SCIBER {SCIBER} | R Documentation |
Batch effect removal with SCIBER
Description
Batch effect removal with SCIBER
Usage
SCIBER(
input_batches,
ref_index = NULL,
batches_meta_data = NULL,
omega = 0.5,
h_fisher = 75,
n_core = parallel::detectCores(),
seed = 7,
k = NULL
)
Arguments
input_batches |
A list contains all the pre-processed matrices with dimension of n_genes*n_cells. |
ref_index |
The index of the reference batch in the object "input_batches" |
batches_meta_data |
A list contains the meta data for all the batches. The order should be consistent with that in "input_batches". Each meta data contains three columns, "cell_id", "cell_type", and "dataset". "dataset" indicates which batch the data comes from. The row names of meta data should match the column names of batch. |
omega |
A list of proportion of matched clusters or a single value between 0 and 1 applied to all query batches. |
h_fisher |
The number of marker genes used for Fisher exact test. |
n_core |
Specify the number of cores otherwise use all the available cores. |
seed |
random seed. |
k |
Number of clusters used for K-means. If not provided, the default is k = the square root of n_0, where n_0 is the number of cells in the reference batch. |
Value
A list which contains the reference and batch-effect-corrected batches. The order is the same as that in input_batches.
Examples
data(HumanDC)
exp <- HumanDC[["exp"]]
meta <- HumanDC[["metadata"]]
omega <- c()
omega[[1]] <- 0.5
res <- SCIBER(input_batches = exp, ref_index = 1,
batches_meta_data = meta, omega = omega, n_core = 1)