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 void setRandomWalkContributionGeneWise(Matrix W) throws IOException {
// 0. get matrix A
this.logger.showTimedMessage("Getting matrix A");
Matrix A = this.getMatrixA();
// 1. multiply both matrices.
this.logger.showTimedMessage("Getting matrix B");
SparseMatrix W_ = W.getSparseMatrix(this.leafIndices, this.allIndices);
this.logger.showMessage(
"Matrix W_ computed. % of sparseness = " + W_.getSparsenessPercentage());
Matrix B = W_.times(A); // TODO: optimize this, A is always very sparse
this.logger.showMessage("Matrix B computed. % of sparseness = " + B.getSparsenessPercentage());
// 2. calculate the RWC
// 2.0 traverse all the products.
// set the value for all eht rows for this column and this row
// for RWC column_index == row_index
this.logger.showTimedMessage("Computing RWC matrix as succesive Jaccard indexes");
final int N = this.RWC.getRowDimension(), M = this.RWC.getColumnDimension();
if (this.weightedJaccard) {
this.logger.showTimedMessage("Jaccard index, _with_ IC");
// precomputing IC for each leaf
final float invMaxAnnot = 1.0f / (float) this.maxNumberOfAnnotations;
float IC[] = new float[this.leafIndices.length];
for (int i = 0; i < this.leafIndices.length; i++) {
// we need to fetch the information content of the nodes if we use weighted jaccard.
IC[i] =
(float)
-Math.log(
this.numAnnotations.get(this.gotermIdByIndex.get(this.leafIndices[i]))
* invMaxAnnot);
}
for (int i = 0; i < N; i++) {
float column_i[] = B.getColumn(i);
for (int j = i; j < M; j++) {
float jaccardIndex = this.getJaccardIndexWithIC(column_i, B.getColumn(j), IC);
this.RWC.set(i, j, jaccardIndex);
this.RWC.set(j, i, jaccardIndex);
}
}
} else {
this.logger.showTimedMessage("Jaccard index, _without_ IC");
float sums[] = new float[N];
for (int i = 0; i < N; i++) {
float sum = 0.0f;
for (float val : B.getColumn(i)) {
sum += val;
}
sums[i] = sum;
}
for (int i = 0; i < N; i++) {
float column_i[] = B.getColumn(i);
for (int j = i; j < M; j++) {
float jaccardIndex =
this.getJaccardIndexWithoutIC(column_i, B.getColumn(j), sums[i], sums[j]);
this.RWC.set(i, j, jaccardIndex);
this.RWC.set(j, i, jaccardIndex);
}
}
}
this.logger.showTimedMessage("RWC set!");
}
