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Find Clusters in Feature Data

Source: R/clustering.R
find_clusters.Rd

Performs cluster analysis on feature data using shared nearest neighbor (SNN) clustering.

Usage

find_clusters(x, ...)

# Default S3 method
find_clusters(
  x,
  metadata_cols,
  k.param = 20,
  prune.SNN = 1/15,
  n.pcs = 20,
  resolution = 0.2,
  n.start = 10,
  verbose = TRUE,
  ...
)

# S3 method for class 'Sap'
find_clusters(
  x,
  segment_type = c("motifs", "syllables", "segments"),
  data_type = c("spectral_feature", "spectrogram"),
  label = NULL,
  k.param = 20,
  prune.SNN = 1/15,
  n.pcs = NULL,
  resolution = NULL,
  n.start = 10,
  verbose = TRUE,
  ...
)

Arguments

x

An object to analyze, either a data frame or SAP object

...

Additional arguments passed to specific methods

metadata_cols

For default method: Column indices for metadata

k.param

Number of nearest neighbors (default: 20)

prune.SNN

Pruning threshold for SNN graph (default: 1/15)

n.pcs

Number of principal components to use

resolution

Resolution parameter for clustering

n.start

Number of random starts (default: 10)

verbose

Whether to print progress messages (default: TRUE)

segment_type

For SAP objects: Type of segments ('motifs', 'syllables', 'segments')

data_type

For SAP objects: Type of feature data ('spectral_feature', "spectrogram")

label

For SAP objects: Specific label to filter data

Value

For default method: Data frame with metadata columns and cluster assignments For SAP objects: Updated object with clustering results in features slot

Details

For feature data frames:

  • Separates metadata and feature columns

  • Finds nearest neighbors using PCA

  • Constructs SNN graph

  • Performs community detection

For SAP objects:

  • Supports multiple segment types

  • Optional label filtering

  • Stores results in features slot

  • Updates feature embeddings

The clustering approach is similar to that used in Seurat V3, implementing:

  • PCA-based neighbor finding

  • SNN graph construction

  • Louvain community detection

See also

run_umap for visualization of clusters

Examples

if (FALSE) { # \dontrun{
# Basic clustering of feature data
clusters <- find_clusters(features,
                         metadata_cols = c(1:5))

# Clustering with custom parameters
clusters <- find_clusters(features,
                         metadata_cols = c(1:5),
                         k.param = 30,
                         resolution = 0.3)

# Cluster SAP object features
sap_obj <- find_clusters(sap_object,
                        segment_type = "motifs",
                        data_type = "spectral_feature")

# Label-specific clustering
sap_obj <- find_clusters(sap_object,
                        segment_type = "syllables",
                        label = "a",
                        resolution = 0.4)
} # }