Single-Cell Technologies and Data Analysis

3 units · Graduate

Recent technological advances have made it possible to obtain genome-wide sequencing data from single cells using high-throughput sequencing, including scRNA-seq, scATAC-seq, scChIP-seq, scHi-C, scDNA-seq, spatial transcriptomics, and scMulti-omics. This course will introduce trainees to the fundamental algorithms and data analysis skills for single-cell omics data modeling and analyses. Students should have a broad overview of the field of bioinformatics; have familiarity with methods to assay gene expression, as well as statistical methods to handle transcriptomics data; familiarity with methods and applications of genome-wide data; and be proficient with the R statistical software environment, as presented in BMI 5750, and applied in BMI 7030 and BMI 7031 (or should be willing to learn that material on their own).