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Diffusion maps for high-dimensional single-cell analysis of differentiation data

机译:扩散图用于分化数据的高维单细胞分析

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Motivation: Single-cell technologies have recently gained popularity in cellular differentiation studies regarding their ability to resolve potential heterogeneities in cell populations. Analyzing such high-dimensional single-cell data has its own statistical and computational challenges. Popularmultivariate approaches are based on data normalization, followed by dimension reduction and clustering to identify subgroups. However, in the case of cellular differentiation, we would not expect clear clusters to be present but instead expect the cells to follow continuous branching lineages.
机译:动机:单细胞技术最近在细胞分化研究中变得越来越流行,这是因为它们具有解决细胞群体中潜在异质性的能力。分析此类高维单细胞数据有其自身的统计和计算挑战。流行的多变量方法基于数​​据规范化,然后进行降维和聚类以识别子组。但是,在细胞分化的情况下,我们不希望存在清晰的簇,而希望细胞遵循连续的分支谱系。

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