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
2022
Multiscale PHATE identifies multimodal signatures of COVID-19
Kuchroo M, Huang J, Wong P, Grenier JC, Shung D, Tong A, Lucas C, Klein J, Burkhardt DB, Gigante S, Godavarthi A, Rieck B, Israelow B, Simonov M, Mao T, Oh JE, Silva J, Takahashi T, Odio CD, Casanovas-Massana A, Fournier J, Farhadian S, Dela Cruz C, Ko A, Hirn M, Wilson F, Hussin J, Wolf G, Iwasaki A, Krishnaswamy S. Multiscale PHATE identifies multimodal signatures of COVID-19. Nature Biotechnology 2022, 40: 681-691. PMID: 35228707, PMCID: PMC10015653, DOI: 10.1038/s41587-021-01186-x.Peer-Reviewed Original ResearchConceptsSingle-cell RNA sequencingTransposase-accessible chromatinSingle-cell sequencingRNA sequencingBiological insightsPopulation groupingsSophisticated computational toolsBiological featuresSequencingFlow cytometryComputational toolsChromatinBiomedical communityDifferent data typesCell responsesCellsPhate