Computational Methods for Single-Cell Data Analysis

ISBN-13: 9781493990566 / Angielski / Twarda / 2019 / 271 str.

Guo-Cheng Yuan

Spis treści

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

Blurb

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.