NCHU Course Outline
Course Name (中) R語言於生物資訊分析應用(6933)
(Eng.) R in Action for Bioinformatic Analysis
Offering Dept Master Program in Plant Health Care
Course Type Elective Credits 2 Teacher Tao-Ho Chang
Department Doctoral Program in Plant Health CarePh.D Language 中/英文 Semester 2025-SPRING
Course Description This course is designed to teach students how to use the R programming language for data analysis in bioinformatics. Through practical case studies, students will master the application of R in various aspects of bioinformatics, including data manipulation, statistical analysis, machine learning techniques, and data visualization. The main content for the course will be as followed:
R Language Fundamentals
Bioinformatics Data Processing
Biological Statistical Analysis
Data Visulisation
Prerequisites
self-directed learning in the course N
Relevance of Course Objectives and Core Learning Outcomes(%) Teaching and Assessment Methods for Course Objectives
Course Objectives Competency Indicators Ratio(%) Teaching Methods Assessment Methods
Develop basic skills in R program
Establishment of Bioinformatic analysis
Expertise the data visulisation
Discussion
Lecturing
Attendance
Assignment
Other
Course Content and Homework/Schedule/Tests Schedule
Week Course Content
Week 1 Course introduction and semester quest of the course
Week 2 Experiment designing in R program
Week 3 Data cleaning in R program
Week 4 Data manipulation in R
Week 5 Statistical analysis in R
Week 6 Graphic language in R: an introduction
Week 7 Basic data visualisation with ggplot2 in R
Week 8 Basic data visualisation with ggplot2 in R
Week 9 Self-learning week
Week 10 Making interactive plot with plotly and ggplot2
Week 11 Bioconductor
Week 12 Transcriptome data analysis: transcriptome data type
Week 13 Transcriptome data analysis: differential expressed genes
Week 14 Transcriptome data analysis: WGCNA analysis, Enrichment analysis, data visulisation
Week 15 Genomic data analysis: genomic data analysis
Week 16 Genomic data analysis: annotation and visualisation
Week 17 Proteomic data analysis: analysis and visualisation
Week 18 Self-learning week
Evaluation
Group discussion participation (30%)
Presentation (20%)
Assignment of practices (50%)
Textbook & other References
Kabacoff, R.I. (2015). R in action: data analysis and graphics 2nd Ed. (USA: Manning publication).
Bioconductor: https://www.bioconductor.org/
Teaching Aids & Teacher's Website
R program and R Studio
Bioconductor
Office Hours
TBD
Sustainable Development Goals, SDGs
include experience courses:N
Please respect the intellectual property rights and use the materials legally.Please respect gender equality.
Update Date, year/month/day:2025/02/11 15:57:26 Printed Date, year/month/day:2025 / 2 / 16
The second-hand book website:http://www.myub.com.tw/