讲座 | Enhancing statistical rigor in single-cell data analysis using synthetic negative controls

学术讲座：Enhancing statistical rigor in single-cell data analysis using synthetic negative controls

主讲人：Jingyi Jessica Li, Professor, Department of Statistics (primary) and the Departments of Biostatistics, Computational Medicine, and Human Genetics (secondary), UCLA

邀请人：Hongyi Xin

时间：14:00, December 9, 2023 (Beijing Time)

地点：Room 200, Yue-Kong Pao Library's Annex

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讲座摘要：

The rapid development of single-cell experimental technologies has propelled fast advances in single-cell computational algorithms. However, the statistical rigor in single-cell data analyses has been often overlooked. Motivated by the mandatory use of negative controls in experiments, I propose to enhance the reliability of single-cell data analysis by using synthetic negative controls generated based on real data under specific null hypotheses. I will demonstrate this strategy using two statistical methods my group developed. First, using permutation to generate a synthetic negative control in which cell-cell relationships are disrupted, we developed the statistical method scDEED to detect dubious two-dimensional cell embeddings, crucial for single-cell data visualization, and to optimize the hyperparameters of embedding methods such as t-SNE and UMAP. Second, using our simulator scDesign3 to generate a synthetic null control in which cells are from one hypothetical cell type, we developed the statistical method ClusterDE to identify potential cell-type markers from differential expression (DE) analysis applied to potential cell types identified through clustering analysis. Overall, leveraging synthetic negative controls is an effective strategy to increase the statistical rigor of single-cell data analysis and thus improve the reliability of analysis results.

References: 

1. bioRxiv, 2023: 2023.04. 21.537839.

2. Nature Biotechnology, 2023: 1-6.

3. Research Square, 2023.

主讲人简介：

Jingyi Jessica Li is a Professor in the Department of Statistics (primary) and the Departments of Biostatistics, Computational Medicine, and Human Genetics (secondary) at UCLA and a Harvard Radcliffe Fellow in the year 2022-23. Before joining UCLA in 2013, Jessica obtained Ph.D. from UC Berkeley, where she worked with Profs. Peter J. Bickel and Haiyan Huang, and B.S. (summa cum laude) from Tsinghua University, China. At UCLA, Jessica leads the group “Junction of Statistics and Biology,” which comprises students from interdisciplinary backgrounds. On the statistical methodology side, her research interests include association measures, asymmetric classification, p-value-free false discovery rate control, and high-dimensional variable selection. On the biomedical application side, her research interests include bulk and single-cell omics, comparative genomics, and information flow in the central dogma. Jessica is the recipient of the Alfred P. Sloan Research Fellowship (2018), the Johnson & Johnson WiSTEM2D Math Scholar Award (2018), the NSF CAREER Award (2019), the MIT Technology Review 35 Innovators Under 35 China (2020), the Harvard Radcliffe Fellowship (2022) and the Overton Prize (2023).

学术兼职：

2023– Editorial Board, Genome Biology

2022– Associate Editor, Journal of American Statistical Association (Applications & Case Studies) 

2021– Editorial Board, Physiological Genomics

2020– Guest Editor, PLOS Computational Biology

2020– Management Committee, Journal of Computational and Graphical Statistics

2015– Associate Editor, PeerJ 

2014– Review Editor, Frontiers in Genetics

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