|
|
|||||||||||||||||
Course Catalog 2014-2015
SGN-53406 High-throughput Data Analysis, 5 cr |
Additional information
The course is implemented only at UTA (BIO4450 High-throughput Data Analysis 5 ECTS). For details, see https://www10.uta.fi/opas/opintojakso.htm?rid=10895&idx=4&uiLang=en&lang=en&lvv=2014
Suitable for postgraduate studies
Person responsible
Juha Kesseli
Lessons
| Study type | P1 | P2 | P3 | P4 | Summer | Implementations | Lecture times and places |
|
|
|
|
|
|
|
|
Requirements
Final examination, weekly exercises, and an assignment.
Completion parts must belong to the same implementation
Learning Outcomes
After the course, the student can: - compare sequencing and microarray technologies used in high-throughput analysis and choose suitable ones for the analysis required. - explain the principles of measurement technologies covered and how various inherent errors and biases of the measurement techniques affect the analysis. - apply common methods and algorithms to extract information from microarray and sequencing measurements. - discuss the statistical principles underlying the data analysis methods above and identify the benefits and weaknesses of each method. - select suitable algorithms for the analysis and justify the choice. - build data analysis pipelines for microarray and sequencing data analysis.
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | Deep sequencing technologies | ||
| 2. | DNA microarrays | ||
| 3. | Statistical methods for the analysis of high-throughput measurement data | ||
| 4. | Data classification and clustering |
Instructions for students on how to achieve the learning outcomes
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 |
| Lecture slides | Kesseli et al. | Yes | English |
Prerequisites
| Course | Mandatory/Advisable | Description |
| SGN-13006 Introduction to Pattern Recognition and Machine Learning | Mandatory | 1 |
| SGN-52606 Processing of Biosignals | Mandatory | 1 |
| SGN-41006 Signal Interpretation Methods | Advisable | |
| SGN-50006 Introduction to Information Technology for Health and Biology | Mandatory |
1 . One of the two courses should be taken as a prerequisite.
Prerequisite relations (Requires logging in to POP)
Correspondence of content
| Course | Corresponds course | Description |
|
|
|
More precise information per implementation
| Implementation | Description | Methods of instruction | Implementation |