Open cemalley opened 6 years ago
Hi @cemalley
Ouija is primarily designed to be used with a small number of marker genes (paper is here) that are a priori known to be involved in the process of interest. So your options are
inference_type = 'vb'
to use much faster variational bayes rather than HMC samplingAlso consider subsampling cells to ~ 200 while you figure out the best range of options.
Hope that helps?
Kieran
Hi Kieran, Thanks for developing ouija. I'm testing it out on a complete 1386 cell x 28000 gene matrix of single cell RNASeq counts. I tested 200GB-1T memory and 1-4 CPUs. It seems to use a steady 600GB memory and cycles between 1 and 2 CPU. With the code from the readme, it has not finished (converged?) in over a day. Is the matrix too big for ouija?
I also notice there are lots of warnings before it says "SAMPLING FOR MODEL 'ouija' NOW (CHAIN 1)", and no other notices beyond that. Thanks for your advice.
library(ouija)
library(Seurat)
load("Seurat.Object.RData")
options(mc.cores = parallel::detectCores())
oui <- ouija(as.matrix(seurat@data))