Comparison and visualisation of agreement for paired lists of rankings

Output from analysis of a high-throughput ‘omics’ experiment very often is a ranked list. One commonly encountered example is a ranked list of differentially expressed genes from a gene expression experiment, with a length of many hundreds of genes. There are numerous situations where interest is in...

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Published inStatistical applications in genetics and molecular biology Vol. 16; no. 1; pp. 31 - 45
Main Authors Donald, Margaret R., Wilson, Susan R.
Format Journal Article
LanguageEnglish
Published Germany De Gruyter 01.03.2017
Walter de Gruyter GmbH
Subjects
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ISSN2194-6302
1544-6115
DOI10.1515/sagmb-2016-0036

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Abstract Output from analysis of a high-throughput ‘omics’ experiment very often is a ranked list. One commonly encountered example is a ranked list of differentially expressed genes from a gene expression experiment, with a length of many hundreds of genes. There are numerous situations where interest is in the comparison of outputs following, say, two (or more) different experiments, or of different approaches to the analysis that produce different ranked lists. Rather than considering exact agreement between the rankings, following others, we consider two ranked lists to be in agreement if the rankings differ by some fixed distance. Generally only a relatively small subset of the top-ranked items will be in agreement. So the aim is to find the point at which the probability of agreement in rankings changes from being greater than 0.5 to being less than 0.5. We use penalized splines and a Bayesian logit model, to give a nonparametric smooth to the sequence of agreements, as well as pointwise credible intervals for the probability of agreement. Our approach produces a point estimate and a credible interval for . R code is provided. The method is applied to rankings of genes from breast cancer microarray experiments.
AbstractList Output from analysis of a high-throughput 'omics' experiment very often is a ranked list. One commonly encountered example is a ranked list of differentially expressed genes from a gene expression experiment, with a length of many hundreds of genes. There are numerous situations where interest is in the comparison of outputs following, say, two (or more) different experiments, or of different approaches to the analysis that produce different ranked lists. Rather than considering exact agreement between the rankings, following others, we consider two ranked lists to be in agreement if the rankings differ by some fixed distance. Generally only a relatively small subset of the k top-ranked items will be in agreement. So the aim is to find the point k at which the probability of agreement in rankings changes from being greater than 0.5 to being less than 0.5. We use penalized splines and a Bayesian logit model, to give a nonparametric smooth to the sequence of agreements, as well as pointwise credible intervals for the probability of agreement. Our approach produces a point estimate and a credible interval for k. R code is provided. The method is applied to rankings of genes from breast cancer microarray experiments.
Output from analysis of a high-throughput ‘omics’ experiment very often is a ranked list. One commonly encountered example is a ranked list of differentially expressed genes from a gene expression experiment, with a length of many hundreds of genes. There are numerous situations where interest is in the comparison of outputs following, say, two (or more) different experiments, or of different approaches to the analysis that produce different ranked lists. Rather than considering exact agreement between the rankings, following others, we consider two ranked lists to be in agreement if the rankings differ by some fixed distance. Generally only a relatively small subset of the top-ranked items will be in agreement. So the aim is to find the point at which the probability of agreement in rankings changes from being greater than 0.5 to being less than 0.5. We use penalized splines and a Bayesian logit model, to give a nonparametric smooth to the sequence of agreements, as well as pointwise credible intervals for the probability of agreement. Our approach produces a point estimate and a credible interval for . R code is provided. The method is applied to rankings of genes from breast cancer microarray experiments.
Output from analysis of a high-throughput ‘omics’ experiment very often is a ranked list. One commonly encountered example is a ranked list of differentially expressed genes from a gene expression experiment, with a length of many hundreds of genes. There are numerous situations where interest is in the comparison of outputs following, say, two (or more) different experiments, or of different approaches to the analysis that produce different ranked lists. Rather than considering exact agreement between the rankings, following others, we consider two ranked lists to be in agreement if the rankings differ by some fixed distance. Generally only a relatively small subset of the k top-ranked items will be in agreement. So the aim is to find the point k at which the probability of agreement in rankings changes from being greater than 0.5 to being less than 0.5. We use penalized splines and a Bayesian logit model, to give a nonparametric smooth to the sequence of agreements, as well as pointwise credible intervals for the probability of agreement. Our approach produces a point estimate and a credible interval for k . R code is provided. The method is applied to rankings of genes from breast cancer microarray experiments.
Author Donald, Margaret R.
Wilson, Susan R.
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Snippet Output from analysis of a high-throughput ‘omics’ experiment very often is a ranked list. One commonly encountered example is a ranked list of differentially...
Output from analysis of a high-throughput 'omics' experiment very often is a ranked list. One commonly encountered example is a ranked list of differentially...
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SubjectTerms Agreements
Bayes Theorem
Bayesian analysis
Breast
Computational Biology - methods
Gene Expression
Gene Expression Profiling
Genes
Humans
Lists
Microarray Analysis
Oligonucleotide Array Sequence Analysis
Probability
Splines
Title Comparison and visualisation of agreement for paired lists of rankings
URI https://www.degruyter.com/doi/10.1515/sagmb-2016-0036
https://www.ncbi.nlm.nih.gov/pubmed/28284040
https://www.proquest.com/docview/1920085930
https://www.proquest.com/docview/1876817511
Volume 16
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