The approach can be significantly enhanced by integrating additional sources of dynamical information, such as RNA velocity and metabolic labeling, into the reference fitting framework. RNA velocity provides insights into the directionality of gene expression changes over time, allowing for a more nuanced understanding of cellular trajectories. By incorporating RNA velocity data, the model can leverage the temporal dynamics of gene expression to refine the inferred trajectories and improve the accuracy of the interaction network.
To achieve this, one could modify the optimization objective to include terms that account for RNA velocity, effectively treating it as an additional constraint or regularization term. This would involve estimating the velocity vector field and incorporating it into the entropic optimal transport framework, thereby guiding the inference process towards biologically plausible trajectories.
Similarly, metabolic labeling can provide complementary information about cellular states and transitions. By integrating metabolic labeling data, the model can capture changes in metabolic activity that may correlate with specific gene regulatory interactions. This can be achieved by augmenting the reference fitting approach with additional data layers that represent metabolic states, allowing for a more comprehensive view of the cellular dynamics.
Overall, the combination of these diverse data sources can lead to a more robust inference of cellular trajectories and gene regulatory networks, as it allows the model to capture the multifaceted nature of biological systems and their responses to various stimuli. By leveraging the strengths of each data type, the reference fitting approach can provide a more accurate and holistic understanding of the underlying biological processes.