CORaL: Comparison of Ranked Lists for Analysis of Gene Expression Data

Antosh, Michael; Fox, David J.; Cooper, Leon N.; Neretti, Nicola

doi:10.1089/cmb.2013.0017

Cited by 9 publications

(9 citation statements)

References 13 publications

(23 reference statements)

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“…Among the first approaches, we find programs such as GeneVenn [6], BioVenn [7] and VennPainters [8] that perform a visual comparison based on more or less flexible forms of Venn diagrams and are therefore limited in terms of the number of lists that may be compared. There are also applications such as CORal [9], Rank–Rank Hypergeometric Overlap [10] and OrderedLists [11] that are based on determining the degree of overlap of two or more ordered lists. One characteristic of all these methods is that if they perform the comparison visually or by using a statistical model, they do not refer to the biological meaning of the list elements.…”

Section: Introductionmentioning

confidence: 99%

An equivalence approach to the integrative analysis of feature lists

2019

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Background Although a few comparison methods based on the biological meaning of gene lists have been developed, the goProfiles approach is one of the few that are being used for that purpose. It consists of projecting lists of genes into predefined levels of the Gene Ontology, in such a way that a multinomial model can be used for estimation and testing. Of particular interest is the fact that it may be used for proving equivalence (in the sense of “enough similarity”) between two lists, instead of proving differences between them, which seems conceptually better suited to the end goal of establishing similarity among gene lists. An equivalence method has been derived that uses a distance–based approach and the confidence interval inclusion principle. Equivalence is declared if the upper limit of a one-sided confidence interval for the distance between two profiles is below a pre-established equivalence limit. Results In this work, this method is extended to establish the equivalence of any number of gene lists. Additionally, an algorithm to obtain the smallest equivalence limit that would allow equivalence between two or more lists to be declared is presented. This algorithm is at the base of an iterative method of graphic visualization to represent the most to least equivalent gene lists. These methods deal adequately with the problem of adjusting for multiple testing. The applicability of these techniques is illustrated in two typical situations: (i) a collection of cancer-related gene lists, suggesting which of them are more reasonable to combine –as claimed by the authors– and (ii) a collection of pathogenesis–based transcript sets, showing which of these are more closely related. The methods developed are available in the goProfiles Bioconductor package. Conclusions The method provides a simple yet powerful and statistically well-grounded way to classify a set of genes or other feature lists by establishing their equivalence at a given equivalence threshold. The classification results can be viewed using standard visualization methods. This may be applied to a variety of problems, from deciding whether a series of datasets generating the lists can be combined to the simplification of groups of lists. Electronic supplementary material The online version of this article (10.1186/s12859-019-3008-x) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

An equivalence approach to the integrative analysis of feature lists

2019

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“…The algorithm of gene set enrichment analysis (GSEA) allows all genes to contribute to overlapping signals in proportion to their degree of differential expression and can detect the weak signals that would be discarded by "threshold" approaches (Efron & Tibshirani, 2007;Subramanian et al, 2005). The algorithm of CORaL estimates the significant set size using the overlaps between sections of the ranked gene lists by maximizing statistical likelihood (Antosh et al, 2013). The algorithm of R2KS particularly emphasizes finding the same items near the top of the ranked gene list (Ni & Vingron, 2012).…”

Section: Introductionmentioning

confidence: 99%

A novel similarity score based on gene ranks to reveal genetic relationships among diseases

Luo

Zhang

et al. 2021

PeerJ

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Knowledge of similarities among diseases can contribute to uncovering common genetic mechanisms. Based on ranked gene lists, a couple of similarity measures were proposed in the literature. Notice that they may suffer from the determination of cutoff or heavy computational load, we propose a novel similarity score SimSIP among diseases based on gene ranks. Simulation studies under various scenarios demonstrate that SimSIP has better performance than existing rank-based similarity measures. Application of SimSIP in gene expression data of 18 cancer types from The Cancer Genome Atlas shows that SimSIP is superior in clarifying the genetic relationships among diseases and demonstrates the tendency to cluster the histologically or anatomically related cancers together, which is analogous to the pan-cancer studies. Moreover, SimSIP with simpler form and faster computation is more robust for higher levels of noise than existing methods and provides a basis for future studies on genetic relationships among diseases. In addition, a measure MAG is developed to gauge the magnitude of association of anindividual gene with diseases. By using MAG the genes and biological processes significantly associated with colorectal cancer are detected.

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“…A similarity measure is a central component in detecting the common genetic basis among different diseases. Several common metrics have been proposed to measure similarities between diseases, such as Pearson, Spearman correlation coefficient, Euclidean distance, Manhattan distance, and Jaccard correlation coefficient (Antosh et al, 2013;Dennis et al, 2003;Serra et al, 2016;Shi et al, 2014). However, with the technological developments in molecular biology, large-scale gene expression profiling datasets produced from diverse technological platforms necessitate new and adaptive similarity measures to reveal meaningful genetic relationships across multiple platform types.…”

Section: Introductionmentioning

confidence: 99%

Peer Review #1 of "A novel similarity score based on gene ranks to reveal genetic relationships among diseases (v0.1)"

2021

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CORaL: Comparison of Ranked Lists for Analysis of Gene Expression Data

Cited by 9 publications

References 13 publications

An equivalence approach to the integrative analysis of feature lists

An equivalence approach to the integrative analysis of feature lists

A novel similarity score based on gene ranks to reveal genetic relationships among diseases

Peer Review #1 of "A novel similarity score based on gene ranks to reveal genetic relationships among diseases (v0.1)"

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