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Meta-analysis approaches to combine multiple gene set enrichment studies.

ABSTRACT: In the field of gene set enrichment analysis (GSEA), meta-analysis has been used to integrate information from multiple studies to present a reliable summarization of the expanding volume of individual biomedical research, as well as improve the power of detecting essential gene sets involved in complex human diseases. However, existing methods, Meta-Analysis for Pathway Enrichment (MAPE), may be subject to power loss because of (1) using gross summary statistics for combining end results from component studies and (2) using enrichment scores whose distributions depend on the set sizes. In this paper, we adapt meta-analysis approaches recently developed for genome-wide association studies, which are based on fixed effect and random effects (RE) models, to integrate multiple GSEA studies. We further develop a mixed strategy via adaptive testing for choosing RE versus FE models to achieve greater statistical efficiency as well as flexibility. In addition, a size-adjusted enrichment score based on a one-sided Kolmogorov-Smirnov statistic is proposed to formally account for varying set sizes when testing multiple gene sets. Our methods tend to have much better performance than the MAPE methods and can be applied to both discrete and continuous phenotypes. Specifically, the performance of the adaptive testing method seems to be the most stable in general situations.

SUBMITTER: Lu W

PROVIDER: S-EPMC5771852 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Meta-analysis approaches to combine multiple gene set enrichment studies.

Lu Wentao W Wang Xinlei X Zhan Xiaowei X Gazdar Adi A

Statistics in medicine 20171019 4

In the field of gene set enrichment analysis (GSEA), meta-analysis has been used to integrate information from multiple studies to present a reliable summarization of the expanding volume of individual biomedical research, as well as improve the power of detecting essential gene sets involved in complex human diseases. However, existing methods, Meta-Analysis for Pathway Enrichment (MAPE), may be subject to power loss because of (1) using gross summary statistics for combining end results from c ...[more]

PMID: 29052247

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Project description:MotivationMuch research effort has been devoted to the identification of enriched gene sets for microarray experiments. However, identified gene sets are often found to be inconsistent among independent studies. This is probably owing to the noisy data of microarray experiments coupled with small sample sizes of individual studies. Therefore, combining information from multiple studies is likely to improve the detection of truly enriched gene classes. As more and more data become available, it calls for statistical methods to integrate information from multiple studies, also known as meta-analysis, to improve the power of identifying enriched gene sets.ResultsWe propose a Bayesian model that provides a coherent framework for joint modeling of both gene set information and gene expression data from multiple studies, to improve the detection of enriched gene sets by leveraging information from different sources available. One distinct feature of our method is that it directly models the gene expression data, instead of using summary statistics, when synthesizing studies. Besides, the proposed model is flexible and offers an appropriate treatment of between-study heterogeneities that frequently arise in the meta-analysis of microarray experiments. We show that under our Bayesian model, the full posterior conditionals all have known distributions, which greatly facilitates the MCMC computation. Simulation results show that the proposed method can improve the power of gene set enrichment meta-analysis, as opposed to existing methods developed by Shen and Tseng (2010, Bioinformatics, 26, 1316-1323), and it is not sensitive to mild or moderate deviations from the distributional assumption for gene expression data. We illustrate the proposed method through an application of combining eight lung cancer datasets for gene set enrichment analysis, which demonstrates the usefulness of the method.Availabilityhttp://qbrc.swmed.edu/software/.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:Recently, microarray data analyses using functional pathway information, e.g., gene set enrichment analysis (GSEA) and significance analysis of function and expression (SAFE), have gained recognition as a way to identify biological pathways/processes associated with a phenotypic endpoint. In these analyses, a local statistic is used to assess the association between the expression level of a gene and the value of a phenotypic endpoint. Then these gene-specific local statistics are combined to evaluate association for pre-selected sets of genes. Commonly used local statistics include t-statistics for binary phenotypes and correlation coefficients that assume a linear or monotone relationship between a continuous phenotype and gene expression level. Methods applicable to continuous non-monotone relationships are needed. Furthermore, for multiple experimental categories, methods that combine multiple GSEA/SAFE analyses are needed.For continuous or ordinal phenotypic outcome, we propose to use as the local statistic the coefficient of multiple determination (i.e., the square of multiple correlation coefficient) R2 from fitting natural cubic spline models to the phenotype-expression relationship. Next, we incorporate this association measure into the GSEA/SAFE framework to identify significant gene sets. Unsigned local statistics, signed global statistics and one-sided p-values are used to reflect our inferential interest. Furthermore, we describe a procedure for inference across multiple GSEA/SAFE analyses. We illustrate our approach using gene expression and liver injury data from liver and blood samples from rats treated with eight hepatotoxicants under multiple time and dose combinations. We set out to identify biological pathways/processes associated with liver injury as manifested by increased blood levels of alanine transaminase in common for most of the eight compounds. Potential statistical dependency resulting from the experimental design is addressed in permutation based hypothesis testing.The proposed framework captures both linear and non-linear association between gene expression level and a phenotypic endpoint and thus can be viewed as extending the current GSEA/SAFE methodology. The framework for combining results from multiple GSEA/SAFE analyses is flexible to address practical inference interests. Our methods can be applied to microarray data with continuous phenotypes with multi-level design or the meta-analysis of multiple microarray data sets.

Project description:BackgroundReproducibility of results can have a significant impact on the acceptance of new technologies in gene expression analysis. With the recent introduction of the so-called next-generation sequencing (NGS) technology and established microarrays, one is able to choose between two completely different platforms for gene expression measurements. This study introduces a novel methodology for gene-ranking stability analysis that is applied to the evaluation of gene-ranking reproducibility on NGS and microarray data.ResultsThe same data used in a well-known MicroArray Quality Control (MAQC) study was also used in this study to compare ranked lists of genes from MAQC samples A and B, obtained from Affymetrix HG-U133 Plus 2.0 and Roche 454 Genome Sequencer FLX platforms. An initial evaluation, where the percentage of overlapping genes was observed, demonstrates higher reproducibility on microarray data in 10 out of 11 gene-ranking methods. A gene set enrichment analysis shows similar enrichment of top gene sets when NGS is compared with microarrays on a pathway level. Our novel approach demonstrates high accuracy of decision trees when used for knowledge extraction from multiple bootstrapped gene set enrichment analysis runs. A comparison of the two approaches in sample preparation for high-throughput sequencing shows that alternating decision trees represent the optimal knowledge representation method in comparison with classical decision trees.ConclusionsUsual reproducibility measurements are mostly based on statistical techniques that offer very limited biological insights into the studied gene expression data sets. This paper introduces the meta-learning-based gene set enrichment analysis that can be used to complement the analysis of gene-ranking stability estimation techniques such as percentage of overlapping genes or classic gene set enrichment analysis. It is useful and practical when reproducibility of gene ranking results or different gene selection techniques is observed. The proposed method reveals very accurate descriptive models that capture the co-enrichment of gene sets which are differently enriched in the compared data sets.

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Meta-analysis approaches to combine multiple gene set enrichment studies.

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Meta-analysis approaches to combine multiple gene set enrichment studies.

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