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This analysis normalized gene family frequencies using a generalized linear model with Poisson canonical logarithmic link function and determined the significance (P-value) using a binomial method, with the Benjamini-Hochberg false-discovery rate correction to adjust q-values for multiple testing.
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We believe that the advantages of the log-binomial method with the likelihood ratio test substantially outweigh those of the Robust Poisson when the true model is log-binomial.
Given a gene, the list of coding heterozygous SNPs for a study individual and the RNA-seq data file, the application performs the heterozygosity test on the RNA data at each input SNP position (using the previously described binomial method with p = q = 0.5, tunable by the user).
The τ is determined by the Beta-binomial method with defined marginal mean and selected intraclass correlation.
For example, a site covered by a single 'C' read (the line with filled circles, Figure 1B) will not be classified as 'methylated' with the binomial method in genomes with the overall methylation levels typical of hymenopteran insects (i.e., < 4%).
A binomial method was used for the analysis with a p-value cutoff of 0.05.
This example demonstrates the pitfalls of the binomial method clearly: two samples with exactly same qualitative information (100% 'C' reads in both cases) are classified as opposite directions due to the low sample size.
Moreover, when combined with the correction for multiple testing with FDR, the power of the binomial method is particularly reduced at low coverage sites in sparsely methylated genomes.
The log-binomial method was performed with the macro from Deddens et al., which in turn used PROC GENMOD (with 1,000 copies when the model failed to converge) [ 12].
The exact binomial method will be used to calculate the corresponding 95% CIs for prevalence values.
The negative binomial method included additional pairwise comparisons among the three stages.
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