Course Description

Foundations for Practical Single Cell RNA-seq Analyses will explore the use and analyses of single cell RNA-seq datasets that will enable precision exploration of the basic unit of life: the cell. This course will examine how single cell RNA-seq experiments are conducted, dive into the computational challenges and solutions to analyze these data, and extract biological meaning from the wealth of information afforded by single cell RNA-seq. It seeks to provide foundational concepts and bioinformatic skills to inquire about the wealth of information (and limitations) of single cell RNA-seq data. In addition to the didactic learning opportunities in the class, there will be practicum lectures and longitudinal project that will provide a hands-on analysis of a published, publicly available single cell dataset. These hands-on analyses will take learners through the computational techniques commonly employed in single cell RNA-seq data analyses.
At the end of the course, you will be able to assess and perform the foundational analyses for a standard single cell RNA-seq dataset using R. You will communicate your findings to peers and evaluate findings of published and peer analyses of single cell RNA-seq data.

Learner Outcomes

• Describe the advantages and limitations of various single cell RNA-seq approaches
• Compare the quality and biases of different single cell RNA-seq datasets generated from the same tissues
• Apply the best practices of single cell RNA-seq analyses to a set of published single cell RNA-seq datasets
• Formulate and conduct an analysis plan to investigate specific hypotheses using single cell RNA-seq data
• Draw biologically meaningful conclusions supported by data and analyses of single cell RNA-seq data

Microcredential(s)

This course applies toward the Bioinformatics Endeavor digital badge.

Textbook Information