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Computational Methods for Single-Cell Data Analysis

  • Book
  • © 2019

Overview

Editors:
  1. Guo-Cheng Yuan

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

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  • Includes cutting-edge techniques
  • Provides step-by-step detail essential for reproducible results
  • Contains key implementation advice from the experts

Part of the book series: Methods in Molecular Biology (MIMB, volume 1935)

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Table of contents (16 protocols)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  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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Keywords

About this book

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.

Editors and Affiliations

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

    Guo-Cheng Yuan

Bibliographic Information

  • Book Title: Computational Methods for Single-Cell Data Analysis

  • Editors: Guo-Cheng Yuan

  • Series Title: Methods in Molecular Biology

  • DOI: https://doi.org/10.1007/978-1-4939-9057-3

  • Publisher: Humana New York, NY

  • eBook Packages: Springer Protocols

  • Copyright Information: Springer Science+Business Media, LLC, part of Springer Nature 2019

  • Hardcover ISBN: 978-1-4939-9056-6Published: 14 February 2019

  • eBook ISBN: 978-1-4939-9057-3Published: 13 February 2019

  • Series ISSN: 1064-3745

  • Series E-ISSN: 1940-6029

  • Edition Number: 1

  • Number of Pages: X, 271

  • Number of Illustrations: 12 b/w illustrations, 156 illustrations in colour

  • Topics: Bioinformatics, Cell Biology

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