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Je něco špatně v tomto záznamu ?
Using of normalizations for gene expression analysis
P. Bubelíny,
Jazyk angličtina Země Spojené státy americké
Typ dokumentu časopisecké články
- MeSH
- algoritmy MeSH
- interpretace statistických dat * MeSH
- lidé MeSH
- normální rozdělení MeSH
- počítačová simulace MeSH
- sekvenční analýza hybridizací s uspořádaným souborem oligonukleotidů metody MeSH
- stanovení celkové genové exprese metody MeSH
- statistické modely MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Normalizations of gene expression data are commonly used in practice. They are used for removing systematic variation which affects the measure of gene expression levels. But one can object to the using of normalized data for testing hypotheses. By using normalized data, tests can break nominal level of multiple testing on which we would like to test the hypotheses. It could bring a lot of false positives, which we would like to prevent. In this chapter, by simulating data with similar correlation structure as real data, we try to find out how quantile, global, and δ-sequence normalizations hold the nominal level of Bonferroni multiple testing procedure.
Citace poskytuje Crossref.org
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- $a Normalizations of gene expression data are commonly used in practice. They are used for removing systematic variation which affects the measure of gene expression levels. But one can object to the using of normalized data for testing hypotheses. By using normalized data, tests can break nominal level of multiple testing on which we would like to test the hypotheses. It could bring a lot of false positives, which we would like to prevent. In this chapter, by simulating data with similar correlation structure as real data, we try to find out how quantile, global, and δ-sequence normalizations hold the nominal level of Bonferroni multiple testing procedure.
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