Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences
Author(s): Charlotte Soneson Institute for Molecular Life Sciences, University of Zurich, Zurich, 8057, Switzerland; SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, 8057, Switzerland
Michael I. Love Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, 02210, USA: Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, 02115, USA
Mark D. Robinson Institute for Molecular Life Sciences, University of Zurich, Zurich, 8057, Switzerland; SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, 8057, Switzerland
Environment
R
Ubuntu
Packages:
edgeR, DESeq, limma, pandoc
Code
R using RStudio
Data
Two simulated and four experimental data sets containing data on genes and transcripts
Results
What's inside
R package to import transcript-level abundance, estimated counts and transcript lengths, and summarizes into matrices for use with downstream statistical analysis packages. Average transcript length, weighted by sample-specific transcript abundance estimates, is provided as a matrix which can be used as an offset for different expression of gene-level counts.
Who uses it
Researchers working with RNA-seq, quantification, gene expression and transcriptomics
Why we like this
An R example on transcript-level abundance, estimated counts and transcript lengths, and summarizes into matrices for use with downstream statistical analysis packages such as edgeR, DESeq2, limma-voom. DESeq2 is a popular pacakge in R
Copy this HTML code to embed this Capsule
<script src="https://codeocean.com/widget.js?slug=2296412" async></script>
For any questions about the Compute Capsule, please contact us at [email protected].
