// **************************************************************************
// **************************************************************************
// UTILITY FUNCTIONS
// **************************************************************************
// **************************************************************************
private int getNumberOfAnnotations(GOTerm currentGoTerm) {
// look for the annotations in the cache, if we find it, return it.
int id = currentGoTerm.getNumericId();
if (this.numAnnotations.containsKey(id)) {
return this.numAnnotations.get(id);
} else { // this number is not cached yet.
int num = this.annotations.countNumberOfGenesForGOTerm(currentGoTerm.getGOid());
this.numAnnotations.put(id, num);
return num;
}
}
private Matrix initialiseTransitionProbabilities() {
// 1. initialise transitionprobabilities
// we use a sparse matrix, so we don't need to put zeroes anywhere.
// Matrix P = new Matrix(this.getNumGoTerms(), this.getNumGoTerms());
Matrix P = new SparseMatrix(this.getNumGoTerms(), this.getNumGoTerms());
// 0.1 check if the node is a leaf, if it is, put a 1 into it.
for (GOTerm currentGoTerm : this.subGoTerms) {
if (this.leafs.contains(currentGoTerm.getNumericId())) {
int leafIndex = this.goTermIndex.get(currentGoTerm.getNumericId());
P.set(leafIndex, leafIndex, 1.0f);
continue;
}
}
for (GOTerm currentGoTerm : this.subGoTerms) {
int N_v = this.getNumberOfAnnotations(currentGoTerm);
int N_vStar = this.getNumberOfAnnotationsStar(currentGoTerm);
this.setTransitionProbabilitiesNonLeaf(currentGoTerm, N_v, N_vStar, P);
}
return P;
}
private void setAllLeafs() {
for (GOTerm term : this.subGoTerms) {
boolean isLeaf = true;
for (String relation : this.relations) {
// is it a leaf for ALL the ontologies? otherwise,
// it is not really a leaf..
if (!this.isLeafISM(term, relation)) {
isLeaf = false;
break;
}
}
if (isLeaf) {
this.leafs.add(term.getNumericId());
}
}
}
private Matrix getMatrixA() throws IOException {
// Matrix A = new Matrix(this.getNumGoTerms(), this.annotations.sizeGenes());
Matrix A = new Matrix(this.getNumGoTerms(), this.RWC.getColumnDimension());
for (GOTerm currentGoTerm : this.subGoTerms) {
// 0. check for NStar value > 0, since this indicates there
// is an annotation
if (this.getNumberOfAnnotationsStar(currentGoTerm) > 0) {
// 0. Get all the genes annotating the current node
// this will hodl the difference of the parent set with the child set
Set<String> uniqueAnnotations =
new HashSet<String>(this.annotations.getProteinsForGOTerm(currentGoTerm.getGOid()));
// 1. Get all the genes annotating the children of the current node
// 1.0 get all the children
Set<GOTerm> children = new HashSet<GOTerm>();
for (String currentRelation : this.relations) {
children.addAll(currentGoTerm.getChildrenForRelation(currentRelation));
}
// 1.1 get all the genes for the children.
Set<String> childrenAnnotations = new HashSet<String>();
for (GOTerm currentChild : children) {
childrenAnnotations.addAll(this.annotations.getProteinsForGOTerm(currentChild.getGOid()));
}
// 2. Traverse the difference and count the number of terms annotating this et of genes.
// 2.0 obtain the difference between the two nodes. that is, the annotations
// that are unique to the current node..
uniqueAnnotations.removeAll(childrenAnnotations);
for (String uniqueAnnotation : uniqueAnnotations) {
// this is a tricky one. We are not sure what "directly" means in the paper.
// but it should not be a very complicated problem to solve.
int count =
this.annotations.getGOTermScoresForProteinId(uniqueAnnotation).keySet().size();
// 0. get the protein id.
