Exprmclust function

Performing Model-based clustering on expression values

this function first uses principal component analysis (PCA) to reduce dimensionality of original data. It then performs model-based clustering on the transformed expression values.

Exprmclust(
  object,
  K = 3,
  modelNames = "VVV",
  reduce = TRUE,
  cluster = NULL,
  quiet = FALSE
)

## S4 method for signature 'DISCBIO'
Exprmclust(
  object,
  K = 3,
  modelNames = "VVV",
  reduce = TRUE,
  cluster = NULL,
  quiet = FALSE
)

## S4 method for signature 'data.frame'
Exprmclust(
  object,
  K = 3,
  modelNames = "VVV",
  reduce = TRUE,
  cluster = NULL,
  quiet = FALSE
)

Arguments

• object: DISCBIO class object.
• K: An integer vector specifying all possible cluster numbers. Default is 3.
• modelNames: model to be used in model-based clustering. By default "ellipsoidal, varying volume, shape, and orientation" is used.
• reduce: A logical vector that allows performing the PCA on the expression data. Default is TRUE.
• cluster: A vector showing the ID of cells in the clusters.
• quiet: if TRUE, suppresses intermediary output

Returns

If object is of class DISCBIO, the output is the same object with the MBclusters slot filled. If the object is a data frame, the function returns a named list containing the four objects that together correspond to the contents of the MBclusters slot.