RankAggreg, an R package for weighted rank aggregation

Pihur, Vasyl; Datta, Susmita; Datta, Somnath

doi:10.1186/1471-2105-10-62

Cited by 285 publications

(270 citation statements)

References 10 publications

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“…In theory, it combines several individual ranked lists to produce a super list which will be as close as possible to all individual lists simultaneously. In our application, we use RankAggreg [17], an R package for weighted rank aggregation. It was illustrated by Lin et al [11] that the utility of ranking aggregation leads to satisfactory simulation results when combining miRNA target lists from different algorithms.…”

Section: Methodsmentioning

confidence: 99%

Comparison and Integration of Target Prediction Algorithms for microRNA Studies

Zhang

Verbeek

2010

Journal of Integrative Bioinformatics

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SummarymicroRNAs are short RNA fragments that have the capacity of regulating hundreds of target gene expression. Currently, due to lack of high-throughput experimental methods for miRNA target identification, a collection of computational target prediction approaches have been developed. However, these approaches deal with different features or factors are weighted differently resulting in diverse range of predictions. The prediction accuracy remains uncertain. In this paper, three commonly used target prediction algorithms are evaluated and further integrated using algorithm combination, ranking aggregation and Bayesian Network classification. Our results revealed that each individual prediction algorithm displays its advantages as was shown on different test data sets. Among different integration strategies, the application of Bayesian Network classifier on the features calculated from multiple prediction methods significantly improved target prediction accuracy.

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Section: Methodsmentioning

confidence: 99%

Comparison and Integration of Target Prediction Algorithms for microRNA Studies

Zhang

Verbeek

2010

Journal of Integrative Bioinformatics

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“…The RankAggreg software package was used to analyze the four different sets of data and combine these four algorithms to establish a consensus ranking. Specifically, the brute force method (using the RankAggreg function) was used to enumerate all possible candidate lists and then the one with the minimum Spearman foot rule distance was selected (36). This method output results in the most stable and least stable reference genes on day 12, however, there may not be consistency in the expression levels of certain reference genes over time under certain conditions (22,37).…”

Section: A B Cmentioning

confidence: 99%

Identification of suitable reference genes for BDV-infected primary rat hippocampal neurons

Mao

Zhang

Guo

et al. 2016

Molecular Medicine Reports

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Abstract. Borna disease virus (BDV) is a neurotropic RNA virus that infects the limbic system of mammals and results in behavioral disorders. The hippocampus is a core region in the limbic system, which contributes to memory and learning and is important in the regulation of emotion. However, no validated microRNA housekeeping genes have yet been identified in BDV-infected rat primary hippocampal neurons. Proper normalization is key in accurate miRNA expression analysis. The present study used reverse transcription-quantitative polymerase chain reaction (RT-qPCR) to evaluate the expression stability of 10 commonly used reference genes 5S, U6, E2 small nucleolar RNA (snoRNA), in BDV-infected rat hippocampal neurons and non-infected controls across 12 days post-infection. The data was analyzed by four statistical algorithms: geNorm, NormFinder, BestKeeper, and the comparative Δ-Ct method. Subsequently, the most suitable reference genes (miR-101a and U87) and the least suitable (snoRNA) were determined by the RankAggreg package. miR-155 was selected as a standard by which to evaluate the most and least suitable reference genes. When normalized to the most stable reference gene there were significant differences between the two groups. However, when the data were normalized to the less stably expressed gene, the results were not significant. miR-101a was recommended as a suitable reference gene for BDV-infected rat primary hippocampal neurons.

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“…Computational implementations can include the representation of each input data set as a separate kernel and the weighted optimized combination of these kernels to reconstruct co-expression patterns [54], as well as Bayesian network-based functions [55], decision trees [56] and weighted rank aggregation [57]. In particular, we tested the RankAggreg R package [58], which exploits the rank aggregation method. This R package takes different lists of ranked elements as input.…”

Section: Comparative Meta-analysis Of Results From Different Platformsmentioning

confidence: 99%

Gene co-expression analyses: an overview from microarray collections inArabidopsis thaliana

et al. 2016

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Bioinformatics web-based resources and databases are precious references for most biological laboratories worldwide. However, the quality and reliability of the information they provide depends on them being used in an appropriate way that takes into account their specific features. Huge collections of gene expression data are currently publicly available, ready to support the understanding of gene and genome functionalities. In this context, tools and resources for gene co-expression analyses have flourished to exploit the 'guilty by association' principle, which assumes that genes with correlated expression profiles are functionally related. In the case of Arabidopsis thaliana, the reference species in plant biology, the resources available mainly consist of microarray results. After a general overview of such resources, we tested and compared the results they offer for gene co-expression analysis. We also discuss the effect on the results when using different data sets, as well as different data normalization approaches and parameter settings, which often consider different metrics for establishing co-expression. A dedicated example analysis of different gene pools, implemented by including/excluding mutant samples in a reference data set, showed significant variation of gene co-expression occurrence, magnitude and direction. We conclude that, as the heterogeneity of the resources and methods may produce different results for the same query genes, the exploration of more than one of the available resources is strongly recommended. The aim of this article is to show how best to integrate data sources and/or merge outputs to achieve robust analyses and reliable interpretations, thereby making use of diverse data resources an opportunity for added value.

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RankAggreg, an R package for weighted rank aggregation

Cited by 285 publications

References 10 publications

Comparison and Integration of Target Prediction Algorithms for microRNA Studies

Comparison and Integration of Target Prediction Algorithms for microRNA Studies

Identification of suitable reference genes for BDV-infected primary rat hippocampal neurons

Gene co-expression analyses: an overview from microarray collections inArabidopsis thaliana

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