/* scan through all columns and identify the set within threshold,
* "fuzziness" of the block is controlled by TOLERANCE (-c)
*/
void scan_block (struct dyStack *gene_set, Block *b_ptr)
{
int i, j;
int block_rows, cur_rows;
block_rows = cur_rows = dsSize(gene_set);
int k;
for (j = 0; j < cols; j++)
for (k=0; k<sigma; k++)
profile[j][k] = 0;
for (j = 0; j< cur_rows ; j++)
seed_update(arr_c[dsItem(gene_set,j)]);
int btolerance = ceil(po->TOLERANCE* block_rows);
for (j = 0; j < cols; j++)
{
/* See if this column satisfies tolerance */
/* here i start from 1 because symbols[0]=0 */
for (i = 1; i < sigma; i++)
{
if ((profile[j][i] >= btolerance))
{
dsPush(b_ptr->conds, j); break;
}
}
}
b_ptr->block_cols = dsSize(b_ptr->conds);
}
void seed_intersect (const discrete *s1, const discrete *s2)
{
seed_update(s1); seed_update(s2);
}
/* Core algorithm */
int cluster (FILE *fw, Edge **el, int n)
{
int block_id = 0;
Block **bb;
int allocated = po->SCH_BLOCK;
AllocArray(bb, allocated);
Edge *e;
Block *b;
struct dyStack *genes, *scores, *b_genes, *allincluster;
int i, j, k, components;
AllocArray(profile, cols);
for (j = 0; j < cols; j++)
AllocArray(profile[j], sigma);
genes = dsNew(rows);
scores = dsNew(rows);
allincluster = dsNew(rows);
bool *candidates;
AllocArray(candidates, rows);
e = *el;
i = 0;
while (i++ < n)
{
/*printf ("%d\n",i);*/
e = *el++;
/* check if both genes already enumerated in previous blocks */
bool flag = TRUE;
/* speed up the program if the rows bigger than 200 */
if (rows > 250)
{
if ( isInStack(allincluster,e->gene_one) && isInStack(allincluster,e->gene_two) )
flag = FALSE;
else if ((po->IS_TFname)&&(e->gene_one!= TFindex)&&(e->gene_two!=TFindex))
flag = FALSE;
else if ((po->IS_list)&&(!sublist[e->gene_one] || !sublist[e->gene_two]))
flag =FALSE;
}
else
{
flag = check_seed(e, bb, block_id);
if ((po->IS_TFname)&&(e->gene_one!= TFindex)&&(e->gene_two!=TFindex))
flag = FALSE;
if ((po->IS_list)&&(!sublist[e->gene_one] || !sublist[e->gene_two]))
flag = FALSE;
}
if (!flag) continue;
for (j = 0; j < cols; j++)
for (k = 0; k < sigma; k++)
profile[j][k] = 0;
/*you must allocate a struct if you want to use the pointers related to it*/
AllocVar(b);
/*initial the b->score*/
b->score = MIN(2, e->score);
/* initialize the stacks genes and scores */
int ii;
dsClear(genes);
dsClear(scores);
for(ii = 0; ii < rows; ii ++)
{
dsPush(genes,-1);
dsPush(scores,-1);
}
dsClear(genes);
dsClear(scores);
/*printf ("%d\t%d\n",e->gene_one,e->gene_two);*/
dsPush(genes, e->gene_one);
dsPush(genes, e->gene_two);
dsPush(scores, 1);
dsPush(scores, b->score);
/* branch-and-cut condition for seed expansion */
int cand_threshold = floor(po->COL_WIDTH * po->TOLERANCE);
if (cand_threshold < 2)
cand_threshold = 2;
/* maintain a candidate list to avoid looping through all rows */
for (j = 0; j < rows; j++)
candidates[j] = TRUE;
candidates[e->gene_one] = candidates[e->gene_two] = FALSE;
components = 2;
/* expansion step, generate a bicluster without noise */
