单细胞数据分析

从本章开始进入单细胞数据分析的学习。这一部分的编排如下：

1. 在接下来的两节（单细胞测序技术介绍和单细胞RNA测序—从raw data到count matrix），简要介绍单细胞测序技术和基本概念

2. 通过学习Seurat官方文档，掌握单细胞数据处理的基本流程和常用函数

3. 在scRNA-seq_online学习材料一章中通过Mary Piper等编写的《scRNA-seq_online: scRNA-seq Lessons from HCBC》以完整案例的形式进一步熟悉单细胞数据处理的每个环节、细节和注意事项

4. 最后在单细胞补充内容中学习单细胞数据处理的其他内容。

Resources

其他scRNA-seq数据分析课程:

• Seurat vignettes

• Seurat cheatsheet

• 《Analysis of single cell RNA-seq data》

• 《ANALYSIS OF SINGLE CELL RNA-SEQ DATA》

• NBIS Workshop: Single Cell RNA-seq Analysis

• 《Single-cell RNA-seq YouTube视频合集》

• Ligand-receptor analysis with CellphoneDB

• Best practices for single-cell analysis across modalities

Resources for scRNA-seq Sample Prep:

• Research protocol分享平台

• “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

Highlighted papers for single-nuclei RNA-seq:

• Single-nucleus and single-cell transcriptomes compared in matched cortical cell types

• A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors