Image of cells

Date: Wednesday, July 21st, 2021

Time: 2:00PM - 3:30PM (EDT) 

Instructors: Tanya Karagiannis, MS and Eric Reed, PhD

Learning Level: Fundamental

Primary Audience: Researchers, PIs, Postdoctoral and Graduate Students, Other Study Team Members

Prerequisite: Basic statistical knowledge is required. Computational experience will be helpful, but is not required.

Skills Domain: Data Management and Informatics, Scientific Concepts and Research Design

What are methods for performing common workflows on scRNAseq data to characterize sub-populations of cell profiles?

Single-cell RNA sequencing (scRNA-seq) allows for transcriptome-wide profiling of individual cells present in a tissue sample. While conceptually similar, scRNSeq and “bulk” RNAseq projects differ so greatly in their overall study design, goals, and statistical caveats that their analytical investigation is distinct. In this session, we will introduce methods for performing common workflows on scRNAseq data to characterize sub-populations of cell profiles, including: data preprocessing and normalization, dimensionality reduction, clustering, and visualization. This course is part of a series of trainings in biomedical data science offered by the Center for Quantitative Methods and Data Science, Tufts Clinical and Translational Science Institute, and Tufts University Data Intensive Studies Center. For the full list of trainings in this series, please visit the Center for Quantitative Methods and Data Science webpage. Basic statistical knowledge is required. Computational experience will be helpful, but is not required.

Dates: Wednesday December 9th 12pm-1:00pm  
Location: Zoom  
 This Course is Free