Faculty Profile

Dylan Cable

Dylan Cable, PhD

  • Assistant Professor, Biostatistics
Dylan Cable is an assistant professor in biostatistics at the University of Michigan. His research involves developing rigorous statistical modeling approaches for emerging high-throughput genomics technologies, such as spatial transcriptomics and single-cell RNA-sequencing. Dr. Cable completed his PhD in computer science at the Massachusetts Institute of Technology and a bachelors degree in mathematics at Stanford University. Dr. Cable is interested in the application of high-throughput genomics technologies to better understand human health and disease, as well as integration with clinical settings and drug discovery pipelines.

  • PhD, Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 2023
  • BS, Mathematics, Stanford University, Stanford, CA, 2018

Research Interests: Statistical methods for genomics, single-cell RNA-seq, spatial transcriptomics, probabalistic modeling, machine learning, applied statistics, statistical computing and optimization

Research Projects:
  • Statistical methodology in spatial transcriptomics: cell type identification, differential expression, multi-sample study design, and spatial region identification.

  • Statistical methods for cellular perturbation experiments: efficient estimation and prediction of perturbation effects in single-cell RNA-seq.

  • Cable, D.M., Murray, E., Shanmugam, V., Zhang, S., Zou, L.S., Diao, M.Z., Chen, H., Ma- cosko, E., Irizarry, R.A., and Chen, F. Cell type-specific differential expression for spatial tran- scriptomics. Nature Methods. September 2022. url: https://www.nature.com/articles/ s41592-022-01575-3

  • Cable, D.M., Murray, E., Zou, L.S., Goeva, A., Macosko, E.Z., Chen, F., and Irizarry, R.A. Robust decomposition of cell type mixtures in spatial transcriptomics. Nature Biotechnology. April 2022. url: https://www.nature.com/articles/s41587-021-00830-w

4614 SPH I
1415 Washington Heights
Ann Arbor, MI 48103-2029

Email: [email protected]

Media inquiries: [email protected]

Areas of Expertise: Biostatistics,  Cancer,  Genetics,  Genomics,  Modeling