Clustering wrapper

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

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

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

Arguments

x

A SummarizedExperiment object.

BLUSPARAM

A BlusterParam object specifying the algorithm to use.

assay.type

a single character value for specifying which assay to use for calculation.

assay_name

a single character value for specifying which assay to use for calculation. (Please use assay.type instead. At some point assay_name will be disabled.)

MARGIN

A single character value for specifying whether the transformation is applied sample (column) or feature (row) wise. (By default: MARGIN = "samples")

full

Logical scalar indicating whether the full clustering statistics should be returned for each method.

name

the name to store the result in metadata

clust.col

A single character value indicating the name of the rowData (or colData) where the data will be stored.

...

Additional parameters to use altExps for example

Value

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

or rowData.

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.

Author

Basil Courbayre

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, 
               MARGIN = "samples", 
               HclustParam(metric = "bray", dist.fun = vegan::vegdist))

# Getting the clusters
colData(tse)$clusters
#>      CL3      CC1      SV1  M31Fcsw  M11Fcsw  M31Plmr  M11Plmr  F21Plmr 
#>        1        2        3        4        5        6        7        8 
#>  M31Tong  M11Tong LMEpi24M SLEpi20M   AQC1cm   AQC4cm   AQC7cm      NP2 
#>        9       10       11       12       13       13       13       14 
#>      NP3      NP5  TRRsed1  TRRsed2  TRRsed3     TS28     TS29    Even1 
#>       15       16       17       18       18       19       20       21 
#>    Even2    Even3 
#>       21       21 
#> Levels: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21