@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},
doi = {10.1038/s41467-018-05988-7},
volume = {9},
user = {backofen},
pmid = {30206223},
pages = {3685},
number = {1},
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.}
}