Analysis of aggregated cell-cell statistical distances within pathways unveils therapeutic-resistance mechanisms in circulating tumor cells.
AuthorSchissler, A Grant
Chen, James L
Billheimer, D Dean
Piegorsch, Walter W
Lussier, Yves A
AffiliationUniversity of Arizona
Ohio State University
MetadataShow full item record
PublisherOXFORD UNIV PRESS
CitationAnalysis of aggregated cell-cell statistical distances within pathways unveils therapeutic-resistance mechanisms in circulating tumor cells. 2016, 32 (12):i80-i89 Bioinformatics
JournalBioinformatics (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 firstname.lastname@example.org
Collection InformationThis 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 email@example.com.
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.
VersionFinal published version
SponsorsThe 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.