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Doublet identification in single-cell sequencing data using scDblFinder .

Pierre-Luc GermainAaron LunCarlos Garcia MeixideWill MacnairMark D Robinson
Published in: F1000Research (2021)
Doublets are prevalent in single-cell sequencing data and can lead to artifactual findings. A number of strategies have therefore been proposed to detect them. Building on the strengths of existing approaches, we developed scDblFinder , a fast, flexible and accurate Bioconductor-based doublet detection method. Here we present the method, justify its design choices, demonstrate its performance on both single-cell RNA and accessibility (ATAC) sequencing data, and provide some observations on doublet formation, detection, and enrichment analysis. Even in complex datasets, scDblFinder can accurately identify most heterotypic doublets, and was already found by an independent benchmark to outcompete alternatives.
Keyphrases
  • single cell
  • rna seq
  • high throughput
  • electronic health record
  • big data
  • loop mediated isothermal amplification
  • real time pcr
  • machine learning
  • high resolution
  • mass spectrometry
  • sensitive detection