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

Persistent Link:
http://hdl.handle.net/10150/620927
Title:
Analysis of aggregated cell-cell statistical distances within pathways unveils therapeutic-resistance mechanisms in circulating tumor cells.
Author:
Schissler, A Grant; Li, Qike; Chen, James L; Kenost, Colleen; Achour, Ikbel; Billheimer, D Dean; Li, Haiquan; Piegorsch, Walter W; Lussier, Yves A
Affiliation:
University of Arizona; Ohio State University
Issue Date:
2016-06-15
Publisher:
OXFORD UNIV PRESS
Citation:
Analysis of aggregated cell-cell statistical distances within pathways unveils therapeutic-resistance mechanisms in circulating tumor cells. 2016, 32 (12):i80-i89 Bioinformatics
Journal:
Bioinformatics (Oxford, England)
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/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
Collection Information:
This 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.
Abstract:
As '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.
Note:
Open access.
ISSN:
1367-4811
PubMed ID:
27307648
DOI:
10.1093/bioinformatics/btw248
Version:
Final published version
Sponsors:
The 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.
Additional Links:
http://bioinformatics.oxfordjournals.org/cgi/pmidlookup?view=long&pmid=27307648

Full metadata record

DC FieldValue Language
dc.contributor.authorSchissler, A Granten
dc.contributor.authorLi, Qikeen
dc.contributor.authorChen, James Len
dc.contributor.authorKenost, Colleenen
dc.contributor.authorAchour, Ikbelen
dc.contributor.authorBillheimer, D Deanen
dc.contributor.authorLi, Haiquanen
dc.contributor.authorPiegorsch, Walter Wen
dc.contributor.authorLussier, Yves Aen
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.en
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/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.comen
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
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