In addition, we generated developmental “vector fields” using RNA-velocities to forecast changes of RNA abundance in the expression landscapes.
Trajectories in this landscape visualize transcriptional programs passed by cells along their developmental paths from stem cells to differentiated tissues.
The method transforms multidimensional gene expression patterns into two dimensional data landscapes, which resemble the metaphoric Waddington epigenetic landscape. Such trajectories can be generated using self-organizing maps machine learning. We here complement developmental trajectories in cell-state space by trajectories in gene-state space to more clearly address this latter aspect. Cell development is driven by alterations of transcriptional programs e.g., by differentiation from stem cells into various tissues or by adapting to micro-environmental requirements. Hereby, cross-sectional snapshots of the diversity of cell transcriptomes were transformed into (pseudo-) longitudinal trajectories of cell differentiation using computational methods, which are based on similarity measures distinguishing cell phenotypes. Single-cell RNA sequencing has become a standard technique to characterize tissue development.
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