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EDISON-WMW: Exact Dynamic Programing Solution of the Wilcoxon–Mann–Whitney Test

In many research disciplines, hypothesis tests are applied to evaluate whether findings are statistically significant or could be explained by chance. The Wilcoxon–Mann–Whitney (WMW) test is among the most popular hypothesis tests in medicine and life science to analyze if two groups of samples are...

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Autores principales: Marx, Alexander, Backes, Christina, Meese, Eckart, Lenhof, Hans-Peter, Keller, Andreas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4792850/
https://www.ncbi.nlm.nih.gov/pubmed/26829645
http://dx.doi.org/10.1016/j.gpb.2015.11.004
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author Marx, Alexander
Backes, Christina
Meese, Eckart
Lenhof, Hans-Peter
Keller, Andreas
author_facet Marx, Alexander
Backes, Christina
Meese, Eckart
Lenhof, Hans-Peter
Keller, Andreas
author_sort Marx, Alexander
collection PubMed
description In many research disciplines, hypothesis tests are applied to evaluate whether findings are statistically significant or could be explained by chance. The Wilcoxon–Mann–Whitney (WMW) test is among the most popular hypothesis tests in medicine and life science to analyze if two groups of samples are equally distributed. This nonparametric statistical homogeneity test is commonly applied in molecular diagnosis. Generally, the solution of the WMW test takes a high combinatorial effort for large sample cohorts containing a significant number of ties. Hence, P value is frequently approximated by a normal distribution. We developed EDISON-WMW, a new approach to calculate the exact permutation of the two-tailed unpaired WMW test without any corrections required and allowing for ties. The method relies on dynamic programing to solve the combinatorial problem of the WMW test efficiently. Beyond a straightforward implementation of the algorithm, we presented different optimization strategies and developed a parallel solution. Using our program, the exact P value for large cohorts containing more than 1000 samples with ties can be calculated within minutes. We demonstrate the performance of this novel approach on randomly-generated data, benchmark it against 13 other commonly-applied approaches and moreover evaluate molecular biomarkers for lung carcinoma and chronic obstructive pulmonary disease (COPD). We found that approximated P values were generally higher than the exact solution provided by EDISON-WMW. Importantly, the algorithm can also be applied to high-throughput omics datasets, where hundreds or thousands of features are included. To provide easy access to the multi-threaded version of EDISON-WMW, a web-based solution of our algorithm is freely available at http://www.ccb.uni-saarland.de/software/wtest/.
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spelling pubmed-47928502016-03-24 EDISON-WMW: Exact Dynamic Programing Solution of the Wilcoxon–Mann–Whitney Test Marx, Alexander Backes, Christina Meese, Eckart Lenhof, Hans-Peter Keller, Andreas Genomics Proteomics Bioinformatics Original Research In many research disciplines, hypothesis tests are applied to evaluate whether findings are statistically significant or could be explained by chance. The Wilcoxon–Mann–Whitney (WMW) test is among the most popular hypothesis tests in medicine and life science to analyze if two groups of samples are equally distributed. This nonparametric statistical homogeneity test is commonly applied in molecular diagnosis. Generally, the solution of the WMW test takes a high combinatorial effort for large sample cohorts containing a significant number of ties. Hence, P value is frequently approximated by a normal distribution. We developed EDISON-WMW, a new approach to calculate the exact permutation of the two-tailed unpaired WMW test without any corrections required and allowing for ties. The method relies on dynamic programing to solve the combinatorial problem of the WMW test efficiently. Beyond a straightforward implementation of the algorithm, we presented different optimization strategies and developed a parallel solution. Using our program, the exact P value for large cohorts containing more than 1000 samples with ties can be calculated within minutes. We demonstrate the performance of this novel approach on randomly-generated data, benchmark it against 13 other commonly-applied approaches and moreover evaluate molecular biomarkers for lung carcinoma and chronic obstructive pulmonary disease (COPD). We found that approximated P values were generally higher than the exact solution provided by EDISON-WMW. Importantly, the algorithm can also be applied to high-throughput omics datasets, where hundreds or thousands of features are included. To provide easy access to the multi-threaded version of EDISON-WMW, a web-based solution of our algorithm is freely available at http://www.ccb.uni-saarland.de/software/wtest/. Elsevier 2016-02 2016-01-29 /pmc/articles/PMC4792850/ /pubmed/26829645 http://dx.doi.org/10.1016/j.gpb.2015.11.004 Text en © 2016 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Original Research
Marx, Alexander
Backes, Christina
Meese, Eckart
Lenhof, Hans-Peter
Keller, Andreas
EDISON-WMW: Exact Dynamic Programing Solution of the Wilcoxon–Mann–Whitney Test
title EDISON-WMW: Exact Dynamic Programing Solution of the Wilcoxon–Mann–Whitney Test
title_full EDISON-WMW: Exact Dynamic Programing Solution of the Wilcoxon–Mann–Whitney Test
title_fullStr EDISON-WMW: Exact Dynamic Programing Solution of the Wilcoxon–Mann–Whitney Test
title_full_unstemmed EDISON-WMW: Exact Dynamic Programing Solution of the Wilcoxon–Mann–Whitney Test
title_short EDISON-WMW: Exact Dynamic Programing Solution of the Wilcoxon–Mann–Whitney Test
title_sort edison-wmw: exact dynamic programing solution of the wilcoxon–mann–whitney test
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4792850/
https://www.ncbi.nlm.nih.gov/pubmed/26829645
http://dx.doi.org/10.1016/j.gpb.2015.11.004
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