MOJITOO: a fast and universal method for integration of multimodal single cell data

Abstract

The advent of multi-modal single cell sequencing techniques have shed new light on molecular mechanisms by simultaneously inspecting transcriptomes, epigenomes and proteomes of the same cell. However, to date, the existing computational approaches for integration of multimodal single cell data are either computationally expensive, require the delineation of parameters or can only be applied to particular modalities. We present a single cell multi-modal integration method, named MOJITOO (Multi-mOdal Joint IntegraTion of cOmpOnents). MOJITOO uses canonical correlation analysis for a fast and parameter free detection of a shared representation of cells from multimodal single cell data. Moreover, estimated canonical components can be used for interpretation, i.e. association of modality specific molecular features with the latent space. We evaluate MOJITOO using bi- and tri-modal single cell data sets and show that MOJITOO outperforms existing methods regarding computational requirements, preservation of original latent spaces and clustering.