Course Details

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Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
7MBG410MICRO ARRAY DATA ANALYSIS3+0+03514.05.2025

 
Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program MOLECULAR BIOLOGY AND GENETICS
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course 1. To provide a comprehensive understanding of the theories and practical applications in microarray data analysis.
2. To develop essential skills in preprocessing, statistically analyzing, and visualizing microarray data.
3. To enable critical interpretation of microarray results and their biological implications.
Course Content This course will provide the theories and applications in data analyses. Topics include general concepts including data preprocessing, feature selection, sampling, using different statistical and machine learning techniques and visualization.
Course Methods and Techniques
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Instructor Dr. Ömer Faruk Bay omerfaruk.bay@agu.edu.tr
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources

Course Category
Mathematics and Basic Sciences %10
Engineering Design %30
Science %40
Health %20

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Quiz/Küçük Sınav 1 % 30
Ödev 1 % 20
Final examination 1 % 50
Total
3
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Yazılı Sınav 1 2 2
F2F Dersi 13 3 39
Ev Ödevi 2 10 20
Okuma 13 2 26
Ders dışı çalışma 13 3 39
Yüz Yüze Ders 13 2 26
Final Sınavı 1 3 3
Total Work Load   Number of ECTS Credits 5 155

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Understand the fundamental principles of microarray technology and the nature of microarray data.
2 Perform essential data preprocessing steps for microarray data, including normalization, background correction, and quality control.
3 Apply various feature selection techniques to identify relevant genes from microarray datasets.
4 Utilize appropriate statistical methods for differential gene expression analysis and clustering of microarray data.
5 Implement basic machine learning techniques for classification and pattern recognition in microarray data.
6 Create informative visualizations to represent and interpret microarray data analysis results.
7 Integrate different analysis steps into a coherent workflow and critically evaluate the outcomes of microarray data analysis.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to Microarray Technology
2 Microarray Experimental Design and Data Acquisition
3 Data Preprocessing I: Background Correction and Quality Control
4 Data Preprocessing II: Normalization
5 Feature Selection I: Filtering and Univariate Methods
6 Statistical Analysis I: Differential Gene Expression
7 Statistical Analysis II: Clustering Techniques
8 Break
9 Active Learning Week
10 Feature Selection II: Multivariate and Embedded Methods
11 Machine Learning I: Classification
12 Machine Learning II: Model Evaluation and Application
13 Pathway and Gene Set Enrichment Analysis
14 Case Studies / Project Work / Advanced Topics
15 Recap

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10
C1 3 4 3 2 3 1 1 1 2 1
C2 3 3 2 5 4 3 3 2 2 2
C3 4 3 2 5 5 4 3 1 2 3
C4 4 3 3 5 5 4 4 2 3 4
C5 4 2 1 5 5 4 4 1 3 4
C6 3 2 1 4 4 3 3 1 3 3
C7 5 3 3 5 5 5 4 2 4 4

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
  https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=75976&lang=en
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