Improving cell-type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning

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

doi:10.1101/2022.10.06.511191

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bioRxiv · 10.1101/2022.10.06.511191

Improving cell-type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning

Alsaggaf, I.·Buchan, D.·Wan, C.

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Abstract

Cell-type identification is an important task for single-cell RNA-seq (scRNA-seq) data analysis. In this work, we proposed a novel Gaussian noise augmented scRNA-seq contrastive learning framework (GsRCL) to learn a type of discriminative feature representations for cell-type prediction tasks. The experimental results suggest that the feature representations learned by GsRCL successfully improved the accuracy of cell-type prediction using scRNA-seq expression profiles.

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Alsaggaf, I., Buchan, D., Wan, C.. 2022-10-08. Improving cell-type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning. https://doi.org/10.1101/2022.10.06.511191

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