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.

Topics

1. Introduction to single cell technologies (C1 and 10X) and programming basics (R/Matlab) for data analysis (7/2 Tue) Guest lecturer: Andrew Hillhouse
2. Getting UMI counts for individual cells (10X cell-ranger and salmon-alevin pipeline) and QC (7/9 Tue) Guest lecturer: John C. Blazier
3. Data modeling, normalization and imputation (7/11 Thr) Guest lecturer: Yan Zhong
4. Dimension reduction and data visualization (PCA, tSNE and PHATE) (7/16 Tue) Guest lecturer: Guanxun Li
5. Clustering analysis, marker gene and cell type identification (7/18 Thr)
6. Feature selection, identification of highly variable genes (7/23 Tue)
7. Differential expression analyses with scRNA-seq and bulk RNA-seq data (7/25 Thr)
8. Pseudotime, trajectory analysis (7/30 Tue)
9. Manifold alignment and coupled nonnegative matrix factorization for combining different types of data, e.g., scRNA-seq and scATAC-seq (8/1 Thr)
10. Construction of single-cell gene regulatory networks (scGRNs) (8/6 Tue)
11. Final exam (?)

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.

R/Matlab

TAMU Virtual Open Access Lab, https://voal.tamu.edu/

TAMU HPRC OnDemand portal, http://portal.hprc.tamu.edu/

[Link 1] [Link 2] [Link 3] [Link 4] [Link 5]