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Parallel-CIBERSORTx

Setting up Environment

Install BLAS and LAPACK

apt-get install libblas-dev liblapack-dev

Install armadillo from source

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

Install Parallel-CIBERSORTx

git clone https://github.com/toeric/Parallel-CIBERSORTx.git
cd lib
make

Run Parallel-CIBERSORTx

//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.

parallel-cibersortx's People

Contributors

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