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An accurate and robust imputation method scImpute for single-cell RNA-seq d...

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Time: 9:00 AM - 10:00 AM PDT (12:00 PM - 1:00 PM EST), Monday, April 30, 2018 link will be sent 24 hours before the event.

Please RSVP, the registration will be closed on 11:59 AM, April 28, 2018.


The emerging single-cell RNA sequencing (scRNA-seq) technologies enable the investigation of transcriptomic landscapes at the single-cell resolution. ScRNA-seq data analysis is complicated by excess zero counts, the so-called dropouts due to low amounts of mRNA sequenced within individual cells. We introduce scImpute, a statistical method to accurately and robustly impute the dropouts in scRNA-seq data. scImpute automatically identifies likely dropouts, and only perform imputation on these values without introducing new biases to the rest data. scImpute also detects outlier cells and excludes them from imputation. Evaluation based on both simulated and real human and mouse scRNA-seq data suggests that scImpute is an effective tool to recover transcriptome dynamics masked by dropouts. scImpute is shown to identify likely dropouts, enhance the clustering of cell subpopulations, improve the accuracy of differential expression analysis, and aid the study of gene expression dynamics.

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Jingyi Jessica Li is an Assistant Professor in the Department of Statistics and the Department of Human Genetics at University of California, Los Angeles (UCLA). She is also a faculty member in the Interdepartmental Ph.D. Program in Bioinformatics and a member in the Jonsson Comprehensive Cancer Center (JCCC) Gene Regulation Research Program Area. Prior to joining UCLA in 2013, Jessica obtained her Ph.D. degree from the Interdepartmental Group in Biostatistics at University of California, Berkeley, where she worked with Profs Peter J. Bickel and Haiyan Huang. Jessica received her B.S. (summa cum laude) from the Department of Biological Sciences and Technology at Tsinghua University, China in 2007. Jessica and her students focus on developing statistical and computational methods motivated by important questions in biomedical sciences and abundant information in big genomic and health related data. On the statistical methodology side, her research interests include association measures, high-dimensional variable selection, and classification metrics. On the biomedical application side, her research interests include next-generation RNA sequencing, comparative genomics, and information flow in the central dogma. Jessica is the recipient of the Hellman Fellowship (2015), the PhRMA Foundation Research Starter Grant in Informatics (2017), and the Alfred P. Sloan Research Fellowship (2018), and the Johnson & Johnson Women in STEM2D Math Scholar Award.

Wei (Vivian) Li is a Ph.D. candidate at UCLA Department of Statistics. She has a B.S. in Statistics from Huazhong University of Science and Technology in China. In her doctoral study, she focuses on developing and improving statistical methods to uncover the hidden information in large-scale genomic data. Her research interests include annotation-based mRNA transcript reconstruction, robust transcript quantification, and gene expression imputation. She is also interested in comparative analysis based on transcriptomic or epigenomic profiles from multiple tissues or cell types.

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