A systematic performance evaluation of clustering methods for single-cell RNA-seq data [version 2; referees: 2 approved]

Cited by papers we hold 6

• Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues · 2023
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• BASS: multi-scale and multi-sample analysis enables accurate cell type clustering and spatial domain detection in spatial transcriptomic studies · 2022
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• Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data · 2021
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• muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data · 2020
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• Eleven grand challenges in single-cell data science · 2020
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• A comparison of automatic cell identification methods for single-cell RNA sequencing data · 2019
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