Remove unwanted variation using control genes within replicates

Adapted from RUVSeq (https://doi.org/10.1038%2Fnbt.2931)

Usage

enRUVs(
  object,
  log = TRUE,
  k = 2,
  tolerance = 1e-08,
  control.idx,
  sc.idx,
  drop = 1
)

Arguments

object: A counts matrix.
log: Whether to perform log2-transformation with 1 offset on data matrix, default: TRUE.
k: The number of factors of unwanted variation to be estimated from the data.
tolerance: Tolerance in the selection of the number of positive singular values, i.e., a singular value must be larger than tolerance to be considered positive.
control.idx: ID of control genes.
sc.idx: A numeric matrix specifying the replicate samples for which to compute the count differences used to estimate the factors of unwanted variation (see details).
drop: The number of singular values to drop in the estimation of unwanted variation, default drop the first singular value that represent the difference between enrichment and input

Value

list contain a matrix of unwanted factors (W) and corrected counts matrix (normalizedCounts).

Details

Each row of sc.idx should correspond to a set of replicate samples. The number of columns is the size of the largest set of replicates; rows for smaller sets are padded with -1 values.

For example, if the sets of replicate samples are (1,2,3),(4,5),(6,7,8), then scIdx should be 1 2 3 4 5 -1 6 7 8