Course Catalog 2009-2010 Basic Basic Pori International Postgraduate Open University |Degrees|     |Study blocks|     |Courses|

Person responsible

Reija Autio

Implementations

No implementations

Requirements

Final examination, weekly exercises, and an assignment
Completion parts must belong to the same implementation

Learning outcomes

After the course, student can: - list the most common microarrays used in high-throughput analysis, - interpret the results of gene expression microarrays, - apply commonly used methods for analyzing microarray data, - compare various methods used in microarray data analysis, - build workflows for microarray data analysis.

Content

Content	Core content	Complementary knowledge	Specialist knowledge
1.	DNA Microarray experiments
2.	Statistical methods for large scale data analysis.
3.	Design and analysis of microarray experiments.
4.	Data classification and clustering.

Evaluation criteria for the course

Final examination, presence in exercises, and assignment.

Assessment scale:

Numerical evaluation scale (1-5) will be used on the course

Partial passing:

Completion parts must belong to the same implementation

Study material

Type	Name	Author	ISBN	URL	Edition, availability, ...	Examination material	Language
Book	Bioinformatics and Computational Biology Solutions Using R and Bioconductor	Gentleman, Carey, Huber, Irizarry and Dudoit					English
Book	DNA Microarray Data Analysis	Tuimala and Laine	952-5520-11-0				English

Prerequisites

Correspondence of content

There is no equivalence with any other courses

Additional information

Student will learn many preprocessing, normalization and analysis methods for data mining of large scale data. In the course, the methods are used for analyzing DNA microarray data, but applicable to other types of large scale data as well. Student will learn the basics of microarray technology with several data mining options.

Last modified	24.02.2010
Modifier	Reija Autio