I would advise that you do not reimplement working methods but interface to them. Bioconductor's BiocSklearn package exposes aspects of scikit and you could have a look at that for one approach. The basilisk package <https://github.com/LTLA/basilisk> in development is a more systematic way of governing interoperability with python and should also be examined.
On Tue, Feb 18, 2020 at 10:53 AM Zhang, David <david.zhang...@ucl.ac.uk> wrote: > Dear all, > > I’m a bioinformatics PhD student at UCL who’s recently been trying to > develop an R package that has some python dependencies. Specifically, areas > of my current pipeline are written in python for speed and any ML has been > implemented through sklearn. I was wondering what you would advise as the > best practice of integrating python code into Bioconductor/R p > packages? E.g. R to python interfaces (such as reticulate) or advising > package users to call python scripts independently or re-writing python > code in R? > > Any advice would be much appreciated. > > Many thanks in advance, > > David > _______________________________________________ > Bioc-devel@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/bioc-devel > -- The information in this e-mail is intended only for the ...{{dropped:18}} _______________________________________________ Bioc-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/bioc-devel
