The ability to jointly profile the transcriptional and chromatin landscape of single-cells has emerged as a powerful technique to identify cellular populations and shed light on their regulation of gene expression. Current computational methods analyze jointly profiled (paired) or individual data modalities (unpaired), but do not offer a principled method to analyze both paired and unpaired samples jointly. Here we present MultiVI, a probabilistic framework that leverages deep neural networks to jointly analyze scRNA, scATAC and multiomic (scRNA scATAC) data. MultiVI creates an informative low-dimensional latent space that accurately reflects both chromatin and transcriptional properties of the cells even when one of the modalities is missing. We use public datasets to demonstrate that MultiVI is stable, easy to use, and outperforms current approaches for the joint analysis of paired and unpaired data.