/***********************************************************************
* A helper function to normalize a subarray.
***********************************************************************/
void normalize_subarray(
int start_index,
int length,
double tolerance,
ARRAY_T* array
) {
ATYPE total;
int i;
/* Compute the total. */
total = 0.0;
for (i = start_index; i < start_index + length; i++) {
total += get_array_item(i, array);
}
assert(total != 0.0);
/* Don't bother if we're close enough. */
if (almost_equal(1.0, total, tolerance)) {
return;
}
/* Divide each element by the total. */
for (i = start_index; i < start_index + length; i++) {
set_array_item(i, get_array_item(i, array) / total, array);
}
}
/**************************************************************************
* Get pseudocount frequencies.
*
* The target_freq matrix only has values for the basic alphabet.
* Fill in the ambiguous character pseudocounts afterwards using
* the average of pseudocounts for letters matching the ambiguous ones.
**************************************************************************/
ARRAY_T *get_pseudocount_freqs(
ALPH_T alph,
ARRAY_T * f, /* Foreground distribution. */
ARRAY_T * b, /* Background distribution. */
MATRIX_T * target_freq /* Target frequency matrix. */
)
{
int i, j;
int asize = alph_size(alph, ALPH_SIZE); // excludes ambigs
ARRAY_T *g = allocate_array(alph_size(alph, ALL_SIZE));// includes ambigs
/*
Create pseudocount frequencies.
*/
for (i = 0; i < asize; i++) { /* non-ambiguous freqs */
double gi = 0;
for (j= 0; j < asize; j++) { /* non-ambiguous freqs */
double qij = get_matrix_cell(i, j, target_freq);
double fj = get_array_item(j, f);
double bj = get_array_item(j, b);
gi += (fj/bj) * qij;
} /* j */
set_array_item(i, gi, g);
if (SUBST_MATRIX_DEBUG) printf("%g %g, ", get_array_item(i, f), gi);
} /* i */
calc_ambigs(alph, FALSE, g); /* takes the average pseudocount */
if (SUBST_MATRIX_DEBUG) printf("\n");
return(g); /* return the pseudocounts */
} /* get_pseudocount_freqs */
/***********************************************************************
* Make all the elements of an array positive by adding a constant to
* each.
***********************************************************************/
void all_positive
(ARRAY_T* array)
{
int i_item;
int num_items;
ATYPE min;
check_null_array(array);
/* Find the minimum value. */
min = get_array_item(0, array);
num_items = get_array_length(array);
for (i_item = 0; i_item < num_items; i_item++) {
if (get_array_item(i_item, array) < min) {
min = get_array_item(i_item, array);
}
}
/* If there are negative elements ... */
if (min < 0.0) {
/* ... then subtract the minimum from all elements. */
for (i_item = 0; i_item < num_items; i_item++) {
incr_array_item(i_item, -min, array);
}
}
}
/*****************************************************************************
* MEME > model > /background_frequencies
****************************************************************************/
void mxml_end_background(void *ctx) {
CTX_T *data;
int i;
bool error;
double sum, delta;
data = (CTX_T*)ctx;
sum = 0;
error = false;
for (i = 0; i < get_array_length(data->nums); i++) {
if (get_array_item(i, data->nums) == -1) {
local_error(data, "Background frequency was not provided for letter %c.\n", alph_char(data->alph, i));
error = true;
} else {
sum += get_array_item(i, data->nums);
}
}
delta = sum - 1.0;
if (delta < 0) delta = -delta;
if (delta > 0.01) {
local_error(data, "The background frequencies summed to %f but they should sum to 1.0.\n", sum);
error = true;
}
if (error) {
free_array(data->nums);
} else {
data->fscope.background = data->nums;
data->nums = NULL;
}
}
/***********************************************************************
* Determine whether two arrays are equal, within a given bound.
