Cluster samples in k clusters

hd_cluster_samples() takes a dataset and returns the clustering of the samples. The user can define the number of clusters or let the function determine the optimal number of clusters using the gap statistic. If the data contain missing values, the function imputes them using the k-nearest neighbors algorithm (k = 5).

Usage

hd_cluster_samples(
  dat,
  distance_method = "euclidean",
  clustering_method = "ward.D2",
  normalize = TRUE,
  k = NULL,
  k_max = 15,
  gap_b = 100,
  seed = 123,
  verbose = FALSE
)

Arguments

dat

An HDAnalyzeR object or a dataset in wide format and sample ID as its first column.

distance_method

The distance method to use. Default is "euclidean".

clustering_method

The clustering method to use. Default is "ward.D2".

normalize

A logical value indicating whether to normalize the data. Z-score normalization is applied using the hd_normalize() function. Default is TRUE.

k

The number of clusters to return. If NULL, the function determines the optimal number of clusters using the gap statistic.

k_max

The maximum number of clusters to test. Default is 15. Valid only if k is NULL.

gap_b

The number of times to repeat the clustering in the gap analysis. Default is 100. Valid only if k is NULL.

seed

The seed to use for reproducibility. Default is 123. Valid only if k is NULL.

verbose

A logical value indicating whether to print the progress of the gap analysis. Default is FALSE. Valid only if k is NULL.

Value

A list with the cluster assignment.

Examples

# Create the HDAnalyzeR object providing the data and metadata
hd_object <- hd_initialize(example_data, example_metadata)

# Clustered data
clustering <- hd_cluster_samples(hd_object, gap_b = 10)
#> Determining optimal number of clusters using gap statistic.
#> Optimal number of clusters:  4

# Access the results
head(clustering[["cluster_res"]])
#> # A tibble: 6 × 2
#>   DAid    Cluster
#>   <chr>     <int>
#> 1 DA00001       1
#> 2 DA00002       2
#> 3 DA00003       3
#> 4 DA00004       4
#> 5 DA00005       1
#> 6 DA00006       2

# Clustered data with defined number of clusters
clustering <- hd_cluster_samples(hd_object, k = 7)
#> Using user-defined number of clusters:  7

# Access the results
head(clustering[["cluster_res"]])
#> # A tibble: 6 × 2
#>   DAid    Cluster
#>   <chr>     <int>
#> 1 DA00001       1
#> 2 DA00002       2
#> 3 DA00003       3
#> 4 DA00004       4
#> 5 DA00005       1
#> 6 DA00006       5