Soneson, Charlotte

The consensus molecular subtypes of colorectal cancer

Guinney, J., Dienstmann, R., Wang, X., de Reyniès, A., Schlicker, A., Soneson, C., Marisa, L., Roepman, P., Nyamundanda, G., Angelino, P., et al. (2015) · Nature Medicine

Colorectal Cancer Treatments and StudiesGenetic factors in colorectal cancer

Cited by 5,170

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

Orchestrating single-cell analysis with Bioconductor

Amezquita, R. A., Lun, A. T. L., Becht, E., Carey, V. J., Carpp, L. N., Geistlinger, L., Marini, F., Rue-Albrecht, K., Risso, D., Soneson, C., et al. (2020) · Nature Methods

Single-cell and spatial transcriptomicsGenomics and Phylogenetic Studies

Cited by 945

A comparison of methods for differential expression analysis of RNA-seq data

Soneson, C., & Delorenzi, M. (2013) · BMC Bioinformatics

Gene expression and cancer classificationGenomics and Phylogenetic Studies

Cited by 887

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

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

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

Serum neurofilament light chain in early relapsing remitting MS is increased and correlates with CSF levels and with MRI measures of disease severity

Kuhle, J., Barro, C., Disanto, G., Mathias, A., Soneson, C., Bonnier, G., Yaldizli, Ö., Regeniter, A., Derfuss, T., Canales, M., et al. (2016) · Multiple Sclerosis Journal

Multiple Sclerosis Research StudiesPeripheral Neuropathies and Disorders

Cited by 257

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

Long-lasting stem cell–like memory CD8 + T cells with a naïve-like profile upon yellow fever vaccination

Fuertes Marraco, S. A., Soneson, C., Cagnon, L., Gannon, P. O., Allard, M., Maillard, S. A., Montandon, N., Rufer, N., Waldvogel, S., Delorenzi, M., et al. (2015) · Science Translational Medicine

T-cell and B-cell ImmunologyImmunotherapy and Immune Responses

Cited by 206

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

Batch Effect Confounding Leads to Strong Bias in Performance Estimates Obtained by Cross-Validation

Soneson, C., Gerster, S., & Delorenzi, M. (2014) · PLoS ONE

Gene expression and cancer classificationMachine Learning and Data Classification

Cited by 84

A Composite Gene Expression Signature Optimizes Prediction of Colorectal Cancer Metastasis and Outcome

Schell, M. J., Yang, M., Missiaglia, E., Delorenzi, M., Soneson, C., Yue, B., Nebozhyn, M. V., Loboda, A., Bloom, G., & Yeatman, T. J. (2016) · Clinical Cancer Research

Cancer Cells and MetastasisColorectal Cancer Treatments and Studies

Cited by 42

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