2023
scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles
Zhu B, Wang Y, Ku L, van Dijk D, Zhang L, Hafler D, Zhao H. scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles. Genome Biology 2023, 24: 292. PMID: 38111007, PMCID: PMC10726524, DOI: 10.1186/s13059-023-03129-y.Peer-Reviewed Original Research
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 ResearchConceptsSingle-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
2020
Uncovering axes of variation among single-cell cancer specimens
Chen WS, Zivanovic N, van Dijk D, Wolf G, Bodenmiller B, Krishnaswamy S. Uncovering axes of variation among single-cell cancer specimens. Nature Methods 2020, 17: 302-310. PMID: 31932777, PMCID: PMC7339867, DOI: 10.1038/s41592-019-0689-z.Peer-Reviewed Original ResearchAlgorithmsAnimalsAntineoplastic AgentsBiopsyBreast NeoplasmsCluster AnalysisCytophotometryDrug Screening Assays, AntitumorEnzyme InhibitorsEpithelial-Mesenchymal TransitionFemaleHumansImage Interpretation, Computer-AssistedMammary Neoplasms, AnimalMiceNeoplasm MetastasisPattern Recognition, AutomatedPhenotypeRecombinant ProteinsSingle-Cell AnalysisSoftwareTransforming Growth Factor beta
2019
Exploring single-cell data with deep multitasking neural networks
Amodio M, van Dijk D, Srinivasan K, Chen WS, Mohsen H, Moon KR, Campbell A, Zhao Y, Wang X, Venkataswamy M, Desai A, Ravi V, Kumar P, Montgomery R, Wolf G, Krishnaswamy S. Exploring single-cell data with deep multitasking neural networks. Nature Methods 2019, 16: 1139-1145. PMID: 31591579, PMCID: PMC10164410, DOI: 10.1038/s41592-019-0576-7.Peer-Reviewed Original Research
2013
Two DNA-encoded strategies for increasing expression with opposing effects on promoter dynamics and transcriptional noise
Dadiani M, van Dijk D, Segal B, Field Y, Ben-Artzi G, Raveh-Sadka T, Levo M, Kaplow I, Weinberger A, Segal E. Two DNA-encoded strategies for increasing expression with opposing effects on promoter dynamics and transcriptional noise. Genome Research 2013, 23: 966-976. PMID: 23403035, PMCID: PMC3668364, DOI: 10.1101/gr.149096.112.Peer-Reviewed Original ResearchConceptsPromoter dynamicsExpression variabilityPromoter transitionsSingle-cell time-lapse microscopyInactive stateSequence changesNucleosome-disfavoring sequencesCis-regulatory elementsProcess of transcriptionActive stateNumber of transcriptsTime-lapse microscopyCell populationsTranscriptional noiseTranscriptional dynamicsSite resultsTranscription factorsDNA sequencesGene expressionMean expressionIdentical populationsIndividual cellsSequence resultsExpression levelsTranscripts