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computeIntrinsicNoise <- function (reporter1, reporter2) {
# input: reporter1, reporter2: vectors of normalized reporter gene expression
# compute sample size
n <- length (reporter1)
# compute sum of squared differences
sum.diff <- sum ((reporter1-reporter2)^2)
# compute n*(\bar{C} - \bar{Y})^2
sum.mean.diff <- n*((mean (reporter1) - mean (reporter2))^2)
# compute sample correlation
sample.cor <- cor (reporter1, reporter2)
# compute estimated ratio of extrinsic vs intrinsic noise
est.ratio <- sample.cor / (1-sample.cor)
# unbiased estimator under general conditions
unbiased.general <- (sum.diff - sum.mean.diff) / 2 / (n-1)
# unbiased estimator assuming that the two repoters have equal means
unbiased.eq.mean <- sum.diff / 2 / n
# min MSE estimator under general conditions
a <- (2*n^3 - 7*n + 6) / (2*(n^2 - n)) + (2 - n) / (n^2 - n) * est.ratio + 1/(2*(n^2 - n)) * est.ratio^2
min.mse.general <- (sum.diff - sum.mean.diff) / 2 / a
# min MSE estimator assuming that the two reporters have equal means
min.mse.eq.mean <- sum.diff / 2 / (n+2)
# asymptotic estimator
asym.general <- (sum.diff - sum.mean.diff) / 2 / n
# return a list of estimators
return (list (ELSS=unbiased.eq.mean, unbiasedGeneral=unbiased.general, unbiasedEqualMean=unbiased.eq.mean, minMSEGeneral=min.mse.general, minMSEEqualMean=min.mse.eq.mean, asymptoticGeneral=asym.general, asymptoticEqualMean=unbiased.eq.mean))
}
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