Time-Series Analysis of Gene Correlation Networks based on Single-Cell Transcriptome Data
Time-Series Analysis of Gene Correlation Networks based on Single-Cell Transcriptome Data
复制标题
基于单细胞转录组数据的基因相关网络的时间序列分析
DOI：
10.1109/bibm52615.2021.9669412
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Ogura Atsushi
中科院分区：
文献类型：
--
作者：
Asano Yasuhito;Ogawa Tatsuro;Shichino Shigeyuki;Ueha Satoshi;Matsushima Koji;Ogura Atsushi
炎症是多种疾病的解剖学和生理学反应，并且没有主观症状的组织病变的检测被认为对于预防慢性炎症是重要的。为此，预计最近开发的用于获得单细胞转录组数据的技术和分析技术（如MAGIC和Monocle）将是有用的。在这项研究中，我们提出了一个基于单细胞转录组数据的基因相关网络作为一种新的分析方法。具体地说，我们提出了一种利用MAGIC构建基因相关网络的方法，以及分析其时间序列变化的可视化和排序方法。为了证实所提出的方法的有用性，我们使用从小鼠通过暴露于二氧化硅颗粒诱导的肺纤维化获得的单细胞转录组的时间序列数据进行实验。我们观察到的显着变化的基因相关网络组成的“负边缘”的疾病状态的进展，以及具有较大的波动排名的特征基因组在疾病的早期阶段发挥重要作用。
Inflammation is an anatomical and physiological response underlying a variety of diseases, and the detection of tissue lesions without subjective symptoms is considered important for preventing of chronic inflammation. For this purpose, it is expected to be useful that recently developed technologies for obtaining single-cell transcriptome data and analytical techniques such as MAGIC and Monocle. In this study, we propose a gene correlation network based on single-cell transcriptome data as a novel analytical approach. Specifically, wep ropose a method for constructing a gene correlation network using MAGIC, and visualization and ranking methods for analyzing its time-series changes. In order to confirm the usefulness of the proposed methods, we conducted experiments using the time-sequence data of single-cell transcriptome obtained from mice induced pulmonary lung fibrosis bye xposure to silica particles. We observed significant changes in the gene correlation network consisting of “negative edges” with the progression of the disease state, as well as that characteristic gene groups with large fluctuations in ranking play important roles in the early stage of the disease.