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.
Experimental data Cellular heterogeneity Computational methods Spatial transcriptomics Sequencing Rare cell-type identification
Editors and affiliations
- DOI https://doi.org/10.1007/978-1-4939-9057-3
- Copyright Information Springer Science+Business Media, LLC, part of Springer Nature 2019
- Publisher Name Humana Press, New York, NY
- eBook Packages Springer Protocols
- Print ISBN 978-1-4939-9056-6
- Online ISBN 978-1-4939-9057-3
- Series Print ISSN 1064-3745
- Series Online ISSN 1940-6029
- Buy this book on publisher's site