***********************************************************************/
BOOLEAN_T equal_arrays
(ATYPE close_enough,
ARRAY_T* array1,
ARRAY_T* array2)
{
int i_item;
int num_items;
check_null_array(array1);
check_null_array(array2);
/* Verify that the arrays are of the same length. */
if (!check_array_dimensions(FALSE, array1, array2)) {
return(FALSE);
}
/* Check to be sure that each value is the same. */
num_items = get_array_length(array1);
for (i_item = 0; i_item < num_items; i_item++) {
if (!almost_equal(get_array_item(i_item, array1)
- get_array_item(i_item, array2), 0.0, close_enough)) {
return(FALSE);
}
}
return(TRUE);
}
/***********************************************************************
* Mix two arrays in log space.
***********************************************************************/
void mix_log_arrays
(float mixing, /* Percent of array2 that will be retained. */
ARRAY_T* array1,
ARRAY_T* array2)
{
int i_item;
int num_items;
ATYPE mixed_value;
check_null_array(array1);
check_null_array(array2);
/* Verify that the arrays are of the same length. */
check_array_dimensions(TRUE, array1, array2);
/* Verify that we've got a reasonable mixing parameter. */
if ((mixing > 1.0) || (mixing < 0.0)) {
die("Invalid mixing parameter (%g).\n", mixing);
}
num_items = get_array_length(array1);
for (i_item = 0; i_item < num_items; i_item++) {
mixed_value
= LOG_SUM(my_log2(1.0 - mixing) + get_array_item(i_item, array1),
my_log2(mixing) + get_array_item(i_item, array2));
set_array_item(i_item, mixed_value, array2);
}
}
/***********************************************************************
* Compute the complexity of a motif as a number between 0 and 1.
*
* Motif complexity is the average K-L distance between the "motif
* background distribution" and each column of the motif. The motif
* background is just the average distribution of all the columns. The
* K-L distance, which measures the difference between two
* distributions, is the same as the information content:
*
* \sum_i p_i log(p_i/f_i)
*
* This value increases with increasing complexity.
***********************************************************************/
double compute_motif_complexity
(MOTIF_T* a_motif)
{
double return_value;
ARRAY_T* motif_background; // Mean emission distribution.
int num_rows;
int i_row;
int num_cols;
int i_col;
num_cols = get_alph_size(ALPH_SIZE);
num_rows = get_num_rows(a_motif->freqs);
// Compute the mean emission distribution.
motif_background = get_matrix_col_sums(a_motif->freqs);
scalar_mult(1.0 / (double)num_rows, motif_background);
// Compute the K-L distance w.r.t. the background.
return_value = 0;
for (i_row = 0; i_row < num_rows; i_row++) {
ARRAY_T* this_emission = get_matrix_row(i_row, a_motif->freqs);
for (i_col = 0; i_col < num_cols; i_col++) {
ATYPE this_item = get_array_item(i_col, this_emission);
ATYPE background_item = get_array_item(i_col, motif_background);
// Use two logs to avoid handling divide-by-zero as a special case.
return_value += this_item
* (my_log(this_item) - my_log(background_item));
}
}
free_array(motif_background);
return(return_value / (double)num_rows);
}
/***********************************************************************
* Discard motifs that are not connected.
***********************************************************************/
static void throw_out_unused_motifs
(MATRIX_T* transp_freq,
MATRIX_T* spacer_ave,
int* num_motifs,
MOTIF_T* motifs)
{
int i_motif, j_motif;
ARRAY_T* row_sums;
ARRAY_T* col_sums;
// Extract the margins of the transition matrix.
row_sums = get_matrix_row_sums(transp_freq);
col_sums = get_matrix_col_sums(transp_freq);
for (i_motif = 0; i_motif < *num_motifs; i_motif++) {
// Is this row or column empty?
if ((get_array_item(i_motif + 1, row_sums) == 0.0) ||
(get_array_item(i_motif + 1, col_sums) == 0.0)) {
if (verbosity >= NORMAL_VERBOSE) {
fprintf(stderr, "Removing unused motif %s. No occurrences of this motif were found.\n",
get_motif_id(&(motifs[i_motif])));
}
// Remove the row and column from the transition matrix.
