@Article{Costa_Grun_Backofen-Graph_graph_appro-2018,
  author =	 {Costa, Fabrizio and Grun, Dominic and Backofen, Rolf},
  title =	 {{GraphDDP}: a graph-embedding approach to detect 
                  differentiation pathways in single-cell-data using prior 
                  class knowledge},
  journal =	 {Nat Commun},
  year =	 {2018},
  volume =	 {9},
  number =	 {1},
  pages =	 {3685},
  user =	 {backofen},
  pmid =	 {30206223},
  doi = 	 {10.1038/s41467-018-05988-7},
  issn = 	 {2041-1723},
  abstract =	 {Cell types can be characterized by expression profiles 
                  derived from single-cell RNA-seq. Subpopulations are 
                  identified via clustering, yielding intuitive outcomes that 
                  can be validated by marker genes. Clustering, however, 
                  implies a discretization that cannot capture the continuous 
                  nature of differentiation processes. One could give up the 
                  detection of subpopulations and directly estimate the 
                  differentiation process from cell profiles. A combination of 
                  both types of information, however, is preferable. 
                  Crucially, clusters can serve as anchor points of 
                  differentiation trajectories. Here we present GraphDDP, 
                  which integrates both viewpoints in an intuitive 
                  visualization. GraphDDP starts from a user-defined cluster 
                  assignment and then uses a force-based graph layout approach 
                  on two types of carefully constructed edges: one emphasizing 
                  cluster membership, the other, based on density gradients, 
                  emphasizing differentiation trajectories. We show on 
                  intestinal epithelial cells and myeloid progenitor data that 
                  GraphDDP allows the identification of differentiation 
                  pathways that cannot be easily detected by other approaches.}
}