Fig. 1: Overview of uniPort algorithm.
From: A unified computational framework for single-cell data integration with optimal transport
uniPort integrates single-cell data by combining a coupled-VAE and Minibatch-UOT. uniPort takes as input a highly variable common gene set of single-cell datasets across different modalities or technologies. a uniPort projects input datasets into a cell-embedding latent space through a shared probabilistic encoder. Then uniPort minimizes a Minibatch-UOT loss between cell embeddings across different datasets. Finally, uniPort reconstructs two terms. The first consists of input datasets by a decoder with different DSBN layers. The second consists of highly variable gene sets corresponding to each dataset by dataset-specific decoders. b uniPort outputs a shared latent space and an optimal transport plan that can be used for downstream analysis, such as visualization, gene imputation and spots deconvolution.
