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clonealign: statistical integration of independent single-cell RNA & DNA-seq from human cancers

By Kieran R Campbell, Adi Steif, Emma Laks, Hans Zahn, Daniel Lai, Andrew McPherson, Hossein Farahani, Farhia Kabeer, Ciara O’Flanagan, Justina Biele, Jazmine Brimhall, Beixi Wang, Pascale Walters, IMAXT Consortium, Alexandre Bouchard-Côté, Samuel Aparicio, Sohrab P. Shah

Posted 11 Jun 2018
bioRxiv DOI: 10.1101/344309 (published DOI: 10.1186/s13059-019-1645-z)

Measuring gene expression of genomically defined tumour clones at single cell resolution would associate functional consequences to somatic alterations, as a prelude to elucidating pathways driving cell population growth, resistance and relapse. In the absence of scalable methods to simultaneously assay DNA and RNA from the same single cell, independent sampling of cell populations for parallel measurement of single cell DNA and single cell RNA must be computationally mapped for genome-transcriptome association. Here we present clonealign, a robust statistical framework to assign gene expression states to cancer clones using single-cell RNA-seq and DNA-seq independently sampled from an heterogeneous cancer cell 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 DNA-Seq or RNA-Seq alone.

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