Open zhangkaicr opened 1 year ago
In R language, we can use the ConsensusClusterPlus package for consistency clustering. For non-negative matrix factorization, we can use the NMF package.
Thank you for opening the issue and suggesting additional features!
In regards to non-negative matrix factorization, it is already implemented in mlr3pipelines as a pipeop. Details are here.
If you want to use it to get cluster assignments as a PredictionClust
object, you can try the following:
library(NMF)
library(mlr3)
library(mlr3cluster)
library(mlr3pipelines)
task = tsk("ruspini")
nmf = po("nmf")
nmf$train(list(task))
p = PredictionClust$new(task = task, partition = as.integer(predict(nmf$state)))
In regards to ConsensusClusterPlus, there are a couple of unusual things happening here:
Do you have any experience with this package? If we can’t address these questions, can you recommend any other packages that implement the same functionality?
I am a bioinformatics PhD and I really appreciate your mlr3cluster package. This package provides many unsupervised clustering algorithms. However, I regret to find that the two most commonly used algorithms in bioinformatics analysis, consistency clustering and non-negative matrix factorization, are not included in this package. These two algorithms are widely used in the medical and biological fields. If these two algorithms are added to the package, the application scope will be greatly increased. I also hope to cite this package in my upcoming doctoral thesis. Thank you very much.