The emergence of single-cell transcriptomics and proteomics approaches has resulted in a wealth of high-dimensional data that are challenging to interpret. Dimensionality reduction methods, such as UMAP and t-SNE, project data points onto a low-dimensional space that preserves cellular similarities from the high-dimensional measurement space. However, the projected dimensions typically have no interpretable biological meaning, and the relationships between measured biomolecular features are obscured completely. These limitations can be overcome by finding embeddings in which each dimension is a function of a distinct and biologically meaningful set of features. Here, we introduce Janus autoencoders, a novel neural network architecture capable of finding such low-dimensional embeddings by jointly optimizing multiple distinct one-dimensional embeddings of a dataset. We demonstrate the utility of Janus autoencoders for (1) visualizing multiomic data such that modality-specific contributions to cell type can be deconvolved and (2) visualizing mass cytometry data such that cell cycle effects can be distinguished from “true” cell state differences. Our initial demonstrations indicate that Janus autoencoders can uncover relationships between cellular states and their underlying cellular features in multiple biological contexts, with the potential to generally enable highly interpretable visualizations of single cell data.