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Analysis of aggregated cell-cell statistical distances within pathways unveils therapeutic-resistance mechanisms in circulating tumor cells.

dc.contributor.authorSchissler, A Grant
dc.contributor.authorLi, Qike
dc.contributor.authorChen, James L
dc.contributor.authorKenost, Colleen
dc.contributor.authorAchour, Ikbel
dc.contributor.authorBillheimer, D Dean
dc.contributor.authorLi, Haiquan
dc.contributor.authorPiegorsch, Walter W
dc.contributor.authorLussier, Yves A
dc.date.accessioned2016-10-11T23:03:36Z
dc.date.available2016-10-11T23:03:36Z
dc.date.issued2016-06-15
dc.identifier.citationAnalysis of aggregated cell-cell statistical distances within pathways unveils therapeutic-resistance mechanisms in circulating tumor cells. 2016, 32 (12):i80-i89 Bioinformaticsen
dc.identifier.issn1367-4811
dc.identifier.pmid27307648
dc.identifier.doi10.1093/bioinformatics/btw248
dc.identifier.urihttp://hdl.handle.net/10150/620927
dc.description.abstractAs 'omics' biotechnologies accelerate the capability to contrast a myriad of molecular measurements from a single cell, they also exacerbate current analytical limitations for detecting meaningful single-cell dysregulations. Moreover, mRNA expression alone lacks functional interpretation, limiting opportunities for translation of single-cell transcriptomic insights to precision medicine. Lastly, most single-cell RNA-sequencing analytic approaches are not designed to investigate small populations of cells such as circulating tumor cells shed from solid tumors and isolated from patient blood samples.
dc.description.sponsorshipThe study was supported in part by the University of Arizona Center for Biomedical Informatics and Biostatistics, The University of Arizona Health Sciences, and the grants NIH K22LM008308 and NIH NCI P30CA023074.en
dc.language.isoenen
dc.publisherOXFORD UNIV PRESSen
dc.relation.urlhttp://bioinformatics.oxfordjournals.org/cgi/pmidlookup?view=long&pmid=27307648en
dc.rights© The Author 2016. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/).en
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.titleAnalysis of aggregated cell-cell statistical distances within pathways unveils therapeutic-resistance mechanisms in circulating tumor cells.en
dc.typeArticleen
dc.contributor.departmentUniversity of Arizonaen
dc.contributor.departmentOhio State Universityen
dc.identifier.journalBioinformatics (Oxford, England)en
dc.description.noteOpen access.en
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
refterms.dateFOA2018-09-11T15:06:08Z
html.description.abstractAs 'omics' biotechnologies accelerate the capability to contrast a myriad of molecular measurements from a single cell, they also exacerbate current analytical limitations for detecting meaningful single-cell dysregulations. Moreover, mRNA expression alone lacks functional interpretation, limiting opportunities for translation of single-cell transcriptomic insights to precision medicine. Lastly, most single-cell RNA-sequencing analytic approaches are not designed to investigate small populations of cells such as circulating tumor cells shed from solid tumors and isolated from patient blood samples.


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© The Author 2016. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/).
Except where otherwise noted, this item's license is described as © The Author 2016. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/).