标杆管理
多样性(控制论)
计算机科学
数据科学
数据挖掘
基本事实
生物学数据
计算生物学
转录组
人工智能
生物信息学
机器学习
生物
基因
基因表达
遗传学
业务
营销
作者
Natalie Charitakis,Mirana Ramialison,Hieu T. Nim
出处
期刊:Springer eBooks
[Springer Nature]
日期:2022-01-01
卷期号:: 165-186
被引量:2
标识
DOI:10.1007/978-3-030-87821-4_7
摘要
The technology to generate Spatially Resolved Transcriptomics (SRT) data is rapidly being improved and applied to investigate a variety of biological tissues. The ability to interrogate how spatially localised gene expression can lend new insight to different tissue development is critical, but the appropriate tools to analyse this data are still emerging. This chapter reviews available packages and pipelines for the analysis of different SRT datasets with a focus on identifying spatially variable genes (SVGs) alongside other aims, while discussing the importance of and challenges in establishing a standardised ‘ground truth’ in the biological data for benchmarking.
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