2023
EPCO-09. CHARACTERIZING THE GBM CELLULAR LANDSCAPE BY LARGE-SCALE SINGLE-NUCLEUS RNA-SEQUENCING
Spitzer A, Nomura M, Garofano L, Johnson K, Nehar-Belaid D, Oh Y, Anderson K, Najac R, Bussema L, Varn F, D’Angelo F, Chowdhury T, Migliozzi S, Park J, Ermini L, Golebiewska A, Niclou S, Das S, Paek S, Moon H, Mathon B, Di Stefano A, Bielle F, Laurenge A, Sanson M, Tanaka S, Saito N, Keir S, Ashley D, Huse J, Yung W, Lasorella A, Verhaak R, Iavarone A, Tirosh I, Suva M. EPCO-09. CHARACTERIZING THE GBM CELLULAR LANDSCAPE BY LARGE-SCALE SINGLE-NUCLEUS RNA-SEQUENCING. Neuro-Oncology 2023, 25: v125-v125. PMCID: PMC10639394, DOI: 10.1093/neuonc/noad179.0473.Peer-Reviewed Original ResearchCellular statesSingle-cell RNA sequencing technologySpecific cellular statesCell typesDNA sequence dataRNA sequencing technologyMalignant cell statesWhole-genome sequencing dataNucleus RNA sequencingRNA sequencing datasetsFunctional enrichment analysisScRNA-seq datasetsScRNA-seq studiesGBM tumor samplesCertain genetic eventsHallmark of glioblastomaCellular landscapeRNA sequencingCell statesEnrichment analysisBaseline expression profilesSequencing dataExpression profilesGlial developmentIntra-tumor heterogeneity
2022
Computational and Statistical Methods for Single-Cell RNA Sequencing Data
Wang Z, Yan X. Computational and Statistical Methods for Single-Cell RNA Sequencing Data. Springer Handbooks Of Computational Statistics 2022, 3-35. DOI: 10.1007/978-3-662-65902-1_1.ChaptersSingle-cell RNA sequencing technologySingle-cell RNA sequencing dataRNA sequencing technologyPhenotype of interestRNA sequencing dataDifferential expression analysisScRNA-seq dataStatistical methodsSequencing technologiesExpression analysisDropout imputationSequencing dataSeq dataDroplet-based technologiesDropout eventsDisease pathogenesisPopulation composition changesData normalizationHigh noise levelsPhenotypeNoise levelTherapeuticsComposition changesA Markov random field model-based approach for differentially expressed gene detection from single-cell RNA-seq data
Zhu B, Li H, Zhang L, Chandra SS, Zhao H. A Markov random field model-based approach for differentially expressed gene detection from single-cell RNA-seq data. Briefings In Bioinformatics 2022, 23: bbac166. PMID: 35514182, PMCID: PMC9487630, DOI: 10.1093/bib/bbac166.Peer-Reviewed Original ResearchConceptsDE genesSeq dataSingle-cell RNA sequencing technologyDifferential expressionSingle-cell RNA-seq dataIdentification of genesRNA sequencing technologySpecific differential expressionSingle-cell resolutionRNA-seq dataMarkov random field modelMarkov random field model-based approachSimilar cell typesNovel statistical modelRandom field modelComplex biological systemsBiological pathwaysGene detectionGenesCell typesStatistical modelMouse datasetsField modelBiological systemsReal data
2021
Bayesian information sharing enhances detection of regulatory associations in rare cell types
Wu A, Peng J, Berger B, Cho H. Bayesian information sharing enhances detection of regulatory associations in rare cell types. Bioinformatics 2021, 37: i349-i357. PMID: 34252956, PMCID: PMC8275330, DOI: 10.1093/bioinformatics/btab269.Peer-Reviewed Original ResearchConceptsScRNA-seq datasetsRegulatory associationsCell typesRegulatory networksCell type-specific gene regulatory networksCell-type specific gene regulationSingle-cell RNA sequencing technologyCell-type specific networksBenchmark scRNA-seq datasetsDiverse cellular contextsGene regulatory network inference methodRNA sequencing technologyGene regulatory networksRare cell typesSingle-cell datasetsSpecific cell typesNetwork inference methodsDynamic biological processesTranscriptional statesGene regulationCellular contextNetwork inference algorithmsComplex rewiringBiological processesGene associationsG2S3: A gene graph-based imputation method for single-cell RNA sequencing data
Wu W, Liu Y, Dai Q, Yan X, Wang Z. G2S3: A gene graph-based imputation method for single-cell RNA sequencing data. PLOS Computational Biology 2021, 17: e1009029. PMID: 34003861, PMCID: PMC8189489, DOI: 10.1371/journal.pcbi.1009029.Peer-Reviewed Original ResearchConceptsSingle-cell transcriptomic datasetsTranscriptomic datasetsGene expressionSingle-cell RNA sequencing technologySingle-cell transcriptomic studiesSingle-cell RNA sequencing dataRNA sequencing technologyRNA sequencing dataSingle-cell resolutionGene expression profilesAdjacent genesTranscriptomic studiesSequencing technologiesSequencing dataExpression profilesGene graphDownstream analysisGenesCell trajectoriesDropout eventsCell subtypesExpressionHigh data sparsityCells
2018
Recovering Gene Interactions from Single-Cell Data Using Data Diffusion
van Dijk D, Sharma R, Nainys J, Yim K, Kathail P, Carr AJ, Burdziak C, Moon KR, Chaffer CL, Pattabiraman D, Bierie B, Mazutis L, Wolf G, Krishnaswamy S, Pe’er D. Recovering Gene Interactions from Single-Cell Data Using Data Diffusion. Cell 2018, 174: 716-729.e27. PMID: 29961576, PMCID: PMC6771278, DOI: 10.1016/j.cell.2018.05.061.Peer-Reviewed Original ResearchManifold 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
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