Calogero / Benes Single Cell Transcriptomics

Methods and Protocols
Erscheinungsjahr 2022
ISBN: 978-1-0716-2756-3
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 2584, 390 Seiten, eBook

Reihe: Methods in Molecular Biology

This volume provides up-to-date methods on single cell wet and bioinformatics protocols 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 protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and comprehensive,  Single Cell Transcriptomics: Methods and Protocols  aims to be a valuable resource for all researchers interested in learning more about this important and developing field.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Calogero, Raffaele A.

Benes, Vladimir

Inhaltsverzeichnis

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 S 3 : A low-cost, portable, high-fidelity platform for massively-parallel single-cell RNA-seq.- A MATQ-seq based protocol 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 Protocol (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.