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Are dropout imputation methods for scRNA-seq effective for scHi-C data?

Briefings in Bioinformatics(2020)10.1093/bib/bbaa289Source: DataRank Database

Are dropout imputation methods for scRNA-seq effective for scHi-C data? is a research paper published in Briefings in Bioinformatics (2020). On theSindex it has a DataRank of 0. It has been cited 18 times.

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Enrichment:Funding (2 grants)OA: greenTopics (22)IDs (PubMed, PMC)Annotations (1)

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Authors (3)

Qing Xie ORCID,Shili Lin ORCID,Chenggong Han

Live enrichment

PubMed:33201180 PMC:PMC8293815

National Institutes of Health

Grant: 1R01GM114142-01

Omics analysis of three-dimensional transcriptional regulation

NIGMS NIH HHS

Grant: R01 GM114142

greenLicense: OUP Standard Publication Reuse

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/8293815

publisherhttp://academic.oup.com/bib/article-pdf/22/4/bbaa289/39129209/bbaa289.pdf

https://doi.org/10.1093/bib/bbaa289

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8293815

https://pubmed.ncbi.nlm.nih.gov/33201180

Fields of Study

0301 basic medicine0303 health sciences03 medical and health sciences

MeSH Terms

HumansRNA, Small CytoplasmicAlgorithmsComputer SimulationSoftwareSingle-Cell AnalysisRNA-Seq

Keywords

DropoutImputationSingle Cell Rna-seqStructural ZeroSingle Cell Hi-cRNA, Small CytoplasmicHumansComputer SimulationRNA-SeqSingle-Cell AnalysisAlgorithmsSoftware

Are dropout imputation methods for scRNA-seq effective for scHi-C data?

FAIR Checklist

Authors (3)

Funding (2 grants)

Open Access

Topics

Annotations (Europe PMC)