CellRouter

CellRouter reconstructs single-cell trajectories to map cell-state transitions and differentiation from multidimensional single-cell omics data.

Key Features:

• Subpopulation Identification: Defines subpopulations manually or via automated graph-clustering by constructing a k-nearest neighbor (kNN) graph from cell-to-cell distances in a low-dimensional embedding space.
• Network Similarity Metrics: Weights kNN graph edges using network similarity metrics such as the Jaccard index to encode phenotypic relatedness for community detection algorithms.
• Trajectory Inference: Uses a flow network algorithm to explore subpopulation structure and reconstruct cell-state transitions across complex, multidimensional omics datasets.
• Gene Regulatory Network Integration: Incorporates gene regulatory networks into the analysis to link regulatory interactions with inferred trajectories.

Scientific Applications:

• Single-cell genomics: Maps cell-state transitions and differentiation pathways from single-cell transcriptomic and other omics data.
• Hematopoietic differentiation: Dissects hematopoietic stem and progenitor cell differentiation into erythrocytes, megakaryocytes, monocytes, and granulocytes.

Methodology:

Constructs a kNN graph from low-dimensional embeddings, weights edges with network similarity metrics such as the Jaccard index, applies community detection/graph-clustering (or allows manual subpopulation definition), runs a flow network algorithm to infer trajectories, and integrates gene regulatory networks into the analysis.