STAT 35510 Statistical Algorithms for Single-Cell Omics and Related Techniques
Description
Single-cell sequencing is a revolutionary technique that allows the analysis of the genetic information within individual cells, providing unprecedented insights into cellular heterogeneity and diversity. This course aims to offer a comprehensive overview of the cutting-edge quantitative methods employed in analyzing single-cell sequencing data. Designed for graduate students with a statistical/quantitative background, the course requires no prior knowledge of biology or experience in analyzing genetics data. We will start with a gentle introduction to basic biological concepts relevant to understanding the data, coupled with a concise overview of single-cell sequencing technologies such as single-cell RNA-seq, single-cell ATAC-seq, spatial transcriptomics, CITE-seq, Perturb-seq, and more. Then, we will discuss common types of computational analyses, such as visualization, denoising, clustering, trajectory analyses, data integration, transfer learning, and the alignment of multi-omics data. A special emphasis will be placed on exploring deep learning models that have been designed for various tasks analyzing single-cell sequencing data. We will also address statistical considerations that arise, including appropriate distributional assumptions on the data, distribution-free tests, “post-estimation” inference, and causal inference.
Course Materials
| Week | Date | Topic | Slides |
|---|---|---|---|
| 1 | 2024-03-20 | Introduction: basic biology, single-cell omics, scRNA-seq technique | Lecture 1 |
| 2 | 2024-03-25 | scRNA-seq count matrix QC, noise and signal distributions | Lecture 2 |
| 2 | 2024-03-27 | scRNA-seq count matrix QC, noise and signal distributions | Lecture 2 |
| 3 | 2024-04-01 | Dimension reduction, highly variable gene selection and visualization | Lecture 3 |
| 3 | 2024-04-03 | Clustering, cell type annotation | Lecture 4 |
| 4 | 2024-04-08 | scRNA-seq denoising, trajectory analysis | Lecture 5 |
| 4 | 2024-04-10 | trajectory analysis (continued), RNA velocity | Lecture 6 |
| 5 | 2024-04-15 & 17 | RNA velocity (continued), “Post-estimation” inference | Lecture 7 |
| 6 | 2024-04-22 | scRNA-seq data integration and batch correction | Lecture 8 |
| 6 | 2024-04-24 | Reference mapping and automatic cell label transfer | Lecture 9 |
| 7 | 2024-04-29 | scATAC-seq: technology, preprocessing and standard pipeline | Lecture 10 |
| 8 | 2024-05-06 | Multi-omics data integration | Lecture 11 |
| 8 | 2024-05-08 | Spatial transcriptomics technology and spatial domain detection | Lecture 12 |
| 9 | 2024-05-13 & 15 | Identify spatially variable genes, cell type deconvolution, imputation | Lecture 13 |