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 their area of scientific interest.  We will conclude by exploring the application of single-cell technologies to disease cohorts with spatial 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 analysis is recommended (BST 281, BST 282, or equivalent; may be taken


Students outside of HMS who wish to cross-register into this course must submit a cross-reg petition in my.harvard that summarizes how they have met the course prerequisites. Submitted cross-reg petitions will be reviewed and approved on a first-come first-served basis beginning January 20, 2023. Cross-registration petitions should still be submitted before this date to optimize chances for approval. 

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