Predicting Clinical Phenotypes by Growth Curve Modeling of Transcriptomic Signatures during Disease Progression

Abstract High-throughput gene expression data analysis has benefited from many statistical tests of differential expression across two or more groups such as t tests, ANOVA, etc. Yet, in complex transcriptomic datasets such as longitudinal or repeated measures, few studied have addressed such key issues as group effects and temporal dependency in expression profiles with a single model that is both practically effective and theoretically grounded. In this study, we used Growth Curve Model (GCM), as a generalization of MANOVA, to identify differentially expressed longitudinal profiles of genes, and thus predicted the associated clinical phenotypes, of pediatric lupus during the progressions of the disease across two different racial groups. In particular, we detected a module of histone genes which was shown to be linked with lupus.