140.688.01 Statistics for Genomics

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:

Introduces statistical genomics with an emphasis on next generation sequencing and microarrays. 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). Also 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. Intended for students with a background in statistics or biology, but not necessarily both. Assumes some familarity 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 microarrays and next generation 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:

Student evaluation is based on data analysis homework assignments and a final project. Students who want to learn the concepts without programming may take the class pass/fail and perform a literature review for a final project.

Instructor Consent:

No consent required