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 and 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)
void	svd(Collection<ExpressionExperiment> ees)
SVDValueObject	svd(ExpressionExperiment ee)
SVDValueObject	svdFactorAnalysis(ExpressionExperiment ee) Compare ExperimentalFactors and BioAssay.processingDates to the PCs.
SVDValueObject	svdFactorAnalysis(PrincipalComponentAnalysis pca) Compare ExperimentalFactors and BioAssay.processingDates to the PCs.

Method Detail

retrieveSvd

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

svd

@Secured(value={"GROUP_USER","ACL_SECURABLE_EDIT"})
void svd(Collection<ExpressionExperiment> ees)
                                                      throws SVDException

Throws:

SVDException

svd

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

Throws:

SVDException

getTopLoadedVectors

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

hasPca

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

getImportantFactors

@Secured(value={"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(value={"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(value={"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