Discovering statistically significant pathways in expression profiling studies

Abstract

Accurate and rapid identification of perturbed pathways through the analysis of genome-wide expression profiles facilitates the generation of biological hypotheses. We propose a statistical framework for determining whether a specified group of genes for a pathway has a coordinated association with a phenotype of interest. Several issues on proper hypothesis-testing procedures are clarified. In particular, it is shown that the differences in the correlation structure of each set of genes can lead to a biased comparison among gene sets unless a normalization procedure is applied. We propose statistical tests for two important but different aspects of association for each group of genes. This approach has more statistical power than currently available methods and can result in the discovery of statistically significant pathways that are not detected by other methods. This method is applied to data sets involving diabetes, inflammatory myopathies, and Alzheimer's disease, using gene sets we compiled from various public databases. In the case of inflammatory myopathies, we have correctly identified the known cytotoxic T lymphocyte-mediated autoimmunity in inclusion body myositis. Furthermore, we predicted the presence of dendritic cells in inclusion body myositis and of an IFN-␣͞␤ response in dermatomyositis, neither of which was previously described. These predictions have been subsequently corroborated by immunohistochemistry.microarrays ͉ gene ontology ͉ normalization ͉ correlated data ͉ inflammatory myopathies

A gene transcription signature of the Akt/mTOR pathway in clinical breast tumors

“…genes induced in AKT1-Tg mouse model) had a coordinated association with a molecular phenotype of interest (e.g. breast tumors with high AKT expression), Q1-Q2 analysis was carried out essentially as described by Tian et al (2005). Briefly, the common population of genes represented in the given human breast tumor profile data set were ranked based on Pearson's correlation with AKT1 mRNA.…”

“…references Bose et al, 2006;Tokunaga et al, 2006), Akt is often measured in its phosphorylated form, whereas here AKT1 mRNA was used as a surrogate for p-Akt. By Q1-Q2 enrichment analysis method (Tian et al, 2005) (results shown in Figure 2b), the set of genes induced in AKT1-Tg were co-expressed as a group with AKT1 mRNA in human breast tumors. In other words, tumors with high levels of Akt also had high expression of a significant number of genes in the AKT1-Tg set.…”

DBGSA: a novel method of distance-based gene set analysis

“…Since the emergence of gene set enrichment analysis (GSEA), an increasing number of GSA approaches based on various statistical methods have been rapidly developed, such as GSEA, 7,8 globaltest, 9 SAM-GS, 10 GlobalANCOVA, 11 ADGO 12,13 and Bayesian network-based pathway analysis. 14 Tian et al 15 classified two types of null hypotheses that test whether a gene set displays a coordinated association with a phenotype of interest. The first type hypothesizes that the genes in a gene set have the same pattern of associations with the given phenotype when compared with the remaining genes (i.e., Q1).…”

Microarray data analysis and mining approaches

“…The authors claimed that their approach has more statistical power than currently available methods and can result in the discovery of statistically significant pathways that are not depicted by other methods [81].…”

“…Since an accurate and rapid identification of perturbed pathways through the analysis of genomewide expression profiles facilitates the generation of biological hypotheses, Tian [81] proposed a statistical framework for determining whether a specified group of genes for a pathway has a coordinated association with a phenotype of interest. In this framework, the overall objective of the analysis is to test whether a group of genes has a coordinated association with a phenotype of interest evaluating the following two null hypothesis:…”