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The specific statistical methods applied and descriptions of replicates can be found in the figure legends.
Going back months later to re-do these experiments with better controls or more replicates can be very frustrating!
Consequently, the commonly used quality control method of applying correlation analysis on technical replicates can be adopted for assessing array performance based on different biological samples using tERCs.
The existence of negative correlation among the replicates can be seen in Figure 2 (more downward spikes than upward).
Multiple analytical replicates can be created for one biological replicate.
The replicates can be those profiled either in the same plates or in different plates.
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In this study, we describe how the optimal number of replicate measures (technical replicates) for each biological sample (biological replicate) can be determined.
The dataset of the biological replicate can be found in the Additional file 2: Figure S1.
The weights w = { w t | t = 1, 2,..., N} of all the trees T i (i = 1,..., N) obtained from primary pseudo-replicates can be computed by solving the following equation system: (9) The solution of the system (9) is as follows (for any t = 1,..., N): (10) Obviously, the bigger the average secondary bootstrap score assigned to a tree, the bigger the tree weight.
This observation suggests that the effects of biological or analytical variation from replicate to replicate can be reduced if comparisons are made between paired samples.
Although some variation was found between replicates, these can be easily avoided by the high number of technical replicates achievable.
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