ImageDoubler: image-based doublet identification in single-cell sequencing

Kaiwen Deng, Xinya Xu, Manqi Zhou, Hongyang Li, Evan T. Keller, Gregory Shelley, Annie Lu, Lana Garmire and Yuanfang Guan ()
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Kaiwen Deng: University of Michigan
Xinya Xu: University of Michigan
Manqi Zhou: University of Michigan
Hongyang Li: University of Michigan
Evan T. Keller: University of Michigan
Gregory Shelley: University of Michigan
Annie Lu: University of Michigan
Lana Garmire: University of Michigan
Yuanfang Guan: University of Michigan

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract Single-cell sequencing provides detailed insights into individual cell behaviors within complex systems based on the assumption that each cell is uniquely isolated. However, doublets—where two or more cells are sequenced together—disrupt this assumption and can lead to potential data misinterpretations. Traditional doublet detection methods primarily rely on simulated genomic data, which may be less effective in homogeneous cell populations and can introduce biases from experimental processes. Therefore, we introduce ImageDoubler in this study, an innovative image-based model that identifies doublets and missing samples leveraging the Fluidigm single-cell sequencing image data. Our approach showcases a notable doublet detection efficacy, achieving a rate up to 93.87% and registering a minimum improvement of 33.1% in F1 scores compared to existing genomic-based methods. This advancement highlights the potential of using imaging to glean insight into developing doublet detection algorithms and exposes the limitations inherent in current genomic-based techniques.

Date: 2025
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DOI: 10.1038/s41467-024-55434-0

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