Robinson, Mark D.

<tt>edgeR</tt> : a Bioconductor package for differential expression analysis of digital gene expression data

Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics

Gene expression and cancer classificationGenomics and Phylogenetic Studies

Cited by 41,364

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

Robinson, M. D., & Oshlack, A. (2010) · Genome Biology

Gene expression and cancer classificationGenomics and Phylogenetic Studies

Cited by 7,876

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

Soneson, C., Love, M. I., & Robinson, M. D. (2015) · F1000Research

Genomics and Phylogenetic StudiesGene expression and cancer classification

Cited by 3,929

Systematic Genetic Analysis with Ordered Arrays of Yeast Deletion Mutants

Tong, A. H. Y., Evangelista, M., Parsons, A. B., Xu, H., Bader, G. D., Pagé, N., Robinson, M., Raghibizadeh, S., Hogue, C. W. V., Bussey, H., et al. (2001) · Science

Fungal and yeast genetics researchBioinformatics and Genomic Networks

Cited by 2,138

Eleven grand challenges in single-cell data science

Lähnemann, D., Köster, J., Szczurek, E., McCarthy, D. J., Hicks, S. C., Robinson, M. D., Vallejos, C. A., Campbell, K. R., Beerenwinkel, N., Mahfouz, A., et al. (2020) · Genome Biology

Single-cell and spatial transcriptomicsGene expression and cancer classification

Cited by 1,327

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

Robinson, M. D., & Smyth, G. K. (2007) · Biostatistics

Gene expression and cancer classificationStatistical Distribution Estimation and Applications

Cited by 1,073

Moderated statistical tests for assessing differences in tag abundance

Robinson, M. D., & Smyth, G. K. (2007) · Bioinformatics

Gene expression and cancer classificationGenomics and Phylogenetic Studies

Cited by 856

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

Soneson, C., & Robinson, M. D. (2018) · Nature Methods

Single-cell and spatial transcriptomicsGene expression and cancer classification

Cited by 770

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

Krieg, C., Nowicka, M., Guglietta, S., Schindler, S., Hartmann, F. J., Weber, L. M., Dummer, R., Robinson, M. D., Levesque, M. P., & Becher, B. (2018) · Nature Medicine

Cancer Immunotherapy and BiomarkersImmunotherapy and Immune Responses

Cited by 735

Doublet identification in single-cell sequencing data using scDblFinder

Germain, P.-L., Lun, A., Garcia Meixide, C., Macnair, W., & Robinson, M. D. (2022) · F1000Research

Single-cell and spatial transcriptomicsGenomics and Phylogenetic Studies

Cited by 684

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

Crowell, H. L., Soneson, C., Germain, P.-L., Calini, D., Collin, L., Raposo, C., Malhotra, D., & Robinson, M. D. (2020) · Nature Communications

Single-cell and spatial transcriptomicsGene expression and cancer classification

Cited by 515

Robustly detecting differential expression in RNA sequencing data using observation weights

Zhou, X., Lindsay, H., & Robinson, M. D. (2014) · Nucleic Acids Research

Gene expression and cancer classificationGenomics and Phylogenetic Studies

Cited by 387

A systematic performance evaluation of clustering methods for single-cell RNA-seq data

Duò, A., Robinson, M. D., & Soneson, C. (2018) · F1000Research

Single-cell and spatial transcriptomicsGene expression and cancer classification

Cited by 296

Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications

Van den Berge, K., Perraudeau, F., Soneson, C., Love, M. I., Risso, D., Vert, J.-P., Robinson, M. D., Dudoit, S., & Clement, L. (2018) · Genome Biology

Single-cell and spatial transcriptomicsGene expression and cancer classification

Cited by 228

stageR: a general stage-wise method for controlling the gene-level false discovery rate in differential expression and differential transcript usage

Van den Berge, K., Soneson, C., Robinson, M. D., & Clement, L. (2017) · Genome Biology

Gene expression and cancer classificationGenomics and Phylogenetic Studies

Cited by 126

snRNA-seq stratifies multiple sclerosis patients into distinct white matter glial responses

Macnair, W., Calini, D., Agirre, E., Bryois, J., Jäkel, S., Smith, R. S., Kukanja, P., Stokar-Regenscheit, N., Ott, V., Foo, L. C., et al. (2025) · Neuron

Multiple Sclerosis Research StudiesSingle-cell and spatial transcriptomics

Cited by 88

Essential guidelines for computational method benchmarking

Weber, L. M., Saelens, W., Cannoodt, R., Soneson, C., Hapfelmeier, A., Gardner, P. P., Boulesteix, A.-L., Saeys, Y., & Robinson, M. D. (2019) · Genome Biology

Scientific Computing and Data ManagementGenomics and Phylogenetic Studies

Cited by 21