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The Class Comparison analyses were performed using BRB ArrayTools [ 17] developed by Dr. Richard Simon and Amy Peng Lam to determine if gene expression profiles were different between groups by applying a random variance t-test to the data.
Thus a random variance model was used.
Differentially expressed genes were identified by a random-variance t-test, with the statistical significance threshold set at p < 0.001.
Genes differentially expressed in involved [LN] and uninvolved [LN] lymph nodes were searched by a random-variance t-test, with the statistical significance threshold set at P<0.01.
Our nearest-neighbor analysis provided a useful method of assessing the phenomenon of meningococcal clustering by taking random variance into account.
The models incorporated CpG sites that were differentially methylated at the 0.001 significance level as assessed by the random variance t-test [23].
The models incorporated genes that were differentially methylated between tumor and surrounding tissue at the 0.001 significance level, as assessed by the random variance t-test.
Significant differential genes among the three groups were filtered by ANOVA and corrected by the random variance model (RVM [ 8].
These algorithms incorporate genes differentially expressed among different classes as assessed by the random variance t-test.
The models incorporated genes that were differentially expressed among genes at the 0.01 significance level as assessed by the random variance t-test [ 13].
To accurately estimate the per-group gene expression value, differentially expressed probe sets from different germination phases were filtered by ANOVA and corrected by the random variance model (RVM) [ 69- 71].
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