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Benchmarking computational methods for single-cell chromatin data analysis.

Siyuan LuoPierre-Luc GermainMark D RobinsonFerdinand von Meyenn
Published in: Genome biology (2024)
Our analysis provides guidelines for choosing analysis methods for different datasets. Overall, feature aggregation, SnapATAC, and SnapATAC2 outperform latent semantic indexing-based methods. For datasets with complex cell-type structures, SnapATAC and SnapATAC2 are preferred. With large datasets, SnapATAC2 and ArchR are most scalable.
Keyphrases
  • data analysis
  • rna seq
  • single cell
  • gene expression
  • machine learning
  • high resolution
  • transcription factor
  • high throughput
  • clinical practice
  • dna methylation