Robust rank aggregation for gene list integration and meta-analysis

Kolde, Raivo; Laur, Sven; Adler, Priit; Vilo, Jaak

doi:10.1093/bioinformatics/btr709

Cited by 880 publications

(903 citation statements)

References 26 publications

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“…To identify the genes that lead to the most consistent decrement in cell viability when suppressed across 14 cell lines, we conducted a rank aggregation on the gene rank lists obtained from basal-level and IR screens, separately. The Robust Rank Aggregation (RAA) algorithm implemented in R package RobustRankAggreg was applied (44). Briefly, the RRA method assumes a null model where the ranks of each gene are uniformly distributed over the rank lists.…”

Section: Methodsmentioning

confidence: 99%

RIG-I–like receptor LGP2 protects tumor cells from ionizing radiation

Widau¹,

Parekh²,

Ranck³

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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An siRNA screen targeting 89 IFN stimulated genes in 14 different cancer cell lines pointed to the RIG-I (retinoic acid inducible gene I)-like receptor Laboratory of Genetics and Physiology 2 (LGP2) as playing a key role in conferring tumor cell survival following cytotoxic stress induced by ionizing radiation (IR). Studies on the role of LGP2 revealed the following: (i) Depletion of LGP2 in three cancer cell lines resulted in a significant increase in cell death following IR, (ii) ectopic expression of LGP2 in cells increased resistance to IR, and (iii) IR enhanced LGP2 expression in three cell lines tested. Studies designed to define the mechanism by which LGP2 acts point to its role in regulation of IFNβ. Specifically (i) suppression of LGP2 leads to enhanced IFNβ, (ii) cytotoxic effects following IR correlated with expression of IFNβ inasmuch as inhibition of IFNβ by neutralizing antibody conferred resistance to cell death, and (iii) mouse embryonic fibroblasts from IFN receptor 1 knockout mice are radioresistant compared with wild-type mouse embryonic fibroblasts. The role of LGP2 in cancer may be inferred from cumulative data showing elevated levels of LGP2 in cancer cells are associated with more adverse clinical outcomes. Our results indicate that cytotoxic stress exemplified by IR induces IFNβ and enhances the expression of LGP2. Enhanced expression of LGP2 suppresses the IFN stimulated genes associated with cytotoxic stress by turning off the expression of IFNβ.innate immunity | cytoplasmic sensor | interferon beta | DHX58

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Section: Methodsmentioning

confidence: 99%

RIG-I–like receptor LGP2 protects tumor cells from ionizing radiation

Widau¹,

Parekh²,

Ranck³

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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“…This approach is similar to those used for differential RNA-Seq analysis [7,8,13]. We rank sgRNAs based on P-values calculated from the NB model, and use a modified robust ranking aggregation (RRA) algorithm [16] named α-RRA to identify positively or negatively selected genes. More specifically, α-RRA assumes that if a gene has no effect on selection, then sgRNAs targeting this gene should be uniformly distributed across the ranked list of all the sgRNAs.…”

Section: Overview Of the Mageck Algorithmmentioning

confidence: 99%

MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens

et al. 2014

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We propose the Model-based Analysis of Genome-wide CRISPR/Cas9 Knockout (MAGeCK) method for prioritizing single-guide RNAs, genes and pathways in genome-scale CRISPR/Cas9 knockout screens. MAGeCK demonstrates better performance compared with existing methods, identifies both positively and negatively selected genes simultaneously, and reports robust results across different experimental conditions. Using public datasets, MAGeCK identified novel essential genes and pathways, including EGFR in vemurafenib-treated A375 cells harboring a BRAF mutation. MAGeCK also detected cell type-specific essential genes, including BCR and ABL1, in KBM7 cells bearing a BCR-ABL fusion, and IGF1R in HL-60 cells, which depends on the insulin signaling pathway for proliferation.

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“…Therefore, we aggregate them using the R package RobustRankAggreg -a robust ranking aggregation method [9] specially designed for similar situations in bioinformatics. Eventually, for each neighborhood, we have a feature vector composing of 150 components, which is a combination of 5 observables, 5 frames and 6 instances.…”

Section: Characterize Neighborhood Behaviours As Feature Vectors By Amentioning

confidence: 99%

Characterization of Neighborhood Behaviours in a Multi-neighborhood Local Search Algorithm

Dang

Causmaecker

2016

Lecture Notes in Computer Science

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Abstract. We consider a multi-neighborhood local search algorithm with a large number of possible neighborhoods. Each neighborhood is accompanied by a weight value which represents the probability of being chosen at each iteration. These weights are fixed before the algorithm runs, and are considered as parameters of the algorithm. Given a set of instances, off-line tuning of the algorithm's parameters can be done by automated algorithm configuration tools (e.g., SMAC). However, the large number of neighborhoods can make the tuning expensive and difficult even when the number of parameters has been reduced by some intuition. In this work, we propose a systematic method to characterize each neighborhood's behaviours, representing them as a feature vector, and using cluster analysis to form similar groups of neighborhoods. The novelty of our characterization method is the ability of reflecting changes of behaviours according to hardness of different solution quality regions. We show that using neighborhood clusters instead of individual neighborhoods helps to reduce the parameter configuration space without misleading the search of the tuning procedure. Moreover, this method is problem-independent and potentially can be applied in similar contexts.

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Robust rank aggregation for gene list integration and meta-analysis

Cited by 880 publications

References 26 publications

RIG-I–like receptor LGP2 protects tumor cells from ionizing radiation

RIG-I–like receptor LGP2 protects tumor cells from ionizing radiation

MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens

Characterization of Neighborhood Behaviours in a Multi-neighborhood Local Search Algorithm

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