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  Course Description
Course Name : Statistical Analysis of Microarrays

Course Code : BT-507

Course Type : Optional

Level of Course : Second Cycle

Year of Study : 1

Course Semester : Fall (16 Weeks)

ECTS : 6

Name of Lecturer(s) : Prof.Dr. ZEYNEL CEBECİ

Learning Outcomes of the Course : Knows statistical analysis of gene expression, gene clustering and other related methods.
Does statistical analysis of microarray data and interpretes the results of analysis
Uses the bioinformatics databases and other related information repositories on gene/genomic researches

Mode of Delivery : Face-to-Face

Prerequisites and Co-Prerequisites : None

Recommended Optional Programme Components : None

Aim(s) of Course : This course aims to teach statistical analysis of data from genomics / microarray experiments with a special reference to gene expressions.

Course Contents : Topics covered in this course include experimental design for microarrays, normalization, quality control and restoration of microarray images, exploratory analysis, and tests for differential expression.

Language of Instruction : Turkish

Work Place : Classroom


  Course Outline /Schedule (Weekly) Planned Learning Activities
Week Subject Student's Preliminary Work Learning Activities and Teaching Methods
1 Introduction to microarrays, basic terms and concepts None None
2 Components of microarrays: Arrays, Probs and Estimation Searching for microarrays in Internet Internet/Computer work
3 Image processing applications Searching for applications of microarray analysis in Internet Internet/Computer work
4 Hybridization, normalization and filtering Reading a tutorial about image processing techniques Reading
5 Descriptive statistics Downloading and installing R software Internet/Computer work
6 Multiple comparison tests Practising a problem on Duncan, Tukey, LSD etc multiple comparison test. Exercise
7 Supervised classification Reading a chapter of textbook about classification and clustering methods Reading
8 Midterm exam Preparing for midterm exam Individual/Group work
9 Gene clustering Reading a textbook chapter about gene clustering Reading
10 Discrimnant analysis Reading a textbook chapter about discrimnant analysis Reading
11 Knowledge and information resources on microarray experiments Searching for bioinformatics rsources on Internet Internet/Computer work
12 Project work and presentation - Case study 1 Preparing for project presentations Internet/Computer work
13 Project work and presentation - Case study 2 Preparing for project presentations Internet/Computer work
14 Project work and presentation - Case study 3 Preparing for project presentations Internet/Computer work
15 Final exam preperation Yarıyıl sonu sınavına hazırlık Individual/Group work
16/17 Final exam Yarıyıl sonu sınavına hazırlık Individual/Group work


  Required Course Resources
Resource Type Resource Name
Recommended Course Material(s)  J van Helden (2002). Statistical analysis of microarray data. http://www.bigre.ulb.ac.be/Users/jvanheld/web_course_microarrays/
 Getting started with microarray analysis in R. http://www.humgen.nl/microarray_analysis_getting_started_with_R.doc
 A Pohl, 2003. Microarray Analysis with R. http://classes.soe.ucsc.edu/bme210/Winter03/lectures/Bio210w03-Lect06-R-Biocond-Intro.pdf
 Using R to draw a Heatmap from Microarray Data. http://www2.warwick.ac.uk/fac/sci/moac/people/students/peter_cock/r/heatmap/
 DNA microarray data analysis using R / Bioconductor. http://www.csc.fi/english/csc/courses/archive/R2007
 Prediction Analysis for Microarrays, for the R package. http://www-stat.stanford.edu/~tibs/PAM/Rdist/index.html
 Microarray analysis exercises 1 - with R. http://jura.wi.mit.edu/bio/education/bioinfo2007/arrays/array_exercises_1R.html
Required Course Material(s)  Statistics in Biotechnology. http://www.intesoft.com/produits/tech/systat/resources/pdf/StatBiotech.pdf
 Microarray - How Does It Work? http://www.unsolvedmysteries.oregonstate.edu/microarray_07
 The GENSCAN Web Server at MIT. http://genes.mit.edu/GENSCAN.html
 
 Microarray Techniques. http://www.cs.columbia.edu/4761/notes07/chapter5.2-microarray.pdf
 Microarrays. The basics. http://faculty.ucr.edu/~tgirke/HTML_Presentations/Manuals/Microarray/arrayBasics.pdf
 Smyth, G. K. (2005). Limma: linear models for microarray data. In: Bioinformatics and Computational Biology Solutions using R and Bioconductor, R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, W. Huber (eds.), Springer, New York, pages 397–420. http://www.statsci.org/smyth/pubs/limma-biocbook-reprint.pdf


  Assessment Methods and Assessment Criteria
Semester/Year Assessments Number Contribution Percentage
    Mid-term Exams (Written, Oral, etc.) 1 40
    Homeworks/Projects/Others 3 60
Total 100
Rate of Semester/Year Assessments to Success 40
 
Final Assessments 100
Rate of Final Assessments to Success 60
Total 100

  Contribution of the Course to Key Learning Outcomes
# Key Learning Outcome Contribution*
1 Evaluates and directs his level of learning in the field of knowledge and skills with his expert level critically. 1
2 Transfers current developments in the field of his work, supporting them with quantitative and qualitative data, systematically to the area outside of the field, written, orally and visually. 2
3 Follows national and international publications and attends social interactions and scientific studies in international level, communicates in at least in one foreign language in order to share studies on international base. 3
4 Uses advanced information and communication technologies along with the required level of their computer software. 5
5 Uses the knowledge in his field for problem solving and / or practical skills in interdisciplinary studies. 4
6 Improves common knowledge accumulation concerning the Biotechnology in the frame of basic theory and practices. 3
7 Is aware of scientific, ethical and social values and handles research process with this frame. 2
8 Handles theories, hypothesis, opinions in the field of Biotechnology with an objective sceptic, logical, analytical manner and evaluates them in critical point of view. 5
9 Gains comprehensive information about natural and applied sciences and its limits with modern techniques and methods applied. 5
10 Improves and increases the knowledge to an expert level in the field of biotechnology 5
11 Understands the interdisciplinary interaction associated with biotechnology. 2
12 Integrates and interprets the knowledge from different disciplines by his expertrise in biology and generate new information 5
13 Analizes the problems encountered in the field of research methods. 2
14 Carries out a study requiring expertise in the field independently. 1
15 Developes new strategic approaches and takes resposibility for analitical solutions for unpredictable complicated problems encountered in applications related to biotechnology. 4
16 Demonstrates leadership in the required environment to solve problems associated with biotechnology. 1
* Contribution levels are between 0 (not) and 5 (maximum).

  Student Workload - ECTS
Works Number Time (Hour) Total Workload (Hour)
Course Related Works
    Class Time (Exam weeks are excluded) 14 4 56
    Out of Class Study (Preliminary Work, Practice) 14 2 28
Assesment Related Works
    Homeworks, Projects, Others 3 10 30
    Mid-term Exams (Written, Oral, etc.) 1 14 14
    Final Exam 1 14 14
Total Workload: 142
Total Workload / 25 (h): 5.68
ECTS Credit: 6