int main(
int argc,
char **argv
)
{
int i;
int order = atoi(argv[1]);
double logcumback[1000];
//char *string = "ACGTT";
char *string = "ATTTTTTTT";
int len = strlen(string);
char *seq = NULL;
Resize(seq, len+1, char);
strcpy(seq, string);
printf("order = %d len = %d string = %s\n", order, len, string);
setup_hash_alph(DNAB);
char *alpha = "ACGTX";
BOOLEAN rc = TRUE;
double *a_cp = get_markov_from_sequence(seq, alpha, rc, order);
printf("# conditional probabilities\n");
print_prob(a_cp, order+1, "", 0, alpha);
log_cum_back(seq, a_cp, order, logcumback);
for (i=0; i<len; i++) printf("%c %f %f\n", seq[i], exp(logcumback[i]),
exp(Log_back(logcumback,i,6)) );
printf("All done\n");
return(0);
} /* main */
double tcm_e_step(
MODEL *model, /* the model */
DATASET *dataset /* the dataset */
)
{
int i, j, k, ii;
THETA logtheta1 = model->logtheta; /* motif log(theta) */
int w = model->w; /* motif width */
int n_samples = dataset->n_samples; /* number of sequences */
BOOLEAN invcomp = model->invcomp; /* use reverse complement strand, too */
int ndir = invcomp ? 2 : 1; /* number of strands */
double log_sigma = log(1.0/ndir); /* log \sigma */
double lambda = model->lambda; /* \lambda of tcm model */
double log_lambda = LOG(lambda); /* log \lambda */
double log_1mlambda = LOG(1-lambda); /* log (1 - \lambda) */
double logpX; /* log likelihood; no erase or smooth */
/* E step */
convert_theta_to_log(model, dataset);
/* calculate all the posterior offset probabilities */
logpX = 0;
for (i=0; i < n_samples; i++) { /* sequence */
SAMPLE *s = dataset->samples[i];
int lseq = s->length;
int m = lseq - w + 1; /* number of possible sites */
double *zi = s->z; /* Pr(z_ij=1 | X_i, \theta) */
double *not_o = s->not_o; /* Pr(v_ij = 1) */
double *lcb = s->logcumback; /* cumulative background probability */
if (lseq < w) continue; /* sequence too short for motif */
/* added by M.H. */
/* use log sigma_ij * lambda * m instead of lambda if secondary structure information is given */
/* NOTE: log_sigma is the prior for + or - strand --> here only + strand --> log_sigma = 0 */
if (dataset->secondaryStructureFilename != NULL) {
/* first check if the maximum sigma * (lambda*m) > 1 --> if so P(Zij=1 | \phi) can be > 1 */
double Pcount = dataset->secondaryStructurePseudocount;
double maxPrior = ((s->max_ss_value + Pcount) / (s->sum_ss_value + (m * Pcount))) * (lambda*m);
if (maxPrior > 1.0 ) {
/* compute new pseudocount that gives sigma_i_max = 1 = (max_ss_value + pseudocount) / (\sum (ss_value[i] + pseudocount)) * lambda * m */
Pcount = (-1 * s->max_ss_value * lambda * m + s->sum_ss_value) / (m * (lambda - 1));
/* for statistics keep maximum adjustment */
if (Pcount - dataset->secondaryStructurePseudocount > MAXADJUST) {
MAXADJUST = Pcount - dataset->secondaryStructurePseudocount;
}
}
/* compute new sigmas with this pseudocount */
double sum = s->sum_ss_value + (m * Pcount); /* \sum ss_value[i] + m*pseudocount */
for (j=0; j < m; j++) {
s->sigma[j] = (s->ss_value[j] + Pcount) / sum;
}
}
for (k=0; k<ndir; k++) { /* strand */
BOOLEAN ic = (k==1); /* doing - strand */
double *szik = s->sz[k]; /* Pr(X_i | z_ij=1, s_ijk=1, \theta) */
for (j=0; j<m; j++) { /* site start */
/* added by M.H. */
/* use the prior instead of lambda */
if (dataset->secondaryStructureFilename != NULL) {
double p = MIN(1.0, ( s->sigma[j] * lambda * m ) ) ; /* rounding */
log_lambda = LOG(p);
log_1mlambda = LOG(1-p);
}
/* log Pr(X_ij | s_ijk=1, \theta0) \sigma (1-\lambda) */
double log_pXijtheta0 = log_sigma + log_1mlambda;
/* log Pr(X_ij | s_ijk=1, \theta1) \sigma \lambda */
double log_pXijtheta1 = log_sigma + log_lambda;
int off = ic ? lseq-w-j : j; /* - strand offset from rgt. */
char *res = ic ? s->resic+off : s->res+off; /* integer sequence */
/* calculate the probability of positions in the site under the
background and foreground models
*/
log_pXijtheta0 += Log_back(lcb, j, w);
for (ii=0; ii<w; ii++) log_pXijtheta1 += logtheta1(ii, (int)res[ii]);
/* set log szik to:
Pr(X_i | z_ij=1, s_ijk=1, \theta) \sigma \lambda
*/
szik[j] = log_pXijtheta1;
/* set z_ij to log Pr(X_ij | \phi): (6-21-99 tlb)
log(
\sigma * sum_{k=0}^{ndir-1} (
Pr(X_i|z_ij=1, s_ijk=1, \theta) \lambda +
Pr(X_i|z_ij=0, s_ijk=1, \theta) (1-\lambda)
)
)
*/
zi[j] = k==0 ? LOGL_SUM(log_pXijtheta0, log_pXijtheta1) :
LOGL_SUM(zi[j], LOGL_SUM(log_pXijtheta0, log_pXijtheta1));
} /* site start */
} /* strand */
/* compute log Pr(X | \phi) = sum_i,j log(Pr(X_ij)) */
for (j=0; j<m; j++) { /* site start */
logpX += zi[j]; /* z_ij = log Pr(X_ij | \phi) */
}
/* sz_ijk : normalize, delog and account for erasing
Pr(z_ij=1, s_ijk=1 | X_i, \phi) \approx
P(z_ij=1, s_ijk=1 | X_i, \phi) P(v_ij = 1)
*/
for (k=0; k<ndir; k++) { /* strand */
double *szik = s->sz[k]; /* Pr(X_i | z_ij=1, s_ijk=1, \phi) */
for (j=0; j<m; j++) { /* site start */
/* note zi[j] holds Pr(X_ij|\phi) */
szik[j] = MIN(1.0, exp(szik[j] - zi[j]) * not_o[j]); /* roundoff */
} /* site */
} /* strand */
/* z_ij : sum of sz_ijk */
for (j=0; j<m; j++) { /* site start */
for (k=zi[j]=0; k<ndir; k++) { /* strand */
zi[j] += s->sz[k][j];
} /* strand */
zi[j] = MIN(1.0, zi[j]); /* avoid roundoff errors */
} /* site */
for (j=m; j<lseq; j++) { /* tail of sequence */
zi[j] = 0;
}
} /* sequence */
/* smooth so no window of size w has z_i which sum to greater than 1.0 */
(void) smooth(w, model, dataset);
return (logpX/log(2.0));
} /* tcm_e_step */
