Online Program Home
My Program

Abstract Details

Activity Number: 535 - Contributed Poster Presentations: Section on Statistics in Genomics and Genetics
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #330442
Title: Probabilistic Inference of Clonal Gene Expression Through Integration of RNA and DNA-Seq at Single-Cell Resolution
Author(s): Kieran Campbell* and Sohrab P Shah and Alexandre Bouchard-Côté
Companies: University of British Columbia and BC Cancer Agency and University of British Columbia
Keywords: Cancer; Multiview; Clustering; Bayesian; Single-cell; RNA-seq
Abstract:

Human cancers form clones - sets of cells that exhibit similar mutations and genomic rearrangements. As clones evolve to resist chemotherapy understanding their molecular properties is crucial to designing effective treatments. While it is possible to measure both the DNA (that defines clonal structure) and RNA (that defines cell state) in single-cells, such assays are time consuming and hard-to-scale, meaning it is far more common to have large datasets where DNA and RNA is measured in separate cells, albeit from the same tumor consisting of similar clones.

Here we present a highly-scalable statistical method to probabilistically assign each cell as measured in gene expression space (scRNA-seq) to a clone defined in copy number space (scDNA-seq). Through simulations we demonstrate that relatively few (< 20%) genes must exhibit CNV-gene expression relationships for such assignment to be feasible. We apply our method to a patient-derived xenograft in breast cancer to characterize the gene expression of expanding clones. Finally, we show how our framework serves as a basis for generalized multiview clustering from unpairable data sources and discuss extensions.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2018 program