slingshot

Slingshot infers continuous, branching lineage structures and pseudotimes from single-cell gene expression data to model developmental trajectories.

Key Features:

• Branching lineage inference: Identifies arbitrarily many branching events in low-dimensional data.
• Pseudotime estimation: Computes pseudotimes that accurately order cells along inferred trajectories.
• Supervised graph construction: Allows incorporation of prior knowledge for lineage structure inference.
• Pipeline integration: Functions as a component after dimensionality reduction and clustering steps.
• Robustness to noise: Combines stable techniques for handling noisy single-cell RNA sequencing data.

Scientific Applications:

• Developmental trajectory modeling: Charts progression from multipotent progenitors to fully differentiated cells in stem cell studies.
• Dynamic temporal gene expression analysis: Enables identification of gene expression changes across inferred pseudotime.
• Heterogeneous cell community characterization: Investigates complex communities of heterogeneous cells via single-cell transcriptomics.

Methodology:

Slingshot infers cell lineages and pseudotimes from single-cell gene expression data using methods for lineage inference in low-dimensional space after dimensionality reduction and clustering.