Evaluate Feature Set Stability — get_feature

Evaluate the stability of clusterings obtained based on incremental subsets of a given feature set.

get_feature_stability(
  data_matrix,
  feature_set,
  steps,
  feature_type,
  n_repetitions = 100,
  seed_sequence = NULL,
  graph_reduction_type = "PCA",
  npcs = 30,
  ecs_thresh = 1,
  ncores = 1,
  algorithm = 4,
  ...
)

Arguments

data_matrix: A data matrix having the features on the rows and the observations on the columns.
feature_set: A set of feature names that can be found on the rownames of the data matrix.
steps: Vector containing the sizes of the subsets; negative values will be interpreted as using all features.
feature_type: A name associated to the feature_set.
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.
graph_reduction_type: The graph reduction type, denoting if the graph should be built on either the PCA or the UMAP embedding.
npcs: The number of principal components.
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.
algorithm: An index 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.
...: additional arguments passed to the umap method.

Value

A list having one field associated with a step value. Each step contains a list with three fields:

ecc - the EC-Consistency of the partitions obtained on all repetitions
embedding - one UMAP embedding generated on the feature subset
most_frequent_partition - the most common partition obtained across repetitions

Note

The algorithm assumes that the feature_set is already sorted when performing the subsetting. For example, if the user wants to analyze highly variable feature set, they should provide them sorted by their variability.

Examples

set.seed(2021)
# create an artificial expression matrix
expr_matrix = matrix(c(runif(100*10), runif(100*10, min = 3, max = 4)), nrow = 200, byrow = TRUE)
rownames(expr_matrix) = as.character(1:200)
colnames(expr_matrix) = paste("feature", 1:10)

feature_stability_result = get_feature_stability(data_matrix = t(expr_matrix),
   feature_set = colnames(expr_matrix),
   feature_type = "feature_name",
   steps = 5,
   npcs = 2,
   n_repetitions = 10,
   algorithm = 1,
   # the following parameters are used by the umap function and are not mandatory
   n_neighbors = 3,
   approx_pow = TRUE,
   n_epochs = 0,
   init = "random",
   min_dist = 0.3)
plot_feature_stability_boxplot(feature_stability_result)