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To exclude the bias caused by different RNA output between samples, we adopted a TMM (trimmed mean of M values) method to calculate a normalization factor introduced by Robinson [36], using calcNormFactors function in edgeR package [69].
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To discriminate between cancer and normal samples, we adopted a gene prioritization approach based on the analysis of PIGs in normal and tumor subsets.
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The relatively sparse sampling scheme we adopted in this study precludes the development of a multi-compartment model.
To test for differences between the samples due to diet, we adopted a partitioning and randomization approach.
We adopted sampling methods which varied between countries according to what was feasible.
Because we adopted a probability sampling technique, our sample is representative of the population of the study area.
We adopted purposeful sampling method to select the cases and controls.
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We adopted a purposive sampling framework, which included geographical region and health / social care agency as sampling parameters.
To avoid any effect of previous experience in the experiment, we adopted a between-subjects design.
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