remove_matrix_row(i_motif + 1, transp_freq);
remove_matrix_col(i_motif + 1, transp_freq);
assert(get_num_rows(transp_freq) == get_num_cols(transp_freq));
remove_matrix_row(i_motif + 1, spacer_ave);
remove_matrix_col(i_motif + 1, spacer_ave);
assert(get_num_rows(spacer_ave) == get_num_cols(spacer_ave));
// Remove the motif from the array.
for (j_motif = i_motif + 1; j_motif < *num_motifs; j_motif++) {
free_motif(&(motifs[j_motif - 1]));
copy_motif(&(motifs[j_motif]), &(motifs[j_motif - 1]));
}
free_motif(&(motifs[j_motif - 1]));
(*num_motifs)--;
i_motif--;
// Recalculate the row and column sums.
free_array(row_sums);
free_array(col_sums);
row_sums = get_matrix_row_sums(transp_freq);
col_sums = get_matrix_col_sums(transp_freq);
}
}
free_array(row_sums);
free_array(col_sums);
}
KARLIN_INPUT_T *make_karlin_input(
MATRIX_T *matrix, /* scoring matrix */
ARRAY_T *probs /* letter freq distribution */
)
{
int i, j;
double escore;
long lowest, highest;
ARRAY_T *score_probs;
int nscores;
int alen = get_num_rows(matrix); /* size of alphabet */
KARLIN_INPUT_T *karlin_input; /* data to return */
/* find the highest and lowest scores in the scoring matrix */
lowest = 1;
highest = -1;
for (i=0; i<alen; i++) {
for (j=0; j<alen; j++) {
double s = get_matrix_cell(i, j, matrix);
if (s < lowest) lowest = s;
if (s > highest) highest = s;
}
}
if (lowest >= 0) die("Lowest score in scoring matrix must be negative, is %f.", (double)lowest);
if (highest<= 0) die("Highest score in scoring matrix must be positve, is %f.", (double)highest);
/* allocate the array of score probabilities and set to 0 */
nscores = highest - lowest + 1;
score_probs = allocate_array(nscores);
init_array(0, score_probs);
/* compute the probabilities of different scores */
escore = 0;
for (i=0; i<alen; i++) {
for (j=0; j<alen; j++) {
int s = get_matrix_cell(i, j, matrix);
double pi = get_array_item(i, probs);
double pj = get_array_item(j, probs);
double sp = get_array_item(s-lowest, score_probs);
set_array_item(s-lowest, sp + pi*pj, score_probs); /* cumulative prob. of score */
escore += pi*pj*s;
/*printf("i %d j %d s %d pi %f pj %f sp %f escore %f\n",i,j,s, pi, pj, sp, escore);*/
}
}
karlin_input = (KARLIN_INPUT_T *)mm_malloc(sizeof(KARLIN_INPUT_T));
karlin_input->low = lowest;
karlin_input->high = highest;
karlin_input->escore = escore;
karlin_input->prob = score_probs;
return(karlin_input);
} /* make_karlin_input */
/***********************************************************************
* Convert array by compute the average of complementary dna frequencies.
*
* Assumes DNA alphabet in order ACGT.
***********************************************************************/
void balance_complementary_dna_freqs
(ARRAY_T* source)
{
double at = (get_array_item(0, source)+get_array_item(3, source))/2.0;
double cg = (get_array_item(1, source)+get_array_item(2, source))/2.0;
set_array_item(0, at, source); // A -> T
set_array_item(1, cg, source); // C -> G
set_array_item(2, cg, source); // G -> C
set_array_item(3, at, source); // T -> A
fill_in_ambiguous_chars(FALSE, source);
}
/***********************************************************************
* Convert array by compute the average of complementary dna frequencies.
*
* Apparently no-one uses this.
*
* Assumes DNA alphabet in order ACGT.
