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 <strong>readme.txt</strong>. 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.