Accurate RNA velocity estimation based on multibatch network reveals complex lineage in batch scRNA-seq data.

ABSTRACT: RNA velocity, as an extension of trajectory inference, is an effective method for understanding cell development using single-cell RNA sequencing (scRNA-seq) experiments. However, existing RNA velocity methods are limited by the batch effect because they cannot directly correct for batch effects in the input data, which comprises spliced and unspliced matrices in a proportional relationship. This limitation can lead to an incorrect velocity stream. This paper introduces VeloVGI, which addresses this issue innovatively in two key ways. Firstly, it employs an optimal transport (OT) and mutual nearest neighbor (MNN) approach to construct neighbors in batch data. This strategy overcomes the limitations of existing methods that are affected by the batch effect. Secondly, VeloVGI improves upon V

SUBMITTER: Huang Z 

PROVIDER: S-EPMC11657662 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature