聚类分析
预处理器
条形码
计算机科学
可视化
计算生物学
数据挖掘
鉴定(生物学)
空间分析
数据科学
生物
人工智能
地理
植物
操作系统
遥感
作者
Oscar E. Ospina,Alex C. Soupir,Brooke L. Fridley
出处
期刊:Methods in molecular biology
日期:2023-01-01
卷期号:: 115-140
被引量:1
标识
DOI:10.1007/978-1-0716-2986-4_7
摘要
Recent developments in spatially resolved transcriptomics (ST) have resulted in a large number of studies characterizing the architecture of tissues, the spatial distribution of cell types, and their interactions. Furthermore, ST promises to enable the discovery of more accurate drug targets while also providing a better understanding of the etiology and evolution of complex diseases. The analysis of ST brings similar challenges as seen in other gene expression assays such as scRNA-seq; however, there is the additional spatial information that warrants the development of suitable algorithms for the quality control, preprocessing, visualization, and other discovery-enabling approaches (e.g., clustering, cell phenotyping). In this chapter, we review some of the existing algorithms to perform these analytical tasks and highlight some of the unmet analytical challenges in the analysis of ST data. Given the diversity of available ST technologies, we focus this chapter on the analysis of barcode-based RNA quantitation techniques.
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