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Differential expression: Differential expression tests were performed using the Reproducible Optimized Test Statistic (ROTS 82 with FDR < 0.05.
To reduce the number of differential expression tests downstream, thereby increasing overall power, 50% of the lowest normalized Log2 probe-set intensities were removed according to recommendations by Hackstadt and Hess (2009) [49].
Differential expression tests were performed using the R package DESeq [ 11].
Obtained counts were analyzed using DESeq2 package, that included library size normalization, dispersions estimation, differential expression tests etc. [ 59].
Cuffdiff then performed the differential expression tests at the level of transcripts, primary transcripts and genes [ 44].
Differential expression tests were performed with the Cyber-T implementation of a variant of the t-test [ 32].
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That is, the ranking order of betweenness scores was almost completely uncorrelated with the results of differential expression test, and the differentially expressed tests only provide a low accuracy for the prioritization of genes.
We also compared naiveLu, a simple method for identifying differentially expressed genes by simply applying a differential expression test directly without accounting for sample heterogeneity.
Moreover, normalization for the differential expression test expects equivalent expression levels for several co-expressed genes [ 14].
Gene expression was analyzed using principal component analysis, differential expression testing, and gene co-expression networks, and the results correlated to season, location, and contaminant burden.
Differential Expression Testing For the 40 subjects, we compared cases and controls stratified by race/ethnicity.
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