addCluster: Clustering wrapper

In microbiome/mia: Microbiome analysis

Clustering wrapper

Description

This function returns a SummarizedExperiment with clustering information in its colData or rowData

Usage

addCluster(
  x,
  BLUSPARAM,
  assay.type = assay_name,
  assay_name = "counts",
  by = MARGIN,
  MARGIN = "rows",
  full = FALSE,
  name = "clusters",
  clust.col = "clusters",
  ...
)

## S4 method for signature 'SummarizedExperiment'
addCluster(
  x,
  BLUSPARAM,
  assay.type = assay_name,
  assay_name = "counts",
  by = MARGIN,
  MARGIN = "rows",
  full = FALSE,
  name = "clusters",
  clust.col = "clusters",
  ...
)

Arguments

x	A SummarizedExperiment object.
BLUSPARAM	A BlusterParam object specifying the algorithm to use.
assay.type	Character scalar. Specifies the name of the assay used in calculation. (Default: "counts")
assay_name	Deprecated. Use assay.type instead.
by	Character scalar. Determines if association is calculated row-wise / for features ('rows') or column-wise / for samples ('cols'). Must be 'rows' or 'cols'.
MARGIN	Deprecated. Use by instead.
full	Logical scalar indicating whether the full clustering statistics should be returned for each method.
name	Character scalar. The name to store the result in metadata
clust.col	Character scalar. Indicates the name of the rowData (or colData) where the data will be stored. (Default: "clusters")
...	Additional parameters to use altExps for example

Details

This is a wrapper for the clusterRows function from the bluster package.

When setting full = TRUE, the clustering information will be stored in the metadata of the object.

By default, clustering is done on the features.

Value

addCluster returns an object of the same type as the x parameter with clustering information named clusters stored in colData or rowData.

Examples

library(bluster)
data(GlobalPatterns, package = "mia")
tse <- GlobalPatterns

# Cluster on rows using Kmeans
tse <- addCluster(tse, KmeansParam(centers = 3))

# Clustering done on the samples using Hclust
tse <- addCluster(tse, 
               by = "samples", 
               HclustParam(metric = "bray", dist.fun = vegan::vegdist))

# Getting the clusters
colData(tse)$clusters

microbiome/mia documentation built on Nov. 4, 2024, 10:21 p.m.