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

ABSTRACT:

Motivation

Modality matching in single-cell omics data analysis-i.e. matching cells across datasets 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 remain out of reach for most multimodal 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

SUBMITTER: Meng-Papaxanthos L 

PROVIDER: S-EPMC10336029 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature