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DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors.

Christopher S McGinnis1, Lyndsay M Murrow1, Zev J Gartner2

  • 1Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA.

Cell Systems
|April 8, 2019
PubMed
Summary

DoubletFinder is a new computational tool that accurately identifies and removes doublets from single-cell RNA sequencing data. This improves the accuracy of downstream analyses and biological discoveries.

Keywords:
doublet detectionmachine learningquality-controlsingle-cell RNA sequencing

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Single-cell RNA sequencing (scRNA-seq) is a powerful technology for studying cellular heterogeneity.
  • Technical artifacts, such as doublets, can compromise the accuracy of scRNA-seq data.
  • Doublets, formed from two cells, can lead to incorrect biological interpretations and reduced cell throughput.

Purpose of the Study:

  • To introduce DoubletFinder, a computational tool for detecting doublets in scRNA-seq data using gene expression profiles.
  • To demonstrate the effectiveness of DoubletFinder in identifying doublets formed from transcriptionally distinct cells.
  • To provide a method for parameter estimation and best practices for applying DoubletFinder across diverse scRNA-seq datasets.

Main Methods:

  • DoubletFinder identifies doublets by assessing the proximity of real cells to artificial doublets generated from random cell pair transcriptomes.
  • The tool utilizes gene expression data exclusively for doublet detection.
  • A method for estimating input parameters is provided to ensure applicability across various datasets.

Main Results:

  • DoubletFinder accurately identifies doublets, particularly those originating from transcriptionally distinct cells.
  • Removal of detected doublets enhances the identification of differentially expressed genes.
  • The tool demonstrates robustness and insensitivity to cell types with hybrid expression features.

Conclusions:

  • DoubletFinder is an effective computational method for accurate doublet detection in scRNA-seq data.
  • Implementing DoubletFinder improves the reliability of downstream analyses and biological insights.
  • The tool offers a practical solution for addressing doublet artifacts in single-cell genomics research.