2015
DOI: 10.1093/nar/gkv007
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limma powers differential expression analyses for RNA-sequencing and microarray studies

Abstract: limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing a… Show more

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Cited by 29,646 publications
(25,362 citation statements)
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References 77 publications
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“…Cell line gene expression principal component analysis was performed using the R prcomp function with the 1000 genes with the largest 10–90% interpercentile range in signal intensities. Gene set analysis was performed using the camera function in the R Bioconductor package limma (Ritchie et al ., 2015; Wu and Smyth, 2012). Seventy gene sets were preselected based on relevance for CRC.…”
Section: Methodsmentioning
confidence: 99%
“…Cell line gene expression principal component analysis was performed using the R prcomp function with the 1000 genes with the largest 10–90% interpercentile range in signal intensities. Gene set analysis was performed using the camera function in the R Bioconductor package limma (Ritchie et al ., 2015; Wu and Smyth, 2012). Seventy gene sets were preselected based on relevance for CRC.…”
Section: Methodsmentioning
confidence: 99%
“…Counts were converted to log2 counts per million, quantile normalized and precision weighted with the voom function of the limma package. 52,53 A linear model was fitted to each gene, and empirical Bayes moderated t-statistics were used to assess differences in expression. 54 A false discovery rate cut-off of 0.15 was applied for calling differentially expressed genes.…”
Section: Methodsmentioning
confidence: 99%
“…Prediction of batch effects was carried out by singular value decomposition (SVD) using the champ bioconductor package (Morris et al ., 2014). Identification of differentially methylated CpG probes between sample groups was performed using a linear model fit of the M values with subsequent empirical Bayes reduction in standard errors from the estimated methylation differences, as described in the limma r package (Ritchie et al ., 2015). As the distribution of unadjusted P ‐values showed no influence of potential confounding factors (Fig.…”
Section: Methodsmentioning
confidence: 99%