GraphDDP: a graph-embedding approach to detect differentiation pathways in single-cell-data using prior class knowledge
Costa F, Grün D, Backofen R.
Cell types can be characterized by expression pro fi les derived from single-cell RNA-seq. Subpopulations are identi fi ed 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 sub-populations and directly estimate the differentiation process from cell pro fi les. 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-de fi ned 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 identi fi cation of differentiation pathways that cannot be easily detected by other approaches.
