Please use this identifier to cite or link to this item: https://doi.org/10.3389/fimmu.2018.02425
Title: A single-cell sequencing guide for immunologists
Authors: See, P
Lum, J
Chen, J
Ginhoux, F 
Keywords: complementary DNA
messenger RNA
transcriptome
animal cell
biological variation
chromatin immunoprecipitation
computer model
cytolysis
dendritic cell
fluorescence activated cell sorting
gene expression
genomics
human
human cell
immunologist
immunology
methodology
microarray analysis
nonhuman
ontogeny
phenotype
reverse transcription
Review
RNA sequence
single cell sequencing
animal
gene expression profiling
high throughput sequencing
immunological technique
immunology
information processing
procedures
sequence analysis
single cell analysis
software
Allergy and Immunology
Animals
Datasets as Topic
Gene Expression Profiling
High-Throughput Nucleotide Sequencing
Humans
Immunologic Techniques
Sequence Analysis, RNA
Single-Cell Analysis
Software
Issue Date: 2018
Citation: See, P, Lum, J, Chen, J, Ginhoux, F (2018). A single-cell sequencing guide for immunologists. Frontiers in Immunology 9 (OCT) : 2425. ScholarBank@NUS Repository. https://doi.org/10.3389/fimmu.2018.02425
Rights: Attribution 4.0 International
Abstract: In recent years there has been a rapid increase in the use of single-cell sequencing (scRNA-seq) approaches in the field of immunology. With the wide range of technologies available, it is becoming harder for users to select the best scRNA-seq protocol/platform to address their biological questions of interest. Here, we compared the advantages and limitations of four commonly used scRNA-seq platforms in order to clarify their suitability for different experimental applications. We also address how the datasets generated by different scRNA-seq platforms can be integrated, and how to identify unknown populations of single cells using unbiased bioinformatics methods. Copyright © 2018 See, Lum, Chen and Ginhoux.
Source Title: Frontiers in Immunology
URI: https://scholarbank.nus.edu.sg/handle/10635/178063
ISSN: 16643224
DOI: 10.3389/fimmu.2018.02425
Rights: Attribution 4.0 International
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