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
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 ResearchMeSH KeywordsAlgorithmsAnimalsBase SequenceComputational BiologyGene Expression ProfilingGenetic MarkersMiceRNASequence Analysis, RNASingle-Cell AnalysisStochastic Processes
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 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