***********************************************************************/
void balance_complementary_dna_freqs
(ARRAY_T* source)
{
double at = (get_array_item(0, source)+get_array_item(3, source))/2.0;
double cg = (get_array_item(1, source)+get_array_item(2, source))/2.0;
set_array_item(0, at, source); // A -> T
set_array_item(1, cg, source); // C -> G
set_array_item(2, cg, source); // G -> C
set_array_item(3, at, source); // T -> A
calc_ambigs(DNA_ALPH, FALSE, source);
}
/**************************************************************************
* get_scaled_lo_prior_dist
*
* Takes a scaled distribution of priors and creates a scaled distribution of
* log odds priors. The parameters for the scaling of the input priors are
* in the PRIOR_DIST_T data structure. The output distribution of log odss
* priors are scaled to be in the same range as the PSSM log odds using
* the input parameters pssm_range, pssm_scale, and pssm_offset.
*
* Special handling is required for a uniform distribution of priors.
* In that case the max_prior == min_prior, and the distribution only
* contains one bin.
*
* Returns a new array containing the scaled log odds priors
**************************************************************************/
ARRAY_T *get_scaled_lo_prior_dist(
PRIOR_DIST_T *prior_dist,
double alpha,
int pssm_range,
double pssm_scale,
double pssm_offset
) {
assert(prior_dist != NULL);
// Alocate enought space for elements in [0 ... pssm_range]
ARRAY_T *scaled_lo_prior_dist = allocate_array(pssm_range + 1);
if (prior_dist != NULL) {
ARRAY_T *dist_array = get_prior_dist_array(prior_dist);
int len_prior_dist = get_array_length(dist_array);
double max_prior = get_prior_dist_maximum(prior_dist);
double min_prior = get_prior_dist_minimum(prior_dist);
double prior_dist_scale = get_prior_dist_scale(prior_dist);
double prior_dist_offset = get_prior_dist_offset(prior_dist);
init_array(0.0L, scaled_lo_prior_dist);
if (max_prior == min_prior) {
// Special case for uniform priors
double value = 1.0;
double lo_prior = my_log2(alpha * max_prior / (1.0L - (alpha * max_prior)));
// Convert lo_prior to PSSM scale
int scaled_index = raw_to_scaled(lo_prior, 1.0L, pssm_scale, pssm_offset);
set_array_item(scaled_index, value, scaled_lo_prior_dist);
}
else {
int prior_index = 0;
for (prior_index = 0; prior_index < len_prior_dist; ++prior_index) {
double value = get_array_item(prior_index, dist_array);
// Convert index giving scaled prior to raw prior.
double scaled_prior = ((double) prior_index) + 0.5L;
double prior \
= scaled_to_raw(scaled_prior, 1, prior_dist_scale, prior_dist_offset);
double lo_prior = my_log2(alpha * prior / (1.0L - (alpha * prior)));
// Scale raw lo_prior using parameters from PSSM.
int scaled_index = raw_to_scaled(lo_prior, 1.0L, pssm_scale, pssm_offset);
if (scaled_index < pssm_range) {
double old_value = get_array_item(scaled_index, scaled_lo_prior_dist);
set_array_item(scaled_index, value + old_value, scaled_lo_prior_dist);
}
}
}
}
return scaled_lo_prior_dist;
}
/***********************************************************************
* Compute the reverse complement of one DNA frequency distribution.
*
* Assumes DNA alphabet in order ACGT.
***********************************************************************/
void complement_dna_freqs
(ARRAY_T* source,
ARRAY_T* dest)
{
set_array_item(0, get_array_item(3, source), dest); // A -> T
set_array_item(1, get_array_item(2, source), dest); // C -> G
set_array_item(2, get_array_item(1, source), dest); // G -> C
set_array_item(3, get_array_item(0, source), dest); // T -> A
//check if the frequencies have ambiguous characters
//for example meme does not use ambiguous characters
if (get_array_length(source) > 4) {
fill_in_ambiguous_chars(FALSE, dest);
}
}
/***********************************************************************
* Apply a pseudocount to the motif pspm.
