这本详细的书提供了最先进的计算方法，以进一步探索由单细胞技术带来的令人兴奋的机会。每一章都详细介绍了一个计算工具箱，旨在克服单细胞分析中的特定挑战，如数据规范化、罕见细胞类型识别和空间转录组分析，所有这些都侧重于分析实验数据的计算方法的实际实现。以高度成功的分子生物学方法系列格式编写的章节包括各自主题的介绍、必要材料和试剂的列表、循序渐进的、易于复制的实验室协议，以及关于故障排除和避免已知缺陷的提示。
权威和前沿的计算方法，单细胞数据分析旨在涵盖广泛的任务，并作为一个重要的手册，为单细胞数据分析。
Experimental data ; Cellular heterogeneity; Computational methods; Spatial transcriptomics Sequencing; Rare cell-type identification;
目录：
Quality Control of Single-Cell RNA-seq Peng Jiang
Normalization for Single-Cell RNA-Seq Data Analysis Rhonda Bacher
Analysis of Technical and Biological Variability in Single-Cell RNA Sequencing Beomseok Kim, Eunmin Lee, Jong Kyoung Kim
Identification of Cell Types from Single-Cell Transcriptomic Data Karthik Shekhar, Vilas Menon
Rare Cell Type Detection Lan Jiang
scMCA: A Tool to Define Mouse Cell Types Based on Single-Cell Digital Expression Huiyu Sun, Yincong Zhou, Lijiang Fei, Haide Chen, Guoji Guo
Differential Pathway Analysis Jean Fan
Pseudotime Reconstruction Using TSCAN Zhicheng Ji, Hongkai Ji
Estimating Differentiation Potency of Single Cells Using Single-Cell Entropy (SCENT) Weiyan Chen, Andrew E. Teschendorff
Inference of Gene Co-expression Networks from Single-Cell RNA-Sequencing Data Alicia T. Lamere, Jun Li
Single-Cell Allele-Specific Gene Expression Analysis Meichen Dong, Yuchao Jiang
- Using BRIE to Detect and Analyze Splicing Isoforms in scRNA-Seq Data Yuanhua Huang, Guido Sanguinetti
Preprocessing and Computational Analysis of Single-Cell Epigenomic Datasets Caleb Lareau, Divy Kangeyan, Martin J. Aryee
Experimental and Computational Approaches for Single-Cell Enhancer Perturbation Assay Shiqi Xie, Gary C. Hon
Antigen Receptor Sequence Reconstruction and Clonality Inference from scRNA-Seq Data Ida Lindeman, Michael J. T. Stubbington
