- Title： MAIB-Talk-018: Applying causal discovery to single-cell analyses using CausalCell
- Date：11:00pm US East time, 06/24/2023
- Date：11:00am Beijing time, 06/25/2023
- Zoom ID：933 1613 9423
- Zoom PWD：416262
- Zoom: https://uwmadison.zoom.us/meeting/register/tJcudu-prTIuGNda1MsF8PKyRQlnGn06TP2E
Presentation Record(Previous Presentation will be showed here if the video is not released for this talk)
Dr. Hao Zhu
Dr. Hao Zhu was initially graduated from a medical school (community school level) and worked for years in a hospital. He then went to the graduate school of the National University of Defense Technology and was trained in computer science (MS degree) After graduating from NUDT, we joined the First Military Medical University and obtained the PhD degree in Pathology and Pathophysiology. ZHU Hao then got the postdoc training in Singapore (Bioinformatics Institute of Singapore) and UK (University of Nottingham, School of Mathematical Sciences); in these years he worked on computational modeling of developmental signaling. Since 2009 he has worked in Southern Medical University, Guangzhou, working on bioinformatics and genome analysis.
Yujian Wen, Jielong Huang, Shuhui Guo, Yehezqel Elyahu, Alon Monsonego, Hai Zhang, Yanqing Ding, Hao Zhu (2023) Applying causal discovery to single-cell analyses using CausalCell eLife 12:e81464
Correlation between objects is prone to occur coincidentally, and exploring correlation or association in most situations does not answer scientific questions rich in causality. Causal discovery (also called causal inference) infers causal interactions between objects from observational data. Reported causal discovery methods and single-cell datasets make applying causal discovery to single cells a promising direction. However, evaluating and choosing causal discovery methods and developing and performing proper workflow remain challenges. We report the workflow and platform CausalCell (http://www.gaemons.net/causalcell/causalDiscovery/) for performing single-cell causal discovery. The workflow/platform is developed upon benchmarking four kinds of causal discovery methods and is examined by analyzing multiple single-cell RNA-sequencing (scRNA-seq) datasets. Our results suggest that different situations need different methods and the constraint-based PC algorithm with kernel-based conditional independence tests work best in most situations. Related issues are discussed and tips for best practices are given. Inferred causal interactions in single cells provide valuable clues for investigating molecular interactions and gene regulations, identifying critical diagnostic and therapeutic targets, and designing experimental and clinical interventions.
This manuscript presents an important tool for causal inference intended for the analysis of single cell datasets but possibly with broader applications. It compares several algorithms and incorporates a number of them in the platform and offers convincing evidence of its usefulness. With the rapid expansion of large datasets, this tool is beneficial in offering several causal inference analysis options and expediting the interpretation of data.