Pathway-Level Information ExtractoR (PLIER) for gene expression data

A major challenge in gene expression analysis is to accurately infer relevant biological insight, such as variation in cell type proportion or pathway activity, from global gene expression studies. We present a general solution for this problem that outperforms available cell proportion inference algorithms, and is more widely useful to automatically identify specific pathways that regulate gene expression. Our method improves replicability and biological insight when applied to trans-eQTL identification. Overall design: Make cell type predictions based on expression data and validate by flow cytometery