时间：4月27日下午13:00-14:00
会议号：891690764
题目：CALLR: a semi-supervised cell type annotation method for single-cell RNA sequencing data
摘要:
Single-cell RNA sequencing (scRNA-seq) technology has been widely applied to capture the heterogeneity of different cell types within complex tissues. An essential step in scRNA-seq data analysis is the annotation of cell types. Traditional cell type annotation is mainly clustering the cells first, and then using the aggregated cluster-level expression profiles and the marker genes to label each cluster. Such methods are greatly dependent on the clustering results, which are insufficient for accurate annotation. In this work, we propose a semi-supervised learning method for cell type annotation called CALLR. It combines unsupervised learning represented by the graph Laplacian matrix constructed from all the cells, and supervised learning using sparse logistic regression. By alternately updating the cell clusters and annotation labels, high annotation accuracy can be achieved. The model is formulated as an optimization problem, and a computationally efficient algorithm is developed to solve it. Experiments on ten real datasets show that CALLR outperforms the compared (semi-) supervised learning methods, and the popular clustering methods.
报告人简介：
张淑芹，复旦大学数学科学学院教授，博士生导师。主要研究数据驱动的数学、统计模型及算法，尤其是生物医学数据中的相关问题，包括网络数据的结构分析，多种生物医学数据的整合，数据子类型的学习等等。