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

RNA Velocity, as an extension of trajectory inference, is an effective method for understanding cell development using single-cell RNA sequencing (scRNA-seq) experiments. Nevertheless, 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 incorrect velocity graphs. 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 VeloVI’s velocity estimation by incorporating the graph structure into the encoder for more effective feature extraction. The effectiveness of VeloVGI was demonstrated in various scenarios, including the mouse spinal cord and olfactory bulb, as well as on several public datasets. The results showed that VeloVGI outperformed other methods in terms of metric performance. Significance Statement RNA Velocity is an effective method for understanding cell development using single-cell RNA sequencing (scRNA-seq) experiments. This paper introduces VeloVGI, which addresses this batch effect issue for existing RNA velocity methods. The effectiveness of VeloVGI was demonstrated in various scenarios, including the mouse spinal cord and olfactory bulb, as well as on several public datasets. The results showed that VeloVGI outperformed other methods in terms of metric performance.