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Background <p>Combining single-cell transcriptome sequencing results from several batches reduces batch effect, which improves our understanding of cellular identity and function.</p> Results <p>This paper introduces CYCLONE, a new method for integrating single-cell gene expression data using a recycle contrastive learning network. The contrastive learning network and the VAE model work together to jointly train the low-dimensional representations. Additionally, they update the indices of inter-batch MNN pairs to generate positive pairs from a reduced-noise low-dimensional space. Meanwhile, CYCLONE cyclically updates the MNN pairs by iteratively training the low-dimensional space to gradually improve the confidence of the positive sample pairs, and augments the MNN pairs with KNN pairs to identify batch-specific cell types, thus avoiding the problems associated with overcorrecting for the batch effect. The performance of CYCLONE was evaluated on simulated and real scRNA-seq datasets, confirming its ability to improve clustering accuracy while successfully eliminating batch effects. In addition, experiments on batch-specific cell types identification validated CYCLONE’s ability to retain batch-specific information while eliminating batch effect, thus preserving batch-specific cell types.</p> Conclusion <p>CYCLONE is an effective integration method based on recycle contrastive learning that improves the accuracy of cell clustering while successfully eliminating batch effects and preserving batch-specific information.</p>

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CYCLONE: recycle contrastive learning for integrating single-cell gene expression data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月30日

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  • Han Ji,
  • Xinwei He,
  • Hongwei Li

摘要

Background

Combining single-cell transcriptome sequencing results from several batches reduces batch effect, which improves our understanding of cellular identity and function.

Results

This paper introduces CYCLONE, a new method for integrating single-cell gene expression data using a recycle contrastive learning network. The contrastive learning network and the VAE model work together to jointly train the low-dimensional representations. Additionally, they update the indices of inter-batch MNN pairs to generate positive pairs from a reduced-noise low-dimensional space. Meanwhile, CYCLONE cyclically updates the MNN pairs by iteratively training the low-dimensional space to gradually improve the confidence of the positive sample pairs, and augments the MNN pairs with KNN pairs to identify batch-specific cell types, thus avoiding the problems associated with overcorrecting for the batch effect. The performance of CYCLONE was evaluated on simulated and real scRNA-seq datasets, confirming its ability to improve clustering accuracy while successfully eliminating batch effects. In addition, experiments on batch-specific cell types identification validated CYCLONE’s ability to retain batch-specific information while eliminating batch effect, thus preserving batch-specific cell types.

Conclusion

CYCLONE is an effective integration method based on recycle contrastive learning that improves the accuracy of cell clustering while successfully eliminating batch effects and preserving batch-specific information.