Exprmclust: Performing Model-based clustering on expression values

In DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics

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 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

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.

DIscBIO documentation built on Nov. 6, 2023, 5:08 p.m.