GraphDDP: a graph-embedding approach to detect differentiation pathways in single-cell-data using prior class knowledge.

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 c

SUBMITTER: Costa F 

PROVIDER: S-EPMC6134144 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature