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Statistical methods for ranking differentially expressed genes
In the analysis of microarray data the identification of differential expression is paramount. Here I outline a method for finding an optimal test statistic with which to rank genes with respect to differential expression. Tests of the method show that it allows generation of top gene lists that giv...
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2003
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC193620/ https://www.ncbi.nlm.nih.gov/pubmed/12801415 http://dx.doi.org/10.1186/gb-2003-4-6-r41 |
Sumario: | In the analysis of microarray data the identification of differential expression is paramount. Here I outline a method for finding an optimal test statistic with which to rank genes with respect to differential expression. Tests of the method show that it allows generation of top gene lists that give few false positives and few false negatives. Estimation of the false-negative as well as the false-positive rate lies at the heart of the method. |
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