Toward deterministic and semiautomated SPADE analysis.

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Sensitive and viable identification of antigen-specific CD8+ T cells by a flow cytometric assay for degranulation. J Immunol Methods (2003) 11.81

Seventeen-colour flow cytometry: unravelling the immune system. Nat Rev Immunol (2004) 7.37

Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat Biotechnol (2011) 5.46

Interpreting flow cytometry data: a guide for the perplexed. Nat Immunol (2006) 5.22

Automated gating of flow cytometry data via robust model-based clustering. Cytometry A (2008) 5.10

Automated high-dimensional flow cytometric data analysis. Proc Natl Acad Sci U S A (2009) 4.91

viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat Biotechnol (2013) 4.88

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Quantum dot semiconductor nanocrystals for immunophenotyping by polychromatic flow cytometry. Nat Med (2006) 4.41

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Statistical mixture modeling for cell subtype identification in flow cytometry. Cytometry A (2008) 3.06

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Data reduction for spectral clustering to analyze high throughput flow cytometry data. BMC Bioinformatics (2010) 2.46

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Elucidation of seventeen human peripheral blood B-cell subsets and quantification of the tetanus response using a density-based method for the automated identification of cell populations in multidimensional flow cytometry data. Cytometry B Clin Cytom (2010) 2.38

A chromatic explosion: the development and future of multiparameter flow cytometry. Immunology (2008) 2.17

B-cell signaling networks reveal a negative prognostic human lymphoma cell subset that emerges during tumor progression. Proc Natl Acad Sci U S A (2010) 2.10

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Automatic clustering of flow cytometry data with density-based merging. Adv Bioinformatics (2009) 1.62

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Hierarchical modeling for rare event detection and cell subset alignment across flow cytometry samples. PLoS Comput Biol (2013) 1.35

The curvHDR method for gating flow cytometry samples. BMC Bioinformatics (2010) 1.33

Misty Mountain clustering: application to fast unsupervised flow cytometry gating. BMC Bioinformatics (2010) 1.13

SWIFT-scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, part 1: algorithm design. Cytometry A (2014) 1.13

Methods for discovery and characterization of cell subsets in high dimensional mass cytometry data. Methods (2015) 1.06

Inferring phenotypic properties from single-cell characteristics. PLoS One (2012) 1.06

CytoSPADE: high-performance analysis and visualization of high-dimensional cytometry data. Bioinformatics (2012) 1.04

Highly multiplexed profiling of single-cell effector functions reveals deep functional heterogeneity in response to pathogenic ligands. Proc Natl Acad Sci U S A (2015) 0.99

SWIFT-scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, part 2: biological evaluation. Cytometry A (2014) 0.98

Computational prediction of manually gated rare cells in flow cytometry data. Cytometry A (2015) 0.80

Individual Motile CD4(+) T Cells Can Participate in Efficient Multikilling through Conjugation to Multiple Tumor Cells. Cancer Immunol Res (2015) 1.25

A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes. Cytometry A (2015) 0.78