Single-cell experiments can measure gene expression levels or protein activities in single cells, unlike traditional experimental studies that measure the average activities based on a large number of cells. Although single-cell experimental data provided unprecedented opportunities to analyze the heterogeneity in cells, the non-time-series data raised challenges to mathematical modeling. Thus the first step in single-cell analysis is to arrange each cell in the pseudo-time trajectory based on the developmental stages of cells. In this talk we will discuss two of our recent works, namely the SCOUT algorithm and DTFLOW algorithm.  

报告人简介：

   田天海，澳大利亚莫那什大学数学学院教授。2001年在澳大利亚昆士兰大学取得博士学位。在基因网络和细胞信号传导等生物系统的随机建模，随机动力系统的数值模拟，模型参数估计，统计计算和大数据分析等方向开展过很多有创新意义的工作，并取得了较多的研究成果。