Fig 1.
Flowchart of the Landscape of Differentiation Dynamics (LDD) method.
A: A pool of single cells, from which we can obtain the gene expression matrix by single cell sequencing. B: After preprocessing, quality control, and dimension reduction, a low-dimensional data matrix is obtained. C: The samples are clustered into different types. Undirected differentiation paths are determined by a transition matrix between clusters. D: After applying the continuous birth-death process to model the whole differentiation process, the potential V(x), differentiation directions, and landscape can be constructed.
Fig 2.
Differentiation landscape, differentiation paths, and gene networks for simulated models.
A and B are the LDD potential landscape and differentiation paths using data from the simulated drift-diffusion process, in which samples/cells were clustered into four groups. C and D are the two-gene and six-gene regulatory networks for simulation, respectively. For the two-gene network, the potential landscape and differentiation paths for clusters are shown in E and F, respectively, where seven clusters were detected. Constant potential V for each cluster was computed by LDD, while the landscape is an illustration constructed by the method in Materials and methods.
Fig 3.
Differentiation landscape and differentiation paths for real datasets.
A, D are the LDD potential landscapes, B, E are the potential values plotted in the two-dimensional reduction space, and C, F are the differentiation paths. A-C use Xu’s dataset, which describes that hepatoblasts (cluster 1) differentiate into hepatocytes (cluster 3) and cholangiocytes (cluster 5). D-F use Furlan’s dataset and show that chromaffin cells (cluster 4) are generated from SCPs (cluster 1).
Table 1.
Properties of different pseudo-time methods.
Table 2.
Comparison between seven pseudo-time methods.