CMOT: Cross Modality Optimal Transport for multimodal inference

Abstract

Biological mechanisms are complex spanning multiple facets, each providing a unique view of the underlying mechanism. Recent single-cell technologies such as scRNA-seq and scATAC-seq have facilitated parallel probing into each of these facets, providing multimodal measurements of individual cells. An integrative analysis of these single-cell modalities can therefore provide a comprehensive understanding of specific cellular and molecular mechanisms. Despite the technological advances, simultaneous profiling of multiple modalities of single cells continues to be challenging as opposed to single modality measurements, and therefore, it may not always be feasible to conduct such profiling experiments (e.g., missing modalities). Furthermore, even with the available multimodalities, data integration remains elusive since modalities may not always have paired samples, leaving partial to no correspondence information. To address those challenges, we developed Cross-Modality Optimal Transport (CMOT), a computational approach to infer missing modalities of single cells based on optimal transport (OT). CMOT first aligns a group of cells (source) within available multi-modal data onto a common latent space. Then, it applies optimal transport to map the cells with a single modality (target) to the aligned cells in source from the same modality by minimizing their cost of transportation using Wasserstein distance. This distance can be regularized by prior knowledge (e.g., cell types) or induced cells clusters to improve mapping, and an entropy of transport to speed up OT computations. Once transported, CMOT uses K-Nearest-Neighbors to infer the missing modality for the cells in target from another modality of mapped cells in source. Furthermore, the alignment of CMOT works for partially corresponding information from multi-modalities, i.e., allowing a fraction of unmatched cells in source. We evaluated CMOT on several emerging multi-modal datasets, e.g., gene and protein expression and chromatin accessibility in developing brain, cancers, and immunology. We found that not only does CMOT outperform existing state-of-art methods, but its inferred gene expression is biologically interpretable such as classifying cell types and cancer types. Finally, CMOT is open source and available on our github: https://github.com/daifengwanglab/CMOT