Introduction to Linear Mixed Effects Models

The Gladstone Data Science Training Program provides learning opportunities and hands-on workshops to improve your skills in bioinformatics and computational analysis. Gain new skills and get support with your questions and data. This program is co-sponsered by UCSF School of Medicine.

More often than not, biological experiments involve repeated measures. Examples include responses measured in cellular assays, where the repeated measures may occur over multiple experimental batches, multiple plates, or multiple wells within a plate. Observations of mouse behavior over consecutive trials would be another example of experiments involving repeated measures. The distinctions between biological and technical replicates become less clear in these scenarios. Standard statistical tests like t-tests and ANOVA are not applicable. Instead, analysis of such data requires more sophisticated approaches, such as Linear Mixed Effect Modeling.

In this course, you will learn the motivation for the use of these models, the underlying description and assumptions behind them, example scenarios where they can be used, and code to implement and interpret these models in R.

Visit the workshop site for more details and materials.