The article discusses the Single-cell variational inference (scVI) model, developed to handle single-cell RNA sequencing data. scVI employs discrete likelihoods to deal with the unique characteristics of single-cell data, allowing for direct learning from raw counts without extensive pre-processing. Additionally, the model incorporates batch ID information in both its encoding and decoding processes to account for batch effects. This approach positions scVI as a scalable and robust solution, capable of achieving superior performance in clustering and differential expression tasks compared to traditional methods.
scVI is a variational autoencoder tuned for single-cell data, excelling in clustering and differential expression tasks while being scalable to large datasets.
It adopts various discrete likelihoods to handle count data, learning a latent space directly from raw expression without conventional pre-processing.
By incorporating batch ID information, scVI effectively mitigates batch effects, ensuring more accurate modeling of single-cell datasets.
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