DIscBIO (version 1.2.2)

Exprmclust: Performing Model-based clustering on expression values

Description

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.

Usage

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

Value

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.

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