double *xtract_init_window(const int N, const int type)
{
double *window;
window = malloc(N * sizeof(double));
switch (type)
{
case XTRACT_GAUSS:
gauss(window, N, 0.4);
break;
case XTRACT_HAMMING:
hamming(window, N);
break;
case XTRACT_HANN:
hann(window, N);
break;
case XTRACT_BARTLETT:
bartlett(window, N);
break;
case XTRACT_TRIANGULAR:
triangular(window, N);
break;
case XTRACT_BARTLETT_HANN:
bartlett_hann(window, N);
break;
case XTRACT_BLACKMAN:
blackman(window, N);
break;
case XTRACT_KAISER:
kaiser(window, N, 3 * PI);
break;
case XTRACT_BLACKMAN_HARRIS:
blackman_harris(window, N);
break;
default:
hann(window, N);
break;
}
return window;
}
void CCA_caseonly(bool perm,
vector<vector<int> > & blperm_case,
Set & S,
Plink & P)
{
///////////////
// Output file
ofstream EPI;
if (!perm)
{
string f = par::output_file_name+".genepi";
P.printLOG("\nWriting gene-based epistasis tests to [ " + f + " ]\n");
EPI.open(f.c_str(), ios::out);
EPI.precision(4);
EPI << setw(12) << "NIND" << " "
<< setw(12) << "GENE1" << " "
<< setw(12) << "GENE2" << " "
<< setw(12) << "NSNP1" << " "
<< setw(12) << "NSNP2" << " "
<< setw(12) << "CC1" << " "
// << setw(12) << "PILLAI" << " "
<< setw(12) << "BART" << " "
<< "\n";
}
//////////////////////////////////
// Canonical correlation analysis
// Number of genes
int ns = P.snpset.size();
// Consider each pair of genes
for (int s1=0; s1 < ns-1; s1++)
{
for (int s2 = s1+1; s2 < ns; s2++)
{
////////////////////////////////////////////////////////
// Step 1. Construct covariance matrix (cases only)
// And partition covariance matrix:
// S_11 S_21
// S_12 S_22
int n1=0, n2=0;
vector<vector<double> > sigma(0);
vector<double> mean(0);
vector<CSNP*> pSNP(0);
/////////////////////////////
// List of SNPs for both loci
for (int l=0; l<P.snpset[s1].size(); l++)
{
if ( S.cur[s1][l] )
{
pSNP.push_back( P.SNP[ P.snpset[s1][l] ] );
n1++;
}
}
for (int l=0; l<P.snpset[s2].size(); l++)
{
if ( S.cur[s2][l] )
{
pSNP.push_back( P.SNP[ P.snpset[s2][l] ] );
n2++;
}
}
// NOTE: we need to make sure that n1 < n2. Migth cause problems below if this is not the case.// *********
int n12 = n1 + n2;
int ne = n1 < n2 ? n1 : n2;// ne = min(p,q)
///////////////////////////////////////////////////
// Choose cases-only
P.setFlags(false);
vector<Individual*>::iterator person = P.sample.begin();
int ncase=0;
while ( person != P.sample.end() )
{
if ( (*person)->aff && !(*person)->missing )
{
(*person)->flag = true;
ncase++;
}
person++;
}
int nind = calcGENEPIMeanVariance(pSNP,
n1,n2,
false,
&P,
mean,
sigma,
P.sample ,
blperm_case[s1],
blperm_case[s2] );
///////////////////////////
// Partition covariance matrix
vector<vector<double> > I11;
vector<vector<double> > I11b;
vector<vector<double> > I12;
vector<vector<double> > I21;
vector<vector<double> > I22;
vector<vector<double> > I22b;
sizeMatrix( I11, n1, n1);
sizeMatrix( I11b, n1, n1);
sizeMatrix( I12, n1, n2);
sizeMatrix( I21, n2, n1);
sizeMatrix( I22, n2, n2);
sizeMatrix( I22b, n2, n2); // For step 4b (eigenvectors for gene2)
for (int i=0; i<n1; i++)
for (int j=0; j<n1; j++)
{
I11[i][j] = sigma[i][j];
I11b[i][j] = sigma[i][j];
}
for (int i=0; i<n1; i++)
for (int j=0; j<n2; j++)
I12[i][j] = sigma[i][n1+j];
for (int i=0; i<n2; i++)
for (int j=0; j<n1; j++)
I21[i][j] = sigma[n1+i][j];
for (int i=0; i<n2; i++)
for (int j=0; j<n2; j++)
{
I22[i][j] = sigma[n1+i][n1+j];
I22b[i][j] = sigma[n1+i][n1+j];
}
////////////////////////////////////////////////////////
// Step 2. Calculate the p x p matrix M1 = inv(sqrt(sig11)) %*% sig12 %*% inv(sig22) %*% sig21 %*% inv(sqrt(sig11))
bool flag = true;
I11 = msqrt(I11);
I11 = svd_inverse(I11,flag);
I22 = svd_inverse(I22,flag);
I22b = msqrt(I22b);// For Step 4b
I22b = svd_inverse(I22b,flag);
I11b = svd_inverse(I11b,flag);
matrix_t tmp;
matrix_t M1;
multMatrix(I11, I12, tmp);
multMatrix(tmp, I22, M1);
multMatrix(M1, I21, tmp);
multMatrix(tmp, I11, M1);
////////////////////////////////////////////////////////
// Step 3. Determine the p eigenvalues of M1. The sqrt(eigen(M)) =
// p canonical correlations Identify the largest can corr
// Compute evalues and evectors
vector_t eigen = eigenvalues(M1);
// Sort eigenvalues
vector<double> sorted_eigen = eigen;
sort(sorted_eigen.begin(),sorted_eigen.end(),greater<double>());
// P-value
// long double pillai_pvalue = pillai(ncase,n1,n2,sorted_eigen[0]);
long double bartlett_pvalue = bartlett(ncase,n1,n2,sorted_eigen);
/////////////////////////////////////////////////////////////////////
// OUTPUT
EPI << setw(12) << ncase << " "
<< setw(12) << P.setname[s1] << " "
<< setw(12) << P.setname[s2] << " "
<< setw(12) << n1 << " "
<< setw(12) << n2 << " "
<< setw(12) << sqrt(sorted_eigen[0]) << " "
// << setw(12) << pillai_pvalue << " "
<< setw(12) << bartlett_pvalue << " "
<< "\n";
} // End of loop over genes2
} // End of loop over genes1
EPI.close();
} // End of CCA_caseonly