报告题目: Imputing dropout events in single cell RNA sequencing data via ensemble learning

报告人：张晓飞 博士

报告时间：2019年6月28日（周五）15:00

报告地点：逸夫楼C座314会议室

摘要：

Imputation of dropout events that may mislead downstream analyses is a key step in analyzing single-cell RNA-sequencing (scRNA-seq) data. We develop EnImpute, an R package that introduces an ensemble learning method for imputing dropout events in scRNA-seq data. EnImpute combines the results obtained from multiple imputation methods to generate a more accurate result. A Shiny application is developed to provide easier implementation and visualization. Experiment results show that EnImpute outperforms the individual state-of-the-art methods in almost all situations. EnImpute is useful for correcting the noisy scRNA-seq data before performing downstream analysis.

报告人简介：

张晓飞，华中师范大学数学与统计学学院副教授，博士研究生导师。主要从事基于机器学习方法的大规模生物医学组学数据挖掘研究。现主持国家自然科学基金面上项目1项、湖北省自然科学基金面上项目1项。曾主持国家自然科学基金青年项目1项，参与国家重点研发计划“精准医学研究”重点专项1项，参与国家自然科学基金重点项目1项。已在Bioinformatics、 IEEE transactions on Cybernetics、 IEEE Transactions on Image Processing、IEEE/ACM Transactions on Computational Biology and Bioinformatics、BMC Bioinformatics、BMC Genomics等学术期刊发表学术论文30余篇，累计影响因子110左右。