Improving cell-type identification with generative adversarial networks-enhanced augmentation-free single-cell RNA-Seq contrastive learning

Alsaggaf, I.; Buchan, D.; Wan, C.

doi:10.64898/2025.11.29.691320

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bioRxiv · 10.64898/2025.11.29.691320

Improving cell-type identification with generative adversarial networks-enhanced augmentation-free single-cell RNA-Seq contrastive learning

Alsaggaf, I.·Buchan, D.·Wan, C.

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Abstract

Cell-type identification plays a fundamental role in single-cell RNA-Seq analytics. Thanks to the recent success of the contrastive learning paradigm, the accuracy of automatic cell-type identification has also been improved. In this work, we propose a novel contrastive learning-based cell-type identification method, namely GAN-RCL, which conducts hard positive sample selection on real and conditional generative adversarial networks-created synthetic instances to improve the performance of augmentation-free contrastive learning. Experimental results confirm that GAN-RCL successfully outperforms other recently-proposed contrastive learning-based methods and shows the state-of-the-art predictive performance on multiple single-cell RNA-Seq cell-type identification tasks.

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Alsaggaf, I., Buchan, D., Wan, C.. 2025-12-02. Improving cell-type identification with generative adversarial networks-enhanced augmentation-free single-cell RNA-Seq contrastive learning. https://doi.org/10.64898/2025.11.29.691320

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