RNA-Seq workflow: gene-level exploratory analysis and differential expression
Author(s): Mike Love Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, Massachusetts, USA
Simon Anders Institute for Molecular Medicine Finland, Helsinki, Finland; European Molecular Biology Laboratory, Heidelberg, Germany
Wolfgang Huber European Molecular Biology Laboratory, Heidelberg, Germany
Environment
R
Ubuntu
Packages:
pandoc, pandoc-citeproc, rstudio-server, BiocStyle, maseqGene, Biomanager
Code
R using RStudio
Data
RNA-seq data from cell lines of airway smooth muscle cells from four human donors treated with dexamethasone and untreated in comparison
Results
What's inside
R package to help users integrate transcript-level cDNA (RNA-seq) abundance estimates from common quantification pipelines into count-based statistical inference engines.
Who uses it
Researchers performing RNA-seq, differential expression, gene expression, using high-throughput sequencing, statistical analysis and visualization
Why we like this
Demonstrates an end-to-end gene-level RNA-seq differential expression workflow using Bioconductor package.
The authors perform exploratory data analysis for quality assessment, explore the relationship between samples, perform differential gene expression analysis, and visually explore the results.
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