R_main_demo_SIMLR_Large_Scale.R

# required external packages for SIMLR large scale
library("Rcpp")
library("Matrix")
library("pracma")
library("RcppAnnoy")
library("RSpectra")

# load the igraph package to compute the NMI
library("igraph")

# load the palettes for the plots
library("grDevices")

# load the SIMLR R package
source("./R/SIMLR_Large_Scale.R")
source("./R/utils.simlr.large.scale.R")
source("./R/utils.simlr.R")
source("./R/SIMLR.Rtsne.R")

# load the C file

# NOTE 1: we make use of an external C program during the computations of SIMLR.
# The code is located in the R directory in the file projsplx_R.c. In order to 
# use SIMLR one needs to compite the program. To do so, one needs to run on the 
# shell the command R CMD SHLIB -c projsplx_R.c. 
# The precompiled projsplx_R.so is already provided for MAC OS X only. 
# If one wants to use SIMLR on other operative systems, the file projsplx_R.so 
# needs to be deleted, and re-compiled. 

# NOTE 2: for Windows, the command dyn.load("./R/projsplx_R.so") needs to be 
# substituted with the command dyn.load("./R/projsplx_R.dll"). 

dyn.load("./R/projsplx_R.so")
sourceCpp("./src/Rtsne.cpp")

# load the datasets
load(file="./data/Test_5_Zeisel.RData")

# test SIMLR.R on the large scale dataset of Zelsel
set.seed(11111)
cat("Performing analysis for Zelsel","\n")
res_large_scale = SIMLR_Large_Scale(X=Zelsel$in_X,c=Zelsel$n_clust,k=30,kk=200)
nmi_large_scale = compare(Zelsel$true_labs[,1],res_large_scale$y$cluster,method="nmi")

# make the scatterd plots
plot(res_large_scale$ydata,col=c(topo.colors(Zelsel$n_clust))[Zelsel$true_labs[,1]],xlab="SIMLR component 1", ylab="SIMLR component 2",pch=20,main="SIMILR 2D visualization for Zelsel")