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Microarray data were then analyzed using the Bioconductor Limma package.
Data preprocessing was performed using the Bioconductor Limma package.
The microarray data were extracted using the Bioconductor limma package [39] and median normalized.
MiRs and mRNAs with significant differential expression were identified using the Bioconductor limma package [ 48].
Filtered raw data was analyzed using the Bioconductor Limma package [ 89].
The text files were imported into R 2.5.1 using the Bioconductor "limma" package 2.8.1 for statistical analysis as reported [ 13].
Similar(47)
We used the Bioconductor limma package (Smyth 2004) to perform ANOVA on normalized probes and calculated significance levels for probes using IBMT (Sartor et al. 2006).
We used the Bioconductor Limma package to detect differentially expressed genes (DEGs) under a linear model with empirical bayes moderation of the variance similar to ANOVA developed to adjust for small sample size in microarray studies (Smyth, 2004).
Differential gene expression were assessed using linear modeling in R using the bioconductor package Limma that allows for identification of differentially expressed genes in a multifactorial experiment79.
Normalisation and statistical analysis of microarray data was performed in R using the Bioconductor packages Limma, Affy and BeadExplorer.
Calculations were performed using the BioConductor package limma [ 40].
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