session Project Intro Problem Formulation Project 3th of January 12th of January 21th of January

19 Track A: The DNA Array Analysis Pipeline Question/hypothesis Design Array or bye a standard array Experimental Design Sample Preparation Hybridization NGS data Expression Index Calculation Image analysis Normalization Comparable Gene Expression Data Mapping Statistical Analysis Fit to Model (time series) Advanced Data Analysis Clustering PCA Gene Annotation Analysis Promoter Analysis Classification Meta analysis Survival analysis Regulatory Network

20 Track C problem formulation Group formation Problem formulation (Finalized, Wednesday week 2) Projects (Initialized Wednesday week 2) Poster session (21th of January, exam)

21 Course web page Course program Lecture Slides Exercises And more