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

By Kelly Street, Davide Risso, Russell B. Fletcher, Diya Das, John Ngai, Nir Yosef, Elizabeth Purdom, Sandrine Dudoit. BMC Genomics, 2017

2,624 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d80cb2a64107c8733dadc2eedd8900452e96d123

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues (2023, positive): “We ﬁ nd that PSTS outperformed Slingshot 29 and Monocle3 30 , which are non-spatial trajectory inference methods (Fig.”
Best practices for single-cell analysis across modalities (2023, methodology): “Changes in composition are frequently observed in development 106 and disease 107 , yet methods for compositional analysis lack an independent benchmark.”
A general and flexible method for signal extraction from single-cell RNA-seq data (2017, positive): “In the original analysis, Slingshot was able to correctly infer the lineages in its supervised mode, by manually specifying lineage endpoints (i.e., clusters corresponding to the mature cell types).”
Lipid-associated macrophages control metabolic homeostasis in a Trem2-dependent manner (2019, methodology): “To infer the most probable molecular differentiation trajectory from monocytes towards the Mac3 subset, we used Slingshot, a recently published method for pseudo-time inference (Street et al., 2018), and characterized a gradual acquisition of Mac3 genes from HFD monocytes.”
Single-Cell RNA-Seq Technologies and Related Computational Data Analysis (2019, methodology): “…tools have been proposed for trajectory inference, e.g., Monocle (Trapnell et al., 2014), Waterfall (Shin et al., 2015), Wishbone (Setty et al., 2016), TSCAN (Ji and Ji, 2016), Monocle2 (Qiu et al., 2017), Slingshot (Street et al., 2018), and CellRouter (Lummertz da Rocha et al., 2018) ( Table 5 ).”
Trajectory-based differential expression analysis for single-cell sequencing data (2019, methodology): “We demonstrate the versatility of tradeSeq by using it downstream of three trajectory inference methods, slingshot [Street et al., 2018], Monocle 2 [Qiu et al.”
Interpretable dimensionality reduction of single cell transcriptome data with deep generative models (2017, methodology): “For analysis, dimensionality reduction projecting high-dimensional data into low-dimensional space (typically two or three dimensions) to visualize the cluster structures 27 – 29 and development trajectories 30 – 33 is commonly used.”
Current best practices in single‐cell RNA‐seq analysis: a tutorial (2019, methodology): “While the popular Seurat tutorials (Butler et al , 2018) generally apply gene scaling, the authors of the Slingshot method opt against scaling over genes in their tutorial (Street et al , 2018).”
DrImpute: imputing dropout events in single cell RNA sequencing data (2017, neutral): “It should be noted that although the cells within each of the time points may not be homogenous, the time labels could be used to represent the overall developmental trajectory, and to evaluate the performance of pseudotime inference algorithms [41 – 45].”