CIBSS Alumni

Dr. Dominic Grün

Dr. Dominic Grün

Contact

Dr. Dominic Grün
Current affiliation: University of Würzburg

T +49 761 5108 490
dominic.gruen(at)uni-wuerzburg.de

Further Information

WWW

10 selected publications:

  • Revealing dynamics of gene expression variability in cell state space.
    Grün D.
    Nat Methods. 2019 Nov 18. [Epub ahead of print]
  • Mapping microglia states in the human brain through the integration of high-dimensional techniques.
    Sankowski R, Böttcher C, Masuda T, Geirsdottir L, Sagar, Sindram E, Seredenina T, Muhs A, Scheiwe C, Shah MJ, Heiland DH, Schnell O, Grün D#, Priller J#, Prinz M# (2019).
    Nat Neurosci. 22(12):2098-2110. #shared corresponding authors
  • A human liver cell atlas reveals heterogeneity and epithelial progenitors.
    Aizarani N, Saviano A, Sagar, Mailly L, Durand S, Herman JS, Pessaux P, Baumert TF, Grün D (2019).
    Nature. 572(7768):199-204
  • Revealing routes of cellular differentiation by single-cell RNA-seq.
    Grün D (2018)
    Curr Opin Sys Biol. 11: 9-17
  • FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data.
    Herman JS, Sagar, Grün D (2018).
    Nature methods, 15(5): 379-386
  • High-Throughput Single-Cell RNA Sequencing and Data Analysis.
    Sagar, Herman JS, Pospisilik JA, Grün D (2018)
    Methods Mol Biol. 1766: 257-283
  • De novo prediction of stem cell identity using single-cell transcriptome data.
    Grün D#, Muraro MJ, Boisset JC, Wiebrands K, Lyubimova A, Dharmadhikari G, van den Born M, van Es J, Jansen E, Clevers H, de Koning Eelco JP, van Oudenaarden A# (2016).
    Cell Stem Cell 19(2): 266-277. #shared corresponding authors
  • Design and analysis of single cell sequencing experiments.
    Grün D and van Oudenaarden A. (2015)
    Cell 163(4): 799-810
  • Single-cell mRNA sequencing reveals rare intestinal cell types.
    Grün D*, Lyubimova A, Kester L, Wiebrands K, Basak O, Sasaki N, Clevers H, van Oudenaarden A. (2015)
    Nature 525: 251-255. *shared first authors
  • Validation of noise models for single-cell transcriptomics enables genome-wide quantification of stochastic gene expression.
    Grün D*, Kester L*, van Oudenaarden A. (2014)
    Nature Methods 11(6): 637-40. *shared first authors