8,812 research outputs found

    Differential meta-analysis of RNA-seq data from multiple studies

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    High-throughput sequencing is now regularly used for studies of the transcriptome (RNA-seq), particularly for comparisons among experimental conditions. For the time being, a limited number of biological replicates are typically considered in such experiments, leading to low detection power for differential expression. As their cost continues to decrease, it is likely that additional follow-up studies will be conducted to re-address the same biological question. We demonstrate how p-value combination techniques previously used for microarray meta-analyses can be used for the differential analysis of RNA-seq data from multiple related studies. These techniques are compared to a negative binomial generalized linear model (GLM) including a fixed study effect on simulated data and real data on human melanoma cell lines. The GLM with fixed study effect performed well for low inter-study variation and small numbers of studies, but was outperformed by the meta-analysis methods for moderate to large inter-study variability and larger numbers of studies. To conclude, the p-value combination techniques illustrated here are a valuable tool to perform differential meta-analyses of RNA-seq data by appropriately accounting for biological and technical variability within studies as well as additional study-specific effects. An R package metaRNASeq is available on the R Forge

    A COMPARISON OF META-ANALYSIS METHODS FOR DETECTING DIFFERENTIALLY EXPRESSED GENES IN MICROARRAY EXPERIMENTS: AN APPLICATION TO MALIGNANT PLEURAL MESOTHELIOMA DATA

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    The proliferation of microarray experiments and the increasing availability of relevant amount of data in public repositories have created a need for meta-analysis methods to efficiently integrate and validate microarray results from independent but related studies. Despite its increasing popularity, meta-analysis of microarray data is not without problems. In fact, although it shares many features with traditional meta-analysis, most classical meta-analysis methods cannot be directly applied to microarray experiments because of their unique issues. Several meta-analysis techniques have been proposed in the context of microarrays. However, only recently a comprehensive framework to carry out microarray data meta-analysis has been proposed. Moreover very few software packages for microarray meta-analysis implementation exist and most of them either have unclear manuals or are not easy to apply. We applied four meta-analysis methods, the Stouffer’s method, the moderated effect size combination approach, the t-based hierarchical modeling and the rank product method, to a set of three microarray studies on malignant pleural mesothelioma. We focused on differential expression analysis between normal and malignant mesothelioma pleural tissues. Both unfiltered and filtered data were analyzed. The lists of differentially expressed genes provided by each method for either kind of data were compared, also by pathway analysis. These comparisons highlighted a poor overlap between the lists of differentially expressed genes and the related pathways obtained using the unfiltered data. Conversely, a higher concordance of the results, both at the gene and the pathway level, was observed when filtered data were considered. The fact that a significant number of genes were identified by only one of the tested methods shows that the gene ranking is based on different perspectives. In fact, the analyzed methods are based on different assumptions and focus on diverse aspects in selecting significant genes. Since so far there is no consensus on what is (are) the ‘best’ meta-analysis method(s), it may be useful to select candidate genes for further analysis using a combination of different meta-analysis methods. In particular, differentially expressed genes detected by more than one method may be considered as the most reliable ones while genes identified by only a single method may be further explored to expand the knowledge of the biological phenomenon of interest

    Study of meta-analysis strategies for network inference using information-theoretic approaches

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    © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Reverse engineering of gene regulatory networks (GRNs) from gene expression data is a classical challenge in systems biology. Thanks to high-throughput technologies, a massive amount of gene-expression data has been accumulated in the public repositories. Modelling GRNs from multiple experiments (also called integrative analysis) has; therefore, naturally become a standard procedure in modern computational biology. Indeed, such analysis is usually more robust than the traditional approaches focused on individual datasets, which typically suffer from some experimental bias and a small number of samples. To date, there are mainly two strategies for the problem of interest: the first one (”data merging”) merges all datasets together and then infers a GRN whereas the other (”networks ensemble”) infers GRNs from every dataset separately and then aggregates them using some ensemble rules (such as ranksum or weightsum). Unfortunately, a thorough comparison of these two approaches is lacking. In this paper, we evaluate the performances of various metaanalysis approaches mentioned above with a systematic set of experiments based on in silico benchmarks. Furthermore, we present a new meta-analysis approach for inferring GRNs from multiple studies. Our proposed approach, adapted to methods based on pairwise measures such as correlation or mutual information, consists of two steps: aggregating matrices of the pairwise measures from every dataset followed by extracting the network from the meta-matrix.Peer ReviewedPostprint (author's final draft

    Application of Volcano Plots in Analyses of mRNA Differential Expressions with Microarrays

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    Volcano plot displays unstandardized signal (e.g. log-fold-change) against noise-adjusted/standardized signal (e.g. t-statistic or -log10(p-value) from the t test). We review the basic and an interactive use of the volcano plot, and its crucial role in understanding the regularized t-statistic. The joint filtering gene selection criterion based on regularized statistics has a curved discriminant line in the volcano plot, as compared to the two perpendicular lines for the "double filtering" criterion. This review attempts to provide an unifying framework for discussions on alternative measures of differential expression, improved methods for estimating variance, and visual display of a microarray analysis result. We also discuss the possibility to apply volcano plots to other fields beyond microarray.Comment: 8 figure

    An adaptively weighted statistic for detecting differential gene expression when combining multiple transcriptomic studies

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    Global expression analyses using microarray technologies are becoming more common in genomic research, therefore, new statistical challenges associated with combining information from multiple studies must be addressed. In this paper we will describe our proposal for an adaptively weighted (AW) statistic to combine multiple genomic studies for detecting differentially expressed genes. We will also present our results from comparisons of our proposed AW statistic to Fisher's equally weighted (EW), Tippett's minimum p-value (minP) and Pearson's (PR) statistics. Due to the absence of a uniformly powerful test, we used a simplified Gaussian scenario to compare the four methods. Our AW statistic consistently produced the best or near-best power for a range of alternative hypotheses. AW-obtained weights also have the additional advantage of filtering discordant biomarkers and providing natural detected gene categories for further biological investigation. Here we will demonstrate the superior performance of our proposed AW statistic based on a mix of power analyses, simulations and applications using data sets for multi-tissue energy metabolism mouse, multi-lab prostate cancer and lung cancer.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS393 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org
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