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

Abstract

MotivationModality 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. ResultsWe 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. AvailabilityLSMMD-MA is freely available at https://github.com/google-research/large_scale_mmdma Contactlpapaxanthos@google.com