LSMMD-MA: Scaling multimodal data integration for single-cell genomics data analysis

Motivation Modality matching in single-cell omics data analysis—i.e., matching cells across data sets collected using different types of genomic assays—has become an important problem, because unifying perspectives across different technologies holds the promise of yielding biological and clinical discoveries. However, single-cell dataset sizes can now reach hundreds of thousands to millions of cells, which remains out of reach for most multi-modal computational methods. Results We propose LSMMD-MA, a large-scale Python implementation of the MMD-MA method for multimodal data integration. In LSMMD-MA we reformulate the MMD-MA optimization problem using linear algebra and solve it with KeOps, a CUDA framework for symbolic matrix computation in Python. We show that LSMMD-MA scales to a million cells in each modality, two orders of magnitude greater than existing implementations. Availability LSMMD-MA is freely available at https://github.com/google-research/large_scale_mmdma Contact [email protected]