Computational Genetics of Big Data

Course Description

The main focus of this course is the application of R programming to the analysis of genetic data, particularly big data sets with multiple measurements. The primary data sets considered will contain RNA-seq and/or other expression data for multiple/all genes in a given set of individuals. This course is for junior or senior students who are thinking of careers at the intersection of life sciences, statistics, and/or computer science, particularly students who are majoring in Genetics. The course fulfills the laboratory requirement for the Genetics major. Students will learn how to acquire such data, format it for R, plot the data, and perform statistical analyses. In addition, students will learn how to simulate data under different hypotheses, and how to perform power and sample size calculations for different statistical methods applied to real or simulated data. Each class consists of a mixture of lecture and computer-based demos and/or exercises, as well as time for students to work on assignments. Guest investigators will frequently make short presentations (in person or by skype) to provide illustrations of how programming and informatics is critical for their research. The course provides the introductory skills needed to conduct basic computational research in the life sciences, including many aspects of computer programming and data analysis. Prerequisites: 01:447:380 or 01:447:384. May be used to fulfill the lab requirement by students majoring in genetics. May be used to fulfill an elective requirement by students majoring in genetics who have already fulfilled the lab requirement with a separate course.

Code

01:447:303

Credits

3

Subject

Genetics

Programs That Include This Course

• Bachelor's Bachelor of Arts in Mathematics
• Bachelor's Major in Genetics
• Bachelor's Major in Genetics and Genomics
• Minor Minor in Data Science (New Brunswick)
• Undergraduate Certificate Undergraduate Certificate in Data Science