block_init(e, b, genes, scores, candidates, cand_threshold, &components, allincluster);
/* track back to find the genes by which we get the best score*/
for(k = 0; k < components; k++)
{
/* printf ("******%d\t%d\n",dsItem(scores,k),b->score);*/
if ((dsItem(scores,k) == b->score)&&(dsItem(scores,k+1)!= b->score)) break;
}
components = k + 1;
/*printf ("%d",components);*/
int ki;
for (ki=0; ki < rows; ki++)
candidates[ki] = TRUE;
for (ki=0; ki < components - 1 ; ki++)
{
seed_update(arr_c[dsItem(genes,ki)]);
candidates[dsItem(genes,ki)] = FALSE;
}
candidates[dsItem(genes,k)] = FALSE;
genes->top = k ;
int cnt = 0;
bool *colcand;
AllocArray(colcand, cols);
for(ki = 0; ki < cols; ki++)
colcand[ki] = FALSE;
/* add columns satisfy the conservative r */
seed_current_modify(arr_c[dsItem(genes,k)], colcand, &cnt, components);
/* add some new possible genes */
int m_cnt=0;
continuous KL_score=0;
discrete *sub_array;
for ( ki = 0; ki < rows; ki++)
{
if (po->IS_list && !sublist[ki]) continue;
m_cnt = intersect_row(colcand, arr_c[dsItem(genes,0)], arr_c[ki]);
if ( candidates[ki] && (m_cnt >= floor(cnt* po->TOLERANCE)) )
{
sub_array = get_intersect_row(colcand,arr_c[dsItem(genes,0)],arr_c[ki],m_cnt);
KL_score = get_KL (sub_array, arr_c[ki], m_cnt, cols);
/*printf ("%d\t%.2f\n",m_cnt,KL_score);*/
if (KL_score>=b->significance * po->TOLERANCE)
{
dsPush(genes,ki);
components++;
candidates[ki] = FALSE;
}
}
}
b->block_rows_pre = components;
/* add genes that negative regulated to the consensus */
for ( ki = 0; ki < rows; ki++)
{
if (po->IS_list && !sublist[ki]) continue;
m_cnt = reverse_row(colcand, arr_c[dsItem(genes,0)], arr_c[ki]);
if ( candidates[ki] && (m_cnt >= floor(cnt * po->TOLERANCE)) )
{
sub_array = get_intersect_reverse_row(colcand,arr_c[dsItem(genes,0)],arr_c[ki],m_cnt);
KL_score = get_KL (sub_array, arr_c[ki], m_cnt, cols);
if (KL_score>=b->significance * po->TOLERANCE)
{
dsPush(genes,ki);
components++;
candidates[ki] = FALSE;
}
}
}
free(colcand);
/* save the current cluster*/
b_genes = dsNew(b->block_rows_pre);
for (ki = 0; ki < b->block_rows_pre; ki++)
dsPush(b_genes, dsItem(genes,ki));
/* store gene arrays inside block */
b->genes = dsNew(components);
b->conds = dsNew(cols);
scan_block(b_genes, b);
if (b->block_cols == 0) continue;
b->block_rows = components;
b->score = b->score;
/* b->score = b->block_rows * b->block_cols; */
dsClear(b->genes);
for ( ki=0; ki < components; ki++)
dsPush(b->genes,dsItem(genes,ki));
for(ki = 0; ki < components; ki++)
if(!isInStack(allincluster, dsItem(genes,ki)))
dsPush(allincluster,dsItem(genes,ki));
/*save the current block b to the block list bb so that we can sort the blocks by their score*/
bb[block_id++] = b;
/* reaching the results number limit */
if (block_id == po->SCH_BLOCK) break;
verboseDot();
}
/* writes character to the current position in the standard output (stdout) and advances the internal file position indicator to the next position.
