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By applying Bonferroni correction to account for multiple testing, sets of significantly correlated transcript-pairs could be identified.
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We used R[ 50] to calculate p-values with Fisher's Exact Test and employed the package 'qvalue'[ 51] to correct for multiple testing setting a false discovery rate level at 0.001.
Often, complex Monte Carlo simulation is required, sometimes within a large-scale multiple testing setting.
Thus, for each gene we calculated its correlation with SPPR and after correction for multiple testing, set a cut-off of absolute correlation 0.8, which corresponds to a false discovery rate of 3.3%.
The Bonferroni procedure is then applied directly to Q i = P i / W i To develop optimal weighting strategies, it is useful to generalize the concept of power to the multiple testing setting by considering the average power of the m2 tests in which the alternative hypothesis is true.
For the multiple testing setting, we propose to augment this procedure by adding a third stopping criterion, namely a q-value threshold α, aiming to run just as many samples as are needed to obtain an accept/reject decision for each test.
To evaluate the accuracy of FS detection, we generated multiple test sets accounting for different sequence lengths and error models.
It is shown on multiple test sets that the MetaGeneTack FS detection performance is comparable or better than the one of earlier developed program FragGeneScan.
We developed a Random Forests-based algorithm using a training set of 158 samples with centrally confirmed IHC status, and subsequently validated this algorithm on multiple test sets with known, locally determined IHC status.
Due to multiple testing, we set the threshold of the HWE test at P = 0.001.
The level of statistical significance with Bonferroni correction for multiple testing was set to p ≤ 0.017.
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