Course Description

Save a bundle and receive 30% off the individual course rate when you register for both BIOF 555 and BIOF 556!

Bundle Courses:

BIOF 555 | Foundations for Practical Single Cell RNA-seq Analyses

BIOF 556 | Advanced Topics in Single Cell Analyses

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 foundational 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.

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 advanced 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

When you complete the course successfully, you will be able to:

• Describe the advantages and limitations of various single cell RNA-seq approaches
• Conduct statistically robust techniques to dissect complex experimental designs using single cell data
• 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
• 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)

These courses apply toward the Biomedical Sciences Endeavor and Bioinformatics Endeavor digital badges.

Textbook Information