Computational methods for single-cell data analysis
Author(s)
Bibliographic Information
Computational methods for single-cell data analysis
（Methods in molecular biology / John M. Walker, series editor, 1935）（Springer protocols）
Humana Press, c2019
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Note
Includes bibliographical references and index
Description and Table of Contents
Description
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.
Table of Contents
1. Quality Control of Single-cell RNA-seq Peng Jiang
2. Normalization for Single-cell RNA-seq Data Analysis
Rhonda Bacher
3. Analysis of Technical and Biological Variability in Single-cell RNA Sequencing
Beomseok Kim, Eunmin Lee, and Jong Kyoung Kim
4. Identification of Cell Types from Single-cell Transcriptomic Data
Karthik Shekhar and Vilas Menon
5. Rare Cell Type Detection
Lan Jiang
6. scMCA- A Tool Defines Cell Types in Mouse Based on Single-cell Digital Expression
Huiyu Sun, Yincong Zhou, Lijiang Fei, Haide Chen, and Guoji Guo
7. Differential Pathway Analysis
Jean Fan
8. Differential Pathway Analysis
Jean Fan
9. Estimating Differentiation Potency of Single Cells using Single Cell Entropy (SCENT)
Weiyan Chen and Andrew E Teschendorff
10. Inference of Gene Co-expression Networks from Single-Cell RNA-sequencing Data
Alicia T. Lamere and Jun Li
11. Single-cell Allele-specific Gene Expression Analysis
Meichen Dong andYuchao Jiang
12. Using BRIE to Detect and Analyse Splicing Isoforms in scRNA-seq Data
Yuanhua Huang and Guido Sanguinetti
13. Preprocessing and Computational Analysis of Single-cell Epigenomic Datasets
Caleb Lareau, Divy Kangeyan, and Martin J. Aryee
14. Experimental and Computational Approaches for Single-cell Enhancer Perturbation Assay
Shiqi Xie and Gary C. Hon
15. Antigen Receptor Sequence Reconstruction and Clonality Inference from scRNA-seq Data
Ida Lindeman and Michael J.T. Stubbington
16. A Hidden Markov Random Field Model for Detecting Domain Organizations from Spatial Transcriptomic Data
Qian Zhu
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