* It is equivalent to putc(character,stdout).*/
putchar('\n');
/* free-up the candidate list */
free(candidates);
free(allincluster);
block_enrichment(fw, bb, block_id);
return report_blocks(fw, bb, block_id);
}
/* Core algorithm */
int cluster (FILE *fw, Edge **el, int n)
{
int block_id = 0;
Block **bb;
int allocated = po->SCH_BLOCK;
AllocArray(bb, allocated);
Edge *e;
Block *b;
struct dyStack *genes, *scores, *b_genes, *allincluster;
int i, j, k, components;
AllocArray(profile, cols);
for (j = 0; j < cols; j++) AllocArray(profile[j], sigma);
genes = dsNew(rows);
scores = dsNew(rows);
allincluster = dsNew(rows);
bool *candidates;
AllocArray(candidates, rows);
e = *el; i = 0;
while (i++ < n)
{
e = *el++;
/*printf("a:%d b:%d score:%d\n",e->gene_one,e->gene_two,e->score);*/
/* check if both genes already enumerated in previous blocks */
bool flag = TRUE;
/* speed up the program if the rows bigger than 200 */
if (rows > 200)
{
if ( isInStack(allincluster,e->gene_one) && isInStack(allincluster,e->gene_two) )
flag = FALSE;
}
else
{
flag = check_seed(e, bb, block_id);
}
if (!flag) continue;
for (j = 0; j < cols; j++)
for (k = 0; k < sigma; k++) profile[j][k] = 0;
AllocVar(b);
b->score = MIN(2, e->score);
/* initialize the stacks genes and scores */
int ii;
dsClear(genes);
dsClear(scores);
for(ii = 0; ii < rows; ii ++)
{
dsPush(genes,-1);
dsPush(scores,-1);
}
dsClear(genes);
dsClear(scores);
dsPush(genes, e->gene_one);
dsPush(genes, e->gene_two);
dsPush(scores, 1);
dsPush(scores, b->score);
/* branch-and-cut condition for seed expansion */
int cand_threshold = floor(po->COL_WIDTH * po->TOLERANCE);
if (cand_threshold < 2) cand_threshold = 2;
/* maintain a candidate list to avoid looping through all rows */
for (j = 0; j < rows; j++) candidates[j] = TRUE;
candidates[e->gene_one] = candidates[e->gene_two] = FALSE;
components = 2;
/* expansion step, generate a bicluster without noise */
block_init(e, b, genes, scores, candidates, cand_threshold, &components, allincluster);
/* track back to find the best score that which genes makes it */
for(k = 0; k < components; k++)
if ((dsItem(scores,k) == b->score)&&(dsItem(scores,k+1)!= b->score)) break;
components = k + 1;
int ki;
for (ki=0; ki < rows; ki++)
candidates[ki] = TRUE;
for (ki=0; ki < components - 1 ; ki++)
{
seed_update(arr_c[dsItem(genes,ki)]);
candidates[dsItem(genes,ki)] = FALSE;
}
candidates[dsItem(genes,k)] = FALSE;
genes->top = k ;
int cnt = 0;
bool *colcand;
AllocArray(colcand, cols);
for(ki = 0; ki < cols; ki++) colcand[ki] = FALSE;
/* add columns satisfy the conservative r */
seed_current_modify(arr_c[dsItem(genes,k)], colcand, &cnt, components);
/* add some new possible genes */
int m_cnt;
for ( ki = 0; ki < rows; ki++)
{
m_cnt = intersect_row(colcand, arr_c[dsItem(genes,0)], arr_c[ki]);
if ( candidates[ki] && (m_cnt >= floor(cnt* po->TOLERANCE)) )
{
dsPush(genes,ki);
components++;
candidates[ki] = FALSE;
}
}
b->block_rows_pre = components;
/* add genes that negative regulated to the consensus */
for ( ki = 0; ki < rows; ki++)
{
m_cnt = reverse_row(colcand, arr_c[dsItem(genes,0)], arr_c[ki]);
if ( candidates[ki] && (m_cnt >= floor(cnt * po->TOLERANCE)) )
{
dsPush(genes,ki);
components++;
candidates[ki] = FALSE;
}
}
free(colcand);
/* save the current cluster*/
b_genes = dsNew(b->block_rows_pre);
for (ki = 0; ki < b->block_rows_pre; ki++)
dsPush(b_genes, dsItem(genes,ki));
/* store gene arrays inside block */
b->genes = dsNew(components);
b->conds = dsNew(cols);
scan_block(b_genes, b);
if (b->block_cols == 0) continue;
b->block_rows = components;
b->score = b->block_rows * b->block_cols;
dsClear(b->genes);
for ( ki=0; ki < components; ki++)
dsPush(b->genes,dsItem(genes,ki));
for(ki = 0; ki < components; ki++)
if(!isInStack(allincluster, dsItem(genes,ki))) dsPush(allincluster,dsItem(genes,ki));
bb[block_id++] = b;
/* reaching the results number limit */
if (block_id == po->SCH_BLOCK) break;
verboseDot();
}
putchar('\n');
/* free-up the candidate list */
free(candidates);
free(allincluster);
return report_blocks(fw, bb, block_id);
}
