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To correct for multiple testing, we created a null distribution of P-values by permuting expression phenotypes relative to genotypes ten times, and then compared the real eQTL P-value distribution to the null distribution.
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A list of WT epicardium-enriched genes was created by a parametric t-test using the Benjamini-Hochberg method to correct for multiple testing with a false discovery rate of 0.05 starting from SOM cluster 1 (from Figure 1).
In order to correct for multiple testing a permutation procedure was adapted to create empirical genome-wide P-values.
This increases mapping precision dramatically but it requires large repositories of patient materials and very closely spaced genetic markers, creating a need for correction for multiple testing, which raises the threshold for claiming statistical significance.
We did not correct for multiple testing.
We do not correct for multiple testing.
As adjusting statistical significance depending on the number of tests performed can create problems (Perneger, 1998), no allowance was made for multiple testing.
A statistical approach that controls for multiple testing.
A Bonferroni test was applied to correct for multiple testing.
P values were not corrected for multiple testing.
However, no comparison survived correction for multiple testing.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com