TY - JOUR UR - http://lib.ugent.be/catalog/pug01:3208289 ID - pug01:3208289 LA - eng TI - An extension of the Wilcoxon-Mann-Whitney test for analyzing RT-qPCR data PY - 2013 JO - (2013) STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY SN - 2194-6302 PB - 2013 AU - De Neve, Jan PP01 002003335239 802000214749 0000-0003-1992-0627 AU - Thas, Olivier AU - Ottoy, Jean-Pierre AU - Clement, Lieven WE02 001995231392 801001441317 0000-0002-9050-4370 AB - Classical approaches for analyzing reverse transcription quantitative polymerase chain reaction (RT-qPCR) data commonly require normalization before assessing differential expression (DE). Normalization often has a substantial effect on the interpretation and validity of the subsequent analysis steps, but at the same time it causes a reduction in variance and introduces dependence among the normalized outcomes. These effects can be substantial, however, they are typically ignored. Most normalization techniques and methods for DE focus on mean expression and are sensitive to outliers. Moreover, in cancer studies, for example, oncogenes are often only expressed in a subsample of the populations during sampling. This primarily affects the skewness and the tails of the distribution and the mean is therefore not necessarily the best effect size measure within these experimental setups. In our contribution, we propose an extension of the Wilcoxon-Mann-Whitney test which incorporates a robust normalization, and the uncertainty associated with normalization is propagated into the final statistical summaries for DE. Our method relies on semiparametric regression models that focus on the probability P{Y <= Y'}, where Y and Y' denote independent responses for different subject groups. This effect size is robust to outliers, while remaining informative and intuitive when DE affects the shape of the distribution instead of only the mean. We also extend our approach for assessing DE for multiple features simultaneously. Simulation studies show that the test has a good performance, and that it is very competitive with standard methods for this platform. The method is illustrated on two neuroblastoma studies ER -Download RIS file
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024 | a 1854/LU-3208289 2 handle | ||
024 | a 10.1515/sagmb-2012-0003 2 doi | ||
040 | a UGent | ||
245 | a An extension of the Wilcoxon-Mann-Whitney test for analyzing RT-qPCR data | ||
260 | c 2013 | ||
520 | a Classical approaches for analyzing reverse transcription quantitative polymerase chain reaction (RT-qPCR) data commonly require normalization before assessing differential expression (DE). Normalization often has a substantial effect on the interpretation and validity of the subsequent analysis steps, but at the same time it causes a reduction in variance and introduces dependence among the normalized outcomes. These effects can be substantial, however, they are typically ignored. Most normalization techniques and methods for DE focus on mean expression and are sensitive to outliers. Moreover, in cancer studies, for example, oncogenes are often only expressed in a subsample of the populations during sampling. This primarily affects the skewness and the tails of the distribution and the mean is therefore not necessarily the best effect size measure within these experimental setups. In our contribution, we propose an extension of the Wilcoxon-Mann-Whitney test which incorporates a robust normalization, and the uncertainty associated with normalization is propagated into the final statistical summaries for DE. Our method relies on semiparametric regression models that focus on the probability P{Y <= Y'}, where Y and Y' denote independent responses for different subject groups. This effect size is robust to outliers, while remaining informative and intuitive when DE affects the shape of the distribution instead of only the mean. We also extend our approach for assessing DE for multiple features simultaneously. Simulation studies show that the test has a good performance, and that it is very competitive with standard methods for this platform. The method is illustrated on two neuroblastoma studies | ||
598 | a A1 | ||
100 | a De Neve, Jan u PP01 0 002003335239 0 802000214749 0 0000-0003-1992-0627 | ||
700 | a Thas, Olivier u LA26 0 801001013507 | ||
700 | a Ottoy, Jean-Pierre u LA26 0 801000228817 | ||
700 | a Clement, Lieven u WE02 0 001995231392 0 801001441317 0 0000-0002-9050-4370 | ||
650 | a Mathematics and Statistics | ||
653 | a probabilistic index model | ||
653 | a normalization | ||
653 | a robustness | ||
653 | a RT-qPCR | ||
653 | a Wilcoxon-Mann-Whitney | ||
653 | a REAL-TIME PCR | ||
653 | a GENE-EXPRESSION | ||
653 | a NEUROBLASTOMA | ||
653 | a MICRORNAS | ||
653 | a MYCN | ||
773 | t STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY g Stat. Appl. Genet. Mol. Biol. 2013. 12 (3) p.333-346 q 12:3<333 | ||
856 | 3 Full Text u https://biblio.ugent.be/publication/3208289/file/3222380 z [ugent] y DeNeveThasOttoyClement2013AnExtensionOfWMWForRTqPCR.pdf | ||
856 | 3 Full Text u https://biblio.ugent.be/publication/3208289/file/3222390 z [no access] y | ||
920 | a article | ||
852 | x WE b WE02 | ||
922 | a UGENT-WE | ||
852 | x BW b LA10 | ||
922 | a UGENT-BW |
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