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

WeekDateTopicSlides
12024-03-20Introduction: basic biology, single-cell omics, scRNA-seq techniqueLecture 1
22024-03-25scRNA-seq count matrix QC, noise and signal distributionsLecture 2
22024-03-27scRNA-seq count matrix QC, noise and signal distributionsLecture 2
32024-04-01Dimension reduction, highly variable gene selection and visualizationLecture 3
32024-04-03Clustering, cell type annotationLecture 4
42024-04-08scRNA-seq denoising, trajectory analysisLecture 5
42024-04-10trajectory analysis (continued), RNA velocityLecture 6
52024-04-15 & 2024-04-17RNA velocity (continued), “Post-estimation” inference in scRNA-seq
Lecture 7