Out-of-distribution prediction with disentangled representations for single-cell RNA sequencing data is a research paper (2021). On theSindex it has a DataRank of 0.345. It has been cited 9 times.
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 β variational autoencoder ( β -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 β -VAE outperforms a state-of-the-art disentanglement method for scRNA-seq in OOD prediction while achieving better disentanglement performance.
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Base Score Contribution
0.345
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0
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