2016
DOI: 10.12688/f1000research.9005.2
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RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR

Abstract: The ability to easily and efficiently analyse RNA-sequencing data is a key strength of the Bioconductor project. Starting with counts summarised at the gene-level, a typical analysis involves pre-processing, exploratory data analysis, differential expression testing and pathway analysis with the results obtained informing future experiments and validation studies. In this workflow article, we analyse RNA-sequencing data from the mouse mammary gland, demonstrating use of the popular package to import, organise,… Show more

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Cited by 254 publications
(210 citation statements)
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“…The number of mapped read-pairs were counted using cufflinks based on the GENCODE v23 annotation 48 . The raw read counts were transformed using voom 49, 50 , TMM-normalized 51 and QC-analysed using the arrayQualityMetrics package in Bioconductor 52 . Samples were scored based on 3 parameters: maplot, boxplot, heatmap.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…The number of mapped read-pairs were counted using cufflinks based on the GENCODE v23 annotation 48 . The raw read counts were transformed using voom 49, 50 , TMM-normalized 51 and QC-analysed using the arrayQualityMetrics package in Bioconductor 52 . Samples were scored based on 3 parameters: maplot, boxplot, heatmap.…”
Section: Methodsmentioning
confidence: 99%
“…For each comparison, the null hypothesis was that there was no difference between the groups being compared. The Bioconductor package Limma was used 50 . The primary output from the statistical analysis is a set of fully annotated (when available) lists of genes differentially expressed in the comparison of interest.…”
Section: Methodsmentioning
confidence: 99%
“…Genes have then been grouped into four classes: unexpressed , male - biased , female - biased and unbiased genes. Unexpressed genes have been determined as such if they displayed less than 1 count per million in at least three libraries as in [69]. Male - biased and female - biased genes have been determined as such using a FDR <0.05.…”
Section: Methodsmentioning
confidence: 99%
“…Differential gene expression was then estimated using the lmFit function in limma::voom, a gene-wise linear model [105], and differentially expressed genes were defined as having an absolute fold-change > 2, with an FDR adjusted p-value < 0.05. Differentially expressed genes were first used "as is" for the gene ontology analysis as described above (previous section).…”
Section: Gene Expression and Ontology Analysis Of Root Transcriptomementioning
confidence: 99%