PMC:3807239 / 28082-29064 JSONTXT

{"project":"2_test","denotations":[{"id":"24163754-11382363-112081905","span":{"begin":436,"end":438},"obj":"11382363"},{"id":"24163754-11382363-112081905","span":{"begin":436,"end":438},"obj":"11382363"},{"id":"24163754-11747612-112081905","span":{"begin":436,"end":438},"obj":"11747612"},{"id":"24163754-11747612-112081905","span":{"begin":436,"end":438},"obj":"11747612"},{"id":"24163754-12169537-112081906","span":{"begin":957,"end":959},"obj":"12169537"},{"id":"24163754-12169537-112081906","span":{"begin":957,"end":959},"obj":"12169537"},{"id":"24163754-12761059-112081907","span":{"begin":960,"end":962},"obj":"12761059"},{"id":"24163754-12761059-112081907","span":{"begin":960,"end":962},"obj":"12761059"},{"id":"24163754-11382363-69479716","span":{"begin":439,"end":441},"obj":"11382363"},{"id":"24163754-11382363-69479716","span":{"begin":439,"end":441},"obj":"11382363"},{"id":"24163754-11747612-69479717","span":{"begin":442,"end":444},"obj":"11747612"},{"id":"24163754-11747612-69479717","span":{"begin":442,"end":444},"obj":"11747612"},{"id":"24163754-12169537-69479718","span":{"begin":957,"end":959},"obj":"12169537"},{"id":"24163754-12169537-69479718","span":{"begin":957,"end":959},"obj":"12169537"},{"id":"24163754-12761059-69479719","span":{"begin":975,"end":977},"obj":"12761059"},{"id":"24163754-12761059-69479719","span":{"begin":975,"end":977},"obj":"12761059"}],"text":"6.3. Generalized Logarithm Transformation\nThe following two-component measurement error model is proposed to model the measured expression levels, (1) y=α+μeη+ϵ where y is the measured raw expression level, α is the mean background noise, μ is the true expression level and η and ϵ are the multiplicative and additive measurement errors, which are assumed to be normally-distributed with mean 0 and variances ση2 and σϵ2, respectively [17,18,19]. The variance of y under this model is Var(y)=μ2Sη2+σϵ2, where Sη2=eση2(eση2−1). To ease the analyses of gene-expression microarrays using some standard statistical techniques, the following generalized logarithm transformation that stabilizes the variance has been proposed:(2) fc(z)=lnz+z2+c22 where c=σϵ/Sη. The performance of the GLOG is further studied, and simulation results show that it is a better choice compared with the “started logarithm” transformation and the “log-linear hybrid” transformation [20,60,61,62,63,64,65,66]."}