getClusterLabelsFromSelection {pagoda2}

Assign names to the clusters, given a clustering vector and a set of selections. This function will use a set of pagoda2 cell seletcion to identify the clusters in a a named factor. It is meant to be used to import user defined annotations that are defined as selections into a more formal categorization of cells that are defined by cluster. To help with this the function allows a percent of cells to have been classified in the selections into multiple groups, something which may be the result of the users making wrong selections. The percent of cells allows to be multiselected in any given group is defined by multiClassCutoff. Furthermore the method will assign each cluster to a selection only if the most popular cluster to the next most popular exceed the ambiguous.ratio in terms of cell numbers. If a cluster does not satisfy this condtiion it is not assigned.

Description

Assign names to the clusters, given a clustering vector and a set of selections. This function will use a set of pagoda2 cell seletcion to identify the clusters in a a named factor. It is meant to be used to import user defined annotations that are defined as selections into a more formal categorization of cells that are defined by cluster. To help with this the function allows a percent of cells to have been classified in the selections into multiple groups, something which may be the result of the users making wrong selections. The percent of cells allows to be multiselected in any given group is defined by multiClassCutoff. Furthermore the method will assign each cluster to a selection only if the most popular cluster to the next most popular exceed the ambiguous.ratio in terms of cell numbers. If a cluster does not satisfy this condtiion it is not assigned.

Usage

getClusterLabelsFromSelection(
  clustering,
  selections,
  multiClassCutoff = 0.3,
  ambiguous.ratio = 0.5
)

Arguments

clustering

a named factor of clusters, where every entry is a cell

selections

a pagoda2 selection object

multiClassCutoff

numeric Percent of cells in any one cluster that can be multiassigned (default=0.3)

ambiguous.ratio

numeric Ratio of first and second cell numbers for any cluster to produce a valid clustering (default=0.5)

Value

a data.frame with two columns, one for cluster and one for selections, each cluster appears only once