get_resolution_importance {ClustAssess}

Evaluate Stability Across Resolution, Number of Neighbors, and Graph Type

Description

Perform a grid search over the resolution, number of neighbors and graph type.

Usage

get_resolution_importance(
  embedding,
  resolution,
  n_neigh,
  n_repetitions = 100,
  seed_sequence = NULL,
  clustering_method = 4,
  graph_type = 0,
  object_name = NULL,
  ecs_thresh = 1,
  ncores = 1
)

Arguments

embedding

The base embedding for the graph construction.

resolution

A sequence of resolution values.

n_neigh

A value or a sequence of number of neighbors used for graph construction.

n_repetitions

The number of repetitions of applying the pipeline with different seeds; ignored if seed_sequence is provided by the user.

seed_sequence

A custom seed sequence; if the value is NULL, the sequence will be built starting from 1 with a step of 100.

clustering_method

An index or a list of indexes indicating which community detection algorithm will be used: Louvain (1), Louvain refined (2), SLM (3) or Leiden (4). More details can be found in the Seurat's FindClusters function.

graph_type

Argument indicating whether the graph should be unweighted (0), weighted (1) or both (2).

object_name

User specified string that uniquely describes the embedding characteristics.

ecs_thresh

The ECS threshold used for merging similar clusterings.

ncores

The number of parallel R instances that will run the code. If the value is set to 1, the code will be run sequentially.

Value

A list having two fields:

• split_by_resolution: A five-level list. The hierarchy is as follows:

  • the configuration name: concatenation between the object name provided by the user, the number of neighbors, the graph type and the clustering method

  • the resolution value \gamma

  • the number of clusters k that can be obtained using the specified resolution

  • the partitions obtained with resolution \gamma and have k clusters

  • the structure of a partitions, which consists in having a mb field with the flat membership vector, freq denoting its frequency and seed, that is the seed used to obtain this partition in this configuration.

• split_by_k: has a similar structure, but the resolution level is removed. The partitions obtained in a configuration with the same number of clusters will be merged into the same list.

Examples

set.seed(2021)
# create an artificial expression matrix
expr_matrix = matrix(runif(500*10), nrow = 500)

# get the PCA embedding of the data
pca_embedding = irlba::irlba(expr_matrix, nv = 2)
pca_embedding = pca_embedding$u %*% diag(pca_embedding$d)
rownames(pca_embedding) = as.character(1:500)

# run the function on the pca embedding
resolution_result = get_resolution_importance(embedding = pca_embedding,
   resolution = c(0.8, 1),
   n_neigh = c(5, 7),
   n_repetitions = 5,
   clustering_method = 1,
   graph_type = 2,
   object_name = "name_example")

plot_k_resolution_corresp(resolution_result)