Figure 1.

Overview of MarkovHC. (A) MarkovHC simultaneously performs hierarchical clustering, transition path tracking, and critical points detecting. (B) The intuitive idea behind MarkovHC. (C) The workflow of MarkovHC: (1) The original input data is the matrix of genes by cells. (2) We calculate sNN (shared Nearest Neighbours) among cells to get the cell by cell similarity matrix. Then we construct a cellular network using the similarity matrix and calculate each cell's degree (D scores) in the network. (3) The Markov transition matrix is calculated using the similarity matrix and D scores. (4) The pseudo-energy matrix is calculated based on the Markov transition matrix. (5) The hierarchical structure is constructed based on attractors, basins, and critical points on each level.