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 in | Statistical applications in genetics and molecular biology Vol. 16; no. 1; pp. 31 - 45 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Germany
De Gruyter
01.03.2017
Walter de Gruyter GmbH |
Subjects | |
Online Access | Get full text |
ISSN | 2194-6302 1544-6115 |
DOI | 10.1515/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. |
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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. |
Author_xml | – sequence: 1 givenname: Margaret R. surname: Donald fullname: Donald, Margaret R. email: m.r.donald@bigpond.com organization: Stats Central, University of New South Wales, Anzac Parade, Kensington, NSW, 2052, Australia – sequence: 2 givenname: Susan R. surname: Wilson fullname: Wilson, Susan R. organization: Australian National University, Canberra, ACT, 0200, Australia |
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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 |
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