The linear mixed model is uniquely suited to interpreting drivers of variation in complex gene expression studies. Yet the lack of a convenient workflow and scalable implementation for analysis and visualization have prevented wider application of this rich statistical framework. Applying this analysis framework to gene expression data currently requires particular expertise in regression modeling, computational statistics, the R programming language and data visualization. Even then, the time required to implement the analysis is often prohibitive.
