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 heterogeneityPartially characterized topology guides reliable anchor-free scRNA-integration
He C, Filippidis P, Kleinstein S, Guan L. Partially characterized topology guides reliable anchor-free scRNA-integration. Communications Biology 2025, 8: 561. PMID: 40185996, PMCID: PMC11971424, DOI: 10.1038/s42003-025-07988-y.Peer-Reviewed Original ResearchConceptsBatch effectsRare cell typesSingle-cell RNA sequencingCell typesDownstream statistical analysisScRNA-seqBiological insightsRNA sequencingBatch correctionCell phenotypeCellular resolutionBiological signalsState-of-the-art methodsAdaptive lossDomain adaptation lossState-of-the-artDiverse setBatch integrationHeterogeneous cell distributionReconstruction lossSequenceTriplet lossPhenotypeSignalCell distributionBatch correcting single-cell spatial transcriptomics count data with Crescendo improves visualization and detection of spatial gene patterns
Millard N, Chen J, Palshikar M, Pelka K, Spurrell M, Price C, He J, Hacohen N, Raychaudhuri S, Korsunsky I. Batch correcting single-cell spatial transcriptomics count data with Crescendo improves visualization and detection of spatial gene patterns. Genome Biology 2025, 26: 36. PMID: 40001084, PMCID: PMC11863647, DOI: 10.1186/s13059-025-03479-9.Peer-Reviewed Original ResearchConceptsBatch effectsVisualization of gene expression patternsSpatial gene patternsGene expression analysis of cellsGene expression patternsGene expression analysisGene expression levelsGene colocalizationAnalysis of cellsGene patternsTranscriptome analysisLigand-receptor interactionsExpression patternsSpatial transcriptomicsSpatial transcriptomic analysisExpression levelsGenesMultiple samplesSpatial patternsTranscriptomeColocalizationAnatomical contextPatternsCount data
2024
BERNN: Enhancing classification of Liquid Chromatography Mass Spectrometry data with batch effect removal neural networks
Pelletier S, Leclercq M, Roux-Dalvai F, de Geus M, Leslie S, Wang W, Lam T, Nairn A, Arnold S, Carlyle B, Precioso F, Droit A. BERNN: Enhancing classification of Liquid Chromatography Mass Spectrometry data with batch effect removal neural networks. Nature Communications 2024, 15: 3777. PMID: 38710683, PMCID: PMC11074280, DOI: 10.1038/s41467-024-48177-5.Peer-Reviewed Original ResearchConceptsLC-MS experimentsLC-MSLiquid chromatography mass spectrometry dataComplex biological samplesMass spectrometry dataLiquid chromatography mass spectrometryChromatography mass spectrometryMass spectrometrySpectrometry dataEffective removalBiological samplesExperimental conditionsBatch effect removalSample processing protocolBatch effectsSpectrometryBatch effect correction methodsCorrecting batch effectsRemoval of batch effectsSingle-cell biclustering for cell-specific transcriptomic perturbation detection in AD progression
Gong Y, Xu J, Wu M, Gao R, Sun J, Yu Z, Zhang Y. Single-cell biclustering for cell-specific transcriptomic perturbation detection in AD progression. Cell Reports Methods 2024, 4: 100742. PMID: 38554701, PMCID: PMC11045878, DOI: 10.1016/j.crmeth.2024.100742.Peer-Reviewed Original ResearchConceptsSnRNA-seq dataGene modulesAD progressionPathogenesis of Alzheimer's diseaseBiologically interpretable resultsSingle-cell data analysisGene regulatory changesFunctional gene modulesGene coexpression patternsAlzheimer's diseaseSingle-cell levelSnRNA-seqBiclustering methodsPolygenic diseaseBatch effectsDropout eventsCoexpression patternsNetwork biomarkersCell typesBiclusteringCellsGenesScRNABiologyComparative analysis
2023
ZINBMM: 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 analysisFive Critical Gene-Based Biomarkers With Optimal Performance for Hepatocellular Carcinoma
Liu Y, Zhang H, Xu Y, Liu Y, Al-Adra D, Yeh M, Zhang Z. Five Critical Gene-Based Biomarkers With Optimal Performance for Hepatocellular Carcinoma. Cancer Informatics 2023, 22: 11769351231190477. PMID: 37577174, PMCID: PMC10413891, DOI: 10.1177/11769351231190477.Peer-Reviewed Original ResearchModel gene-gene interactionsCorrecting batch effectsGene-gene interactionsPublished transcriptomic studiesAnalysis of human cancersGene-based biomarkersWhole-transcriptome datasetsGenomic levelTranscriptome dataTranscriptomic studiesBatch effectsEffective therapeutic targetDEGsHuman cancersDisease etiologyMolecular backgroundCaucasian cohortTherapeutic targetIdentified 5Signature patternsIdentification of biomarkersConceptual advancesMiniaturized setting
2021
Diagnostics and correction of batch effects in large‐scale proteomic studies: a tutorial
Čuklina J, Lee C, Williams E, Sajic T, Collins B, Rodríguez Martínez M, Sharma V, Wendt F, Goetze S, Keele G, Wollscheid B, Aebersold R, Pedrioli P. Diagnostics and correction of batch effects in large‐scale proteomic studies: a tutorial. Molecular Systems Biology 2021, 17: msb202110240. PMID: 34432947, PMCID: PMC8447595, DOI: 10.15252/msb.202110240.Peer-Reviewed Original ResearchConceptsBatch effectsProteomic studiesLarge-scale proteomic studiesCorrection of batch effectsMass spectrometry-based proteomicsStep-by-step protocolProteomic datasetsProteomic dataSystems biologyBatch correctionMultiple experimental designsProteomic ChallengeR packageProteomicsClinical proteomicsBiological signalsTechnical variabilityStatistical powerIntensity driftBiology
2017
GRAPE: a pathway template method to characterize tissue-specific functionality from gene expression profiles
Klein MI, Stern DF, Zhao H. GRAPE: a pathway template method to characterize tissue-specific functionality from gene expression profiles. BMC Bioinformatics 2017, 18: 317. PMID: 28651562, PMCID: PMC5485588, DOI: 10.1186/s12859-017-1711-z.Peer-Reviewed Original ResearchConceptsGene expression profilesExpression profilesIndividual gene expression profilesTissue-specific functionalityEnrichment-based methodsPathway scoresGene expression levelsBatch effectsPathway genesPresent genesPerturbed pathwaysPathway expressionExpression levelsIndividual samplesTissue typesPathwayGenesTCGA subtypesAbnormal pathwaysIndependent datasetsBreast cancer subtypesNon-competitive approachIndividual tumorsCancer subtypesGrapesRemoval 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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