Course catalogue doctoral education - HT21

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Title Practical course in microarray data analysis: mRNA, DNA-methylation and DNA arrays
Course number 2667
Programme X- Används inte längre - Regenerativ medicin
Language English
Credits 1.5
Date 2015-05-04 -- 2015-05-13
Responsible KI department Institutionen för medicin, Solna
Specific entry requirements (1) Basic knowledge in descriptive statistics. (2) Basic knowledge in R-programming language.
Intended learning outcomes At the end of the course the students should be able to
- Understand the overall theoretical principles behind the individual steps in the analysis of mRNA, DNA-methylation and DNA arrays, including the preconditions and assumptions in the individual steps of the analysis.
- Show an ability to perform independent microarray analysis.
- Design microarray-based experiments for an optimum statistical analysis.
- Perform integrative data analysis of DNA-methylation and mRNA based experiments.
- Analyze the obtained data functionally.
Contents of the course The students will be provided with tools to explore and analyze microarray data sets. With ""example-oriented"" exercises the most important cases and methods under R environment will be reviewed. One will learn new concepts in R including the necessary background to increase ones experience in R by using available documentation and public tools. Specifically the Bioconductor, an open software source for bioinformatics will be used.
Thirdly, students will be provided with tools to integrate their data with public available resources in addition to combine several data types.


1. Introduction to R & array analysis: Bioconductor.
2. Overview of the microarray technology -Theory and Practice.
3. Introduction to array processing:
a. Quality control.
b. Normalization.
c. Batch effect.
4. Analyze mRNA-arrays: Affymetrix, Illumina.
a. Differential Expression Analysis: the identification of genes varying among different conditions.
b. Fold change analysis.
5. Pipelines to analyze DNA-methylation arrays: Nimblegen, Illumina 450K.
a. Estimation of DNA-methylation by arrays.
b. Normalization in DNA-methylation arrays.
c. Differential methylation.
6. Pipelines to analyze genotype arrays: computing odd ratios.
7. Advance tools to analyze microarray data:
a. Distance measures and clustering.
b. Class Discovery (i.e. PCA).
8. Functional analysis of the results:
a. Gene Set Enrichment Analysis: the identification of pathways.
b. Network Analysis.
9. How to become independent in R-Bioconductor.
10. Notes on how to integrate different data types?
Teaching and learning activities Lectures: 30%
Computer Labs: 50%
Project work: 20%
Compulsory elements Attendance is compulsory. In case of absence, lectures can be compensated with extra reading and computing assignments according to instructions from the course leaders.
Examination The examination will take into account two elements:
- Resolution of the exercises during the computer labs. (with support from the lecturers.)
- Project: individually, a data set must be analyzed by the different methods (and supporting the results with plots) shown in the course. The data set can come from the students, or if not a data set will be provided.
Literature and other teaching material [A] About R:
A Beginner's Guide to R. Zuur AF , Ieno EN, Meesters E. Springer.

[B] R and Statistics:
Statistical Computing: An Introduction to Data Analysis using S-Plus. Crawley MJ. Wiley.
A Handbook of Statistical Analyses Using R. Everitt BS, Hothorn T. Chapman and Hall/CRC.

[C] Microarray analysis.
Bioconductor Case Studies, Hahne et al, Springer
Statistics and Data Analysis for Microarrays Using R and Bioconductor, Second Edition, Draghici, CRC Press
Bioinformatics and Computational Biology Solutions Using R and Bioconductor (Statistics for Biology and Health), Gentleman et al, Springer,

[D] There are plenty of free manuals available in Internet. For instance:
Number of students 5 - 15
Selection of students Selection is based on the relevance of the course content for the doctoral project, and the personal motivation included in the course application
More information The course takes place at CMB at KI Solna campus, in the seminar room A216. Entrance Berzelius v 35. The participants are required to bring their own laptop computers with the R software package installed according to the information from the course directors. The course will be conducted between 9 and 13:00 for the following days: 4 ,5 ,6,7,8,11 and 12 of May. The last day (13th May) that the course will start at 9:00 and will finish at 17:00.
Additional course leader The course directors are Dr. David Gomez- Cabrero and Dr. Francesco Marabita, both at the Department of Medicine, Solna.
Latest course evaluation Course evaluation report
Course responsible Matti Nikkola
Institutionen för cell- och molekylärbiologi
Contact person David Gomez-Cabrero
Institutionen för medicin, Solna

Francesco Marabita
Institutionen för onkologi-patologi