Uses of Class
ubic.gemma.core.analysis.preprocess.OutlierDetails

Packages that use OutlierDetails

Package	Description
ubic.gemma.core.analysis.preprocess	This package contains classes for preprocessing expression data.

Uses of OutlierDetails in ubic.gemma.core.analysis.preprocess

Fields in ubic.gemma.core.analysis.preprocess with type parameters of type OutlierDetails

Modifier and Type	Field	Description
static final Comparator<OutlierDetails>	OutlierDetails.FIRST_QUARTILE_COMPARATOR	Compare outliers by first quartile Note: this comparator imposes orderings that are inconsistent with equals
static final Comparator<OutlierDetails>	OutlierDetails.MEDIAN_COMPARATOR	Compare outliers by median correlation Note: this comparator imposes orderings that are inconsistent with equals

Methods in ubic.gemma.core.analysis.preprocess that return types with arguments of type OutlierDetails

Modifier and Type	Method	Description
Optional<Collection<OutlierDetails>>	OutlierDetectionService.getOutlierDetails(ExpressionExperiment ee)	Obtain existing outlier details from a previous OutlierDetectionService.identifyOutliersByMedianCorrelation(ExpressionExperiment) invocation.
Optional<Collection<OutlierDetails>>	OutlierDetectionServiceImpl.getOutlierDetails(ExpressionExperiment ee)
Collection<OutlierDetails>	OutlierDetectionService.identifyOutliersByMedianCorrelation(DoubleMatrix<BioAssay,BioAssay> cormat)	Identify outliers by sorting by median, then looking for non-overlap of first quartile-second quartile range This is exposed for efficiency in geeq score calculation, use this#identifyOutliers(ExpressionExperiment, boolean, boolean) to have the correlation matrix computed correctly for you.
Collection<OutlierDetails>	OutlierDetectionService.identifyOutliersByMedianCorrelation(ExpressionExperiment ee)	Identify outliers by median correlation for the given dataset.
Collection<OutlierDetails>	OutlierDetectionServiceImpl.identifyOutliersByMedianCorrelation(DoubleMatrix<BioAssay,BioAssay> cormat)
Collection<OutlierDetails>	OutlierDetectionServiceImpl.identifyOutliersByMedianCorrelation(ExpressionExperiment ee)