Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks

TLDR

Gene expression profiles are increasingly used to reverse engineer cellular networks, yet normalization methods designed for differential expression lack metrics for assessing suitability in network reconstruction and correlation measurement. The study aims to benchmark normalization procedures for reverse engineering gene networks and propose an alternative to eliminate artifacts. The authors benchmark MAS5, RMA, GCRMA, and Li‑Wong on replicate, randomized, and human B‑cell datasets using established reverse‑engineering algorithms, and propose an alternative implementation to remove GCRMA artifacts. MAS5 produced the most accurate network reconstructions, while GCRMA’s artifact step caused systematic overestimation of pairwise correlations, impacting reverse‑engineering and clustering methods. Contact: [e-mail].

Abstract

Abstract Motivation: An increasingly common application of gene expression profile data is the reverse engineering of cellular networks. However, common procedures to normalize expression profiles generated using the Affymetrix GeneChips technology were originally developed for a rather different purpose, namely the accurate measure of differential gene expression between two or more phenotypes. As a result, current evaluation strategies lack comprehensive metrics to assess the suitability of available normalization procedures for reverse engineering and, in general, for measuring correlation between the expression profiles of a gene pair. Results: We benchmark four commonly used normalization procedures (MAS5, RMA, GCRMA and Li-Wong) in the context of established algorithms for the reverse engineering of protein–protein and protein–DNA interactions. Replicate sample, randomized and human B-cell data sets are used as an input. Surprisingly, our study suggests that MAS5 provides the most faithful cellular network reconstruction. Furthermore, we identify a crucial step in GCRMA responsible for introducing severe artifacts in the data leading to a systematic overestimate of pairwise correlation. This has key implications not only for reverse engineering but also for other methods, such as hierarchical clustering, relying on accurate measurements of pairwise expression profile correlation. We propose an alternative implementation to eliminate such side effect. Contect: [e-mail]