#include "metastats.h"
+//The following code has been modified using the original Metastats program from White, J.R., Nagarajan, N. & Pop, M. Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLoS Comput Biol 5, e1000352 (2009).
+
int metastat_main (char* outputFileName, int numRows, int numCols, double threshold, int numPermutations, double** data, int secondGroupingStart){
int size,c=0,i=0,j=0,k,counter=0, bflag=0;
}
// Initialize the matrices
- size = row*col;
- double matrix[row][col];
- double pmatrix[size],pmatrix2[size],permuted[size];
- double storage[row][9];
-
- for (i=0;i<row;i++){
- for (j =0;j<9;j++){
- storage[i][j]=0;
- }
- }
-
+ size = row*col;
+
+ double ** matrix, * pmatrix, * permuted, ** storage;
+ matrix = malloc(row*sizeof(double *));
+ storage = malloc(row*sizeof(double *));
+ for (i = 0; i<row; i++){
+ matrix[i] = malloc(col*sizeof(double));
+ }
+ for (i = 0; i<row;i++){
+ storage[i] = malloc(9*sizeof(double));
+ }
+
+ pmatrix = (double *) malloc(size*sizeof(double *));
+ permuted = (double *) malloc(size*sizeof(double *));
+
+
+
for(i=0; i<row; i++){
for(j=0; j<col;j++){
matrix[i][j]=data[i][j];
permutes = &B;
ptt_size = B*row;
-
+
//changing ptt_size to row
- double permuted_ttests[row], pvalues[row], tinitial[row];
+ double * permuted_ttests, * pvalues, * tinitial;
+ permuted_ttests = (double *) malloc(size*sizeof(double *));
+ pvalues = (double *) malloc(size*sizeof(double *));
+ tinitial = (double *) malloc(size*sizeof(double *));
for(i=0;i<row;i++){
permuted_ttests[i]=0;}
tinitial[i]=0; }
// Find the initial values for the matrix.
+
start(pmatrix,gvalue,nr,nc,tinitial,storage);
// Start the calculations.
if ( (col==2) || ((g-1)<8) || ((col-g+1) < 8) ){
- double fish[row], fish2[row];
+ double * fish, *fish2;
+ fish = (double *) malloc(size*sizeof(double *));
+ fish2 = (double *) malloc(size*sizeof(double *));
+
for(i=0;i<row;i++){
fish[i]=0;
fish2[i]=0;}
// f21 f22
int *nr, *nc, *ldtabl, *work;
- int nrow=2, ncol=2, ldtable=2, workspace=100000;
+ int nrow=2, ncol=2, ldtable=2;
+ int workspace = 2*(row*col*sizeof(double *));
double *expect, *prc, *emin,*prt,*pre;
double e=0, prc1=0, emin1=0, prt1=0, pre1=0;
+
+ prt = (double *) malloc(size*sizeof(double *));
+ prc = (double *) malloc(size*sizeof(double *));
nr = &nrow;
nc = &ncol;
emin=&emin1;
prt=&prt1;
pre=&pre1;
-
+
fexact(nr,nc,data, ldtabl,expect,prc,emin,prt,pre,work);
if (*pre>.999999999){
}
}
else{
-
+printf("here before testp\n");
testp(permuted_ttests, permutes, permuted,pmatrix, nc, nr, gvalue,tinitial,pvalues);
// Checks to make sure the matrix isn't sparse.
- double sparse[row], sparse2[row];
+ double * sparse, * sparse2;
+ sparse = (double *) malloc(size*sizeof(double *));
+ sparse2 = (double *) malloc(size*sizeof(double *));
+
for(i=0;i<row;i++){
sparse[i]=0;
sparse2[i]=0;}
double data[] = {f11, f12, f21, f22};
int *nr, *nc, *ldtabl, *work;
- int nrow=2, ncol=2, ldtable=2, workspace=10000000; // I added two zeros for larger data sets
+ int nrow=2, ncol=2, ldtable=2, workspace=INT_MAX; // I added two zeros for larger data sets
double *expect, *prc, *emin,*prt,*pre;
double e=0, prc1=0, emin1=0, prt1=0, pre1=0;
emin=&emin1;
prt=&prt1;
pre=&pre1;
-
+printf("here before fexact2\n");
fexact(nr,nc,data, ldtabl,expect,prc,emin,prt,pre,work);
if (*pre>.999999999){
}
// Calculates the mean of counts (not normalized)
- double temp[row][2];
-
+ double ** temp;
+ temp = malloc(row*sizeof(double *));
+ for (i = 0; i<row; i++){
+ temp[i] = malloc(col*sizeof(double));
+ }
+printf("here\n");
for(j=0;j<row;j++){
for(i=0;i<2;i++){
temp[j][i]=0;
if (! seeded) {
seeded = 1;
- srandom(time(NULL));
+ srand(time(NULL));
}
for (i = 0; i < n; i++) {
- int selection = random() % (n - i);
+ int selection = rand() % (n - i);
int tmp = array[i + selection];
array[i + selection] = array[i];
array[i] = tmp;
}
void start(double *Imatrix,int *g,int *nr,int *nc,double *initial,
- double storage[][9]){
+ double** storage){
int i, a = *nr;
a*=4;
-
+
double store[a], tool[a], C1[*nr][3], C2[*nr][3];
double nrows,ncols,gvalue, xbardiff=0, denom=0;
-
+
nrows = (double) *nr;
ncols = (double) *nc;
gvalue= (double) *g;
-
+
meanvar(Imatrix,g,nr,nc,store);
-
+
for(i=0;i<=a-1;i++){
tool[i]=store[i];
}
- for (i=0; i<*nr;i++){
+
+ for (i=0; i<*nr;i++){
C1[i][0]=tool[i]; //mean group 1
storage[i][0]=C1[i][0];
C1[i][1]=tool[i+*nr+*nr]; // var group 1
storage[i][1]=C1[i][1];
C1[i][2]=C1[i][1]/(gvalue-1);
storage[i][2]=sqrt(C1[i][2]);
-
+ printf("here2\n");
C2[i][0]=tool[i+*nr]; // mean group 2
- storage[i][4]=C2[i][0];
+ storage[i][4]=C2[i][0];
C2[i][1]=tool[i+*nr+*nr+*nr]; // var group 2
- storage[i][5]=C2[i][1];
- C2[i][2]=C2[i][1]/(ncols-gvalue+1);
+ storage[i][5]=C2[i][1];
+ C2[i][2]=C2[i][1]/(ncols-gvalue+1);
storage[i][6]=sqrt(C2[i][2]);
+
}
+
for (i=0; i<*nr; i++){
xbardiff = C1[i][0]-C2[i][0];
denom = sqrt(C1[i][2]+C2[i][2]);
initial[i]=fabs(xbardiff/denom);
- }
+ }
}
projects / mothur.git / blobdiff