Course Catalog 2014-2015
Basic

Basic Pori International Postgraduate Open University

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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
Lectures
Excercises
Assignment



 



 



 
 4 h/week
 2 h/week
 2 h/week



 
SGN-53406 2014-01  

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:

Completion parts must belong to the same implementation

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 
SGN-53406 High-throughput Data Analysis, 5 cr SGN-6176 Microarray Data Analysis, 5 cr  

More precise information per implementation

Implementation Description Methods of instruction Implementation
SGN-53406 2014-01        

Last modified17.12.2014