Package ubic.gemma.core.analysis.preprocess.svd

Interface SVDService

All Known Implementing Classes:
SVDServiceImpl
public interface SVDService
Performs Singular value decomposition on experiment data to get eigengenes, and does comparison of those PCs to factors recorded in the experimental design.
Author:
paul

  • Method Details

    • getTopLoadedVectors

      @Secured({"IS_AUTHENTICATED_ANONYMOUSLY","ACL_SECURABLE_READ"}) Map<ProbeLoading,DoubleVectorValueObject> getTopLoadedVectors(ExpressionExperiment ee, int component, int count)

    • hasSvd

      @Secured({"IS_AUTHENTICATED_ANONYMOUSLY","ACL_SECURABLE_READ"}) boolean hasSvd(ExpressionExperiment ee)
      Check if a dataset has a SVD analysis.

    • getSvd

      @Nullable @Secured({"IS_AUTHENTICATED_ANONYMOUSLY","ACL_SECURABLE_READ"}) SVDResult getSvd(ExpressionExperiment ee)
      Obtain the SVD analysis for the given experiment, if it exists.

      If you only need to check if an SVD analysis exists, use hasSvd(ExpressionExperiment) instead as this method may retrieve a large matrix.

    • svd

      @Secured({"GROUP_USER","ACL_SECURABLE_EDIT"}) SVDResult svd(ExpressionExperiment ee) throws SVDException
      Compute and update the SVD analysis fo the given experiment.
      Throws:
      SVDException

    • getSvdFactorAnalysis

      @Secured({"IS_AUTHENTICATED_ANONYMOUSLY","ACL_SECURABLE_READ"}) SVDResult getSvdFactorAnalysis(ExpressionExperiment ee)
      Compare ExperimentalFactors and BioAssay.processingDates to the PCs.
      Parameters:
      ee - the experiment
      Returns:
      SVD VO

    • getSvdFactorAnalysis

      @Secured({"IS_AUTHENTICATED_ANONYMOUSLY","ACL_SECURABLE_READ"}) SVDResult getSvdFactorAnalysis(PrincipalComponentAnalysis pca)
      Compare ExperimentalFactors and BioAssay.processingDates to the PCs.
      Parameters:
      pca - PCA
      Returns:
      SVD VO

    • getImportantFactors

      @Secured({"IS_AUTHENTICATED_ANONYMOUSLY","ACL_SECURABLE_READ"}) Set<ExperimentalFactor> getImportantFactors(ExpressionExperiment ee, Collection<ExperimentalFactor> experimentalFactors, Double importanceThreshold)
      Parameters:
      ee - the expression experiment
      experimentalFactors - to consider
      importanceThreshold - threshold for pvalue of association with factor. Suggested value might be 0.01.
      Returns:
      factors which are "significantly" associated with one of the first three PCs