clonealign: statistical integration of independent single-cell RNA and DNA sequencing data from human cancers View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-12

AUTHORS

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

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. More... »

PAGES

54

References to SciGraph publications

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  • Journal

    TITLE

    Genome Biology

    ISSUE

    1

    VOLUME

    20

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s13059-019-1645-z

    DOI

    http://dx.doi.org/10.1186/s13059-019-1645-z

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1112739198

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30866997


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