Default parameters for Harmony v2 batch correction

Usage

params_sc_harmony_v2(
  k = NULL,
  sigma = 0.1,
  theta = 2,
  lambda = 1,
  block_size = 0.2,
  max_iter_kmeans = 4L,
  max_iter_harmony = 10L,
  epsilon_kmeans = 0.001,
  epsilon_harmony = 0.01,
  window_size = 3L,
  alpha = 0.2,
  tau = 0,
  batch_proportion_cutoff = 1e-05,
  use_dynamic_lambda = FALSE,
  kmeans = list()
)

Arguments

k: Integer or NULL. Number of clusters for k-means clustering. If not provided, it will be automatically determined as min(round(N / 30), 100). Defaults to NULL.
sigma: Numeric vector. Per-cluster diversity weights. Either a single value (broadcast to all clusters) or a vector of length k. Defaults to 0.1.
theta: Numeric vector. Per-variable diversity penalties. Either a single value (broadcast to all variables) or a vector of length equal to the number of batch variables. Defaults to 2.0.
lambda: Numeric vector. Ridge regression penalty for the linear model. Typically a single value that is broadcast to all design matrix columns. Ignored when use_dynamic_lambda = TRUE. Defaults to 1.0.
block_size: Numeric. Fraction of cells to update per block during optimisation (0.0-1.0). Lower values reduce memory usage but increase computation time. Defaults to 0.2.
max_iter_kmeans: Integer. Maximum number of k-means iterations per Harmony round. Defaults to 4L.
max_iter_harmony: Integer. Maximum number of Harmony outer iterations. Defaults to 10L.
epsilon_kmeans: Numeric. Convergence threshold for k-means clustering. Stops when the relative change in cluster assignments falls below this value. Defaults to 0.001.
epsilon_harmony: Numeric. Convergence threshold for Harmony. Stops when the relative change in the objective function falls below this value. Defaults to 0.01.
window_size: Integer. Number of previous iterations to consider when checking convergence. Defaults to 3L.
alpha: Numeric. Scaling factor for dynamic lambda estimation. Must be in (0, 1). Only relevant when use_dynamic_lambda = TRUE. Defaults to 0.2.
tau: Numeric. Scaling factor for theta based on batch size. A value of 0 disables batch-size scaling of theta. Defaults to 0.0.
batch_proportion_cutoff: Numeric. Cutoff for pruning batches with small proportions during ridge regression. Defaults to 1e-05.
use_dynamic_lambda: Boolean. If TRUE, lambda is estimated dynamically per cluster instead of using the fixed lambda value. Defaults to FALSE.
kmeans: List. Optional overrides for the k-means clustering algorithm Possible parameters are "k_means_iter", "k_means_init", "gemm" and "hamerly", see params_kmeans_defaults(). See params_kmeans_defaults() for the available elements. Defaults to list().

Value

A named list with the following elements:

k - Integer or NULL. Number of clusters for k-means clustering. If not provided, it will be automatically determined as min(round(N / 30), 100). Defaults to NULL.
sigma - Numeric vector. Per-cluster diversity weights. Either a single value (broadcast to all clusters) or a vector of length k. Defaults to 0.1.
theta - Numeric vector. Per-variable diversity penalties. Either a single value (broadcast to all variables) or a vector of length equal to the number of batch variables. Defaults to 2.0.
lambda - Numeric vector. Ridge regression penalty for the linear model. Typically a single value that is broadcast to all design matrix columns. Ignored when use_dynamic_lambda = TRUE. Defaults to 1.0.
block_size - Numeric. Fraction of cells to update per block during optimisation (0.0-1.0). Lower values reduce memory usage but increase computation time. Defaults to 0.2.
max_iter_kmeans - Integer. Maximum number of k-means iterations per Harmony round. Defaults to 4L.
max_iter_harmony - Integer. Maximum number of Harmony outer iterations. Defaults to 10L.
epsilon_kmeans - Numeric. Convergence threshold for k-means clustering. Stops when the relative change in cluster assignments falls below this value. Defaults to 0.001.
epsilon_harmony - Numeric. Convergence threshold for Harmony. Stops when the relative change in the objective function falls below this value. Defaults to 0.01.
window_size - Integer. Number of previous iterations to consider when checking convergence. Defaults to 3L.
alpha - Numeric. Scaling factor for dynamic lambda estimation. Must be in (0, 1). Only relevant when use_dynamic_lambda = TRUE. Defaults to 0.2.
tau - Numeric. Scaling factor for theta based on batch size. A value of 0 disables batch-size scaling of theta. Defaults to 0.0.
batch_proportion_cutoff - Numeric. Cutoff for pruning batches with small proportions during ridge regression. Defaults to 1e-05.
use_dynamic_lambda - Boolean. If TRUE, lambda is estimated dynamically per cluster instead of using the fixed lambda value. Defaults to FALSE.
The elements of params_kmeans_defaults(), overridden by kmeans, spliced in at this position.