sc3_prepare: Prepare the 'SingleCellExperiment' object for 'SC3'...
In SC3: Single-Cell Consensus Clustering

Description Usage Arguments Value

This function prepares an object of SingleCellExperiment class for SC3 clustering. It creates and populates the following items of the sc3 slot of the metadata(object):

kmeans_iter_max - the same as the kmeans_iter_max argument.
kmeans_nstart - the same as the kmeans_nstart argument.
n_dim - contains numbers of the number of eigenvectors to be used in kmeans clustering.
rand_seed - the same as the rand_seed argument.
svm_train_inds - if SVM is used this item contains indexes of the training cells to be used for SC3 clustering and further SVM prediction.
svm_study_inds - if SVM is used this item contains indexes of the cells to be predicted by SVM.
n_cores - the same as the n_cores argument.

sc3_prepare.SingleCellExperiment(object, gene_filter, pct_dropout_min,
  pct_dropout_max, d_region_min, d_region_max, svm_num_cells, svm_train_inds,
  svm_max, n_cores, kmeans_nstart, kmeans_iter_max, rand_seed)

## S4 method for signature 'SingleCellExperiment'
sc3_prepare(object, gene_filter = TRUE,
  pct_dropout_min = 10, pct_dropout_max = 90, d_region_min = 0.04,
  d_region_max = 0.07, svm_num_cells = NULL, svm_train_inds = NULL,
  svm_max = 5000, n_cores = NULL, kmeans_nstart = NULL,
  kmeans_iter_max = 1e+09, rand_seed = 1)

`object`	an object of `SingleCellExperiment` class.
`gene_filter`	a boolen variable which defines whether to perform gene filtering before SC3 clustering.
`pct_dropout_min`	if `gene_filter = TRUE`, then genes with percent of dropouts smaller than `pct_dropout_min` are filtered out before clustering.
`pct_dropout_max`	if `gene_filter = TRUE`, then genes with percent of dropouts larger than `pct_dropout_max` are filtered out before clustering.
`d_region_min`	defines the minimum number of eigenvectors used for kmeans clustering as a fraction of the total number of cells. Default is `0.04`. See `SC3` paper for more details.
`d_region_max`	defines the maximum number of eigenvectors used for kmeans clustering as a fraction of the total number of cells. Default is `0.07`. See `SC3` paper for more details.
`svm_num_cells`	number of randomly selected training cells to be used for SVM prediction. The default is `NULL`.
`svm_train_inds`	a numeric vector defining indeces of training cells that should be used for SVM training. The default is `NULL`.
`svm_max`	define the maximum number of cells below which SVM is not run.
`n_cores`	defines the number of cores to be used on the user's machine. If not set, 'SC3' will use all but one cores of your machine.
`kmeans_nstart`	nstart parameter passed to `kmeans` function. Default is `1000` for up to `2000` cells and `50` for more than `2000` cells.
`kmeans_iter_max`	iter.max parameter passed to `kmeans` function. Default is `1e+09`.
`rand_seed`	sets the seed of the random number generator. `SC3` is a stochastic method, so setting the `rand_seed` to a fixed values can be used for reproducibility purposes.