%0 Journal Article
%T 用于空间分辨转录组学数据分析的统计方法<br> Statistical Methods for Spatially Re-solved Transcriptomic Data Analysis
%A 王琳
%A 赵桂华
%J Bioprocess
%P 57-63
%@ 2164-5582
%D 2023
%I Hans Publishing
%R 10.12677/BP.2023.131008
%X 近年来，空间转录组学的发展使得对细胞转录组及其空间位置进行多重分析得以实现。伴随着实验技术能力与效率的日益提升，发展分析方法的要求也逐渐显现。生成空间分辨转录组(SRT, Spatially Resolved Transcriptome)数据的技术正在迅速改进，并应用于研究各种生物组织。研究空间定位基因表达如何为不同组织发育提供新的见解是至关重要的。我们回顾了用于分析不同SRT数据集的可用包，重点是识别空间可变基因(SVGs, Spatially Variable Genes)。另外，在测序方案不断开发的过程中，有必要对现有分析方法中的基本假设进行重新评价与调整，以便使用越来越复杂的数据。为启发和协助今后模型开发工作，这里将对空间转录组学统计学习方法研究新进展进行综述，归纳出有用资源并介绍今后的挑战与机遇。<br />
In recent years, the development of spatial tran-scriptomics has enabled multiple analyses of cell transcriptome and its spatial location. With the increasing ability and efficiency of experimental technology, the requirement of developing analyt-ical methods has gradually emerged. Techniques for generating Spatially Resolved Transcriptome (SRT) data are rapidly improving and being applied to study a variety of biological tissues. It is crit-ical to study how spatially localized gene expression provides new insights into different tissue de-velopment. This paper reviews the packages available for analysis of different SRT data sets, with emphasis on the identification of Spatially Variable Genes (SVGs, Spatially Variable Genes). In addi-tion, as sequencing protocols continue to be developed, it is necessary to reevaluate and adjust the basic assumptions in existing analytical methods in order to use increasingly complex data. In order to inspire and assist future model development, this paper reviews the recent progress of statistical learning methods in spatial transcriptomics, summarizes useful resources, and introduces future challenges and opportunities.
%K 基因表达模式，空间分辨转录组，统计分析<br> Gene Expression Patterns
%K Spatially Resolved Transcriptome
%K Statistical Analysis
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=63233