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 | 2025-01-07 | Introduction: basic biology, single-cell omics, scRNA-seq technique | Lecture 1 |
| 1 | 2025-01-09 | scRNA-seq technique and count matrix QC | Lecture 2 |
| 2 | 2025-01-14 | scRNA-seq noise and signal distributions | Lecture 3 |
| 2 | 2025-01-16 | scRNA-seq analysis workflow, normalization, visualization | Lecture 4 |
| 3 | 2025-01-21 | scRNA-seq clustering and cell type annotation | Lecture 5 |
| 3 | 2025-01-23 | scRNA-seq denoising | Lecture 6 |
| 4 | 2025-01-30 | scRNA-seq trajectory analysis | Lecture 7 |
| 5 | 2025-02-04 | RNA velocity | Lecture 8 |
| 5 | 2025-02-06 | Data integration and batch correction | Lecture 9 |
| 6 | 2025-02-13 | Reference mapping and transfer learning | Lecture 10 |
| 7 | 2025-02-18 | scATAC-seq technology, preprocessing and dimension reduction | Lecture 11 |
| 8 | 2025-02-25 | Single-cell multi-omics integration | Lecture 12 |
| 8 | 2025-02-27 | Spatial transcriptomics and spatial domain detection | Lecture 13 |
| 9 | 2025-03-04 | Identify SVG, cell type deconvolution, imputation for spatial transcriptomics | Lecture 14 |