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Differential expression was determined using the LIMMA model.
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Quality control and differential analysis of summary data was performed using the limma (linear models for microarray data) bioconductor package.
Paired t-tests between patients (n = 6) and controls (n = 6) were performed using the Limma (linear models for microarray data) package to identify differential expression [3].
Standard deviations of the logarithm of fold change values were also estimated using the limma (Linear Models for Microarray Analysis) package [41].
Differential expression of transcripts between human psoriatic skin samples and control skin samples was evaluated using the Limma linear modeling package [57], with P-value adjustment using the Benjamini-Hochberg method [58].
Differential expression analysis was carried out using the Limma linear modeling package available in the R Bioconductor software suite [ 76].
Differential expression testing was performed using the Limma linear modeling package available in the R Bioconductor software suite [ 83].
Differential expression between treatments was determined using the limma linear modeling method, and the significance of differences was ranked by the moderated t-statistic.
Empirical Bayes ANOVA analysis [ 21] was performed using the LIMMA (Linear Models for Microarray Data) package [ 22] as part of the R Bioconductor suite [ 23].
Between-array analysis of H3K4me3 in wild-type and atf1Δ experiments was performed using the limma (linear models for microarray data) package after interarray quantile normalization.
We compared the gene expression patterns of the tumor epithelium and stroma at each stage of progression (DCIS or IDC) with their respective normal state using the limma (linear models of microarrays) software package [ 12].
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