Green Biotec UG Bremen Germany
Microarray Analysis
| Microarray Analysis | |
|---|---|
| Course Description | This course focuses on understanding, designing, and analyzing various platforms of microarrays. It emphasizes transcriptomic data analysis and visualization techniques for handling high-dimensional data generated from organisms, including humans. Students will learn computational approaches, statistical techniques, and biological interpretations essential for analyzing microarray datasets. This course is tailored for students and professionals in biology, medicine, and computational sciences. |
| Recommended Books | 1. DNA Microarray Analysis Using Bioconductor by Jarno Tuimala 2. Statistics and Data Analysis for Microarrays Using R and Bioconductor, Second Edition by Sorin Draghici 3. Advanced Analysis of Gene Expression Microarray Data by Aidong Zhang |
| Course Learning Outcomes | After completing this course, students will: 1. Analyze and interpret different platforms of microarray data using R. 2. Apply computational techniques and algorithms for microarray analysis. 3. Conduct expression microarray data analyses. 4. Extract biological insights from transcriptomic datasets. |
| Assessment System | Quizzes: 10-15% Assignments: 5-10% Midterms: 30-40% End Semester Exam: 40-50% |
| Lecture Plan | ||
|---|---|---|
| S.No. | Description | Quizzes/Assignment |
| 1 | Introduction: Central Dogma of Molecular Biology and Microarray Overview | Quiz 1 |
| 2 | High Throughput Genomic Technologies and cDNA Microarrays | |
| 3 | Introduction to R Programming for Microarray Data Analysis | Assignment 1 |
| 4 | Microarray Platforms: Affymetrix, Illumina, and Agilent Structures | |
| 5 | File Formats, Experimental Designs, and Gene Ontology (GO) Analysis | Quiz 2 |
| 6 | Statistical Techniques: Parametric and Non-Parametric Approaches | |
| 7 | Data Preprocessing: Background Correction, Normalization, and Log Transformation | Assignment 2 |
| 8 | Principal Component Analysis and Quality Control | Quiz 3 |
| 9 | Microarray Data Analysis Using Bioconductor in R and Linux | |
| 10 | Analysis of Illumina Microarrays: One-Color and Two-Color Channels | Assignment 3 |
| 11 | Analysis of Agilent Microarrays: Data Integration and Analysis Techniques | Quiz 4 |
| 12 | Meta-Analysis Techniques for Microarray Datasets | |
| 13 | Case Studies: Insights from Microarray Experiments in Biology and Medicine | Assignment 4 |
| 14 | Challenges and Future Trends in Microarray Analysis | |
| 15 | Project Presentations and Final Feedback | |
