biostudies-literatureSchissler AGNCI NIH HHSNLM NIH HHSi80-i89https://www.ebi.ac.uk/biostudies/studies/S-EPMC4908332biostudies-literatureUnknown32(12)<h4>Motivation</h4>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.<h4>Results</h4>In response to these characteristics and limitations in current single-cell RNA-sequencing methodology, we introduce an analytic framework that models transcripBioinformatics (Oxford, England)Analysis of aggregated cell-cell statistical distances within pathways unveils therapeutic-resistance mechanisms in circulating tumor cells.PMC4908332K22 LM008308P30 CA023074Li HLussier YALi QBillheimer DDPiegorsch WWKenost CSchissler AGChen JLAchour IfalseAnalysis of aggregated cell-cell statistical distances within pathways unveils therapeutic-resistance mechanisms in circulating tumor cells.<h4>Motivation</h4>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.<h4>Results</h4>In response to these characteristics and limitations in current single-cell RNA-sequencing methodology, we introduce an analytic framework that models transcrip2016-01-01T00:00:00Z2016 Jun2026-05-30T08:32:12.526Z2019-03-27T02:16:04ZS-EPMC49083322730764810.1093/bioinformatics/btw248