Single-Cell Analysis for Functional Genomics of Disease

2 credits - Spring Term

Single-cell technologies promise unparalleled insights into human biology, but first, these data require a new computational and statistical toolkit.  In this course, we will compare single-cell and bulk data analysis paradigms, and explore new methods to quality control, analyze, and interpret single-cell-RNA-sequencing data.  Students will learn about single-cell technologies and experimental design, build pipelines to process sequencing data, and use R packages for quality control and analysis.  Students will learn about frameworks for interpreting single-cell data - eg., trajectories, differential abundance - and use them to answer biological questions with single-cell datasets in diverse domains.  We will conclude by exploring the application of single-cell technologies to disease cohorts with spatial profiling, reference mapping, and multimodal approaches.  Class activities will include lectures, coding activities, and paper discussions.

Prerequisites: R and command-line programming (BMI 713 or equivalent), basic statistics (BMI 715, or equivalent), familiarity with genomic data  (BST 281, BST 282, or equivalent coursework or research experience; may be taken concurrently) 

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Aparna Nathan

Aparna Nathan, PhD

Lecturer in Biomedical Informatics
Associate Director, Master of Biomedical Informatics (MBI) Program