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

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
doi: https://doi.org/10.1101/344309
Kieran R Campbell
1Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
2Department of Statistics, University of British Columbia, Vancouver, British Columbia, Canada
3UBC Data Science Institute, University of British Columbia, Vancouver, British Columbia, Canada
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Adi Steif
1Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
4Genome Science and Technology Graduate Program, University of British Columbia, Vancouver, British Columbia, Canada
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Emma Laks
1Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
4Genome Science and Technology Graduate Program, University of British Columbia, Vancouver, British Columbia, Canada
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Hans Zahn
1Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
7Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
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Daniel Lai
1Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
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Andrew McPherson
1Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
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Hossein Farahani
1Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
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Farhia Kabeer
1Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
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Ciara O’Flanagan
1Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
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Justina Biele
1Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
5Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
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Jazmine Brimhall
1Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
5Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
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Beixi Wang
1Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
5Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
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Pascale Walters
1Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
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8CRUK IMAXT Grand Challenge Consortium
Alexandre Bouchard-Côté
2Department of Statistics, University of British Columbia, Vancouver, British Columbia, Canada
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Samuel Aparicio
1Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
5Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
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Sohrab P Shah
1Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada
5Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
6Memorial Sloan Kettering Cancer Center, New York, New York, USA
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Abstract

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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Posted June 11, 2018.
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clonealign: statistical integration of independent single-cell RNA & DNA-seq from human cancers
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
bioRxiv 344309; doi: https://doi.org/10.1101/344309
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clonealign: statistical integration of independent single-cell RNA & DNA-seq from human cancers
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
bioRxiv 344309; doi: https://doi.org/10.1101/344309

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