Single Cell Transcriptomics: Methods and Protocols

by Raffaele A. Calogero (Editor), Vladimir Benes (Editor)

Overview

This volume provides up-to-date methods on single cell wet and bioinformatics prools based on the researcher experiment requirements. Chapters detail basic analytical procedures, single-cell data QC, dimensionality reduction, clustering, cluster-specific features selection, RNA velocity, multi-modal data integration, and single cell RNA editing. 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.

Cutting-edge and comprehensive, Single Cell Transcriptomics: Methods and Prools aims to be a valuable resource for all researchers interested in learning more about this important and developing field.

Product Details

ISBN-13:	9781071627587
Publisher:	Springer US
Publication date:	12/11/2023
Series:	Methods in Molecular Biology , #2584
Edition description:	1st ed. 2023
Pages:	390
Product dimensions:	7.01(w) x 10.00(h) x (d)

Table of Contents

Guidance on processing the 10x Genomics Single Cell Gene Expression Assay.- BD Rhapsody™ Single-Cell Analysis System Workflow: From sample to multimodal single cell sequencing data.- Profiling transcriptional heterogeneity with Seq-Well S³: A low-cost, portable, high-fidelity platform for massively-parallel single-cell RNA-seq.- A MATQ-seq based prool for single-cell RNA-seq in bacteria.- Full-length single-cell RNA-sequencing with FLASH-seq.- Plant single cell/nucleus RNA-seq workflow.- Ensuring Quality Cell Input for Single Cell Sequencing Experiments by Viability and Singlet Enrichment using Cell Sorting.- Tissue RNA integrity in Visium Spatial Prool (Fresh Frozen Samples).- Single cell RNAseq data QC and preprocessing.- Single cell RNAseq complexity reduction.- Functional-feature-based data reduction using sparsely connected autoencoders.- Single cell RNAseq clustering.- Identifying Gene Markers AssociatedTo Cell Subpopulations.- A guide to trajectory inference and RNA velocity.- Integration of scATAC-seq with scRNA-seq data.- Using “Galaxy-rCASC”, a public Galaxy instance for single-cell RNA-Seq data analysis.- Bringing cell subpopulation discovery on a cloud-HPC using rCASC and StreamFlow.- Profiling RNA editing in single cells.