2019
Visualizing structure and transitions in high-dimensional biological data
Moon KR, van Dijk D, Wang Z, Gigante S, Burkhardt DB, Chen WS, Yim K, Elzen AVD, Hirn MJ, Coifman RR, Ivanova NB, Wolf G, Krishnaswamy S. Visualizing structure and transitions in high-dimensional biological data. Nature Biotechnology 2019, 37: 1482-1492. PMID: 31796933, PMCID: PMC7073148, DOI: 10.1038/s41587-019-0336-3.Peer-Reviewed Original ResearchConceptsSingle-cell RNA sequencing datasetsSingle-cell RNA sequencingUnique biological insightsRNA sequencing datasetsGerm layer differentiationMain developmental branchesHigh-throughput technologiesGut microbiome dataRNA sequencingUndescribed subpopulationsHigh-dimensional biological dataSequencing datasetsBiological insightsDevelopmental branches
2014
Quantitative Arbor Analytics: Unsupervised Harmonic Co-Clustering of Populations of Brain Cell Arbors Based on L-Measure
Lu Y, Carin L, Coifman R, Shain W, Roysam B. Quantitative Arbor Analytics: Unsupervised Harmonic Co-Clustering of Populations of Brain Cell Arbors Based on L-Measure. Neuroinformatics 2014, 13: 47-63. PMID: 25086878, DOI: 10.1007/s12021-014-9237-2.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAnimalsBrainBrain MappingHumansImage Processing, Computer-AssistedPattern Recognition, AutomatedConceptsCo-clustering methodAnalytics systemSynthetic datasetsThree-dimensional visualizationAnalysis ToolkitHeterogeneous ensembleDistance measureAlgorithmMultivariate data pointsData smoothingData pointsWavelet basisData matrixHarmonic analysis theoryL-measureNeuroMorpho databaseDatasetAnalysis theoryToolkitVisualizationEnsembleRobustnessDatabaseSuperiorityMethod
2009
Detecting consistent common lines in cryo-EM by voting
Singer A, Coifman RR, Sigworth FJ, Chester DW, Shkolnisky Y. Detecting consistent common lines in cryo-EM by voting. Journal Of Structural Biology 2009, 169: 312-322. PMID: 19925867, PMCID: PMC2826584, DOI: 10.1016/j.jsb.2009.11.003.Peer-Reviewed Original Research