140.688.01
Statistics For Genomics

Location
East Baltimore
Term
3rd Term
Department
Biostatistics
Credit(s)
3
Academic Year
2024 - 2025
Instruction Method
In-person
Class Time(s)
Tu, Th, 1:30 - 2:50pm
Lab Times
Tuesday, 3:30 - 4:30pm (01)
Auditors Allowed
Yes, with instructor consent
Available to Undergraduate
No
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Kasper Hansen
Weiqiang Zhou
Contact Name
Kasper Hansen
Frequency Schedule
Every Year
Prerequisite

Some familarity with the R statistical language will be assumed; a student without any experience in this language can still take the class but will need to set aside additional time to learn R. A suitable background class is 140.776.01 – Statistical Computing

Description
Intended for students with a background in statistics or biology, but not necessarily both.
Introduces statistical genomics with an emphasis on the next generation sequencing, including the single sequencing technology, microbiome sequencing, and bulk RNA sequencing. Covers the key capabilities of the Bioconductor project (a widely used open source software project for the analysis of high-throughput experiments in genomics and molecular biology and rooted in the open source statistical computing environment R). Introduces statistical concepts and tools necessary to interpret and critically evaluate the bioinformatics and computational biology literature. Includes an overview of preprocessing and normalization, batch effects, statistical inference, multiple comparisons. Assumes some familiarity with the R statistical language (a student without any experience in this language can still take the class but will need to set aside additional time to learn R).
Learning Objectives
Upon successfully completing this course, students will be able to:
  1. Describe the basics of how various high-throughput assays works, including microarray, next generation sequencing, microbiome sequencing and single cell RNA sequencing
  2. Critique existing methodology for the analysis of high-throughput biological data
  3. Write R code to import and analyze microarray and next generation sequencing data
Methods of Assessment
This course is evaluated as follows:
  • 40% Homework
  • 60% Project(s)
Instructor Consent
No consent required
Special Comments

Students will also need to attend one hour-long lab session per week (Tuesdays 3:30-4:30).