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CB2 improves power of cell detection in droplet-based single-cell RNA sequencing data.

Zijian NiShuyang ChenJared BrownChristina Kendziorski
Published in: Genome biology (2020)
An important challenge in pre-processing data from droplet-based single-cell RNA sequencing protocols is distinguishing barcodes associated with real cells from those binding background reads. Existing methods test barcodes individually and consequently do not leverage the strong cell-to-cell correlation present in most datasets. To improve cell detection, we introduce CB2, a cluster-based approach for distinguishing real cells from background barcodes. As demonstrated in simulated and case study datasets, CB2 has increased power for identifying real cells which allows for the identification of novel subpopulations and improves the precision of downstream analyses.
Keyphrases
  • single cell
  • rna seq
  • high throughput
  • cell therapy
  • stem cells
  • induced apoptosis
  • bone marrow
  • cell death
  • deep learning
  • transcription factor
  • machine learning
  • quantum dots
  • label free
  • big data
  • dna binding
  • pi k akt