On the assumption of random error distribution, our approach was to use the individual muscle as the unit of analysis and to analyze multiple muscle samples.

The continuous approximation is necessary not just for computational convenience, but because the form of the error distributions was continuous.

We also created synthetic array datasets with error distributions taken from real data.

In the second step, error is added to the synthetic patterns using an experimentally derived error distribution.

Known also as the exponential power distribution, or the generalized error distribution, this is a parametric family of symmetric distributions.

For error distributions that belong to the Exponential family, a link function may be used to transform the parameters under the Generalized linear model framework.

Otherwise the error distribution depends both on the used projection and also on the projection parameters.

It is important to note that this result is for an 'ideal' detector, with homogeneous acceptance and efficiency, normal error distributions and zero background.

Empirical likelihood estimates require few assumptions about the error distribution compared to similar methods like maximum likelihood.

This means that the quality scores of confirming reads can simply be added, as long as the error distributions are sufficiently independent.