GRAPE: a pathway template method to characterize tissue-specific functionality from gene expression profiles

Michael I. Klein, David F. Stern, Hongyu Zhao

Personalizing treatment regimes based on gene expression profiles of individual tumors will facilitate management of cancer. Although many methods have been developed to identify pathways perturbed in tumors, the results are often not generalizable across independent datasets due to the presence of platform/batch effects. There is a need to develop methods that are robust to platform/batch effects and able to identify perturbed pathways in individual samples. We present Gene-Ranking Analysis of Pathway Expression (GRAPE) as a novel method to identify abnormal pathways in individual samples that is robust to platform/batch effects in gene expression profiles generated by multiple platforms. GRAPE first defines a template consisting of an ordered set of pathway genes to characterize the normative state of a pathway based on the relative rankings of gene expression levels across a set of reference samples. This template can be used to assess whether a sample conforms to or deviates from the typical behavior of the reference samples for this pathway. We demonstrate that GRAPE performs well versus existing methods in classifying tissue types within a single dataset, and that GRAPE achieves superior robustness and generalizability across different datasets. A powerful feature of GRAPE is the ability to represent individual gene expression profiles as a vector of pathways scores. We present applications to the analyses of breast cancer subtypes and different colonic diseases. We perform survival analysis of several TCGA subtypes and find that GRAPE pathway scores perform well in comparison to other methods. GRAPE templates offer a novel approach for summarizing the behavior of gene-sets across a collection of gene expression profiles. These templates offer superior robustness across distinct experimental batches compared to existing methods. GRAPE pathway scores enable identification of abnormal gene-set behavior in individual samples using a non-competitive approach that is fundamentally distinct from popular enrichment-based methods. GRAPE may be an appropriate tool for researchers seeking to identify individual samples displaying abnormal gene-set behavior as well as to explore differences in the consensus gene-set behavior of groups of samples. GRAPE is available in R for download at https://CRAN.R-project.org/package=GRAPE .