Statistical and machine learning methods for spatially resolved transcriptomics with histology.
Statistical and machine learning methods for spatially resolved transcriptomics with histology.
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DOI：
10.1016/j.csbj.2021.06.052
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发表时间：
2021
影响因子：
6
通讯作者：
Li M
中科院分区：
文献类型：
--
作者：
Hu J;Schroeder A;Coleman K;Chen C;Auerbach BJ;Li M
空间分辨转录组学（SRT）技术的最新发展使科学家能够在其形态学背景下对细胞进行综合理解。这些技术在不同组织和疾病中的应用改变了我们对转录复杂性的看法。大多数已发表的研究利用为单细胞RNA测序（scRNA-seq）开发的工具进行数据分析。然而，SRT数据表现出与scRNA-seq不同的性质。为了充分利用此类数据中空间位置信息的额外维度，需要为SRT量身定制的新方法。此外，SRT数据通常具有伴随的高分辨率组织学信息。在基因表达分析中阐明组织学特征是一个探索不足的领域。在这篇综述中，我们将集中在统计和机器学习方面的SRT数据分析，并讨论如何空间位置和组织学信息可以与基因表达相结合，以提高我们的理解转录的复杂性。最后指出了该领域存在的问题和未来的研究方向。
Recent developments in spatially resolved transcriptomics (SRT) technologies have enabled scientists to get an integrated understanding of cells in their morphological context. Applications of these technologies in diverse tissues and diseases have transformed our views of transcriptional complexity. Most published studies utilized tools developed for single-cell RNA sequencing (scRNA-seq) for data analysis. However, SRT data exhibit different properties from scRNA-seq. To take full advantage of the added dimension on spatial location information in such data, new methods that are tailored for SRT are needed. Additionally, SRT data often have companion high-resolution histology information available. Incorporating histological features in gene expression analysis is an underexplored area. In this review, we will focus on the statistical and machine learning aspects for SRT data analysis and discuss how spatial location and histology information can be integrated with gene expression to advance our understanding of the transcriptional complexity. We also point out open problems and future research directions in this field.
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影响因子：
4.6
作者：
Bankhead P;Loughrey MB;Fernández JA;Dombrowski Y;McArt DG;Dunne PD;McQuaid S;Gray RT;Murray LJ;Coleman HG;James JA;Salto-Tellez M;Hamilton PW
通讯作者：
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作者：
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通讯作者：
Lundeberg, Joakim
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作者：
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