Out-of-distribution prediction with disentangled representations for single-cell RNA sequencing data

Lotfollahi, M.; Dony, l.; Agarwala, H.; Theis, F. J.

doi:10.1101/2021.09.01.458535

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bioRxiv · 10.1101/2021.09.01.458535

Out-of-distribution prediction with disentangled representations for single-cell RNA sequencing data

Lotfollahi, M.·Dony, l.·Agarwala, H.·Theis, F. J.

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Abstract

Learning robust representations can help uncover underlying biological variation in scRNA-seq data. Disentangled representation learning is one approach to obtain such informative as well as interpretable representations. Here, we learn disentangled representations of scRNA-seq data using {beta} variational autoencoder ({beta}-VAE) and apply the model for out-of-distribution (OOD) prediction. We demonstrate accurate gene expression predictions for cell-types absent from training in a perturbation and a developmental dataset. We further show that {beta}-VAE outperforms a state-of-the-art disentanglement method for scRNA-seq in OOD prediction while achieving better disentanglement performance.

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Lotfollahi, M., Dony, l., Agarwala, H., Theis, F. J.. 2021-09-02. Out-of-distribution prediction with disentangled representations for single-cell RNA sequencing data. https://doi.org/10.1101/2021.09.01.458535

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