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To investigate differences in gene expression profiles, we analyzed genes between both libraries using the IDEG6 modeling methods [ 51].
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A web tool IDEG6 was used for detection of differentially expressed genes between two libraries using Fisher exact test [ 91].
Pairwise comparisons of differential expression for each gene were completed between libraries using the DESeq package (Anders and Huber, 2010) for R 2.15.0 (http://www.R-project.org).org
To assess the technical reproducibility, we compared methylation of common fragments between these technical libraries using a Bland-Altman (BA) plot.
TMM normalization was applied to account for compositional difference between libraries using the function calcNormFactors [ 32].
To compare differentially expressed miRNAs between multiple samples through the miRDeep2-method, read count of each identified miRNA was normalized between libraries using upper-quartile normalization [ 78].
No consensus number was found for the matched contigs between the two libraries using BLASTN in reciprocal searches.
Signature abundance was also compared statistically between the two libraries using a Z-test [ 25], with the resulting statistical significance expressed as a p-value.
This value was even greater for comparisons between libraries using the common linker sequence (average Spearman's rho of 0.88) [Additional file 2: Figure S1 and Additional file 5: Table S3], indicating that the common linker reduced biases based on barcode difference, consistent with results reported in [ 9].
Signature frequency was also compared statistically between the two libraries using the Z-score method according to Kal et al. [ 34], which uses the p-value and a statistical significance value.
Signature frequency was also compared statistically between the two libraries using Z-score method according to Kal et al. (1999) [ 41], which use p-value as statistical significance level.
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