空间分析
杠杆(统计)
转录组
生物
数据集
计算生物学
集合(抽象数据类型)
数据科学
数据挖掘
计算机科学
遗传学
机器学习
人工智能
统计
数学
基因表达
基因
程序设计语言
作者
Eleftherios Zormpas,Rachel Queen,Alexis Comber,Simon Cockell
出处
期刊:Cell
[Elsevier]
日期:2023-12-01
卷期号:186 (26): 5677-5689
标识
DOI:10.1016/j.cell.2023.11.003
摘要
RNA sequencing in situ allows for whole-transcriptome characterization at high resolution, while retaining spatial information. These data present an analytical challenge for bioinformatics-how to leverage spatial information effectively? Properties of data with a spatial dimension require special handling, which necessitate a different set of statistical and inferential considerations when compared to non-spatial data. The geographical sciences primarily use spatial data and have developed methods to analye them. Here we discuss the challenges associated with spatial analysis and examine how we can take advantage of practice from the geographical sciences to realize the full potential of spatial information in transcriptomic datasets.
科研通智能强力驱动
Strongly Powered by AbleSci AI