EAGS: efficient and adaptive gaussian smoothing applied to high-resolved spatial transcriptomics

This dataset is used to preserve the mouse brain and mouse olfactory bulb data (in h5ad format) involved in the EAGS study.

You can get details of the different datasets from readme.txt.

Abstract of the EAGS study:

The emergence of high-resolved spatial transcriptomics (ST) technology has facilitated the research of novel methods to investigate biological development, growth and other complex biological processes. High-resolution and whole transcriptomics ST datasets require customized imputation methods to improve signal-to-noise ratio and the data quality. We propose an efficient and adaptive gaussian smoothing (EAGS) method for imputation on high-resolved ST. Its adaptive two-factor smoothing creates patterns based on the spatial and expression information of the cells, creates adaptive weights for the smoothing of cells in the same pattern, then utilizes the weights to restore the gene expression profiles. The performance and efficiency of EAGS are verified on high-resolved ST data of mouse brain and olfactory bulb. Compared with other competitive methods, EAGS shows higher clustering accuracy, better biological interpretation and a significant advantage in computational consumption.

本数据集用于存储EAGS研究中涉及的小鼠脑及小鼠嗅球数据（格式为h5ad）。
研究者可通过readme.txt文件获取各数据集的详细信息。

EAGS研究摘要：
高分辨空间转录组学（spatial transcriptomics, ST）技术的出现，为研发探究生物发育、生长及其他复杂生物过程的新型研究方法提供了重要支撑。高分辨全转录组空间转录组数据集需借助定制化插补方法，以提升信噪比与数据质量。我们提出了一种用于高分辨空间转录组数据插补的高效自适应高斯平滑（efficient and adaptive gaussian smoothing, EAGS）方法。该方法通过自适应双因子平滑，基于细胞的空间位置与表达信息构建细胞模式，并为同一模式内的细胞平滑计算自适应权重，最终利用这些权重恢复基因表达谱。我们在小鼠脑与嗅球的高分辨空间转录组数据上验证了EAGS的性能与效率。与其他同类竞争方法相比，EAGS具备更高的聚类精度、更优异的生物学可解释性，且在计算开销上具有显著优势。