TY  - JOUR
AU  - Law, CW
AU  - Alhamdoosh, M
AU  - Su, S
AU  - Dong, X
AU  - Tian, L
AU  - Smyth, GK
AU  - Ritchie, ME
Y2  - 2021/02/05
Y1  - 2016
SN  - 2046-1402
UR  - http://hdl.handle.net/11343/259992
AB  - 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 edgeR package to import, organise, filter and normalise the data, followed by the limma package with its voom method, linear modelling and empirical Bayes moderation to assess differential expression and perform gene set testing. This pipeline is further enhanced by the Glimma package which enables interactive exploration of the results so that individual samples and genes can be examined by the user. The complete analysis offered by these three packages highlights the ease with which researchers can turn the raw counts from an RNA-sequencing experiment into biological insights using Bioconductor.
LA  - eng
PB  - F1000 Research Ltd
T1  - RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR.
DO  - 10.12688/f1000research.9005.3
IS  - F1000Research
VL  - 5
SP  - 1408-1408
L1  - /bitstream/handle/11343/259992/PMC4937821.pdf?sequence=1&isAllowed=y
ER  -