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This is particularly suitable for fields where experiments are registered before they are performed, and are standardized, so that overall estimates can be generated by pooling several experiments.
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This was done by pooling several individuals from three laboratory nests per set.
≥ 50 cells pooled from several experiments were analyzed per time point.
For our experiments we also pooled several independent RNA isolations and amplified them prior to dye labelling.
Due to the absence of sequenced sample replicates and aware that our experiment has been pooled several treatment conditions it was not possible to immediately identify differentially expressed genes by pairwise comparisons between the sample groups.
We demonstrate how to pool data from several experiments in a theoretically rigorous way with a Bayesian hierarchical model.
However, by pooling results from previous experiments, we were able to detect additional patterns and sources of variation.
Several measures were done by pooling the samples of several individuals.
The detection principle is validated by several experiments.
Their efficiency will be exhibited by several experiments and simulations.
Furthermore, we introduce a Bayesian hierarchical approach to pool data from several experiments and present results for estimation of mRNA stability.
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