Course Description

Advanced Topics in Single Cell Analyses will explore the use and analyses of complex single cell experiments designed to precisely answer specific questions using multi-modal, multi-dataset, of multi-species measurements from the basic unit of life: the cell. This course will examine how experimental and computational challenges to multi-faceted measurements of single cell data reveal seemingly unlimited wealth of biological complexity. As with the introductory class, there will be a paired combination of didactic and practicum lectures to provide concepts along with practice. Learners will design a structured longitudinal project to dive deeper into one or more advanced topics discussed on one or more publicly available single cell datasets. These hands-on analyses will take learners through the advanced computational techniques employed in single cell data analyses.
At the end of the course, you will be able to assess and perform advanced analyses for a complexly designed single cell RNA-seq with multi-modal or multi-species datasets 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 advanced techniques in single cell genomic approaches
• Apply introductory analyses of single cell multi-modal analyses to a set of published single cell datasets
• Conduct statistically robust techniques to dissect complex experimental designs using single cell data
• Formulate and conduct an analysis plan to investigate specific hypotheses using single cell RNA-seq or multi-omic data
• Draw biologically meaningful conclusions supported by data and analyses of single cell RNA-seq or multi-omic data

Microcredential(s)

This course applies toward the Bioinformatics Endeavor digital badge.

Textbook Information