PDF(Pubmed)
Abstract：
The emergence of high-resolved spatial transcriptomics (ST) has facilitated the research of novel methods to investigate biological development, organism growth, and other complex biological processes. However, high-resolved and whole transcriptomics ST datasets require customized imputation methods to improve the signal-to-noise ratio and the data quality.
We propose an efficient and adaptive Gaussian smoothing (EAGS) imputation method for high-resolved ST. The adaptive 2-factor smoothing of EAGS creates patterns based on the spatial and expression information of the cells, creates adaptive weights for the smoothing of cells in the same pattern, and then utilizes the weights to restore the gene expression profiles. We assessed the performance and efficiency of EAGS using simulated and high-resolved ST datasets of mouse brain and olfactory bulb.
Compared with other competitive methods, EAGS shows higher clustering accuracy, better biological interpretations, and significantly reduced computational consumption.
We propose an efficient and adaptive Gaussian smoothing (EAGS) imputation method for high-resolved ST. The adaptive 2-factor smoothing of EAGS creates patterns based on the spatial and expression information of the cells, creates adaptive weights for the smoothing of cells in the same pattern, and then utilizes the weights to restore the gene expression profiles. We assessed the performance and efficiency of EAGS using simulated and high-resolved ST datasets of mouse brain and olfactory bulb.
Compared with other competitive methods, EAGS shows higher clustering accuracy, better biological interpretations, and significantly reduced computational consumption.
摘要：
背景：高分辨空间转录组学（ST）的出现促进了研究生物学发育的新方法的研究，有机体生长,和其他复杂的生物过程。然而,高分辨率和完整的转录组学ST数据集需要定制的插补方法来提高信噪比和数据质量。
结果：我们提出了一种用于高分辨ST的高效且自适应的高斯平滑（EAGS）插补方法。EAGS的自适应2因子平滑基于细胞的空间和表达信息创建模式，为同一模式中的单元格的平滑创建自适应权重，然后利用权重来恢复基因表达谱。我们使用小鼠大脑和嗅球的模拟和高分辨率ST数据集评估了EAGS的性能和效率。
结论：与其他竞争方法相比，EAGS显示出更高的聚类精度，更好的生物学解释，并显著减少计算消耗。
结果：我们提出了一种用于高分辨ST的高效且自适应的高斯平滑（EAGS）插补方法。EAGS的自适应2因子平滑基于细胞的空间和表达信息创建模式，为同一模式中的单元格的平滑创建自适应权重，然后利用权重来恢复基因表达谱。我们使用小鼠大脑和嗅球的模拟和高分辨率ST数据集评估了EAGS的性能和效率。
结论：与其他竞争方法相比，EAGS显示出更高的聚类精度，更好的生物学解释，并显著减少计算消耗。
