Computational Methods for Single-Cell Data Analysis

1. Front Matter

   Pages i-x

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2. Quality Control of Single-Cell RNA-seq

   • Peng Jiang

   Pages 1-9

3. Normalization for Single-Cell RNA-Seq Data Analysis

   • Rhonda Bacher

   Pages 11-23

4. Analysis of Technical and Biological Variability in Single-Cell RNA Sequencing

   • Beomseok Kim, Eunmin Lee, Jong Kyoung Kim

   Pages 25-43

5. Identification of Cell Types from Single-Cell Transcriptomic Data

   • Karthik Shekhar, Vilas Menon

   Pages 45-77

6. Rare Cell Type Detection

   • Lan Jiang

   Pages 79-89

7. scMCA: A Tool to Define Mouse Cell Types Based on Single-Cell Digital Expression

   • Huiyu Sun, Yincong Zhou, Lijiang Fei, Haide Chen, Guoji Guo

   Pages 91-96

8. Differential Pathway Analysis

   • Jean Fan

   Pages 97-114

9. Pseudotime Reconstruction Using TSCAN

   • Zhicheng Ji, Hongkai Ji

   Pages 115-124

10. Estimating Differentiation Potency of Single Cells Using Single-Cell Entropy (SCENT)

    • Weiyan Chen, Andrew E. Teschendorff

    Pages 125-139

11. Inference of Gene Co-expression Networks from Single-Cell RNA-Sequencing Data

    • Alicia T. Lamere, Jun Li

    Pages 141-153

12. Single-Cell Allele-Specific Gene Expression Analysis

    • Meichen Dong, Yuchao Jiang

    Pages 155-174

13. Using BRIE to Detect and Analyze Splicing Isoforms in scRNA-Seq Data

    • Yuanhua Huang, Guido Sanguinetti

    Pages 175-185

14. Preprocessing and Computational Analysis of Single-Cell Epigenomic Datasets

    • Caleb Lareau, Divy Kangeyan, Martin J. Aryee

    Pages 187-202

15. Experimental and Computational Approaches for Single-Cell Enhancer Perturbation Assay

    • Shiqi Xie, Gary C. Hon

    Pages 203-221

16. Antigen Receptor Sequence Reconstruction and Clonality Inference from scRNA-Seq Data

    • Ida Lindeman, Michael J. T. Stubbington

    Pages 223-249

17. A Hidden Markov Random Field Model for Detecting Domain Organizations from Spatial Transcriptomic Data

    • Qian Zhu

    Pages 251-268

18. Back Matter

    Pages 269-271

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This detailed book provides state-of-art computational approaches to further explore the exciting opportunities presented by single-cell technologies. Chapters each detail a computational toolbox aimed to overcome a specific challenge in single-cell analysis, such as data normalization, rare cell-type identification, and spatial transcriptomics analysis, all with a focus on hands-on implementation of computational methods for analyzing experimental data. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls.

Authoritative and cutting-edge, Computational Methods for Single-Cell Data Analysis aims to cover a wide range of tasks and serves as a vital handbook for single-cell data analysis.

• Experimental data
• Cellular heterogeneity
• Computational methods
• Spatial transcriptomics
• Sequencing
• Rare cell-type identification

• Dana–Farber Cancer Institute and Harvard Chan, School of Public Health, Boston, USA

  Guo-Cheng Yuan

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