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Course Catalog 2011-2012
SGN-6176 Microarray Data Analysis, 5 cr |
Additional information
Student will learn preprocessing, normalization and analysis methods for data mining of large scale data. In the course, the methods are used for analyzing DNA microarray and next generation sequencing data, but applicable to other types of large scale data as well. Student will learn the basics of microarray and sequencing technologies with several data mining options that are available.
Suitable for postgraduate studies
Person responsible
Reija Autio
Lessons
| Study type | P1 | P2 | P3 | P4 | Summer | Implementations | Lecture times and places |
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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. | Deep sequencing technologies | ||
| 3. | Statistical methods for the analysis of high-throughput measurement data | ||
| 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 | |||
| Lecture slides | Kesseli et al. | English |
Prerequisites
| Course | Mandatory/Advisable | Description |
| SGN-6106 Computational Systems Biology | Advisable |
Prerequisite relations (Requires logging in to POP)
Correspondence of content
There is no equivalence with any other courses
More precise information per implementation
| Implementation | Description | Methods of instruction | Implementation |
| 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 and modern sequencing data, but applicable to other types of large scale data as well. Student will learn the basics of microarray and sequencing technologies with several data mining options. |