2019
Development of a 2-dimensional atlas of the human kidney with imaging mass cytometry
Singh N, Avigan ZM, Kliegel JA, Shuch BM, Montgomery RR, Moeckel GW, Cantley LG. Development of a 2-dimensional atlas of the human kidney with imaging mass cytometry. JCI Insight 2019, 4: e129477. PMID: 31217358, PMCID: PMC6629112, DOI: 10.1172/jci.insight.129477.Peer-Reviewed Original ResearchConceptsCell typesIndividual cell typesCritical baseline dataRenal cell typesMass cytometryQuantitative atlasNormal human samplesHuman kidneyRelative abundanceDevelopment of therapiesHuman kidney diseaseKidney diseaseMetal-conjugated antibodiesQuantitative interrogationScarce samplesMachine-learning pipelineDiscovery purposesFuture quantitative analysisNovel abnormalityNormal human kidneySingle tissue sectionHuman samplesRenal biopsyImmune cellsCells
2017
Gating mass cytometry data by deep learning
Li H, Shaham U, Stanton KP, Yao Y, Montgomery RR, Kluger Y. Gating mass cytometry data by deep learning. Bioinformatics 2017, 33: 3423-3430. PMID: 29036374, PMCID: PMC5860171, DOI: 10.1093/bioinformatics/btx448.Peer-Reviewed Original ResearchRemoval 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 ResearchMeSH KeywordsComputational BiologyCytophotometryData AccuracyHumansMachine LearningSequence Analysis, RNASingle-Cell AnalysisStatistics as TopicConceptsMeasurement 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