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.
Highlights
Single-cell transcriptomics allows researchers to investigate complex communities of heterogeneous cells
Slingshot divides the problem of multiple lineage inference into two stages: 1. Identification of lineages, i.e., ordered sets of cell clusters, where all lineages share a starting cluster and each leads to a unique terminal cluster
One of the main challenges of single-cell RNA sequencing (RNA-Seq) data analysis is the high level of variability
Summary
Single-cell transcriptomics allows researchers to investigate complex communities of heterogeneous 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 Traditional transcription assays, such as bulk microarrays and RNA sequencing (RNA-Seq), offer a bird’s-eye view of transcription. Newly-developed single-cell assays can give us a much more detailed picture [1] This higher resolution allows researchers to distinguish between closely-related populations of cells, potentially revealing functionally distinct groups with complex relationships [2]. There are not clear distinctions between cellular states, but instead a smooth transition, where individual cells represent points along a continuum or lineage Cells in these systems change states by undergoing gradual transcriptional changes, with progress being driven by an underlying temporal variable
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