{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Schissler AG"],"funding":["NCI NIH HHS","NLM NIH HHS"],"pagination":["i80-i89"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC4908332"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["32(12)"],"pubmed_abstract":["Motivation
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.Results
In response to these characteristics and limitations in current single-cell RNA-sequencing methodology, we introduce an analytic framework that models transcrip"],"journal":["Bioinformatics (Oxford, England)"],"pubmed_title":["Analysis of aggregated cell-cell statistical distances within pathways unveils therapeutic-resistance mechanisms in circulating tumor cells."],"pmcid":["PMC4908332"],"funding_grant_id":["K22 LM008308","P30 CA023074"],"pubmed_authors":["Li H","Lussier YA","Li Q","Billheimer DD","Piegorsch WW","Kenost C","Schissler AG","Chen JL","Achour I"],"additional_accession":[]},"is_claimable":false,"name":"Analysis of aggregated cell-cell statistical distances within pathways unveils therapeutic-resistance mechanisms in circulating tumor cells.","description":"Motivation
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.Results
In response to these characteristics and limitations in current single-cell RNA-sequencing methodology, we introduce an analytic framework that models transcrip","dates":{"release":"2016-01-01T00:00:00Z","publication":"2016 Jun","modification":"2026-05-30T08:32:12.526Z","creation":"2019-03-27T02:16:04Z"},"accession":"S-EPMC4908332","cross_references":{"pubmed":["27307648"],"doi":["10.1093/bioinformatics/btw248"]}}