sc3_prepare {SC3} | R Documentation

## Prepare the `SingleCellExperiment` object for `SC3` clustering.

### Description

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.

### Usage

    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)

### Arguments

`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.

### Value

an object of `SingleCellExperiment` class

[Package SC3 version 1.8.0 Index]