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

Interface SVDServiceHelper

All Known Implementing Classes:

SVDServiceHelperImpl

public interface SVDServiceHelper

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 Summary

Modifier and Type	Method	Description
Set<ExperimentalFactor>	getImportantFactors(ExpressionExperiment ee, Collection<ExperimentalFactor> experimentalFactors, Double importanceThreshold)
Map<ProbeLoading,DoubleVectorValueObject>	getTopLoadedVectors(ExpressionExperiment ee, int component, int count)
boolean	hasPca(ExpressionExperiment ee)
SVDValueObject	retrieveSvd(ExpressionExperiment ee)
SVDValueObject	svd(ExpressionExperiment ee)
SVDValueObject	svdFactorAnalysis(PrincipalComponentAnalysis pca)	Compare ExperimentalFactors and BioAssay.processingDates to the PCs.
SVDValueObject	svdFactorAnalysis(ExpressionExperiment ee)	Compare ExperimentalFactors and BioAssay.processingDates to the PCs.

Method Detail

retrieveSvd

@Secured({"IS_AUTHENTICATED_ANONYMOUSLY","ACL_SECURABLE_READ"})
SVDValueObject retrieveSvd(ExpressionExperiment ee)

svd

@Secured({"GROUP_USER","ACL_SECURABLE_EDIT"})
SVDValueObject svd(ExpressionExperiment ee)
            throws SVDException

Throws:

SVDException

getTopLoadedVectors

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

hasPca

@Secured({"IS_AUTHENTICATED_ANONYMOUSLY","ACL_SECURABLE_READ"})
boolean hasPca(ExpressionExperiment ee)

getImportantFactors

@Secured({"IS_AUTHENTICATED_ANONYMOUSLY","ACL_SECURABLE_READ"})
Set<ExperimentalFactor> getImportantFactors(ExpressionExperiment ee,
                                            Collection<ExperimentalFactor> experimentalFactors,
                                            Double importanceThreshold)

Parameters:

experimentalFactors - to consider

importanceThreshold - threshold for pvalue of association with factor. Suggested value might be 0.01.

ee - the expression experiment

Returns:

factors which are "significantly" associated with one of the first three PCs

svdFactorAnalysis

@Secured({"IS_AUTHENTICATED_ANONYMOUSLY","ACL_SECURABLE_READ"})
SVDValueObject svdFactorAnalysis(PrincipalComponentAnalysis pca)

Compare ExperimentalFactors and BioAssay.processingDates to the PCs.

Parameters:

pca - PCA

Returns:

SVD VO

svdFactorAnalysis

@Secured({"IS_AUTHENTICATED_ANONYMOUSLY","ACL_SECURABLE_READ"})
SVDValueObject svdFactorAnalysis(ExpressionExperiment ee)

Compare ExperimentalFactors and BioAssay.processingDates to the PCs.

Parameters:

ee - the experiment

Returns:

SVD VO