Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics

TLDR

Single‑cell transcriptomics enables mapping of heterogeneous cell communities, yet accurately inferring multiple branching lineages remains challenging, making lineage inference essential for understanding dynamic gene expression. The study introduces Slingshot, a novel method for inferring cell lineages and pseudotimes from single‑cell gene expression data. Slingshot combines highly stable techniques suited for noisy single‑cell data with flexible trajectory‑identification capabilities to infer multiple branching lineages and pseudotimes. In published datasets, Slingshot accurately identifies one to three branching trajectories, and simulation studies show it infers more accurate pseudotimes than leading methods.

Abstract

Single-cell transcriptomics allows researchers to investigate complex communities of heterogeneous cells. It can be applied to stem cells and their descendants in order to chart the progression from multipotent progenitors to fully differentiated cells. While a variety of statistical and computational methods have been proposed for inferring cell lineages, the problem of accurately characterizing multiple branching lineages remains difficult to solve.We introduce Slingshot, a novel method for inferring cell lineages and pseudotimes from single-cell gene expression data. In previously published datasets, Slingshot correctly identifies the biological signal for one to three branching trajectories. Additionally, our simulation study shows that Slingshot infers more accurate pseudotimes than other leading methods.Slingshot is a uniquely robust and flexible tool which combines the highly stable techniques necessary for noisy single-cell data with the ability to identify multiple trajectories. Accurate lineage inference is a critical step in the identification of dynamic temporal gene expression.