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A Comparative Study of Mouse Hepatic and Intestinal Gene Expression Profiles under PPAR? Knockout by Gene Set Enrichment Analysis.

ABSTRACT: Gene expression profiling of PPAR? has been used in several studies, but fewer studies went further to identify the tissue-specific pathways or genes involved in PPAR? activation in genome-wide. Here, we employed and applied gene set enrichment analysis to two microarray datasets both PPAR? related respectively in mouse liver and intestine. We suggested that the regulatory mechanism of PPAR? activation by WY14643 in mouse small intestine is more complicated than in liver due to more involved pathways. Several pathways were cancer-related such as pancreatic cancer and small cell lung cancer, which indicated that PPAR? may have an important role in prevention of cancer development. 12 PPAR? dependent pathways and 4 PPAR? independent pathways were identified highly common in both liver and intestine of mice. Most of them were metabolism related, such as fatty acid metabolism, tryptophan metabolism, pyruvate metabolism with regard to PPAR? regulation but gluconeogenesis and propanoate metabolism independent of PPAR? regulation. Keratan sulfate biosynthesis, the pathway of regulation of actin cytoskeleton, the pathways associated with prostate cancer and small cell lung cancer were not identified as hepatic PPAR? independent but as WY14643 dependent ones in intestinal study. We also provided some novel hepatic tissue-specific marker genes.

SUBMITTER: He K

PROVIDER: S-EPMC3147148 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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A Comparative Study of Mouse Hepatic and Intestinal Gene Expression Profiles under PPARα Knockout by Gene Set Enrichment Analysis.

He Kan K Wang Qishan Q Yang Yumei Y Wang Minghui M Pan Yuchun Y

PPAR research 20110727

Gene expression profiling of PPARα has been used in several studies, but fewer studies went further to identify the tissue-specific pathways or genes involved in PPARα activation in genome-wide. Here, we employed and applied gene set enrichment analysis to two microarray datasets both PPARα related respectively in mouse liver and intestine. We suggested that the regulatory mechanism of PPARα activation by WY14643 in mouse small intestine is more complicated than in liver due to more involved pat ...[more]

PMID: 21811494

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Project description:BackgroundThe analysis of high-throughput gene expression data with respect to sets of genes rather than individual genes has many advantages. A variety of methods have been developed for assessing the enrichment of sets of genes with respect to differential expression. In this paper we provide a comparative study of four of these methods: Fisher's exact test, Gene Set Enrichment Analysis (GSEA), Random-Sets (RS), and Gene List Analysis with Prediction Accuracy (GLAPA). The first three methods use associative statistics, while the fourth uses predictive statistics. We first compare all four methods on simulated data sets to verify that Fisher's exact test is markedly worse than the other three approaches. We then validate the other three methods on seven real data sets with known genetic perturbations and then compare the methods on two cancer data sets where our a priori knowledge is limited.ResultsThe simulation study highlights that none of the three method outperforms all others consistently. GSEA and RS are able to detect weak signals of deregulation and they perform differently when genes in a gene set are both differentially up and down regulated. GLAPA is more conservative and large differences between the two phenotypes are required to allow the method to detect differential deregulation in gene sets. This is due to the fact that the enrichment statistic in GLAPA is prediction error which is a stronger criteria than classical two sample statistic as used in RS and GSEA. This was reflected in the analysis on real data sets as GSEA and RS were seen to be significant for particular gene sets while GLAPA was not, suggesting a small effect size. We find that the rank of gene set enrichment induced by GLAPA is more similar to RS than GSEA. More importantly, the rankings of the three methods share significant overlap.ConclusionThe three methods considered in our study recover relevant gene sets known to be deregulated in the experimental conditions and pathologies analyzed. There are differences between the three methods and GSEA seems to be more consistent in finding enriched gene sets, although no method uniformly dominates over all data sets. Our analysis highlights the deep difference existing between associative and predictive methods for detecting enrichment and the use of both to better interpret results of pathway analysis. We close with suggestions for users of gene set methods.

Project description:Enrichment analysis is a popular approach to identify pathways or sets of genes which are significantly enriched in the context of differentially expressed genes. The traditional gene set enrichment approach considers a pathway as a simple gene list disregarding any knowledge of gene or protein interactions. In contrast, the new group of so called pathway topology-based methods integrates the topological structure of a pathway into the analysis.We comparatively investigated gene set and pathway topology-based enrichment approaches, considering three gene set and four topological methods. These methods were compared in two extensive simulation studies and on a benchmark of 36 real datasets, providing the same pathway input data for all methods.In the benchmark data analysis both types of methods showed a comparable ability to detect enriched pathways. The first simulation study was conducted with KEGG pathways, which showed considerable gene overlaps between each other. In this study with original KEGG pathways, none of the topology-based methods outperformed the gene set approach. Therefore, a second simulation study was performed on non-overlapping pathways created by unique gene IDs. Here, methods accounting for pathway topology reached higher accuracy than the gene set methods, however their sensitivity was lower.We conducted one of the first comprehensive comparative works on evaluating gene set against pathway topology-based enrichment methods. The topological methods showed better performance in the simulation scenarios with non-overlapping pathways, however, they were not conclusively better in the other scenarios. This suggests that simple gene set approach might be sufficient to detect an enriched pathway under realistic circumstances. Nevertheless, more extensive studies and further benchmark data are needed to systematically evaluate these methods and to assess what gain and cost pathway topology information introduces into enrichment analysis. Both types of methods for enrichment analysis require further improvements in order to deal with the problem of pathway overlaps.

Dataset Information

A Comparative Study of Mouse Hepatic and Intestinal Gene Expression Profiles under PPAR? Knockout by Gene Set Enrichment Analysis.

Publications

A Comparative Study of Mouse Hepatic and Intestinal Gene Expression Profiles under PPARα Knockout by Gene Set Enrichment Analysis.

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