Trajectory inference algorithms based on single-cell omics data are powerful tools for predicting and dissecting cell differentiation. However, most existing tools are tailored to specific datasets and lack generalizability across diverse data types. To ad-dress this limitation, we developed CellFateExplorer, systematically evaluates the performance of x trajectory inference methods across y datasets. Through an interactive web interface, CellFateExplorer provides guidance on method selection and down-stream analysis for specific datasets. In summary, CellFateExplorer is an integrated platform for exploring cell fate.

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 VeloVI’s velocity estimation by incorporating the graph structure into the encoder for more effective feature extraction. The effectiveness of VeloVGI is demonstrated in various scenarios, including the mouse spinal cord and olfactory bulb tissue, as well as on several public datasets. The results show that VeloVGI outperformed other methods in terms of metric performance.