2022
Zero-preserving imputation of single-cell RNA-seq data
Linderman GC, Zhao J, Roulis M, Bielecki P, Flavell RA, Nadler B, Kluger Y. Zero-preserving imputation of single-cell RNA-seq data. Nature Communications 2022, 13: 192. PMID: 35017482, PMCID: PMC8752663, DOI: 10.1038/s41467-021-27729-z.Peer-Reviewed Original Research
2021
Graph of graphs analysis for multiplexed data with application to imaging mass cytometry
Lin YE, Shnitzer T, Talmon R, Villarroel-Espindola F, Desai S, Schalper K, Kluger Y. Graph of graphs analysis for multiplexed data with application to imaging mass cytometry. PLOS Computational Biology 2021, 17: e1008741. PMID: 33780435, PMCID: PMC8032202, DOI: 10.1371/journal.pcbi.1008741.Peer-Reviewed Original Research
2019
Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data
Linderman GC, Rachh M, Hoskins JG, Steinerberger S, Kluger Y. Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data. Nature Methods 2019, 16: 243-245. PMID: 30742040, PMCID: PMC6402590, DOI: 10.1038/s41592-018-0308-4.Peer-Reviewed Original Research
2018
DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network
Katzman JL, Shaham U, Cloninger A, Bates J, Jiang T, Kluger Y. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Medical Research Methodology 2018, 18: 24. PMID: 29482517, PMCID: PMC5828433, DOI: 10.1186/s12874-018-0482-1.Peer-Reviewed Original ResearchConceptsDeep neural networksPersonalized treatment recommendationsTreatment recommendationsNeural networkTreatment optionsPatient covariatesRecommender systemsSurvival methodsCox proportional hazards modelDifferent treatment optionsProportional hazards modelSurvival modelsExtensive feature engineeringIndividual treatment recommendationsPrior medical knowledgeSet of patientsLinear Cox proportional hazards modelsPatient characteristicsClinical studiesPatient featuresSurvival timeFeature engineeringHazards modelArt survival modelsTreatment effectiveness
2017
Ritornello: high fidelity control-free chromatin immunoprecipitation peak calling
Stanton KP, Jin J, Lederman RR, Weissman SM, Kluger Y. Ritornello: high fidelity control-free chromatin immunoprecipitation peak calling. Nucleic Acids Research 2017, 45: e173-e173. PMID: 28981893, PMCID: PMC5716106, DOI: 10.1093/nar/gkx799.Peer-Reviewed Original ResearchConceptsChIP-seqNext-generation high-throughput DNA sequencing technologiesHigh-throughput DNA sequencing technologiesGenome-wide localizationGenome-wide scaleTF-binding sitesTranscription factor bindingDNA sequencing technologiesENCODE consortiumResultant readsChromatin immunoprecipitationReference genomeDiverse biological effectsModification eventsFactor bindingOmics techniquesSequencing technologiesOmics experimentsSequencing costsWide localizationPeak callingChip targetArtifactual peaksPeak callersBiological effects
2013
TrAp: a tree approach for fingerprinting subclonal tumor composition
Strino F, Parisi F, Micsinai M, Kluger Y. TrAp: a tree approach for fingerprinting subclonal tumor composition. Nucleic Acids Research 2013, 41: e165-e165. PMID: 23892400, PMCID: PMC3783191, DOI: 10.1093/nar/gkt641.Peer-Reviewed Original ResearchConceptsGenome-wide experimentsEvolutionary relationshipsMutational profileSequencing technologiesMixed cell populationsSilico analysisTumor samplesCell subpopulationsEvolutionary frameworkNumber of subpopulationsSingle cellsEvolutionary pathCell populationsCollective signalClonal compositionMetastatic potentialNumerous cellsTumor karyotypeComputational approachSubpopulationsCellsMixed subpopulationsAbundanceDistinct metastasesTumor composition
2006
Characterizing disease states from topological properties of transcriptional regulatory networks
Tuck DP, Kluger HM, Kluger Y. Characterizing disease states from topological properties of transcriptional regulatory networks. BMC Bioinformatics 2006, 7: 236. PMID: 16670008, PMCID: PMC1482723, DOI: 10.1186/1471-2105-7-236.Peer-Reviewed Original ResearchConceptsTranscriptional regulatory networksRegulatory networksTranscription factorsTranscriptional networksRegulated genesGene deregulationExpression profilesDiseased statesGene regulatory networksCentrality of genesGene expression experimentsGene expression profilesGene expression studiesGene centralityRegulatory linkExpression experimentsExpression studiesGene linksGenesCell typesExpression datasetsGene subsetsDifferential activityNormal cellsRemarkable degree