平滑的
计算机科学
高斯分布
转录组
计算生物学
数据科学
生物系统
生物
化学
计算机视觉
计算化学
生物化学
基因
基因表达
作者
Tongxuan Lv,Ying Zhang,Mei Li,Qiang Kang,Shuangsang Fang,Yong Zhang,Susanne Brix Pedersen,Xun Xu
出处
期刊:GigaScience
[Oxford University Press]
日期:2024-01-01
卷期号:13 (1)
标识
DOI:10.1093/gigascience/giad097
摘要
Abstract Background 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. Findings 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. Conclusions Compared with other competitive methods, EAGS shows higher clustering accuracy, better biological interpretations, and significantly reduced computational consumption.
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