Single Cell Transcriptomics, 1st ed. 2023 Methods and Protocols Methods in Molecular Biology Series, Vol. 2584
Coordonnateurs : Calogero Raffaele A., Benes Vladimir
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.
1. Guidance on processing the 10x Genomics Single Cell Gene Expression Assay
Katharina Danielski
2. BD Rhapsody™ Single-Cell Analysis System Workflow: From sample to multimodal single cell sequencing data
Jannes Ulbrich, Vadir Lopez-Salmeron, and Ian Gerrard
3. Profiling transcriptional heterogeneity with Seq-Well S3: A low-cost, portable, high-fidelity platform for massively-parallel single-cell RNA-seq
Riley S Drake, Martin Arreola Villanueva, Mike Vilme, Daniela D Russo, Andrew Navia, J. Christopher Love, and Alex K. Shalek
4. A MATQ-seq based protocol for single-cell RNA-seq in bacteria
Christina Homberger, Antoine-Emmanuel Saliba, and Jörg Vogel
5. Full-length single-cell RNA-sequencing with FLASH-seq
Vincent Hahaut and Simone Picelli
6. Plant single cell/nucleus RNA-seq workflow
Sandra Thibivilliers, Andrew Farmer, Susan Schroeder, and Marc Libault
7. Ensuring Quality Cell Input for Single Cell Sequencing Experiments by Viability and Singlet Enrichment using Cell Sorting
Malte Paulsen
8. Tissue RNA integrity in Visium Spatial Protocol (Fresh Frozen Samples)
Federica Antico, Marta Gai, and Maddalena Arigoni
9. Single cell RNAseq data QC and preprocessing
Martina Olivero and Raffaele A Calogero
10. Single cell RNAseq complexity reduction
Francesca Corderoand Raffaele A Calogero
11. Functional-feature-based data reduction using sparsely connected autoencoders
Luca Alessandri and Raffaele A Calogero
12. Single cell RNAseq clustering
Marco Beccuti and Raffaele A Calogero
13. Identifying Gene Markers Associated To Cell Subpopulations
Maria Luisa Ratto and Luca Alessandri
14. A guide to trajectory inference and RNA velocity
Philipp Weiler, Koen Van den Berge, Kelly Street, and Simone Tiberi
15. Integration of scATAC-seq with scRNA-seq data
Ivan Berest and Andrea Tangherloni
16. Using “Galaxy-rCASC”, a public Galaxy instance for single-cell RNA-Seq data analysis
Pietro Mandreoli, Luca Alessandri, Raffaele A. Calogero, Marco Antonio Tangaro, Federico Zambelli
17. Bringing cell subpopulation discovery on a cloud-HPC using rCASC and StreamFlow
Sandro G. Contaldo, Luca Alessandri, Iacopo Colonnelli, Marco Beccuti and Marco Aldinucci
18. Profiling RNA editing in single cells
Adriano Fonzino, Graziano Pesole, and Ernesto Picardi
Includes cutting-edge methods and protocols
Provides step-by-step detail essential for reproducible results
Contains key notes and implementation advice from the experts
Date de parution : 12-2023
Ouvrage de 390 p.
17.8x25.4 cm
Date de parution : 12-2022
Ouvrage de 390 p.
17.8x25.4 cm
Thème de Single Cell Transcriptomics :
Mots-clés :
RNAseq; CITE-seq; optimal clustering; Spatial transcriptomics; fastq files
