Hi,

I am trying to use the new runNFM() from scater. I have two questions.

First, I am trying to perform first reduce dimension using NMF rather than PCA, just to test it if it better represent the data, which I would obviously plot using UMAP. So, I am trying to get 50 NMF components rather than just. Do you think I am doing something conceptually wrong?

Because I am trying to get 50 NMF components, it is running for very long. So, I tried to use multiple cores. As it uses nmf function, where parallel computations can be specified using .opt = 'p6' or .pbackend= 8, which uses doParallel package, I am still not able to use multiple cores. How can I fix that? I did call registerDoParallel(cl=10). Still no speed up.

Thanks!

Piyush

I suspect the parallel mode only works when you are requesting multiple runs - each run seems to be assigned to a single core. If you only have one run, it doesn't appear to parallelize _within_ that run.

To illustrate:

library(Biobase)
data(esGolub)
out <- nmf(esGolub, 3, nrun=5, .opt='vp8')
## NMF algorithm: 'brunet'
## Multiple runs: 5
## # NOTE - CRAN check detected: limiting maximum number of cores [2/12]
## Mode: parallel (2/12 core(s))
## Runs: |==================================================| 100%
## System time:
##    user  system elapsed 
##   6.014   0.561   7.831

Running top at the same time indicates that, indeed, multiple R processes have been started up. But if we remove the nrun= argument:

out <- nmf(esGolub, 3, .opt='vp8')
## NMF algorithm: 'brunet'
## NMF seeding method: random
## Iterations:  600/2000 
## DONE (converged at 610/2000 iterations)

Note the lack of any commentary about parallelization in the latter.

Personally, I found NMF (or at least, the implementation in NMF) too slow to serve as a routine dimensionality reduction technique. The main appeal of NMF lies in the interpretability of the factors but that wasn't something I really cared about for my analyses, so I just stuck to PCA. I should mention that scater originally used the NNLM package, which was much faster; unfortunately, that got kicked off CRAN.