2021
Quantifying the effect of experimental perturbations at single-cell resolution
Burkhardt DB, Stanley JS, Tong A, Perdigoto AL, Gigante SA, Herold KC, Wolf G, Giraldez AJ, van Dijk D, Krishnaswamy S. Quantifying the effect of experimental perturbations at single-cell resolution. Nature Biotechnology 2021, 39: 619-629. PMID: 33558698, PMCID: PMC8122059, DOI: 10.1038/s41587-020-00803-5.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsCluster AnalysisComputational BiologyComputer SimulationHumansLikelihood FunctionsSequence Analysis, RNASingle-Cell AnalysisTranscriptomeConceptsSingle-cell RNA sequencing datasetsClusters of cellsRNA sequencing datasetsSingle-cell resolutionSingle-cell levelTranscriptomic spaceSequencing datasetsExperimental perturbationsCell populationsGene signatureVertex frequencyDiscrete regionsCellsEffects of perturbationsMultiple conditionsPerturbation responseClustersPopulationPerturbationsLikelihood estimatesGraph signal processing
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