run_stability_analysis {scITD} | R Documentation |
Test stability of a decomposition by subsampling or bootstrapping donors. Note that running this function will replace the decomposition in the project container with one resulting from the tucker parameters entered here.
Description
Test stability of a decomposition by subsampling or bootstrapping donors. Note that running this function will replace the decomposition in the project container with one resulting from the tucker parameters entered here.
Usage
run_stability_analysis(
container,
ranks,
tucker_type = "regular",
rotation_type = "hybrid",
subset_type = "subset",
sub_prop = 0.75,
n_iterations = 100,
ncores = container$experiment_params$ncores
)
Arguments
container |
environment Project container that stores sub-containers for each cell type as well as results and plots from all analyses |
ranks |
numeric The number of donor, gene, and cell type ranks, respectively, to decompose to using Tucker decomposition. |
tucker_type |
character The 'regular' type is the only one implemented with sparsity constraints (default='regular') |
rotation_type |
character Set to 'hybrid' to optimize loadings via our hybrid method (see paper for details). Set to 'ica_dsc' to perform ICA rotation on resulting donor factor matrix. Set to 'ica_lds' to optimize loadings by the ICA rotation. (default='hybrid') |
subset_type |
character Set to either 'subset' or 'bootstrap' (default='subset') |
sub_prop |
numeric The proportion of donors to keep when using subset_type='subset' (default=.75) |
n_iterations |
numeric The number of iterations to perform (default=100) |
ncores |
numeric The number of cores to use (default=container$experiment_params$ncores) |
Value
The project container with the donor scores stability plot in container$plots$stability_plot_dsc and the loadings stability plot in container$plots$stability_plot_lds
Examples
test_container <- run_stability_analysis(test_container, ranks=c(2,4),
tucker_type='regular', rotation_type='hybrid', subset_type='subset',
sub_prop=0.75, n_iterations=5, ncores=1)