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clonealign: statistical integration of independent single-cell RNA and DNA sequencing data from human cancers Campbell, Kieran R.; Steif, Adi; Laks, Emma; Zahn, Hans; Lai, Daniel; McPherson, Andrew; Farahani, Hossein; Kabeer, Farhia; O’Flanagan, Ciara; Biele, Justina; et al.

Abstract

Measuring gene expression of tumor clones at single-cell resolution links functional consequences to somatic alterations. Without scalable methods to simultaneously assay DNA and RNA from the same single cell, parallel single-cell DNA and RNA measurements from independent cell populations must be mapped for genome-transcriptome association. We present clonealign, which assigns gene expression states to cancer clones using single-cell RNA and DNA sequencing independently sampled from a heterogeneous population. We apply clonealign to triple-negative breast cancer patient-derived xenografts and high-grade serous ovarian cancer cell lines and discover clone-specific dysregulated biological pathways not visible using either sequencing method alone.

Item Metadata

Title
clonealign: statistical integration of independent single-cell RNA and DNA sequencing data from human cancers
Creator
Campbell, Kieran R.; Steif, Adi; Laks, Emma; Zahn, Hans; Lai, Daniel; McPherson, Andrew; Farahani, Hossein; Kabeer, Farhia; O’Flanagan, Ciara; Biele, Justina; Brimhall, Jazmine; Wang, Beixi; Walters, Pascale; Consortium, IMAXT; Bouchard-Côté, Alexandre; Aparicio, Samuel, 1963-; Shah, Sohrab P.
Contributor
University of British Columbia. Data Science Institute; Michael Smith Laboratories
Publisher
BioMed Central
Date Issued
2019-03-12
Description
Measuring gene expression of tumor clones at single-cell resolution links functional consequences to somatic alterations. Without scalable methods to simultaneously assay DNA and RNA from the same single cell, parallel single-cell DNA and RNA measurements from independent cell populations must be mapped for genome-transcriptome association. We present clonealign, which assigns gene expression states to cancer clones using single-cell RNA and DNA sequencing independently sampled from a heterogeneous population. We apply clonealign to triple-negative breast cancer patient-derived xenografts and high-grade serous ovarian cancer cell lines and discover clone-specific dysregulated biological pathways not visible using either sequencing method alone.
Genre
Article
Type
Text
Language
eng
Date Available
2019-03-18
Provider
Vancouver : University of British Columbia Library
Rights
Attribution 4.0 International (CC BY 4.0)
DOI
10.14288/1.0377124
URI
http://hdl.handle.net/2429/68902
Affiliation
Graduate and Postdoctoral Studies; Medicine, Faculty of; Science, Faculty of; Other UBC; Non UBC; Pathology and Laboratory Medicine, Department of; Statistics, Department of
Citation
Genome Biology. 2019 Mar 12;20(1):54
Publisher DOI
10.1186/s13059-019-1645-z
Peer Review Status
Reviewed
Scholarly Level
Faculty
Copyright Holder
The Author(s)
Rights URI
http://creativecommons.org/licenses/by/4.0/
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Rights

Attribution 4.0 International (CC BY 4.0)