***********************************************************************/
void apply_pseudocount_to_motif
(MOTIF_T* motif, ARRAY_T *background, double pseudocount)
{
int pos, letter, len, asize, sites;
double prob, count, total;
ARRAY_T *temp;
// no point in doing work when it makes no difference
if (pseudocount == 0) return;
assert(pseudocount > 0);
// motif dimensions
asize = alph_size(motif->alph, ALPH_SIZE);
len = motif->length;
// create a uniform background if none is given
temp = NULL;
if (background == NULL) {
temp = allocate_array(asize);
get_uniform_frequencies(motif->alph, temp);
background = temp;
}
// calculate the counts
sites = (motif->num_sites > 0 ? motif->num_sites : DEFAULT_SITE_COUNT);
total = sites + pseudocount;
for (pos = 0; pos < len; ++pos) {
for (letter = 0; letter < asize; ++letter) {
prob = get_matrix_cell(pos, letter, motif->freqs);
count = (prob * sites) + (pseudocount * get_array_item(letter, background));
prob = count / total;
set_matrix_cell(pos, letter, prob, motif->freqs);
}
}
if (temp) free_array(temp);
}
/***********************************************************************
* Normalize an array in log space.
***********************************************************************/
void log_normalize
(ATYPE close_enough,
ARRAY_T* array)
{
int i_item;
int num_items;
ATYPE total;
ATYPE this_value;
/* Get the sum of the elements. */
total = log_array_total(array);
/* If the array already sums to zero, don't bother. */
if (almost_equal(total, 0.0, close_enough)) {
return;
}
/* If there's nothing in the array, then return all zeroes. */
if (total < LOG_SMALL) {
init_array(LOG_ZERO, array);
return;
}
num_items = get_array_length(array);
for (i_item = 0; i_item < num_items; i_item++) {
this_value = get_array_item(i_item, array) - total;
/* If this value is small enough, just make it zero. */
if (this_value < LOG_SMALL) {
set_array_item(i_item, LOG_ZERO, array);
} else {
set_array_item(i_item, this_value, array);
}
}
}
/**************************************************************************
* get_min_pvalue
*
* Return the minimum p-value for a given pssm.
*
**************************************************************************/
static double get_min_pvalue(
PSSM_T *pssm // The PSSM.
)
{
int i, j;
int max_score;
int r = pssm->w;
int c = pssm->alphsize;
double min_p_value;
// Get the largest score in each row and sum them.
max_score = 0;
for (i=0; i<r; i++) {
double large = -BIG;
for (j=0; j<c; j++) {
double x = get_matrix_cell(i, j, pssm->matrix);
large = MAX(large, x);
}
max_score += large;
}
min_p_value = get_array_item(max_score, pssm->pv);
return(min_p_value);
} /* get_min_pvalue */
/***********************************************************************
* Divide corresponding elements in two arrays.
***********************************************************************/
void dot_divide
(ARRAY_T* array1,
ARRAY_T* array2)
{
int i_item;
int num_items;
check_null_array(array1);
check_null_array(array2);
check_array_dimensions(TRUE, array1, array2);
num_items = get_array_length(array1);
for (i_item = 0; i_item < num_items; i_item++) {
set_array_item(i_item, get_array_item(i_item, array1) /
get_array_item(i_item, array2), array2);
}
}
/***********************************************************************
* Compute the dot product of two arrays.
***********************************************************************/
double dot_product
(ARRAY_T* array1,
ARRAY_T* array2)
{
int i_item;
int num_items;
double return_value;
check_array_dimensions(TRUE, array1, array2);
num_items = get_array_length(array1);
return_value = 0.0;
for (i_item = 0; i_item < num_items; i_item++) {
return_value += get_array_item(i_item, array1) *
get_array_item(i_item, array2);
}
return(return_value);
}
/***********************************************************************
* Compute the sum of the squares of the differences between two arrays.
