R CMD INSTALL DESeq2_1.16.1.tgz
* installing to library ‘/Library/Frameworks/R.framework/Versions/3.6/Resources/library’
* installing *binary* package ‘DESeq2’ ...
* DONE (DESeq2)
but getting this error:
library(DESeq2)
Error: package or namespace load failed for ‘DESeq2’:
package ‘DESeq2’ was installed by an R version with different internals; it needs to be reinstalled for use with this R version
Is there a particular reason you want to use an older version of the package with a newer version of R? This is generally not recommended and the latest version of the package is normally kept up to date with bug corrections and improvements and Bioconductor releases and closely tied with versions of R?
Thanks for your reply.
I want to use this specific older version of DESeq2 because newer version of DESeq2 is not working for samples without replicates.
I deprecated this functionality because I’m skeptical that it’s useful at all. If you want to estimate the LFC you can apply the VST to the data and just take the difference.
Just to add to Lori's comment, if you want to reproduce an exact result from v1.16.1, you should download the exact version of R and Bioconductor. You will likely encounter errors otherwise, as that version of DESeq2 may have had specific code that may not work with current Bioconductor classes and methods. There's not a good reason to try to mix versions if you are trying to obtain exact results from a previous version.
Otherwise, it's recommended to use the latest version of DESeq2.
Is there a particular reason you want to use an older version of the package with a newer version of R? This is generally not recommended and the latest version of the package is normally kept up to date with bug corrections and improvements and Bioconductor releases and closely tied with versions of R?
Thanks for your reply. I want to use this specific older version of DESeq2 because newer version of DESeq2 is not working for samples without replicates.
I deprecated this functionality because I’m skeptical that it’s useful at all. If you want to estimate the LFC you can apply the VST to the data and just take the difference.