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• W4311673709 abstract "Technological developments have led to an explosion of high-throughput single-cell data, which are revealing unprecedented perspectives on cell identity. Recently, significant attention has focused on investigating, from single-cell RNA-sequencing (scRNA-seq) data, cellular dynamic processes, such as cell differentiation, cell cycle and cell (de)activation. In particular, trajectory inference methods, by ordering cells along a trajectory, allow estimating a differentiation tree of cells. While trajectory inference tools typically work with gene expression levels, common scRNA-seq protocols allow the identification and quantification of unspliced pre-mRNAs and mature spliced mRNAs for each gene. By exploiting the abundance of unspliced and spliced mRNA, one can infer the RNA velocity of individual cells, i.e., the time derivative of the gene expression state of cells. Whereas traditional trajectory inference methods reconstruct cellular dynamics given a population of cells of varying maturity, RNA velocity relies on a dynamical model describing splicing dynamics. Here, we initially discuss conceptual and theoretical aspects of both approaches, then illustrate how they can be combined together, and finally present an example use case on real data." @default.
• W4311673709 created "2022-12-28" @default.
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• W4311673709 date "2022-12-11" @default.
• W4311673709 modified "2023-10-15" @default.
• W4311673709 title "A Guide to Trajectory Inference and RNA Velocity" @default.
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• W4311673709 doi "https://doi.org/10.1007/978-1-0716-2756-3_14" @default.
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