***********************************************************************/
ATYPE sum_of_square_diffs
(ARRAY_T* array1,
ARRAY_T* array2)
{
int i_item;
int num_items;
ATYPE diff; /* The difference between two corresponding array values. */
ATYPE total = 0; /* The sum of the squares. */
check_null_array(array1);
check_null_array(array2);
check_array_dimensions(TRUE, array1, array2);
num_items = get_array_length(array1);
for (i_item = 0; i_item < num_items; i_item++) {
diff = get_array_item(i_item, array1) - get_array_item(i_item, array2);
total += diff * diff;
}
return(total);
}
ATYPE get_array_maximum
(ARRAY_T* array)
{
check_null_array(array);
int num_items = get_array_length(array);
if (num_items == 0) {
die("Attempted to retrieve maximum value from an empty array.\n");
}
ATYPE return_value = get_array_item(0, array);
int i_item;
for (i_item = 1; i_item < num_items; i_item++) {
ATYPE item = get_array_item(i_item, array);
if (item > return_value) {
return_value = item;
}
}
return(return_value);
}
/*
* Replace the elements an array of frequences with the average
* over complementary bases.
*/
void average_freq_with_complement(ALPH_T alph, ARRAY_T *freqs) {
int a_index, t_index, g_index, c_index;
double at_freq, gc_freq;
assert(alph == DNA_ALPH);
a_index = alph_index(alph, 'A');
t_index = alph_index(alph, 'T');
g_index = alph_index(alph, 'G');
c_index = alph_index(alph, 'C');
at_freq = (get_array_item(a_index, freqs) +
get_array_item(t_index, freqs)) / 2.0;
gc_freq = (get_array_item(g_index, freqs) +
get_array_item(c_index, freqs)) / 2.0;
set_array_item(a_index, at_freq, freqs);
set_array_item(t_index, at_freq, freqs);
set_array_item(g_index, gc_freq, freqs);
set_array_item(c_index, gc_freq, freqs);
}
/*
* Take the counts from an ambiguous character and evenly distribute
* them among the corresponding concrete characters.
*
* This function operates in log space.
*/
static void dist_ambig(ALPH_T alph, char ambig, char *concrete_chars,
ARRAY_T* freqs) {
PROB_T ambig_count, concrete_count;
int ambig_index, num_concretes, i, concrete_index;
// Get the count to be distributed.
ambig_index = alph_index(alph, ambig);
ambig_count = get_array_item(ambig_index, freqs);
// Divide it by the number of corresponding concrete characters.
num_concretes = strlen(concrete_chars);
ambig_count -= my_log2((PROB_T)num_concretes);
// Distribute it in equal portions to the given concrete characters.
for (i = 0; i < num_concretes; i++) {
concrete_index = alph_index(alph, concrete_chars[i]);
concrete_count = get_array_item(concrete_index, freqs);
// Add the ambiguous counts.
concrete_count = LOG_SUM(concrete_count, ambig_count);
set_array_item(concrete_index, concrete_count, freqs);
}
// Set the ambiguous count to zero.
set_array_item(ambig_index, LOG_ZERO, freqs);
}
/*************************************************************************
* Test whether a given array is already sorted.
*************************************************************************/
BOOLEAN_T is_sorted
(BOOLEAN_T good_score_is_low,
ARRAY_T* my_array)
{
ATYPE prev_value = get_array_item(0, my_array);
int index = 0;
int length = get_array_length(my_array);
for (index = 1; index < length; index++) {
ATYPE this_value = get_array_item(index, my_array);
if (good_score_is_low) {
if (this_value < prev_value) {
return(FALSE);
}
} else {
if (this_value > prev_value) {
return(FALSE);
}
}
prev_value = this_value;
}
return(TRUE);
}
/*****************************************************************************
* MEME > motifs > motif > probabilities > alphabet_matrix > /alphabet_array
* Check that all letters have a probability and update the current matrix row.
****************************************************************************/
void mxml_end_probability_pos(void *ctx) {
CTX_T *data;
ARRAY_T *pos;
int i;
data = (CTX_T*)ctx;
pos = get_matrix_row(data->current_pos, data->mscope.motif->freqs);
for (i = 0; i < get_array_length(pos); i++) {
if (get_array_item(i, pos) == -1) {
local_error(data, "Probability for letter %c in position %d is missing.\n", alph_char(data->alph, i), i + 1);
}
}
data->current_pos++;
}
void unlog_array
(ARRAY_T* array)
{
int i_item;
int num_items;
check_null_array(array);
num_items = get_array_length(array);
for (i_item = 0; i_item < num_items; i_item++) {
set_array_item(i_item, EXP2(get_array_item(i_item, array)), array);
}
}
/***********************************************************************
* Multiply each element of an array by a scalar value.
