init | R Documentation |
Initialize the parameters of the mixture model using k-means or hierarchical clustering on the PCs.
init(x, k_init = 20, use = "kmeans", droplets.use = NULL, n_sample = NULL, nstart_init = 30, min_size_init = 10, fixed = NULL, model = "mltn", psc = 1e-10, seedn = 1, threads = threads, verbose = TRUE, ...)
x |
An SCE object. |
k_init |
The number of clusters to initialize, not including fixed background clusters. |
use |
The method to use for clustering, one of either "kmeans" or "hclust." |
droplets.use |
Specify droplet IDs to use for initializing parameters. |
n_sample |
The number of droplets to sample from for initializing parameters. |
nstart_init |
The number of starts to use in k-means for for the initialization. |
min_size_init |
The minimum number of droplets that must belong to an initialized cluster. |
fixed |
A named integer vector that specifies which droplets to
fix to which clusters. If |
model |
The mixture model to assume. Can be either "DM" for a Dirichlet-multinomial or "mltn" for a multinomial. |
psc |
Pseudocount to add to estimation of gene probabilities. |
seedn |
Random seed. |
threads |
Number of threads. |
verbose |
Verbosity. |
... |
Additional parameters to pass to hclust or kmeans. |
An SCE object
# Initialize parameters with default values and multiple threads sce <- init(sce, threads = 8) # Specify initial k of 10 cell type clusters sce <- init(sce, k_init = 10, threads = 8) # Initialize parameters for Dirichlet-multinomial sce <- init(sce, k_init = 10, model = "DM", threads = 8) # Set seedn to NULL for random starts sce <- init(sce, k_init = 10, seedn = NULL, threads = 8) sce <- init(sce, k_init = 10, seedn = NULL, threads = 8) sce <- init(sce, k_init = 10, seedn = NULL, threads = 8) # Specify initial k of 30 cell type clusters and # Only allow clusters with at least 30 droplets sce <- init(sce, k_init = 30, min_size_init = 30, threads = 8)
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