VIBS-689 Special Topics in Single Cell Analyses
Description of Course
Single cell technologies have made genomic analysis on the single cell level more accessible than ever. Recent development of these technologies, especially single-cell RNA-seq (scRNA-seq), has revolutionized genomic research. This course focuses on the computational biology of scRNA-seq analysis.
- Introduction to single cell technologies (C1 and 10X) and programming basics (R/Matlab) for data analysis (7/2 Tue) Guest lecturer: Andrew Hillhouse
- Getting UMI counts for individual cells (10X cell-ranger and salmon-alevin pipeline) and QC (7/9 Tue) Guest lecturer: John C. Blazier
- Data modeling, normalization and imputation (7/11 Thr) Guest lecturer: Yan Zhong
- Dimension reduction and data visualization (PCA, tSNE and PHATE) (7/16 Tue) Guest lecturer: Guanxun Li
- Clustering analysis, marker gene and cell type identification (7/18 Thr)
- Feature selection, identification of highly variable genes (7/23 Tue)
- Differential expression analyses with scRNA-seq and bulk RNA-seq data (7/25 Thr)
- Pseudotime, trajectory analysis (7/30 Tue)
- Manifold alignment and coupled nonnegative matrix factorization for combining different types of data, e.g., scRNA-seq and scATAC-seq (8/1 Thr)
- Construction of single-cell gene regulatory networks (scGRNs) (8/6 Tue)
- Final exam (8/8 Thr)
Class Notes and Resource Materials
Class notes are distributed in class. There is no required textbook.
Additional readings: you are responsible for additional readings that will be announced in class.