9/9 – Jingyi Jessica Li, UCLA
CBQB Seminar (in-person event)
September 9, 2024
12:00 PM - 1:00 PM
This is an in-person event in room COMRB 8175 on west campus (directions). You can also watch seminar live here>>
Speaker:
Jingyi Jessica Li, Ph.D.
Professor
Department of Statistics
University of California, Los Angeles
Title:
Using Synthetic Null Data to Enhance Statistical Rigor in Single-cell and Spatial Transcriptomics
Abstract:
The rapid development of genomics technologies has propelled fast advances in computational algorithms. However, the statistical rigor in data analysis has been often overlooked. Motivated by the mandatory use of negative controls in experiments, I propose to enhance the reliability of single-cell and spatial omics data analysis by using synthetic null data 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 synthetic null data in which cell-cell relationships are disrupted, we developed a statistical method, scDEED (https://doi.org/10.1101/2023.04.21.537839), 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 (https://www.nature.com/articles/s41587-023-01772-1) to generate synthetic null single-cell and spatial transcriptomics data, we developed a statistical method, ClusterDE (https://doi.org/10.1101/2023.07.21.550107), to identify potential cell-type markers (or spatial domain markers) from differential expression (DE) analysis applied to potential cell types (or spatial domains) identified through clustering analysis. Overall, using synthetic null data is an effective strategy to increase the statistical rigor of complex data analysis and improve the reliability of analysis results.
Date posted
Sep 9, 2024
Date updated
Sep 9, 2024