| 报告题目： |
基于单细胞网络的单细胞数据分析 |
| 报告人： |
陈洛南 教授 |
| 报告人单位： |
中科院上海生命科学研究院 |
| 报告时间： |
4月14号（星期天）上午10：00-12：00 |
| 报告地点： |
科技楼北410 |
| 报告摘要： |
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Single-cell RNA sequencing (scRNA-seq) is able to give an insight into the gene–gene associations or transcriptional networks among cell populations based on the sequencing of a large number of cells. However, traditional network methods are limited to the grouped cells instead of each single cell, and thus the heterogeneity of single cells will be erased. We present a new method to construct a cell-specific network (CSN) for each single cell from scRNA-seq data (i.e. one network for one cell), which transforms the data from‘unstable’gene expression form to‘stable’gene association form on a single-cell basis. In particular, it is for the first time that we can identify the gene associations/network at a single-cell resolution level. By CSN method, scRNA-seq data can be analyzed for clustering and pseudo-trajectory from network perspective by any existing method, which opens a new way to scRNA-seq data analyses. In addition, CSN is able to find differential gene associations for each single cell, and even‘dark’genes that play important roles at the network level but are generally ignored by traditional differential gene expression analyses. In addition, CSN can be applied to construct individual network of each sample bulk RNA-seq data. Experiments on various scRNA-seq datasets validated the effectiveness of CSN in terms of accuracy and robustness. |
| 报告摘要： |
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陈洛南，中国科学院上海生命科学研究院系统生物学重点实验室执行主任。华中科技大学学士，Tohoku University (Japan)硕士，Tohoku University(Japan)博士。中国运筹学会《计算系统生物学分会》理事长，IEEE-SMC《系统生物学委员会》主席，中国细胞生物学会《功能基因组学与系统生物学分会》副会长，中国药理学会《网络药理学专业委员会》副主任委员。国家基金委重大研究计划专家组，国家重点研发计划重点专项首席科学家。近年，在计算系统生物学和复杂疾病研究领域发表了300余篇SCI 期刊论文及10余部专著及编著书籍，被引14000 余次，特别是提出疾病诊断的网络标志物，及疾病预测的动态网络标志物，开发生物网络30余软件。详细介绍及文章目录见报告人主页http://sysbio.sibcb.ac.cn/cb/chenlab/LuonanChen.htm。 |
