Computational Methods for Single-Cell Data Analysis
Editors: Yuan, Guo-Cheng (Ed.)
Free Preview- Includes cutting-edge techniques
- Provides step-by-step detail essential for reproducible results
- Contains key implementation advice from the experts
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- About this book
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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.
- Table of contents (16 chapters)
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Quality Control of Single-Cell RNA-seq
Pages 1-9
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Normalization for Single-Cell RNA-Seq Data Analysis
Pages 11-23
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Analysis of Technical and Biological Variability in Single-Cell RNA Sequencing
Pages 25-43
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Identification of Cell Types from Single-Cell Transcriptomic Data
Pages 45-77
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Rare Cell Type Detection
Pages 79-89
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Table of contents (16 chapters)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Computational Methods for Single-Cell Data Analysis
- Editors
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- Guo-Cheng Yuan
- Series Title
- Methods in Molecular Biology
- Series Volume
- 1935
- Copyright
- 2019
- Publisher
- Humana Press
- Copyright Holder
- Springer Science+Business Media, LLC, part of Springer Nature
- eBook ISBN
- 978-1-4939-9057-3
- DOI
- 10.1007/978-1-4939-9057-3
- Hardcover ISBN
- 978-1-4939-9056-6
- Series ISSN
- 1064-3745
- Edition Number
- 1
- Number of Pages
- X, 271
- Number of Illustrations
- 12 b/w illustrations, 156 illustrations in colour
- Topics
