Package 'coreCollection'

The coreCollection package

Description

This package can be used to create a CoreCollection object.

Author(s)

Matthijs Brouwer <[email protected]>

References

Odong, T.L. (2012) Quantative methods for sampling of germplasm collections - Getting the best out of molecular markers when creating core collections. PhD diss., Wageningen University and Research, Wageningen, The Netherlands. http://edepot.wur.nl/212422

Jansen, J & Hintum, Theo. (2007) Genetic distance sampling: A novel sampling method for obtaining core collections using genetic distances with an application to cultivated lettuce. TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik. 114. 421-8. 10.1007/s00122-006-0433-9

See Also

- vcfR provides a suite of tools for input and output of variant call format (VCF) files, manipulation of their content and visualization.
- adegenet provides the genlight class for genome-wide SNP data, and includes a method to create a distance matrix.

Other core collection: CoreCollection()

---

The CoreCollection class

Description

R6 class for creating a core collection based on the provided distanceMatrix, required size of the core n and optionally a set of preselected accessions to be included into the core.

Usage

CoreCollection(
  distanceMatrix,
  n,
  preselected = c(),
  coreSelectMethod = "A-NE",
  adjustedGroupMethod = "split",
  algorithm = "randomDescent",
  seed = NULL
)

Arguments

distanceMatrix	A distance matrix; can be either a matrix or a dist
n	The number of items in the core
preselected	An optional list of preselected accessions to be included in the core collection; the provided accessions should occur in the labels or rownames of the provided distanceMatrix
coreSelectMethod	The method for computing core accessions within the groups: A-NE (accession nearest entry), E-NE (entry nearest entry) or E-E (entry entry)
adjustedGroupMethod	The method to handle adjusting groups when multiple preselected accessions occur within a single group: split to just split the initial groups with multiple accessions or recompute to recompute the division of accessions over the groups.
algorithm	Algorithm applied to compute a solution: currently, only randomDescent is available
seed	The seed used when generating the core collection. If no seed is provided, a random seed is chosen and each time the recompute() method is called on the object, a new seed will be used.

Details

Based on a provided distanceMatrix and required number n of accessions within the core, a random set of accessions is created, implicitly dividing the full population into initial groups based on the nearest randomly chosen random accession. If a set of preselected accessions is provided, this initial division is adjusted using the adjustedGroupMethod. Then, using the coreSelectMethod in the algorithm, the core accessions within these groups are calculated, resulting in the final core collection.

Fields

adjustedBasedGroups

A list describing the initial random division of all accessions into groups, adjusted for the set of preselected accessions by using the defined adjustedGroupMethod.

adjustedGroupMethod

The method to handle adjusting groups when multiple preselected accessions occur within a single group.

adjustedSelected

A data.frame representing the intial random selection of accesions, adjusted for the set of preselected accessions by using the defined adjustedGroupMethod, with the accession names as labels and the following columns:

• contains: the (positive) number of accessions that have this accessions as the closest random selected accession

• preselects: the number of these closest accessions that were preselected

• preselected: a boolean indicating if the random selected accession was preselected

• random: a boolean indiciating if the selected accesion was initially randomly chosen or introduced later by the applied adjustedGroupMethod.

algorithm

The applied algorithm to compute the solution.

core

A data.frame representing the core collection with the accession names as labels and in the first and only column a boolean value indicating whether or not the accession was preselected.

coreSelectMethod

The applied method to select the core accessions based on the computed adjustedBasedGroups.

distanceMatrix

The distance matrix; this will allways be a dist object.

n

The required core size

pop

A data.frame representing the whole collection with the accession names as labels and in the first and only column:

• result: a string describing if the accession is marked as other or as included in the core, and if in the core because it was preselected or because of the applied coreSelectMethod.

preselected

The list of preselected accessions.

randomBasedGroups

A list with the initial division into groups based on the initial random selection of accessions described by randomSelected. Each item describes all accessions that have the random selected accesion from the label as the nearest neighbour, including the random selected accession.

randomSelected

A data.frame representing the intial random selection of accesions with the accession names as labels and the following columns:

• contains: the (positive) number of accessions that have this accessions as the closest random selected accession

• preselects: the number of these closest accessions that were preselected

• preselected: a boolean indicating if the random selected accession was preselected

• random: a boolean indiciating if the random selected accesion was randomly chosen. This will always be TRUE for this field, but including this column makes the output comparable with adjustedSelected.

seed

The last applied seed for the randomizer. This will only change when the recompute() method is called and no initial seed is defined.

Methods

alternativeCore(n)

The nth alternative core with n a positive integer. Provides for each accession in the core, if available, the nth nearest accession from within the same group as an alternative.

clone(deep = FALSE)

The default R6Class clone method.

initialize(distanceMatrix, n, preselected, coreSelectMethod, adjustedGroupMethod, algorithm, seed)

Initialisation of the object, is called automatically on creation or recomputing.

measure(coreSelectMethod)

The measure for the provided coreSelectMethod. If no value is provided, the current selected coreSelectMethod is used. The measure is used by the algorithm to compute the core collection.

measures()

A data.frame with the available coreSelectMethods as labels and in the first and only column the measures for these methods.

recompute()

Recompute the core collection: If on initialisation of the object a seed was provided, this same seed will be applied and therefore the same core collection will be created. Otherwise, a new seed is generated, resulting in a new core.

print()

Create a summary of the core collection object, same as summary().

summary()

Create a summary of the core collection object, same as print().

See Also

Other core collection: coreCollection-package

---