In the world of single-cell gene expression data analysis (scRNA-seq), two models based on variational autoencoders have recently stood out: scVI and LDVAE. These models offer innovative approaches to dimensionality reduction, taking into account particularities such as count data, batch effects, and library size.
scVI focuses on efficiently addressing noise in scRNA-seq count data, while also considering batch effects to improve the quality of representation of individual cells. On the other hand, LDVAE focuses on library normalization to correct size effects that can bias the results of subsequent analyses.
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