Mark D. Robinson

Research interests

Publications

• Doublet identification in single-cell sequencing data using scDblFinder

  F1000Research · 2021 · 10.12688/f1000research.73600.1

• muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data

  Nature Communications · 2020 · 10.1038/s41467-020-19894-4

• Eleven grand challenges in single-cell data science

  Genome biology · 2020 · https://doi.org/10.1186/s13059-020-1926-6

• CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets

  F1000Research · 2019 · 10.12688/f1000research.11622.3

• Bias, robustness and scalability in single-cell differential expression analysis

  Nature Methods · 2018 · 10.1038/nmeth.4612

• High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy

  Nature Medicine · 2018 · 10.1038/nm.4466

• Compensation of Signal Spillover in Suspension and Imaging Mass Cytometry

  Cell Systems · 2018 · 10.1016/j.cels.2018.02.010

• Treatment of a metabolic liver disease by in vivo genome base editing in adult mice

  Nature Medicine · 2018 · 10.1038/s41591-018-0209-1

• ALT-803, an IL-15 superagonist, in combination with nivolumab in patients with metastatic non-small cell lung cancer: a non-randomised, open-label, phase 1b trial

  The Lancet Oncology · 2018 · 10.1016/s1470-2045(18)30148-7

• CyTOF workflow: Differential discovery in high-throughput high-dimensional cytometry datasets

  F1000Research · 2017 · 10.12688/f1000research.11622.1

• Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences

  F1000Research · 2015 · https://doi.org/10.12688/f1000research.7563.1

• Robustly detecting differential expression in RNA sequencing data using observation weights

  Nucleic Acids Research · 2014 · 10.1093/nar/gku310

• Count-based differential expression analysis of RNA sequencing data using R and Bioconductor

  Nature Protocols · 2013 · https://doi.org/10.1038/nprot.2013.099

• A scaling normalization method for differential expression analysis of RNA-seq data

  Genome biology · 2010 · https://doi.org/10.1186/gb-2010-11-3-r25

• From RNA-seq reads to differential expression results

  Genome biology · 2010 · 10.1186/gb-2010-11-12-220

• edgeR : a Bioconductor package for differential expression analysis of digital gene expression data

  Bioinformatics · 2009 · https://doi.org/10.1093/bioinformatics/btp616

• Large‐scale mapping of human protein–protein interactions by mass spectrometry

  Molecular Systems Biology · 2007 · 10.1038/msb4100134

• Moderated statistical tests for assessing differences in tag abundance

  Bioinformatics · 2007 · 10.1093/bioinformatics/btm453

• Small-sample estimation of negative binomial dispersion, with applications to SAGE data

  Biostatistics · 2007 · 10.1093/biostatistics/kxm030

• Global landscape of protein complexes in the yeast Saccharomyces cerevisiae

  Nature · 2006 · https://doi.org/10.1038/nature04670

• High-Throughput Mapping of a Dynamic Signaling Network in Mammalian Cells

  Science · 2005 · 10.1126/science.1105776

• ESHRE PGD Consortium ‘Best practice guidelines for clinical preimplantation genetic diagnosis (PGD) and preimplantation genetic screening (PGS)’

  Human Reproduction · 2004 · 10.1093/humrep/deh579

• Systematic Genetic Analysis with Ordered Arrays of Yeast Deletion Mutants

  Science · 2001 · https://doi.org/10.1126/science.1065810

Current projects

No projects listed.