We find that dictionary learning is a powerful tool for enabling cross-modality bridge integration at single-cell resolution. Our key insight is to treat a multi-omic dataset as a dictionary, with each individual cell’s multi-omic profile representing an atom. We learn a ‘dictionary representation’ of each unimodal dataset based on these atoms. For clarity, we emphasize that in contrast to the original applications of dictionary learning where the atoms represent a set of features33,34, we utilize individual instances (cells) as dictionary elements. This transformation takes datasets in which completely different sets of features were measured and represents them each in a space where the defining features represent the same set of atoms (Fig. 1b). Once different modalities can be represented using the same set of features, they can be readily aligned in a final step.
