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
Detection of differentially abundant cell subpopulations in scRNA-seq data
Zhao J, Jaffe A, Li H, Lindenbaum O, Sefik E, Jackson R, Cheng X, Flavell RA, Kluger Y. Detection of differentially abundant cell subpopulations in scRNA-seq data. Proceedings Of The National Academy Of Sciences Of The United States Of America 2021, 118: e2100293118. PMID: 34001664, PMCID: PMC8179149, DOI: 10.1073/pnas.2100293118.Peer-Reviewed Original ResearchMeSH KeywordsAgingB-LymphocytesBrainCell LineageCOVID-19CytokinesDatasets as TopicDendritic CellsGene Expression ProfilingGene Expression RegulationHigh-Throughput Nucleotide SequencingHumansMelanomaMonocytesPhenotypeRNA, Small CytoplasmicSARS-CoV-2Severity of Illness IndexSingle-Cell AnalysisSkin NeoplasmsT-LymphocytesTranscriptomeConceptsDA subpopulationsIll COVID-19 patientsImmune checkpoint therapyCOVID-19 patientsSingle-cell RNA sequencing analysisCheckpoint therapyBrain tissueCell subpopulationsRNA sequencing analysisTime pointsSubpopulationsDiseased individualsDistinct phenotypesOriginal studyCell typesAbundant subpopulationSequencing analysisCellsDA measuresPhenotypeImportant differencesNonrespondersPatientsTherapy