2024
Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning
Steach H, Viswanath S, He Y, Zhang X, Ivanova N, Hirn M, Perlmutter M, Krishnaswamy S. Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning. Lecture Notes In Computer Science 2024, 14758: 235-252. DOI: 10.1007/978-1-0716-3989-4_15.Peer-Reviewed Original ResearchSingle-cell resolutionMetabolic networksStructure of metabolic networksBiological processesGlobal metabolic networkMetabolic stateMeasure gene expressionGenomic informationTranscriptomic dataTranscriptome dataPost-translationallyEpigenetic modificationsMultimodal regulationGene expressionSingle-cellTissue scaleBiological featuresCellsTranscriptomeMetabolomicsTranscriptionFlux ratesMultiomicsScRNAseqBiology
2023
Integrated transcriptome and trajectory analysis of cutaneous T-cell lymphoma identifies putative precancer populations
Ren J, Qu R, Rahman N, Lewis J, King A, Liao X, Mirza F, Carlson K, Huang Y, Gigante S, Evans B, Rajendran B, Xu S, Wang G, Foss F, Damsky W, Kluger Y, Krishnaswamy S, Girardi M. Integrated transcriptome and trajectory analysis of cutaneous T-cell lymphoma identifies putative precancer populations. Blood Advances 2023, 7: 445-457. PMID: 35947128, PMCID: PMC9979716, DOI: 10.1182/bloodadvances.2022008168.Peer-Reviewed Original ResearchConceptsCutaneous T-cell lymphomaMalignant CTCL cellsDiverse transcriptomic profilesT cellsSingle-cell RNACTCL cellsDevelopment of CTCLIntegrated transcriptomeT-cell receptor sequencingT cell exhaustion phenotypeCommon antigenic stimulusPeripheral blood CD4Transcriptomic profilesGene expressionT-cell lymphomaIntegrative analysisPotential therapeutic targetProliferation advantageLimited diversityBlood CD4Blood involvementMutation levelsExhaustion phenotypeWorse prognosisAntigenic stimulus
2018
Manifold learning-based methods for analyzing single-cell RNA-sequencing data
Moon K, Stanley J, Burkhardt D, van Dijk D, Wolf G, Krishnaswamy S. Manifold learning-based methods for analyzing single-cell RNA-sequencing data. Current Opinion In Systems Biology 2018, 7: 36-46. DOI: 10.1016/j.coisb.2017.12.008.Peer-Reviewed Original ResearchSingle-cell RNA-sequencing dataSingle-cell RNA sequencing technologyRNA sequencing technologyRNA-sequencing dataThousands of cellsGene regulationCellular statesPhenotypic diversityCellular developmentGene interactionsSequencing technologiesGene expressionSeq dataUnderlying biological signalManifold learning-based methodsSingle experimentBiological signalsRecent advancesDiversityDeeper insightRegulationExpression