/**
* @param set
* @param restrictionSet
* @return intersection of set (ConciseSet) and restrictionSet (if restrictionSet non-null &
* non-empty); otherwise return set
*/
private static ExtendedSet<Integer> intersect(
final ExtendedSet<Integer> set, final Set<Integer> restrictionSet) {
if (restrictionSet != null && !restrictionSet.isEmpty()) {
int prevSize = set.size();
FastSet intersection = new FastSet(set);
intersection.retainAll(restrictionSet);
log.debug(
prevSize != 0
? ("Size saving by retainAll = "
+ (((prevSize - intersection.size()) * 100) / prevSize))
+ "%"
: "");
return intersection;
}
return set;
}
/**
* The core scoring method for statistics queries
*
* @param statisticsQuery query to be peformed on statisticsStorage
* @param statisticsStorage core data for Statistics qeries
* @param scoringExps an out parameter.
* <p>- If null, experiment counts result of statisticsQuery should be returned. if - If
* non-null, it serves as a flag that an optimised statisticsQuery should be performed to just
* collect Experiments for which non-zero counts exist for Statistics query. A typical call
* scenario in this case is just one efv per statisticsQuery, in which we can both: 1. check
* if the efv Attribute itself is a scoring one 2. map this Attribute and Experimeants in
* scoringExps to efo terms - via the reverse mapping efv-experiment-> efo term in EfoIndex
* (c.f. atlasStatisticsQueryService.getScoringAttributesForGenes())
* @return Multiset of aggregated experiment counts, where the set of scores genes is intersected
* across statisticsQuery.getConditions(), and union-ed across attributes within each
* condition in statisticsQuery.getConditions().
*/
public static Multiset<Integer> scoreQuery(
StatisticsQueryCondition statisticsQuery,
final StatisticsStorage statisticsStorage,
Set<ExperimentInfo> scoringExps) {
// gatherScoringExpsOnly -> experiment counts should be calculated for statisticsQuery
// !gatherScoringExpsOnly -> scoring experiments should be collected (into scoringExps) only
boolean gatherScoringExpsOnly = scoringExps != null;
Set<StatisticsQueryOrConditions<StatisticsQueryCondition>> andStatisticsQueryConditions =
statisticsQuery.getConditions();
Multiset<Integer> results = null;
if (andStatisticsQueryConditions.isEmpty()) { // End of recursion
Set<Integer> bioEntityIdRestrictionSet = statisticsQuery.getBioEntityIdRestrictionSet();
Set<EfAttribute> attributes = statisticsQuery.getAttributes();
if (attributes.isEmpty()) {
// No attributes were provided - we have to use pre-computed scores across all attributes
Multiset<Integer> scoresAcrossAllEfos =
statisticsStorage.getScoresAcrossAllEfos(statisticsQuery.getStatisticsType());
results = intersect(scoresAcrossAllEfos, bioEntityIdRestrictionSet);
} else {
results = HashMultiset.create();
setQueryExperiments(statisticsQuery, statisticsStorage);
// For each experiment in the query, traverse through all attributes and add all gene
// indexes into one ConciseSet. This way a gene can score
// only once for a single experiment - across all OR attributes in this query. Once all
// attributes have been traversed for a single experiment,
// add ConciseSet to Multiset results
for (ExperimentInfo exp : statisticsQuery.getExperiments()) {
FastSet statsForExperiment = new FastSet();
for (EfAttribute attr : attributes) {
Map<ExperimentInfo, ConciseSet> expsToStats =
getStatisticsForAttribute(
statisticsQuery.getStatisticsType(), attr, statisticsStorage);
if (expsToStats != null) {
if (expsToStats.isEmpty()) {
log.debug(
"Failed to retrieve stats for stat: "
+ statisticsQuery.getStatisticsType()
+ " and attr: "
+ attr);
} else {
if (expsToStats.get(exp) != null) {
if (!gatherScoringExpsOnly) {
statsForExperiment.addAll(
intersect(expsToStats.get(exp), bioEntityIdRestrictionSet));
} else if (containsAtLeastOne(expsToStats.get(exp), bioEntityIdRestrictionSet)) {
// exp contains at least one non-zero score for at least one gene index in
// bioEntityIdRestrictionSet -> add it to scoringExps
scoringExps.add(exp);
}
} else {
log.debug(
"Failed to retrieve stats for stat: "
+ statisticsQuery.getStatisticsType()
+ " exp: "
+ exp.getAccession()
+ " and attr: "
+ attr);
}
}
}
}
if (!gatherScoringExpsOnly) {
results.addAll(statsForExperiment);
}
}
}
} else {
// run over all AND conditions, do "OR" inside (cf. scoreOrStatisticsQueryConditions()) ,
// "AND"'ing over the whole thing
for (StatisticsQueryOrConditions<StatisticsQueryCondition> orConditions :
andStatisticsQueryConditions) {
// Pass gene restriction set down to orConditions
orConditions.setGeneRestrictionSet(statisticsQuery.getBioEntityIdRestrictionSet());
// process OR conditions
Multiset<Integer> condGenes =
getScoresForOrConditions(orConditions, statisticsStorage, scoringExps);
if (results == null) results = condGenes;
else {
Iterator<Multiset.Entry<Integer>> resultGenes = results.entrySet().iterator();
while (resultGenes.hasNext()) {
Multiset.Entry<Integer> entry = resultGenes.next();
if (!condGenes.contains(
entry.getElement())) // AND operation between different top query conditions
resultGenes.remove();
else
// for all gene ids belonging to intersection of all conditions seen so far, we
// accumulate experiment counts
results.setCount(
entry.getElement(), entry.getCount() + condGenes.count(entry.getElement()));
}
}
}
}
if (results == null) {
results = HashMultiset.create();
}
return results;
}