Computational Methods for Single Cell Data Analysis 1st Edition by Guo Cheng Yuan ISBN 9781493990566 149399056X

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Product details:

ISBN 10:  149399056X

ISBN 13: 9781493990566

Author: Guo-Cheng Yuan

 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

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

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

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

5. Rare Cell Type Detection

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

7. Differential Pathway Analysis

8. Pseudotime Reconstruction Using TSCAN

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

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

11. Single-Cell Allele-Specific Gene Expression Analysis

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

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

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

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

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

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Tags: Guo Cheng Yuan, Computational Methods, Single Cell, Data Analysis