2024
SDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data
Liu Y, Li N, Qi J, Xu G, Zhao J, Wang N, Huang X, Jiang W, Wei H, Justet A, Adams T, Homer R, Amei A, Rosas I, Kaminski N, Wang Z, Yan X. SDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data. Genome Biology 2024, 25: 271. PMID: 39402626, PMCID: PMC11475911, DOI: 10.1186/s13059-024-03416-2.Peer-Reviewed Original Research
2022
Comprehensive visualization of cell–cell interactions in single-cell and spatial transcriptomics with NICHES
Raredon M, Yang J, Kothapalli N, Lewis W, Kaminski N, Niklason L, Kluger Y. Comprehensive visualization of cell–cell interactions in single-cell and spatial transcriptomics with NICHES. Bioinformatics 2022, 39: btac775. PMID: 36458905, PMCID: PMC9825783, DOI: 10.1093/bioinformatics/btac775.Peer-Reviewed Original ResearchConceptsCell-cell interactionsCell-cell signalingSingle-cell resolutionSingle-cell dataLocal cellular microenvironmentSingle-cell levelSpatial transcriptomics dataCell clustersExtracellular signalingTranscriptomic dataGene expression valuesSpatial transcriptomicsSignaling mechanismCellular microenvironmentNicheExpression valuesSupplementary dataSignalingTranscriptomicsComprehensive visualizationBioinformaticsInteraction
2019
LCox: a tool for selecting genes related to survival outcomes using longitudinal gene expression data
Sun J, Herazo-Maya JD, Wang JL, Kaminski N, Zhao H. LCox: a tool for selecting genes related to survival outcomes using longitudinal gene expression data. Statistical Applications In Genetics And Molecular Biology 2019, 18: 20170060. PMID: 30759070, DOI: 10.1515/sagmb-2017-0060.Peer-Reviewed Original Research
2018
iDREM: Interactive visualization of dynamic regulatory networks
Ding J, Hagood JS, Ambalavanan N, Kaminski N, Bar-Joseph Z. iDREM: Interactive visualization of dynamic regulatory networks. PLOS Computational Biology 2018, 14: e1006019. PMID: 29538379, PMCID: PMC5868853, DOI: 10.1371/journal.pcbi.1006019.Peer-Reviewed Original ResearchConceptsDynamic regulatory networksRegulatory networksHigh-throughput time series dataInteraction dataProtein-DNA interaction dataSingle-cell RNA-seqTime series gene expression dataStatic datasetsInteractive visualizationGene expression dataData typesRNA-seqTime series dataBiological processesExpression dataMiRNA expressionNetworkSeries dataImportant challengeNew versionDevelopmental dataNovel hypothesisUnified modelMultiple labsRecent years
2014
Missing value imputation in high-dimensional phenomic data: imputable or not, and how?
Liao SG, Lin Y, Kang DD, Chandra D, Bon J, Kaminski N, Sciurba FC, Tseng GC. Missing value imputation in high-dimensional phenomic data: imputable or not, and how? BMC Bioinformatics 2014, 15: 346. PMID: 25371041, PMCID: PMC4228077, DOI: 10.1186/s12859-014-0346-6.Peer-Reviewed Original ResearchConceptsImputation methodsSTS schemeReal data analysisData imputationMissing valuesDifferent imputation methodsBest imputation methodOrdinal data typeComplete data matrixValue imputation methodsMultivariate imputationWeighted hybridData matrixR packageValue imputationContinuous intensityImputation errorPhenomic dataSelection schemeReal datasetsSchemeMost methodsImputationSimulationsMicroarray experiments
2012
An R package suite for microarray meta-analysis in quality control, differentially expressed gene analysis and pathway enrichment detection
Wang X, Kang DD, Shen K, Song C, Lu S, Chang LC, Liao SG, Huo Z, Tang S, Ding Y, Kaminski N, Sibille E, Lin Y, Li J, Tseng GC. An R package suite for microarray meta-analysis in quality control, differentially expressed gene analysis and pathway enrichment detection. Bioinformatics 2012, 28: 2534-2536. PMID: 22863766, PMCID: PMC3463115, DOI: 10.1093/bioinformatics/bts485.Peer-Reviewed Original ResearchConceptsDifferent operation systemsMulti-core parallel computingUser-friendly softwareParallel computingPathway detectionSoftware suiteFlexible inputFast implementationOperation systemVisualization plotsSupplementary dataNew algorithmMetapathsNew challengesSummary outputMarker detectionPathway databasesLittle effortMeta-analysis pipelineRapid advancesHigh-throughput genomic technologiesGenomic dataSystematic pipelineComputingPipeline
2011
Finding subtypes of transcription factor motif pairs with distinct regulatory roles
Bais AS, Kaminski N, Benos PV. Finding subtypes of transcription factor motif pairs with distinct regulatory roles. Nucleic Acids Research 2011, 39: e76-e76. PMID: 21486752, PMCID: PMC3113591, DOI: 10.1093/nar/gkr205.Peer-Reviewed Original ResearchConceptsTF binding sitesTranscription factorsDownstream regulationMotif pairsTF-DNA binding specificityBinding preferencesDNA binding specificityDNA binding preferencesDistinct regulatory rolesDownstream regulatory effectsMultiple regulatory pathwaysDifferent binding preferencesDyad motifDNA sequencesSequence elementsRegulatory pathwaysBinding specificityRegulatory roleDifferential recruitmentBinding sitesMotif discoveryRegulationCofactorMotifDistinct modes
2005
Comparison of normalization methods for CodeLink Bioarray data
Wu W, Dave N, Tseng GC, Richards T, Xing EP, Kaminski N. Comparison of normalization methods for CodeLink Bioarray data. BMC Bioinformatics 2005, 6: 309. PMID: 16381608, PMCID: PMC1373657, DOI: 10.1186/1471-2105-6-309.Peer-Reviewed Original ResearchAnalysis of Microarray Experiments for Pulmonary Fibrosis
Davé NB, Kaminski N. Analysis of Microarray Experiments for Pulmonary Fibrosis. Methods In Molecular Medicine 2005, 117: 333-358. PMID: 16118461, DOI: 10.1385/1-59259-940-0:333.Peer-Reviewed Original Research
2002
Statistical Methods for Analyzing Gene Expression Data for Cancer Research
Friedman N, Kaminski N. Statistical Methods for Analyzing Gene Expression Data for Cancer Research. Ernst Schering Foundation Symposium Proceedings 2002, 109-131. PMID: 12060998, DOI: 10.1007/978-3-662-04747-7_6.Peer-Reviewed Original Research