Get clusters of elements (e.g., samples or transcripts)

cluster_elements() takes as input A `tbl` (with at least three columns for sample, feature and transcript abundance) or `SummarizedExperiment` (more convenient if abstracted to tibble with library(tidySummarizedExperiment)) and identify clusters in the data.

cluster_elements(
  .data,
  .element = NULL,
  .feature = NULL,
  .abundance = NULL,
  method,
  of_samples = TRUE,
  transform = log1p,
  action = "add",
  ...,
  log_transform = NULL
)

# S4 method for class 'spec_tbl_df'
cluster_elements(
  .data,
  .element = NULL,
  .feature = NULL,
  .abundance = NULL,
  method,
  of_samples = TRUE,
  transform = log1p,
  action = "add",
  ...,
  log_transform = NULL
)

# S4 method for class 'tbl_df'
cluster_elements(
  .data,
  .element = NULL,
  .feature = NULL,
  .abundance = NULL,
  method,
  of_samples = TRUE,
  transform = log1p,
  action = "add",
  ...,
  log_transform = NULL
)

# S4 method for class 'tidybulk'
cluster_elements(
  .data,
  .element = NULL,
  .feature = NULL,
  .abundance = NULL,
  method,
  of_samples = TRUE,
  transform = log1p,
  action = "add",
  ...,
  log_transform = NULL
)

# S4 method for class 'SummarizedExperiment'
cluster_elements(
  .data,
  .element = NULL,
  .feature = NULL,
  .abundance = NULL,
  method,
  of_samples = TRUE,
  transform = log1p,
  action = "add",
  ...,
  log_transform = NULL
)

# S4 method for class 'RangedSummarizedExperiment'
cluster_elements(
  .data,
  .element = NULL,
  .feature = NULL,
  .abundance = NULL,
  method,
  of_samples = TRUE,
  transform = log1p,
  action = "add",
  ...,
  log_transform = NULL
)

Arguments

.data: A `tbl` (with at least three columns for sample, feature and transcript abundance) or `SummarizedExperiment` (more convenient if abstracted to tibble with library(tidySummarizedExperiment))
.element: The name of the element column (normally samples).
.feature: The name of the feature column (normally transcripts/genes)
.abundance: The name of the column including the numerical value the clustering is based on (normally transcript abundance)
method: A character string. The cluster algorithm to use, at the moment k-means is the only algorithm included.
of_samples: A boolean. In case the input is a tidybulk object, it indicates Whether the element column will be sample or transcript column
transform: A function that will tranform the counts, by default it is log1p for RNA sequencing data, but for avoinding tranformation you can use identity
action: A character string. Whether to join the new information to the input tbl (add), or just get the non-redundant tbl with the new information (get).
...: Further parameters passed to the function kmeans
log_transform: DEPRECATED - A boolean, whether the value should be log-transformed (e.g., TRUE for RNA sequencing data)

Value

A tbl object with additional columns with cluster labels

A `SummarizedExperiment` object

Details

`r lifecycle::badge("maturing")`

identifies clusters in the data, normally of samples. This function returns a tibble with additional columns for the cluster annotation. At the moment only k-means (DOI: 10.2307/2346830) and SNN clustering (DOI:10.1016/j.cell.2019.05.031) is supported, the plan is to introduce more clustering methods.

Underlying method for kmeans do.call(kmeans(.data, iter.max = 1000, ...)

Underlying method for SNN .data Seurat::CreateSeuratObject() Seurat::ScaleData(display.progress = TRUE,num.cores = 4, do.par = TRUE) Seurat::FindVariableFeatures(selection.method = "vst") Seurat::RunPCA(npcs = 30) Seurat::FindNeighbors() Seurat::FindClusters(method = "igraph", ...)

Examples



    cluster_elements(tidybulk::se_mini,  centers = 2, method="kmeans")
#> Warning: tidybulk says: highly abundant transcripts were not identified (i.e. identify_abundant()) or filtered (i.e., keep_abundant), therefore this operation will be performed on unfiltered data. In rare occasions this could be wanted. In standard whole-transcriptome workflows is generally unwanted.
#> class: SummarizedExperiment 
#> dim: 527 5 
#> metadata(0):
#> assays(1): count
#> rownames(527): ABCB4 ABCB9 ... ZNF324 ZNF442
#> rowData names(1): entrez
#> colnames(5): SRR1740034 SRR1740035 SRR1740043 SRR1740058 SRR1740067
#> colData names(6): Cell.type time ... dead cluster_kmeans