Publication | Open Access
scClassify: hierarchical classification of cells
21
Citations
53
References
2019
Year
Unknown Venue
EngineeringMachine LearningTranscriptomics TechnologyGenomicsTrajectory AnalysisData ScienceData MiningPattern RecognitionSingle Cell SequencingCell NumberHierarchical ClassificationEnsemble LearningRna SequencingSingle-cell GenomicsSingle-cell AnalysisBioinformaticsFunctional GenomicsCell BiologyCell Type HierarchyComputational BiologySingle-cell BiologyClassifier SystemSystems BiologyMedicineEnsemble AlgorithmCell Detection
Abstract Cell type identification is a key computational challenge in single-cell RNA-sequencing (scRNA-seq) data. To capitalize on the large collections of well-annotated scRNA-seq datasets, we present scClassify, a hierarchical classification framework based on ensemble learning. scClassify can identify cells from published scRNA-seq datasets more accurately and more finely than in the original publications. We also estimate the cell number needed for accurate classification anywhere in a cell type hierarchy.
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