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Course Catalog 2010-2011
SGN-6176 Microarray Data Analysis, 5 cr |
Person responsible
Reija Autio
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:
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 |
Prerequisite relations (Requires logging in to POP)
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.
Suitable for postgraduate studies