***********************************************************************/
void scalar_mult
(ATYPE value,
ARRAY_T* array)
{
int i_item;
int num_items;
check_null_array(array);
num_items = get_array_length(array);
for (i_item = 0; i_item < num_items; i_item++) {
set_array_item(i_item, get_array_item(i_item, array) * value, array);
}
}
/**************************************************************************
* Convert a given array to or from logs.
**************************************************************************/
void convert_to_from_log_array
(BOOLEAN_T to_log,
ARRAY_T* source_array,
ARRAY_T* target_array)
{
int num_items;
int i_item;
ATYPE new_value;
// If the source is null, just return.
if (source_array == NULL)
return;
num_items = get_array_length(source_array);
for (i_item = 0; i_item < num_items; i_item++) {
if (to_log) {
new_value = my_log2(get_array_item(i_item, source_array));
} else {
new_value = EXP2(get_array_item(i_item, source_array));
}
set_array_item(i_item, new_value, target_array);
}
}
/*************************************************************************
* Converts the motif frequency matrix into a odds matrix: taken from old ama-scan.c
*************************************************************************/
void convert_to_odds_matrix(MOTIF_T* motif, ARRAY_T* bg_freqs){
const int asize = alph_size(get_motif_alph(motif), ALPH_SIZE);
int motif_position_index,alph_index;
MATRIX_T *freqs;
freqs = get_motif_freqs(motif);
const int num_motif_positions = get_num_rows(freqs);
for (alph_index=0;alph_index<asize;++alph_index){
double bg_likelihood = get_array_item(alph_index, bg_freqs);
for (motif_position_index=0;motif_position_index<num_motif_positions;++motif_position_index){
freqs->rows[motif_position_index]->items[alph_index] /= bg_likelihood;
}
}
}
MATRIX_T *convert_score_to_target(
MATRIX_T *score, /* score matrix */
ARRAY_T *prob /* letter frequencies */
)
{
int i, j;
KARLIN_INPUT_T *karlin_input;
double lambda, K, H;
MATRIX_T *target; /* target freq. matrix */
int alen = get_num_rows(score); /* alphabet length */
/* make input for karlin() */
karlin_input = make_karlin_input(score, prob);
/* get lambda */
karlin(karlin_input->low, karlin_input->high, karlin_input->prob->items,
&lambda, &K, &H);
/*printf("lambda %f K %f H %f\n", lambda, K, H);*/
/* calculate target frequencies */
target = allocate_matrix(alen, alen);
for (i=0; i<alen; i++) {
for (j=0; j<alen; j++) {
double pi = get_array_item(i, prob);
double pj = get_array_item(j, prob);
double sij = get_matrix_cell(i, j, score);
double f = pi * pj * exp(lambda * sij);
set_matrix_cell(i, j, f, target);
}
}
// Free local dynamic memory.
free_array(karlin_input->prob);
myfree(karlin_input);
return(target);
} /* convert_score_to_target */
/*************************************************************************
* Copies the motif frequency matrix and converts it into a odds matrix
*************************************************************************/
MATRIX_T* create_odds_matrix(MOTIF_T *motif, ARRAY_T* bg_freqs){
const int asize = alph_size(get_motif_alph(motif), ALPH_SIZE);
int pos, aidx;
MATRIX_T *odds;
odds = duplicate_matrix(get_motif_freqs(motif));
const int num_pos = get_num_rows(odds);
for (aidx = 0; aidx < asize; ++aidx) {
double bg_likelihood = get_array_item(aidx, bg_freqs);
for (pos = 0; pos < num_pos; ++pos) {
odds->rows[pos]->items[aidx] /= bg_likelihood;
}
}
return odds;
}