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Single cell RNA sequencing and Whole Genome Sequencing on different cells from the same sample for a triple negative patient derived xenograft and ovarian cancer cell lines.

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.

Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data

Dataset ID Description Technology Samples
EGAD00001004552 NextSeq 550 3
Publications Citations
clonealign: statistical integration of independent single-cell RNA and DNA sequencing data from human cancers.
Genome Biol 20: 2019 54
Computational Methods for Single-cell Multi-omics Integration and Alignment.
Genomics Proteomics Bioinformatics 20: 2022 836-849
Reconstructing clonal tree for phylo-phenotypic characterization of cancer using single-cell transcriptomics.
Nat Commun 14: 2023 982