33,422 research outputs found
Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics.
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
Low-latency compression of mocap data using learned spatial decorrelation transform
Due to the growing needs of human motion capture (mocap) in movie, video
games, sports, etc., it is highly desired to compress mocap data for efficient
storage and transmission. This paper presents two efficient frameworks for
compressing human mocap data with low latency. The first framework processes
the data in a frame-by-frame manner so that it is ideal for mocap data
streaming and time critical applications. The second one is clip-based and
provides a flexible tradeoff between latency and compression performance. Since
mocap data exhibits some unique spatial characteristics, we propose a very
effective transform, namely learned orthogonal transform (LOT), for reducing
the spatial redundancy. The LOT problem is formulated as minimizing square
error regularized by orthogonality and sparsity and solved via alternating
iteration. We also adopt a predictive coding and temporal DCT for temporal
decorrelation in the frame- and clip-based frameworks, respectively.
Experimental results show that the proposed frameworks can produce higher
compression performance at lower computational cost and latency than the
state-of-the-art methods.Comment: 15 pages, 9 figure
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