ClustDist-class: Class '"ClustDist"'

In pRoloc: A unifying bioinformatics framework for spatial proteomics

Description

The ClustDist summaries algorithm information, from running the clustDist function, such as the number of k's tested for the kmeans, and mean and normalised pairwise (Euclidean) distances per numer of component clusters tested.

Objects from the Class

Object of this class are created with the clustDist function.

Slots

k:

Object of class "numeric" storing the number of k clusters tested.

dist:

Object of class "list" storing the list of distance matrices.

term:

Object of class "character" describing GO term name.

id:

Object of class "character" describing the GO term ID.

nrow:

Object of class "numeric" showing the number of instances in the set

clustsz:

Object of class "list" describing the number of instances for each cluster for each k tested

components:

Object of class "vector" storing the class membership of each protein for each k tested.

fcol:

Object of class "character" showing the feature column name in the corresponding MSnSet where the protein set information is stored.

Methods

plot

Plots the kmeans clustering results.

show

Shows the object.

Author(s)

Lisa M Breckels <[e-mail]>

Examples

  showClass("ClustDist")
  
  library('pRolocdata')
  data(dunkley2006)
  par <- setAnnotationParams(inputs =
                    c("Arabidopsis thaliana genes",
                      "Gene stable ID"))
                    
  ## add protein set/annotation information                  
  xx <- addGoAnnotations(dunkley2006, par)
  
  ## filter
  xx <- filterMinMarkers(xx, n = 50)
  xx <- filterMaxMarkers(xx, p = .25)
  
  ## get distances for protein sets
  dd <- clustDist(xx)
  
  ## plot clusters for first 'ClustDist' object 
  ## in the 'ClustDistList'
  plot(dd[[1]], xx)
  
  ## plot distances for all protein sets 
  plot(dd)

pRoloc documentation built on Nov. 8, 2020, 6:26 p.m.