Reconstructing Cell Cycle Pseudo Time-Series Via Single-Cell Transcriptome Data

Simple item page
dc.contributor.authorLiu, Zehuaen_US
dc.contributor.authorLou, Huazheen_US
dc.contributor.authorXie, Kaikunen_US
dc.contributor.authorWang, Haoen_US
dc.contributor.authorChen, Ningen_US
dc.contributor.authorAparicio, Oscar M.en_US
dc.contributor.authorZhang, Michael Q.en_US
dc.contributor.authorJiang, Ruien_US
dc.contributor.authorChen, Tingen_US
dc.contributor.utdAuthorZhang, Michael Q.en_US
dc.date.accessioned2018-08-24T21:15:16Z
dc.date.available2018-08-24T21:15:16Z
dc.date.created2017-06-19en_US
dc.date.issued2018-08-24
dc.descriptionIncludes supplementary materialen_US
dc.description.abstractSingle-cell mRNA sequencing, which permits whole transcriptional profiling of individual cells, has been widely applied to study growth and development of tissues and tumors. Resolving cell cycle for such groups of cells is significant, but may not be adequately achieved by commonly used approaches. Here we develop a traveling salesman problem and hidden Markov model-based computational method named reCAT, to recover cell cycle along time for unsynchronized single-cell transcriptome data. We independently test reCAT for accuracy and reliability using several data sets. We find that cell cycle genes cluster into two major waves of expression, which correspond to the two well-known checkpoints, G1 and G2. Moreover, we leverage reCAT to exhibit methylation variation along the recovered cell cycle. Thus, reCAT shows the potential to elucidate diverse profiles of cell cycle, as well as other cyclic or circadian processes (e.g., in liver), on single-cell resolution.en_US
dc.description.departmentSchool of Natural Sciences and Mathematicsen_US
dc.description.sponsorshipNational Science Foundation of China [61673241, 61561146396], National Basic Research Program of China [2012CB316504, 2012CB316503]; Hi-tech Research and Development Program of China [2012AA020401]; NSFC [61305066, 91010016, 91519326, 31361163004]; NIH/NHGRI [5U01HG006531-03; 4R01HG006465]en_US
dc.identifier.bibliographicCitationLiu, Zehua, Huazhe Lou, Kaikun Xie, Hao Wang, et al. 2017. "Reconstructing cell cycle pseudo time-series via single-cell transcriptome data." Nature Communications 8, doi:10.1038/s41467-017-00039-zen_US
dc.identifier.issn2041-1723en_US
dc.identifier.urihttp://hdl.handle.net/10735.1/6029
dc.identifier.volume8en_US
dc.language.isoenen_US
dc.publisherNature Publishing Groupen_US
dc.relation.urihttp://dx.doi.org/10.1038/s41467-017-00039-z
dc.rightsCC BY 4.0 (Attribution)en_US
dc.rights©2017 The Authorsen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.sourceNature Communications
dc.subjectGene Expressionen_US
dc.subjectSequence Analysis, RNAen_US
dc.subjectDNA Methylationen_US
dc.subjectStem Cellsen_US
dc.subjectGenetic Heterogeneityen_US
dc.subjectPluripotent Stem Cellsen_US
dc.subjectData Miningen_US
dc.subjectCell Divisionen_US
dc.titleReconstructing Cell Cycle Pseudo Time-Series Via Single-Cell Transcriptome Dataen_US
dc.type.genrearticleen_US

Files

Original bundle

Now showing 1 - 2 of 2
Thumbnail Image
Name:
NSM-3154-8184.30.pdf
Size:
915.79 KB
Format:
Adobe Portable Document Format
Description:
Article
Download
Thumbnail Image
Name:
NSM-3154-8184.30_S1.pdf
Size:
4.14 MB
Format:
Adobe Portable Document Format
Description:
Supplement
Download

Collections

Zhang, Michael Q.