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

Abstract

BackgroundSingle-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.ResultsWe 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.ConclusionsSlingshot 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.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-4772-0) contains supplementary material, which is available to authorized users.

Injury Activates Transient Olfactory Stem Cell States with Diverse Lineage Capacities

“…To map the routes by which activated HBCs contribute to regeneration of the olfactory epithelium following injury, we employed Slingshot, a lineage analysis algorithm that can predict branching lineage trajectories from single-cell RNA-seq data (Fletcher et al, 2017; Street et al, 2017). Because almost all HBCs have transitioned to the HBC*1 stage and none remain in the resting HBC cluster at 24 HPI (Figure 2F), we removed the uninjured HBCs contributing to the resting HBC cluster from our input to Slingshot and selected the activated HBC cluster (HBC*1) as the root of the trajectories.…”

“…We used a recently developed cell lineage inference algorithm, Slingshot (Street et al, 2017; Version 0.0.3-4, available as an open-source R package slingshot at https://github.com/kstreet13/slingshot), to identify lineage trajectories and bifurcations and to order cells along trajectories. Slingshot takes as input a matrix of reduced dimension normalized expression measures (e.g., PCA) and cell clustering assignments.…”

Current best practices in single‐cell RNA‐seq analysis: a tutorial

“…There is currently no consensus on whether or not to perform normalization over genes. While the popular Seurat tutorials (Butler et al , ) generally apply gene scaling, the authors of the Slingshot method opt against scaling over genes in their tutorial (Street et al , ). The preference between the two choices revolves around whether all genes should be weighted equally for downstream analysis, or whether the magnitude of expression of a gene is an informative proxy for the importance of the gene.…”

“…Instead, TI methods should be selected based on the complexity of the expected trajectory. The comparison revealed that Slingshot (Street et al , ) outperformed other methods for simple trajectories that range from linear to bi‐ and multifurcating models. If more complex trajectories are expected, PAGA (Wolf et al , ) was recommended by the authors.…”

Computational approaches for high‐throughput single‐cell data analysis

“…linear , bifurcating or cyclical ). Some methods require specific input , while others are capable of inferring the trajectory structure in an unbiased way . A recent comparative review assessed the performance of more than thirty TI methods on both synthetic and real scRNA‐Seq datasets, providing useful practical guidelines to choose the most appropriate methods.…”