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Single-cell multimodal sequencing methods, which measure multiple different modalities simultaneously (such as gene expression, chromatin accessibility, and surface protein data) are an exciting new space in the field of genomics as they provide a more comprehensive picture of cellular state than technologies that assay a single modality. Here I present PolyVI, a suite of three deep generative models to analyze DOGMA-seq (gene expression, protein, chromatin), ASAP-seq (chromatin, protein), and SNARE-seq (gene expression, chromatin) datasets. PolyVI is able to map the data to a low dimensional latent space, batch correct, and de-noise the data.

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