生物信息学研究中心
学术报告


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Speaker:

Dr.Jingyi Jessica Li,University of California, Los Angeles

Inviter: 孙六全 研究员
Title:
Statistical Methods for Bulk and Single-cell RNA Sequencing Data
Time & Venue:

2019.1.19 10:00 S705

Abstract:

In this talk, I will introduce two recent statistical methods my group JSB@UCLA has developed for bulk and single-cell RNA-seq data. The first part of my talk will be about the discovery of novel mRNA isoforms from bulk RNA-seq data. Our method AIDE (Annotation-assisted Isoform Discovery and abundance Estimation) is the first computational approach that directly controls false isoform discoveries by identifying isoforms in a stepwise and conservative manner. AIDE prioritizes the annotated isoforms and precisely identifies novel isoforms whose addition significantly improves the explanation of observed RNA-seq reads. As a robust bioinformatics tool for transcriptome analysis, AIDE will enable researchers to discover novel isoforms with high confidence. The second part of my talk will be about the imputation of dropout gene expression values in single-cell RNA-seq (scRNA-seq) data. I will introduce scImpute, a statistical method to accurately and robustly impute the dropouts in scRNA-seq data. scImpute automatically identifies likely dropouts and only performs imputation on these values without introducing new biases to the rest data. scImpute also detects outlier cells and excludes them from imputation. scImpute is shown to enhance the clustering of cell subpopulations, improve the accuracy of differential expression analysis, and recover gene expression dynamics.

Affiliation:  

学术报告中国科学院数学与系统科学研究院应用数学研究所
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