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The first distribution, moderate sample variation, is estimated from the background mutations of the data from Ding et al. (2008).
For moderate sample variation, the P-values for false positives are 0.0001 and 0.0008, and the P-values for true positives are less than 10−16.
To see the effect of variation of mutation rates across samples, we change the sample ID in which mutations occurred by sampling a new sample ID under two different distributions namely, moderate sample variation and high sample variation.
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In addition, our moderate sample size did not allow for exploration of race or ethnic variations in predictors of insulin action.
To assess the variation in the microarray expression level for a specified gene in an experiment of moderate sample size and possibly including paired samples, we designed a block bootstrapping procedure to analyze the microarray data from 66 lung samples, containing 27 pairs of patient adjacent normal-tumor samples and 12 un-paired samples.
All the samples were run in triplicates in order to minimize the sample to sample variation.
To minimize sample variation, we used high quality RNA.
Such sample variation may influence the results.
Most assays can accommodate only moderate sample throughput.
Dynamic marginal structural models lack precision at moderate sample sizes.
One limitation of our study is its moderate sample size.
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