Sentence examples for using the limma library from inspiring English sources

Differential gene expression control versus case samples were carried out using the Limma library [ 88] from Bioconductor.

Raw data from Genepix was imported into R (1.9) [ 47] and analysed using the LIMMA library (Linear Models for Microarray Data version 1.7, [ 48]).

Data was analyzed in the R environment using the Limma library (Smyth, 2004) for loess normalization and calculation of p-values between treatments.

For each study relevant cel files were quality assessed using standard metrics and subsequent expression values RMA normalised [ 42] before differential expression profiles were generated using the LIMMA library [ 43].

Background subtracted intensity values for all probes considered present were imported into R where within-print-tip Lowess normalisation and the identification of statistically significant, differential gene expression was performed using the LIMMA library in the Bioconductor software package [ 29].

Raw data from Genepix was imported into R and analyzed using the LIMMA library (Linear Models for Microarray Data,[ 57]) for within-array print-tip loess normalization of intensities, identification of statistically significant regulation (moderated t-statistics using empirical Bayes shrinkage of the standard errors), and calculation of average fold-changes.

Using the Limma Bioconductor library, 2,251 and 2,615 probe sets were found to be significantly differentially regulated in Mφs and DCs respectively (Figure 1B).

This uRNAList is a new defined R class similar to the class RGList that is used by the limma library [ 16], which uses names that are more appropriate to the Agilent microRNA data.

MicroRNA-expression levels were tested for significant differential expression between the HTs and EMs libraries using the Limma [ 34] package in R. The Benjamini-Hochberg method (BH-FDR) [ 35] was used to control the false discovery rate.

We computed moderated t-statistics, log-odds ratios of differential expression (based on empirical Bayes shrinkage of the standard errors towards a common value), and adjusted p-values (obtained using the Bonferroni correction) using functions in the limma library of the Bioconductor software package.

Univariate differential expression analysis was performed using methods from the limma library [ 13], and multiple testing was controlled using the Benjamini and Hochberg method [ 16].