Retrieves or sets parameters used for filtering, normalization, and prediciton of compartments.

defaultHiCDOCParameters

parameters(object)

parameters(object) <- value

Arguments

object

A HiCDOCDataSet.

value

a named list containing the names and valued of the parameters to change (see Details).

Format

An object of class list of length 7.

Details

A HiCDOCDataSet's parameters are automatically set to default values retrieved from defaultHiCDOCParameters. They are accessed by filtering, normalization, and compartment detection functions. If those functions are called with custom arguments, the object's parameters are updated to record the actual parameters used. If the object's parameters are customized before calling the functions, the custom parameters will be used.

All parameters are listed here:

smallChromosomeThreshold

The minimum length (number of positions) for a chromosome to be kept when filtering with filterSmallChromosomes. Defaults to defaultHiCDOCParameters$smallChromosomeThreshold = 100.

sparseReplicateThreshold

The minimum percentage of non-zero interactions for a chromosome replicate to be kept when filtering with filterSparseReplicates. If a chromosome replicate's percentage of non-zero interactions is lower than this value, it is removed. Defaults to defaultHiCDOCParameters$smallChromosomeThreshold = 30

weakPositionThreshold

The minimum average interaction for a position to be kept when filtering with filterWeakPositions. If a position's average interaction with the entire chromosome is lower than this value in any of the replicates, it is removed from all replicates and conditions. Defaults to defaultHiCDOCParameters$smallChromosomeThreshold = 1.

loessSampleSize

The number of positions used as a sample to estimate the effect of distance on proportion of interactions when normalizing with normalizeDistanceEffect Defaults to defaultHiCDOCParameters$loessSampleSize = 20000.

kMeansDelta

The convergence stop criterion for the clustering when detecting compartments with detectCompartments. When the centroids' distances between two iterations is lower than this value, the clustering stops. Defaults to defaultHiCDOCParameters$kMeansDelta = 0.0001.

kMeansIterations

The maximum number of iterations during clustering when detecting compartments with detectCompartments. Defaults to defaultHiCDOCParameters$kMeansIterations = 50.

kMeansRestarts

The amount of times the clustering is restarted when detecting compartments with detectCompartments. For each restart, the clustering iterates until convergence or reaching the maximum number of iterations. The clustering that minimizes inner-cluster variance is selected. Defaults to defaultHiCDOCParameters$kMeansRestarts = 20.

Functions

Examples

data(exampleHiCDOCDataSet) # Retrieve parameters parameters(exampleHiCDOCDataSet)
#> $smallChromosomeThreshold #> [1] 100 #> #> $sparseReplicateThreshold #> [1] 0.3 #> #> $weakPositionThreshold #> [1] 1 #> #> $loessSampleSize #> [1] 20000 #> #> $kMeansDelta #> [1] 1e-04 #> #> $kMeansIterations #> [1] 50 #> #> $kMeansRestarts #> [1] 20 #>
# Set parameters parameters(exampleHiCDOCDataSet) <- list("smallChromosomeThreshold" = 50) parameters(exampleHiCDOCDataSet) <- list( "weakPositionThreshold" = 10, "kMeansRestarts" = 30 )