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Before the experiment, batch cultures of each genotype were acclimated to experimental conditions of nutrients and light for four weeks in 150 ml flasks filled with 100 ml of medium.
Sex, age and sample grouping during microarray experiment (batch) were included as covariates.
The algorithm was then applied to all images within that specific experiment (batch analysis).
Using the empirical Bayes method to correct for both within and between experiment batch effects (Model 1), we identified 629 differentially expressed genes. Figure 6 is a clustered heat map based on these 629 genes.
There was a significant difference between batches in the mean weight over the course of the experiment: Batch 1, 36.0±0.2 kg; Batch 2 39.3±0.2 kg, (P<0.01).
In our experiment, batch (representing time of year and associated environmental conditions) was highly significant, with the June/July batch having 8% greater MY3 and 11% greater DMP adjusted for LW than the February/March batch.
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The experimental procedures lead to differences in the conditions of the color reaction and signal development among experiment batches (see differences between Figs. 1 and 4).
The removal of As III) was studied by performing a series of biosorption experiments (batch and column).
A series of adsorption experiments (batch and column) were performed utilizing iron impregnated sugarcane carbon (Fe SCC), a composite adsorbent, to remove arsenic from aqueous systems.
In this section we show that the empirical Bayes method successfully adjusts for within-experiment batch effects and that these can be of even greater magnitude than the between-experiment batch effects.
These numbers show that the empirical Bayes method is very effective at removing within-experiment batch effects.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com