PomaClust: Cluster Analysis

In POMA: User-friendly Workflow for Pre-processing and Statistical Analysis of Mass Spectrometry Data

Description

This function performs a classical multidimensional scaling (MDS) using all features in the data and computes a cluster analysis for k clusters. Then, the calculated clusters will be represented on a MDS plot.

Usage

PomaClust(
  data,
  method = "euclidean",
  k = NA,
  k_max = 15,
  show_clusters = TRUE,
  labels = FALSE,
  show_group = FALSE
)

Arguments

data	A MSnSet object. First pData column must be the subject group/type.
method	Distance measure method to perform MDS. Options are "euclidean", "maximum", "manhattan", "canberra" and "minkowski". See ?dist().
k	Number of clusters (default is NA). The optimum number of clusters will be used by default.
k_max	Number of clusters among which the optimal one will be selected.
show_clusters	Logical indicating if clusters should be plotted or not. If this parameter is set to FALSE the resultant plot will be a classical 2-dimension MDS plot.
labels	Logical indicating if sample names should be plotted or not.
show_group	Logical indicating if the original sample group from pData should be plotted instead of sample ID or not. Only works if labels is set to TRUE.

Value

A list with the results.

Author(s)

Pol Castellano-Escuder

Examples

data("st000284")

PomaClust(st000284)

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