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.
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。