2021
Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-supervised Edge Features and Graph Neural Networks
Sehanobish A, Ravindra N, Van Dijk D. Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-supervised Edge Features and Graph Neural Networks. Proceedings Of The AAAI Conference On Artificial Intelligence 2021, 35: 4864-4873. DOI: 10.1609/aaai.v35i6.16619.Peer-Reviewed Original ResearchSARS-CoV-2 infectionSingle-cell omics dataCOVID-19 severitySingle-cell RNA sequencing datasetsCell typesRNA sequencing datasetsSARS-CoV-2Transcriptomic patternsSequencing datasetsOmics dataCellular determinantsCellular understandingIndividual cellsBronchoalveolar lavage fluid samplesInfected cellsSevere COVID-19Lavage fluid samplesCOVID-19Lung organoidsDisease statesInfectionTranscriptomeCellsSeverityFluid samplesQuantifying 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
Learning General Transformations of Data for Out-of-Sample Extensions
Amodio M, van Dijk D, Wolf G, Krishnaswamy S. Learning General Transformations of Data for Out-of-Sample Extensions. 2023 IEEE 33rd International Workshop On Machine Learning For Signal Processing (MLSP) 2020, 00: 1-6. PMID: 34557339, PMCID: PMC8456777, DOI: 10.1109/mlsp49062.2020.9231660.Peer-Reviewed Original ResearchSingle-cell RNA sequencing datasetsRNA sequencing datasetsSample of cellsSequencing datasetsDrug perturbationsSpecific distributionCells
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