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	Master Program in Plant Health Care/Graduate	Language	中/英文	Semester	2026-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	Y

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 Proteomic data analysis: analysis and visualisation Self-learning week self-directed learning
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(Link URL)
include experience courses：N

Please respect the intellectual property rights and use the materials legally.Please respect gender equality.
Update Date, year/month/day：None	Printed Date, year/month/day：2026 / 1 / 09
The second-hand book website：http://www.myub.com.tw/