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- Publisher Website: 10.1016/j.coisb.2021.100375
- Scopus: eid_2-s2.0-85122795967
- WOS: WOS:000850439600006
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Article: Uncertainty versus variability: Bayesian methods for analysis of scRNA-seq data
Title | Uncertainty versus variability: Bayesian methods for analysis of scRNA-seq data |
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Authors | |
Keywords | scRNA-seq data Bayesian methods Gene expression Alternative splicing |
Issue Date | 2021 |
Publisher | Elsevier Ltd. The Journal's web site is located at https://www.journals.elsevier.com/current-opinion-in-systems-biology/ |
Citation | Current Opinion in Systems Biology, 2021, v. 28, article no. 100375 How to Cite? |
Abstract | Single-cell ‘omics technologies have the potential to revolutionize our understanding of stochasticity and heterogeneity in biology, yet such measurements are inevitably affected by high levels of noise and technical artifacts. To distinguish genuine biological variability from confounding factors, it is therefore essential to adopt analysis methodologies that model such noisy effects. In this review, we discuss model-based approaches that tackle this problem within the framework of Bayesian statistics. We start by revisiting the fundamental concepts and illustrate how they are used in a number of single-cell RNA sequencing analyses, highlighting the merits and still unmet challenges within this expanding area of research. |
Persistent Identifier | http://hdl.handle.net/10722/304618 |
ISSN | 2023 Impact Factor: 3.4 2023 SCImago Journal Rankings: 1.676 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Huang, Y | - |
dc.contributor.author | Sanguinetti, G | - |
dc.date.accessioned | 2021-10-05T02:32:43Z | - |
dc.date.available | 2021-10-05T02:32:43Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Current Opinion in Systems Biology, 2021, v. 28, article no. 100375 | - |
dc.identifier.issn | 2452-3100 | - |
dc.identifier.uri | http://hdl.handle.net/10722/304618 | - |
dc.description.abstract | Single-cell ‘omics technologies have the potential to revolutionize our understanding of stochasticity and heterogeneity in biology, yet such measurements are inevitably affected by high levels of noise and technical artifacts. To distinguish genuine biological variability from confounding factors, it is therefore essential to adopt analysis methodologies that model such noisy effects. In this review, we discuss model-based approaches that tackle this problem within the framework of Bayesian statistics. We start by revisiting the fundamental concepts and illustrate how they are used in a number of single-cell RNA sequencing analyses, highlighting the merits and still unmet challenges within this expanding area of research. | - |
dc.language | eng | - |
dc.publisher | Elsevier Ltd. The Journal's web site is located at https://www.journals.elsevier.com/current-opinion-in-systems-biology/ | - |
dc.relation.ispartof | Current Opinion in Systems Biology | - |
dc.subject | scRNA-seq data | - |
dc.subject | Bayesian methods | - |
dc.subject | Gene expression | - |
dc.subject | Alternative splicing | - |
dc.title | Uncertainty versus variability: Bayesian methods for analysis of scRNA-seq data | - |
dc.type | Article | - |
dc.identifier.email | Huang, Y: yuanhua@hku.hk | - |
dc.identifier.authority | Huang, Y=rp02649 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.coisb.2021.100375 | - |
dc.identifier.scopus | eid_2-s2.0-85122795967 | - |
dc.identifier.hkuros | 325781 | - |
dc.identifier.volume | 28 | - |
dc.identifier.spage | article no. 100375 | - |
dc.identifier.epage | article no. 100375 | - |
dc.identifier.isi | WOS:000850439600006 | - |
dc.publisher.place | United Kingdom | - |