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Cell lineage/pseudotime inference software tools | Single-cell RNA sequencing data analysis

Inference of lineage structure has been referred to as “pseudotemporal reconstruction” and it can help us understand how cells change state and how cell fate decisions are made (Bendall et al., 2014; Campbell et al., 2015; Trapnell et al., 2014). Furthermore, many systems contain lineages that share a common initial state but branch and terminate at different states. These complicated lineage structures require additional analysis to distinguish between cells that fall along different lineages (Ji and Ji, 2016; Setty et al., 2016; Shin et al., 2015).

(Bendall et al., 2014) Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell.
(Campbell et al., 2015) Laplacian eigenmaps and principal curves for high resolution pseudotemporal ordering of single-cell RNA-seq profiles. bioRxiv.
(Trapnell et al., 2014) The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol.
(Ji and Ji, 2016) TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. Nucleic Acids Res.
(Setty et al., 2016) Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat Biotechnol.
(Shin et al., 2015) Single-Cell RNA-Seq with Waterfall Reveals Molecular Cascades underlying Adult Neurogenesis. Cell Stem Cell.

Source text:
(Street et al., 2017) Slingshot: Cell lineage and pseudotime inference for single-cell transcriptomics. bioRxiv.

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