Robust rank aggregation for gene list integration and meta-analysis

By Raivo Kolde, Sven Laur, Priit Adler, J. Vilo. Bioinform., 2012

1,052 citations recorded by the source corpus.

Motivation: The continued progress in developing technological platforms, availability of many published experimental datasets, as well as different statistical methods to analyze those data have allowed approaching the same research question using various methods simultaneously. To get the best out of all these alternatives, we need to integrate their results in an unbiased manner. Prioritized gene lists are a common result presentation method in genomic data analysis applications. Thus, the rank aggregation methods can become a useful and general solution for the integration task. Results: Standard rank aggregation methods are often ill-suited for biological settings where the gene lists are inherently noisy. As a remedy, we propose a novel robust rank aggregation (RRA) method. Our method detects genes that are ranked consistently better than expected under null hypothesis of uncorrelated inputs and assigns a significance score for each gene. The underlying probabilistic model makes the algorithm parameter free and robust to outliers, noise and errors. Significance scores also provide a rigorous way to keep only the statistically relevant genes in the final list. These properties make our approach robust and compelling for many settings. Availability: All the methods are implemented as a GNU R package RobustRankAggreg, freely available at the Comprehensive R Archive Network http://cran.r-project.org/. Contact: vilo@ut.ee Supplementary information Supplementary data are available at Bioinformatics online.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ca097155934e8eed360295d21e44cafb0c0d38ec

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Citations in the corpus

A feature selection model based on genetic rank aggregation for text sentiment classification (2017, neutral): “In this model, features that are ranked better compared with the null hypothesis of uncorrelated inputs are given a significance score [49].”
SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis (2015, methodology): “In addition to our own innovation, the pipeline incorporated several R and Bioconductor packages, including ROCR [67] for evaluating and visualizing classifier/prediction performance, RobustRankAggreg [55] for the rank aggregation in validating cell type assignment using the expression patterns of m”
Reuse of public genome-wide gene expression data (2012, methodology): “…use archived data for performance evaluation address, for example, fundamental methods of array data processing and transcript quantification 61 , regulatory mechanisms 90,91,92 , gene function 93 , RNA interference effects 94 , cancer prognosis 95 , gene ranking 96 or differential expression 97 .”
Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning (2016, methodology): “The 8 methods included standard linear methods including PCA [18], FA [33], and probabilistic PCA (PPCA) [28]; nonlinear methods including t-SNE [23], Laplacian eigenmaps [25], multidimensional scaling (MDS) [26], and Sammon mapping [27]; and model-based methods specifically designed for single-cell”
Ensembles for feature selection: A review and future trends (2019, positive): “Robust Rank Aggregation [46] was then proposed to improve the limitations of Stuart and other classical methods.”
MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens (2014, methodology): “98e-3, ranked 1st out of 7,115) was found to be specifically essential in HL-60, which is consistent with the observation that an IGF1R inhibitor reduces proliferation and induces apoptosis in HL-60 cells [16,30].”