What if We Ignore the Random Effects When Analyzing RNA-seq Data in a Multifactor Experiment?
Identifying differentially expressed (DE) genes between different conditions is one of the main goals of RNA-seq data analysis. Although a large amount of RNA-seq data are produced for two-group comparison with small sample sizes at early stage, more and more RNA-seq data are being produced in the setting of complex experimental designs such as split-plot designs and repeated measure designs. Data arising from such experiments are traditionally analyzed by mixed-effects models. Therefore an appropriate statistical approach for analyzing RNA-seq data from such designs should be generalized linear mixed models (GLMM) or similar approaches that allow for random effects. However, common practices for analyzing such data in literature either treat random effects as fixed or completely ignore the experimental design and focus on two-group comparison using partial data.
In this seminar, we examine the effect of ignoring the random effects when analyzing RNA-seq data. We accomplish this goal by comparing the standard GLMM model to the methods that ignore the random effects through simulation studies and real data analysis.
Dr. Qiu is an assistant professor of Statistics at the Department of Applied economics and Statistics, University of Delaware. She graduated from Cornell University in 2004 and was faculty at the department of statistics at the University of Missouri for 11 years before joining UD. At Missouri, she held a joint appointment at the college of Agriculture, Food Science and Natural Resources (CAFNR) to provide statistical consulting service to the CAFNR where she had many opportunities to analyze microarray data and RNA-seq data. Her research interest is in statistical analysis of gene expression data, Bayesian modeling of RNA-seq data and DNA-methylation data, multiple testing, and high dimensional equivalence test.
Thursday Tech Talks, sponsored by the Delaware Clinical & Translational Science ACCEL program and the Christiana Care Value Institute, are monthly seminars intended to present statistical, epidemiological and bioinformatics methods and how they can be applied to medical research studies. Presentations focus on the technical aspects of methods as well as their application to real world situations.
These free talks are held the first Thursday of each month at noon at Christiana Hospital but can be viewed from your home or office computer. Earn CMEs by participating in-person or online. Lunch is served and all are welcome to attend.
To see the full calendar of events, visit the Value Institute Events page or the ACCEL website, or subscribe to the ID Series mailing list.
Contact Sarahfaye Dolman at sarahfaye.f.dolman@christianacare.org with any questions.