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Although there are several methods to control for population structure in association mapping that were also applied here [31], it is still useful to estimate the extent of false positives using a null distribution of association tests P-values from a random set of markers.
False discovery rate estimates (q-values) were estimated using a null distribution of CyberT's from permuted data.
Significantly differentially expressed genes were determined by estimating the false discovery rate using a null distribution obtained from the self-self experiments, as described in the Background section.
We conjectured that by using a null distribution that appropriately controls for the shared-ancestry signal, tree-ignorant methods would exhibit equivalent statistical power to tree-aware methods.
For each pair of genes, the method computes a Z-score using a null distribution obtained from the scores between these two genes and all other genes.
We computed p-values for each term's iGA score using a null distribution obtained via 1000 random permutations of the original gene order.
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For instance, it was recently shown that such population relationships greatly increased the rate of false positives in tests of selection based on FST which use a null distribution generated under a simple island model of differentiation [31].
This was then used as a null distribution from which the FDR was calculated, as in [56].
For each metabolite, the "IncMSE" values of each marker from 1000 RF runs on permuted metabolites were stored, and uses as a "null distribution" of the IncMSE value to assess the significance threshold of each marker.
Neither (1) fitting the null distribution parameters (to all test statistics) by way of maximum likelihood nor (2) use of a null distribution, yielded calibrated P-values.
The distribution of perturbation scores from permuted samples is used as a null distribution to estimate the significance of true samples.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com