scRNA-seq_online学习材料
本节内容来自Mary Piper等编写的scRNA-seq_online: scRNA-seq Lessons from HCBC (first release)
代码文件下载自GitHub仓库：scRNA-seq_online
在线版本：https://hbctraining.github.io/scRNA-seq_online/
GitHub仓库更新日期：2023年12月13日
This repository has teaching materials for a hands-on Introduction to single-cell RNA-seq analysis workshop. This workshop will instruct participants on how to design a single-cell RNA-seq experiment, and how to efficiently manage and analyze the data starting from count matrices. This will be a hands-on workshop in which we will focus on using the Seurat package using R/RStudio. Working knowledge of R is required or completion of the Introduction to R workshop.
Learning Objectives
- Explain common considerations when designing a single-cell RNA-seq experiment
- Discuss the steps involved in taking raw single-cell RNA-sequencing data and generating a count (gene expression) matrix
- Compute and assess QC metrics at every step in the workflow
- Cluster cells based on expression data and derive the identity of the different cell types present
- Perform integration of different sample conditions
Resources
其他scRNA-seq数据分析课程:
Resources for scRNA-seq Sample Prep:
“Sampling time-dependent artifacts in single-cell genomics studies.” Massoni-Badosa et al.2019
“Dissociation of solid tumor tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses.” O’Flanagan et al. 2020
“Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows.” Denisenko et al. 2020