A universal differential expression prediction tool for single-cell and spatial genomics data
A universal differential expression prediction tool for single-cell and spatial genomics data
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DOI：
10.1101/2022.11.13.516355
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发表时间：
2023-05
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影响因子：
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通讯作者：
A. Vandenbon;Diego Diez
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文献类型：
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作者：
A. Vandenbon;Diego Diez
随着单细胞和空间基因组数据的日益复杂，公正和高效的探索性数据分析工具的重要性越来越大。一个常见的探索性数据分析步骤是预测在一个组织内的细胞或位置的子集中具有不同活性水平的基因。我们之前开发了SingleCellHayStack，一种从单细胞转录组数据预测差异表达基因的方法，而不依赖于细胞集群。在这里，我们提出了SingleCellHayStack的更新，它现在是一种普遍适用的预测差异活动特征的方法：1)SingleCellHayStack现在接受连续的特征，可以是RNA或蛋白质表达、染色质可获得性或来自单细胞、空间甚至大量基因组数据的模块分数；2)它可以处理一维轨迹、二维空间坐标以及更高维的潜在空间作为输入坐标。性能得到了显著提高，计算时间减少了多达10倍，并可扩展到数百万个单元，使SingleCellHayStack成为适合用于地图集水平数据集探索性分析的工具。SingleCellHayStack以R包和Python模块的形式提供
With the growing complexity of single-cell and spatial genomics data, there is an increasing importance of unbiased and efficient exploratory data analysis tools. One common exploratory data analysis step is the prediction of genes with different levels of activity in a subset of cells or locations inside a tissue. We previously developed singleCellHaystack, a method for predicting differentially expressed genes from single-cell transcriptome data, without relying on clustering of cells. Here we present an update to singleCellHaystack, which is now a universally applicable method for predicting differentially active features: 1) singleCellHaystack now accepts continuous features that can be RNA or protein expression, chromatin accessibility or module scores from single-cell, spatial and even bulk genomics data, and 2) it can handle 1D trajectories, 2-3D spatial coordinates, as well as higher-dimensional latent spaces as input coordinates. Performance has been drastically improved, with up to ten times reduction in computational time and scalability to millions of cells, making singleCellHaystack a suitable tool for exploratory analysis of atlas level datasets. singleCellHaystack is available as an R package and Python module