Analysis of aggregated cell-cell statistical distances within pathways unveils therapeutic-resistance mechanisms in circulating tumor cells.
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Bioinformatics-2016-Schissler- ...
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Author
Schissler, A GrantLi, Qike
Chen, James L
Kenost, Colleen
Achour, Ikbel
Billheimer, D Dean
Li, Haiquan
Piegorsch, Walter W
Lussier, Yves A
Affiliation
University of ArizonaOhio State University
Issue Date
2016-06-15
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OXFORD UNIV PRESSCitation
Analysis of aggregated cell-cell statistical distances within pathways unveils therapeutic-resistance mechanisms in circulating tumor cells. 2016, 32 (12):i80-i89 BioinformaticsJournal
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/).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
1367-4811PubMed ID
27307648Version
Final published versionSponsors
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.ae974a485f413a2113503eed53cd6c53
10.1093/bioinformatics/btw248
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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/).
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