Computational Methods for Single-Cell Data Analysis

by Guo-Cheng Yuan (Editor)

Overview

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 prools, 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.

Product Details

ISBN-13:	9781493990566
Publisher:	Springer New York
Publication date:	02/14/2019
Series:	Methods in Molecular Biology , #1935
Edition description:	1st ed. 2019
Pages:	271
Product dimensions:	7.01(w) x 10.00(h) x (d)

Table of Contents

Quality Control of Single-cell RNA-seq.- Normalization for Single-cell RNA-seq Data Analysis.- Analysis of Technical and Biological Variability in Single-cell RNA Sequencing.- Identification of Cell Types from Single-cell Transcriptomic Data.- Rare Cell Type Detection.- scMCA- A Tool Defines Cell Types in Mouse Based on Single-cell Digital Expression.- Differential Pathway Analysis.- Differential Pathway Analysis.- Estimating Differentiation Potency of Single Cells using Single Cell Entropy (SCENT).- Inference of Gene Co-expression Networks from Single-Cell RNA-sequencing Data.- Single-cell Allele-specific Gene Expression Analysis.- Using BRIE to Detect and Analyse Splicing Isoforms in scRNA-seq Data.- Preprocessing and Computational Analysis of Single-cell Epigenomic Datasets.- Experimental and Computational Approaches for Single-cell Enhancer Perturbation Assay.- Antigen Receptor Sequence Reconstruction and Clonality Inference from scRNA-seq Data.- A Hidden Markov Random Field Modelfor Detecting Domain Organizations from Spatial Transcriptomic Data.