Please use this identifier to cite or link to this item:
https://doi.org/10.3389/fimmu.2018.02425
DC Field | Value | |
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dc.title | A single-cell sequencing guide for immunologists | |
dc.contributor.author | See, P | |
dc.contributor.author | Lum, J | |
dc.contributor.author | Chen, J | |
dc.contributor.author | Ginhoux, F | |
dc.date.accessioned | 2020-10-20T04:59:00Z | |
dc.date.available | 2020-10-20T04:59:00Z | |
dc.date.issued | 2018 | |
dc.identifier.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 | |
dc.identifier.issn | 16643224 | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/178063 | |
dc.description.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. | |
dc.rights | Attribution 4.0 International | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.source | Unpaywall 20201031 | |
dc.subject | complementary DNA | |
dc.subject | messenger RNA | |
dc.subject | transcriptome | |
dc.subject | animal cell | |
dc.subject | biological variation | |
dc.subject | chromatin immunoprecipitation | |
dc.subject | computer model | |
dc.subject | cytolysis | |
dc.subject | dendritic cell | |
dc.subject | fluorescence activated cell sorting | |
dc.subject | gene expression | |
dc.subject | genomics | |
dc.subject | human | |
dc.subject | human cell | |
dc.subject | immunologist | |
dc.subject | immunology | |
dc.subject | methodology | |
dc.subject | microarray analysis | |
dc.subject | nonhuman | |
dc.subject | ontogeny | |
dc.subject | phenotype | |
dc.subject | reverse transcription | |
dc.subject | Review | |
dc.subject | RNA sequence | |
dc.subject | single cell sequencing | |
dc.subject | animal | |
dc.subject | gene expression profiling | |
dc.subject | high throughput sequencing | |
dc.subject | immunological technique | |
dc.subject | immunology | |
dc.subject | information processing | |
dc.subject | procedures | |
dc.subject | sequence analysis | |
dc.subject | single cell analysis | |
dc.subject | software | |
dc.subject | Allergy and Immunology | |
dc.subject | Animals | |
dc.subject | Datasets as Topic | |
dc.subject | Gene Expression Profiling | |
dc.subject | High-Throughput Nucleotide Sequencing | |
dc.subject | Humans | |
dc.subject | Immunologic Techniques | |
dc.subject | Sequence Analysis, RNA | |
dc.subject | Single-Cell Analysis | |
dc.subject | Software | |
dc.type | Review | |
dc.contributor.department | MICROBIOLOGY AND IMMUNOLOGY | |
dc.description.doi | 10.3389/fimmu.2018.02425 | |
dc.description.sourcetitle | Frontiers in Immunology | |
dc.description.volume | 9 | |
dc.description.issue | OCT | |
dc.description.page | 2425 | |
Appears in Collections: | Staff Publications Elements |
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