Clustering wrapper — getCluster • mia

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

getCluster(x, ...)

addCluster(x, ...)

# S4 method for class 'SummarizedExperiment'
addCluster(
  x,
  BLUSPARAM,
  assay.type = assay_name,
  assay_name = NULL,
  by = MARGIN,
  MARGIN = "rows",
  name = "clustering",
  clust.col = "cluster",
  full = FALSE,
  ...
)

# S4 method for class 'SingleCellExperiment'
addCluster(
  x,
  BLUSPARAM,
  assay.type = assay_name,
  assay_name = NULL,
  dimred = NULL,
  by = MARGIN,
  MARGIN = "rows",
  name = "clustering",
  clust.col = "cluster",
  full = FALSE,
  ...
)

# S4 method for class 'SummarizedExperiment'
getCluster(
  x,
  BLUSPARAM,
  assay.type = assay_name,
  assay_name = NULL,
  by = MARGIN,
  MARGIN = "rows",
  ...
)

# S4 method for class 'SingleCellExperiment'
getCluster(
  x,
  BLUSPARAM,
  assay.type = assay_name,
  assay_name = NULL,
  dimred = NULL,
  by = MARGIN,
  MARGIN = "rows",
  ...
)

# S4 method for class 'ANY'
getCluster(x, BLUSPARAM, by = MARGIN, MARGIN = "rows", full = FALSE, ...)

Arguments

x: A SummarizedExperiment object.
...: Additional parameters to use altExps for example
BLUSPARAM: A BlusterParam object specifying the algorithm to use.
assay.type: Character scalar. Specifies the name of assay used in calculation.
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.
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")
full: Logical scalar indicating whether the full clustering statistics should be returned for each method.
dimred: Character scalar or integer scalar. Specifies dimension reduction results used in calculation. Either dimred or assay.type must be specified.

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.

Examples

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

# Cluster on rows using Kmeans
tse <- addCluster(
    tse,
    assay.type = "counts",
    BLUSPARAM = KmeansParam(centers = 3)
)

# Clustering done on the samples using Hclust
res <- getCluster(
    tse,
    assay.type = "counts",
    by = "samples",
    BLUSPARAM = HclustParam(metric = "bray", dist.fun = vegan::vegdist)
)
res |> head()
#>     CL3     CC1     SV1 M31Fcsw M11Fcsw M31Plmr 
#>       1       2       3       4       5       6 
#> Levels: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

# Apply clustering to PCA results
library(scater)
#> Loading required package: scuttle
#> Loading required package: ggplot2
tse <- transformAssay(tse, method = "rclr")
tse <- runPCA(tse, assay.type = "rclr")
#> Warning: more singular values/vectors requested than available
#> Warning: You're computing too large a percentage of total singular values, use a standard svd instead.
tse <- addCluster(
    tse,
    dimred = "PCA",
    BLUSPARAM = KmeansParam(centers = 3),
    by = 2
)
tse$cluster |> head()
#>     CL3     CC1     SV1 M31Fcsw M11Fcsw M31Plmr 
#>       1       1       1       2       2       3 
#> Levels: 1 2 3