apt-get install libblas-dev liblapack-dev
wget http://sourceforge.net/projects/arma/files/armadillo-10.5.1.tar.xz
tar Jxvf armadillo-10.5.1.tar.xz
cd armadillo-10.5.1
cmake
make
make install
git clone https://github.com/toeric/Parallel-CIBERSORTx.git
cd lib
make
//main.cpp
#include <armadillo>
#include <string>
#include "lib/CIBERSORTx.h"
int main(int arcg, char *argv[]) {
std::string M = argv[1]; // Bulk data file name
std::string S = argv[2]; // Signature Matrix file name
// false: deconvolution without batch correction
// true: process B-mode correction
bool batch = false;
//Number of thread
int thread_num = 48;
CIBERSORTx model(M, S);
model.dodecomposition(batch, thread_num);
return 0;
}
Run the specific data:
./test data/Mix_10.txt data/Sig.txt
Actually, we had run bulk data with 10, 600 and 10000 samples to test our pakage. Since the bulk data with 600 and 10000 samples are too big, we just upload bulk data with 10 samples (Mix_10.txt) to github.
The output of Parallel-CIBERSORTx would be "CIBERSORTx_result.txt". Each row in the output txt file represents a sample, column represent each cell type. The value in the row i and column j represents the cell type proportion of j in sample i.