A.set(
this.goTermIndex.get(currentGoTerm.getNumericId()),
this.proteinIndices.get(uniqueAnnotation),
1.0f / count);
}
}
}
this.logger.showMessage("Matrix A computed. % of sparseness = " + A.getSparsenessPercentage());
return A;
}
private int getNumberOfAnnotationsStar(GOTerm currentGoTerm) throws IllegalArgumentException {
// look for the annotations in the cache, if we find it, return it.
int id = currentGoTerm.getNumericId();
if (this.numAnnotationsStar.containsKey(id)) {
return this.numAnnotationsStar.get(id);
} else { // this number is not cached yet, so we need to compute it.
// 0. get number of annotations for this node.
int currentGoTermAnnotationCount = this.getNumberOfAnnotations(currentGoTerm);
// 1. get all annotations for the children.
Set<GOTerm> children = new HashSet<GOTerm>();
for (String currentRelation : this.relations) { // again, we consider all relations at once.
children.addAll(currentGoTerm.getChildrenForRelation(currentRelation));
}
Set<String> proteinsAnnotatedToChildren = new HashSet<String>();
for (GOTerm child : children) {
proteinsAnnotatedToChildren.addAll(this.annotations.getProteinsForGOTerm(child.getGOid()));
}
// there is no need to actually make the set difference. Once we
// get the number of annotations in the parent, we should just
// substract to that number the number of unique annotations in the
// children. This would be the number of annotations in the parent
// that belong to none of the children.
int childrenAnnotationCount = proteinsAnnotatedToChildren.size();
int retVal = currentGoTermAnnotationCount - childrenAnnotationCount;
this.numAnnotationsStar.put(id, retVal);
return retVal;
}
}
private void setTransitionProbabilitiesNonLeaf(
GOTerm currentGoTerm, int N_v, int N_vStar, Matrix P) {
// P(v,c) = (1 - N_v* / N_v) N_c/(Sum{u: v->u} N_u_)
// P(v,c) = A * N_C/B
if (N_v == N_vStar) {
// The fact that N_v is equal to N_vStar
// gives a 0.0 in the matrix entries, because of the leading factor
// 1-N_v/N_vStar, which does not to be specified due to the sparse
// matrix used...
return;
}
float A = (1.0f - ((float) N_vStar / (float) N_v));
// we get all the children
// now, this is a tricky one.
// the way I understand it so far is that all relations are the same, that is,
// the random walker will not differentiate between a part_of and a is_a relation
// so, we get ALL the children, adding them all up in a list, disregarding the relations
// completely. This is viable, since the random walker would be able to reach that
// node following either one of the relations.
// children will the contain ALL the children, following every possible relation
// the number of children per node is not very large, ArrayList is O(1) insertion, and O(n)
// so it's ok.
Set<GOTerm> children = new HashSet<GOTerm>();
for (String currentRelation : this.relations) {
children.addAll(currentGoTerm.getChildrenForRelation(currentRelation));
}
// we count all the annotations in the children
// B keesp the sum of N_u for every child.
// this number is the same given eery child of v.
int N_u = 0;
for (GOTerm currentChild : children) {
N_u += this.getNumberOfAnnotations(currentChild);
}
// we need to use two loops. First we compute the total sum of
// annotations in the children, and the we modify the matrix P.
// keep in mind that these nodes are allways non leaf nodes, therefore, B
// should not remain zero unless the children are actually not annotating
// any genes.
// this is why we specify an initial value for B. This will put
// a very low transition probability to that node.
// P(v,c) = A * N_C/B
final float inv_N_u = 1.0f / N_u;
Map<Integer, GOTerm> sortedChildren = new TreeMap<Integer, GOTerm>();
for (GOTerm currentChild : children) {
sortedChildren.put(currentChild.getNumericId(), currentChild);
}
for (int currentChildId : sortedChildren.keySet()) {
GOTerm currentChild = sortedChildren.get(currentChildId);
int N_c = this.getNumberOfAnnotations(currentChild);
// we check whether the annotations are in excess of zero.
// if the node does not have any annotations, then there is no need
// in putting anything in it, since the structure is a sparse matrix.
if (N_c > 0) {
float newEntry = A * (N_c * inv_N_u);
int v = this.goTermIndex.get(currentGoTerm.getNumericId());
int c = this.goTermIndex.get(currentChild.getNumericId());
P.set(c, v, newEntry);
}
}
}
