2025
A benchmarking study of copy number variation inference methods using single-cell RNA-sequencing data
Chen X, Fang L, Chen Z, Chen W, Wu H, Zhu B, Moos M, Farmer A, Zhang X, Xiong W, Gong S, Jones W, Mason C, Wu S, Xiao C, Wang C. A benchmarking study of copy number variation inference methods using single-cell RNA-sequencing data. Precision Clinical Medicine 2025, 8: pbaf011. PMID: 40584741, PMCID: PMC12204187, DOI: 10.1093/pcmedi/pbaf011.Peer-Reviewed Original ResearchScRNA-seq datasetsStudy genetic heterogeneityScRNA-seq dataCopy number variationsSingle-cell RNA sequencingRNA sequencing dataInference methodsRead lengthSequencing depthSingle-cell levelSequencing tissueScRNA-seqTranscriptome dataNumber variationsGenetic heterogeneityRNA sequencingBatch effectsHuman lung adenocarcinoma cell lineLung adenocarcinoma cell linesAdenocarcinoma cell lineCell linesInferCNVCancer researchSCCNVTumor heterogeneityDifferences in immune cells and gene expression in human milk by parity on integrated scRNA sequencing
Yi D, Park H, Shin M, Kim H, Lee S, Kang I. Differences in immune cells and gene expression in human milk by parity on integrated scRNA sequencing. Clinical And Experimental Pediatrics 2025, 68: 141-152. PMID: 39810510, PMCID: PMC11825117, DOI: 10.3345/cep.2024.01585.Peer-Reviewed Original ResearchGene expressionAdaptive immune cellsInfant immune systemImmune cellsImmune cell heterogeneityScRNA-seq datasetsHuman breast milkCell heterogeneityGene Set Enrichment AnalysisMultiparous groupT/B cellsProportion of innate immune cellsResponse-related genesImmune systemScRNA-seqImmune response-related genesImmune cell profilesInnate immune cellsEnrichment analysisGenesProportion of monocytesEffect of parityImmune mediatorsImmune clustersBreast milk
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 heterogeneityZINBMM: a general mixture model for simultaneous clustering and gene selection using single-cell transcriptomic data
Li Y, Wu M, Ma S, Wu M. ZINBMM: a general mixture model for simultaneous clustering and gene selection using single-cell transcriptomic data. Genome Biology 2023, 24: 208. PMID: 37697330, PMCID: PMC10496184, DOI: 10.1186/s13059-023-03046-0.Peer-Reviewed Original ResearchConceptsSingle-cell transcriptomic dataCell heterogeneitySingle-cell RNA sequencing data analysisRNA sequencing data analysisCluster-specific genesGene selectionScRNA-seq datasetsSequencing data analysisNegative binomial mixture modelTranscriptomic dataCell lineagesCell typesBinomial mixture modelsBiological understandingBatch effectsDropout eventsLineagesGenesRaw countsCritical componentSelectionSystemic analysis
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 associations
2017
Removal of batch effects using distribution-matching residual networks
Shaham U, Stanton KP, Zhao J, Li H, Raddassi K, Montgomery R, Kluger Y. Removal of batch effects using distribution-matching residual networks. Bioinformatics 2017, 33: 2539-2546. PMID: 28419223, PMCID: PMC5870543, DOI: 10.1093/bioinformatics/btx196.Peer-Reviewed Original ResearchConceptsMeasurement errorNovel deep learning approachRandom measurement errorMultivariate distributionsResidual neural networkDeep learning approachNovel biological technologiesMaximum mean discrepancyPhysical phenomenaResidual networkNeural networkLearning approachSystematic componentSupplementary dataSystematic errorsMean discrepancyScRNA-seq datasetsBatch effectsErrorNetworkStatistical analysis
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